From dd29580968ef1a922461c7180d3a22b7c3088aac Mon Sep 17 00:00:00 2001 From: Michael W Regan Date: Tue, 2 Apr 2024 09:09:01 -0400 Subject: [PATCH 001/687] remove uint8 data types (#8377) Co-authored-by: Howard Bushouse --- CHANGES.rst | 9 ++++++--- jwst/ramp_fitting/ramp_fit_step.py | 6 +++--- 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 68e6b918b5..e7b932e861 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,7 +1,12 @@ 1.14.1 (unreleased) =================== -- +ramp_fitting +------------ + +- Changed the data type for several variables in ramp_fitting + to use uint16 instead of uint8, in order to avoid potential + overflow/wraparound problems. [#8377] 1.14.0 (2024-03-29) =================== @@ -24,7 +29,6 @@ ami - Disable ``AmiAverage`` step. [#7862] - associations ------------ @@ -102,7 +106,6 @@ documentation - Adds documentation on the 1-D residual fringe correction for MIRI MRS data that is done in ``extract_1d``. [#8371] - emicorr ------- diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index 933577d538..4bfe500ef2 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -221,7 +221,7 @@ def compute_RN_variances(groupdq, readnoise_2d, gain_2d, group_time): imshape = (nrows, ncols) cubeshape = (ngroups,) + imshape - segs_4 = np.zeros((nint,) + (ngroups,) + imshape, dtype=np.uint8) + segs_4 = np.zeros((nint,) + (ngroups,) + imshape, dtype=np.uint16) var_r4 = np.zeros((nint,) + (ngroups,) + imshape, dtype=np.float32) + LARGE_VARIANCE var_r3 = np.zeros((nint,) + imshape, dtype=np.float32) + LARGE_VARIANCE s_inv_var_r3 = np.zeros((nint,) + imshape, dtype=np.float32) @@ -317,7 +317,7 @@ def calc_segs(rn_sect, gdq_sect, group_time): imshape = (asize2, asize1) gdq_2d = gdq_sect[:, :, :].reshape((ngroups, npix)) segs = np.zeros((ngroups, npix), dtype=np.int32) - sr_index = np.zeros(npix, dtype=np.uint8) + sr_index = np.zeros(npix, dtype=np.uint16) i_read = 0 while i_read < ngroups: @@ -341,7 +341,7 @@ def calc_segs(rn_sect, gdq_sect, group_time): i_read += 1 - segs = segs.astype(np.uint8) + segs = segs.astype(np.uint16) segs_beg_3 = segs.reshape(ngroups, imshape[0], imshape[1]) segs_beg_3 = utils.remove_bad_singles(segs_beg_3) From 74bec9985b6c2f234118cc3b3fb9a43f12ce38c2 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 2 Apr 2024 08:58:27 -0700 Subject: [PATCH 002/687] JP-3574: fix stage 2 pipeline steps not respecting output_file parameter (#8368) Co-authored-by: Howard Bushouse --- CHANGES.rst | 7 + docs/jwst/pipeline/calwebb_image2.rst | 6 +- docs/jwst/pipeline/calwebb_spec2.rst | 6 +- jwst/pipeline/calwebb_image2.py | 11 +- jwst/pipeline/calwebb_spec2.py | 15 +- jwst/pipeline/tests/test_calwebb_image2.py | 134 ++++++++++++++++++ jwst/pipeline/tests/test_calwebb_spec2.py | 156 +++++++++++++++++++++ jwst/pipeline/tests/test_calwebspec2.py | 13 -- 8 files changed, 321 insertions(+), 27 deletions(-) create mode 100644 jwst/pipeline/tests/test_calwebb_image2.py create mode 100644 jwst/pipeline/tests/test_calwebb_spec2.py delete mode 100644 jwst/pipeline/tests/test_calwebspec2.py diff --git a/CHANGES.rst b/CHANGES.rst index e7b932e861..b284e1dc72 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,12 @@ 1.14.1 (unreleased) =================== +pipeline +-------- + +- Fixed a bug in the ``calwebb_spec2`` and ``calwebb_image2`` pipelines + that was causing them not to respect the ``output_file`` parameter. [#8368] + ramp_fitting ------------ @@ -8,6 +14,7 @@ ramp_fitting to use uint16 instead of uint8, in order to avoid potential overflow/wraparound problems. [#8377] + 1.14.0 (2024-03-29) =================== diff --git a/docs/jwst/pipeline/calwebb_image2.rst b/docs/jwst/pipeline/calwebb_image2.rst index f2b607a2e1..b5102c4542 100644 --- a/docs/jwst/pipeline/calwebb_image2.rst +++ b/docs/jwst/pipeline/calwebb_image2.rst @@ -61,9 +61,9 @@ Inputs The input to ``Image2Pipeline`` is a countrate exposure, in the form of either "_rate" or "_rateints" -data. A single input file can be processed or an ASN file listing -multiple inputs can be used, in which case the processing steps will be -applied to each input exposure, one at a time. If "_rateints" products are +data. A single input FITS file can be processed or an ASN file can be used, +as long as there is only one output product specified in +the association. If "_rateints" products are used as input, each step applies its algorithm to each integration in the exposure, where appropriate. diff --git a/docs/jwst/pipeline/calwebb_spec2.rst b/docs/jwst/pipeline/calwebb_spec2.rst index daddc6e2ff..fa53774c89 100644 --- a/docs/jwst/pipeline/calwebb_spec2.rst +++ b/docs/jwst/pipeline/calwebb_spec2.rst @@ -253,9 +253,9 @@ Inputs :File suffix: _rate or _rateints The input to the ``Spec2Pipeline`` pipeline is a countrate exposure, in the form -of either "_rate" or "_rateints" data. A single input file can be processed or an -ASN file listing multiple inputs can be used, in which case the processing steps -will be applied to each input exposure, one at a time. +of either "_rate" or "_rateints" data. A single input FITS file can be processed +or an ASN file can be used, as long as there is only one output product specified +in the association. If "_rateints" products are used as input, for modes other than NIRSpec Fixed Slit, each step applies its algorithm to each integration in the exposure, where appropriate. diff --git a/jwst/pipeline/calwebb_image2.py b/jwst/pipeline/calwebb_image2.py index 9d8b6c1bd4..64aab36d80 100644 --- a/jwst/pipeline/calwebb_image2.py +++ b/jwst/pipeline/calwebb_image2.py @@ -51,13 +51,16 @@ def process(self, input): # Retrieve the input(s) asn = LoadAsLevel2Asn.load(input, basename=self.output_file) + if len(asn['products']) > 1 and self.output_file is not None: + self.log.warning("Multiple products in input association. Output file name will be ignored.") + self.output_file = None # Each exposure is a product in the association. # Process each exposure. results = [] for product in asn['products']: self.log.info('Processing product {}'.format(product['name'])) - if self.save_results: + if (self.save_results) & (self.output_file is None): self.output_file = product['name'] try: getattr(asn, 'filename') @@ -74,8 +77,9 @@ def process(self, input): suffix = 'cal' if isinstance(result, datamodels.CubeModel): suffix = 'calints' - result.meta.filename = self.make_output_path(suffix=suffix) + result.meta.filename = self.make_output_path(basepath=self.output_file, suffix=suffix) results.append(result) + self.output_file = None self.log.info('... ending calwebb_image2') @@ -88,7 +92,7 @@ def process_exposure_product( self, exp_product, pool_name=' ', - asn_file=' ' + asn_file=' ', ): """Process an exposure found in the association product @@ -131,6 +135,7 @@ def process_exposure_product( # Record ASN pool and table names in output input.meta.asn.pool_name = pool_name input.meta.asn.table_name = asn_file + input.meta.filename = self.make_output_path(basepath=self.output_file) # Do background processing, if necessary if len(members_by_type['background']) > 0: diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 5f254def44..85ddabec94 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -108,6 +108,9 @@ def process(self, data): # Retrieve the input(s) asn = self.load_as_level2_asn(data) + if len(asn['products']) > 1 and self.output_file is not None: + self.log.warning('Multiple products in input association. Output file name will be ignored.') + self.output_file = None # Each exposure is a product in the association. # Process each exposure. Delay reporting failures until the end. @@ -115,7 +118,8 @@ def process(self, data): failures = [] for product in asn['products']: self.log.info('Processing product {}'.format(product['name'])) - self.output_file = product['name'] + if self.output_file is None: + self.output_file = product['name'] try: getattr(asn, 'filename') except AttributeError: @@ -124,7 +128,7 @@ def process(self, data): result = self.process_exposure_product( product, asn['asn_pool'], - asn.filename + asn.filename, ) except NoDataOnDetectorError as exception: # This error merits a special return @@ -137,6 +141,7 @@ def process(self, data): else: if result is not None: results.append(result) + self.output_file = None #handles multiple products in the association if len(failures) > 0 and self.fail_on_exception: raise RuntimeError('\n'.join(failures)) @@ -153,7 +158,7 @@ def process_exposure_product( self, exp_product, pool_name=' ', - asn_file=' ' + asn_file=' ', ): """Process an exposure found in the association product @@ -277,7 +282,7 @@ def process_exposure_product( # Record ASN pool and table names in output calibrated.meta.asn.pool_name = pool_name calibrated.meta.asn.table_name = op.basename(asn_file) - calibrated.meta.filename = self.make_output_path(suffix=suffix) + calibrated.meta.filename = self.make_output_path(basepath=self.output_file, suffix=suffix) # Produce a resampled product, either via resample_spec for # "regular" spectra or cube_build for IFU data. No resampled @@ -304,7 +309,7 @@ def process_exposure_product( resampled = calibrated.copy() resampled = self.cube_build(resampled) if not self.cube_build.skip: - self.save_model(resampled[0], 's3d') + self.save_model(resampled[0], suffix='s3d') else: resampled = calibrated diff --git a/jwst/pipeline/tests/test_calwebb_image2.py b/jwst/pipeline/tests/test_calwebb_image2.py new file mode 100644 index 0000000000..e0389f42e3 --- /dev/null +++ b/jwst/pipeline/tests/test_calwebb_image2.py @@ -0,0 +1,134 @@ +import pytest +import os +import shutil +from jwst.stpipe import Step +from jwst.datamodels import ImageModel + + +INPUT_FILE = "dummy_rate.fits" +INPUT_FILE_2 = "dummy2_rate.fits" +INPUT_ASN = "dummy_asn.json" +OUTPUT_FILE = "custom_name.fits" +OUTPUT_FILE_ASN = "custom_name_asn.fits" #cannot reuse because everything runs in same cwd +LOGFILE = "run_asn.log" +LOGCFG = "test_logs.cfg" + +@pytest.fixture(scope='module') +def make_dummy_rate_file(tmp_cwd_module): + ''' + Make and save a dummy rate file in the temporary working directory + Partially copied from test_background.py + ''' + + image = ImageModel((2048, 2048)) + image.data[:, :] = 1 + image.meta.instrument.name = 'NIRCAM' + image.meta.instrument.filter = 'CLEAR' + image.meta.exposure.type = 'NRC_IMAGE' + image.meta.observation.date = '2019-02-27' + image.meta.observation.time = '13:37:18.548' + image.meta.date = '2019-02-27T13:37:18.548' + image.meta.subarray.xstart = 1 + image.meta.subarray.ystart = 1 + + image.meta.subarray.xsize = image.data.shape[-1] + image.meta.subarray.ysize = image.data.shape[-2] + + image.meta.instrument.channel = 'SHORT' + image.meta.instrument.module = 'A' + image.meta.instrument.detector = 'NRCA1' + + # bare minimum wcs info to get assign_wcs step to pass + image.meta.wcsinfo.crpix1 = 693.5 + image.meta.wcsinfo.crpix2 = 512.5 + image.meta.wcsinfo.v2_ref = -453.37849 + image.meta.wcsinfo.v3_ref = -373.810549 + image.meta.wcsinfo.roll_ref = 272.3237653262276 + image.meta.wcsinfo.ra_ref = 80.54724018120017 + image.meta.wcsinfo.dec_ref = -69.5081101864959 + + with image as dm: + dm.save(INPUT_FILE) + + +@pytest.fixture(scope='module') +def make_dummy_association(make_dummy_rate_file): + + shutil.copy(INPUT_FILE, INPUT_FILE_2) + os.system(f"asn_from_list -o {INPUT_ASN} -r DMSLevel2bBase {INPUT_FILE} {INPUT_FILE_2}") + + +@pytest.fixture(scope='module', params=[OUTPUT_FILE]) +def run_image2_pipeline_file(make_dummy_rate_file, request): + ''' + Run pipeline, skipping most steps + ''' + args = ["calwebb_image2", INPUT_FILE, + "--steps.flat_field.skip=true", + "--steps.photom.skip=true", + "--steps.resample.skip=true", + f"--output_file={request.param}",] + + Step.from_cmdline(args) + + +@pytest.fixture(scope='module', params=[OUTPUT_FILE_ASN]) +def run_image2_pipeline_asn(make_dummy_association, request): + ''' + Two-product association passed in. This should trigger a warning + and the output_file parameter should be ignored. + ''' + # save warnings to logfile so can be checked later + logcfg_content = f"[*] \n \ + level = INFO \n \ + handler = file:{LOGFILE}" + with open(LOGCFG, 'w') as f: + f.write(logcfg_content) + + args = ["calwebb_image2", INPUT_ASN, + f"--logcfg={LOGCFG}", + "--steps.flat_field.skip=true", + "--steps.photom.skip=true", + "--steps.resample.skip=true", + f"--output_file={request.param}",] + + Step.from_cmdline(args) + + +def test_output_file_rename_file(run_image2_pipeline_file): + ''' + Covers a bug where the output_file parameter was not being + respected in calls to Image2Pipeline. + ''' + assert os.path.exists(INPUT_FILE) #ensures tmp_cwd_module is working + custom_stem = OUTPUT_FILE.split('.')[0] + for extension in ['cal']: + assert os.path.exists(f'{custom_stem}_{extension}.fits') + + +def test_output_file_norename_asn(run_image2_pipeline_asn): + ''' + Ensure output_file parameter is ignored, with warning, + when multiple products are in the same association. + ''' + # ensure tmp_cwd_module is successfully keeping all files in cwd + assert os.path.exists(INPUT_ASN) + assert os.path.exists(INPUT_FILE) + assert os.path.exists(INPUT_FILE_2) + + custom_stem = OUTPUT_FILE_ASN.split('.')[0] + input_stem = INPUT_FILE.split('_')[0] + input_stem_2 = INPUT_FILE_2.split('_')[0] + + # ensure default filenames were written, and not the custom one + for extension in ['cal']: + assert not os.path.exists(f'{custom_stem}_{extension}.fits') + assert os.path.exists(f'{input_stem}_{extension}.fits') + assert os.path.exists(f'{input_stem_2}_{extension}.fits') + + # ensure warning goes to log file + assert os.path.exists(LOGFILE) + with open(LOGFILE, 'r') as f: + log_content = f.read() + + assert "Multiple products in input association. Output file name will be ignored." in log_content \ No newline at end of file diff --git a/jwst/pipeline/tests/test_calwebb_spec2.py b/jwst/pipeline/tests/test_calwebb_spec2.py new file mode 100644 index 0000000000..59b2148bc1 --- /dev/null +++ b/jwst/pipeline/tests/test_calwebb_spec2.py @@ -0,0 +1,156 @@ +import pytest +import os +import shutil +from jwst.pipeline.calwebb_spec2 import Spec2Pipeline +from jwst.stpipe import Step +from jwst.datamodels import IFUImageModel + + +INPUT_FILE = "dummy_rate.fits" +INPUT_FILE_2 = "dummy2_rate.fits" +INPUT_ASN = "dummy_asn.json" +OUTPUT_FILE = "custom_name.fits" +OUTPUT_FILE_ASN = "custom_name_asn.fits" #cannot reuse because everything runs in same cwd +LOGFILE = "run_asn.log" +LOGCFG = "test_logs.cfg" + +@pytest.fixture(scope='module') +def make_dummy_rate_file(tmp_cwd_module): + ''' + Make and save a dummy rate file in the temporary working directory + Partially copied from test_background.py + ''' + image = IFUImageModel((2048, 2048)) + image.data[:, :] = 1 + + image.meta.instrument.name = 'NIRSPEC' + image.meta.instrument.detector = 'NRS1' + image.meta.instrument.filter = 'CLEAR' + image.meta.instrument.grating = 'PRISM' + image.meta.exposure.type = 'NRS_IFU' + image.meta.observation.date = '2019-02-27' + image.meta.observation.time = '13:37:18.548' + image.meta.date = '2019-02-27T13:37:18.548' + image.meta.subarray.xstart = 1 + image.meta.subarray.ystart = 1 + image.meta.subarray.xsize = image.data.shape[-1] + image.meta.subarray.ysize = image.data.shape[-2] + image.meta.instrument.gwa_tilt = 37.0610 + + # bare minimum wcs info to get assign_wcs step to pass + image.meta.wcsinfo.crpix1 = 693.5 + image.meta.wcsinfo.crpix2 = 512.5 + image.meta.wcsinfo.v2_ref = -453.37849 + image.meta.wcsinfo.v3_ref = -373.810549 + image.meta.wcsinfo.roll_ref = 272.3237653262276 + image.meta.wcsinfo.ra_ref = 80.54724018120017 + image.meta.wcsinfo.dec_ref = -69.5081101864959 + + with image as dm: + dm.save(INPUT_FILE) + + +@pytest.fixture(scope='module') +def make_dummy_association(make_dummy_rate_file): + + shutil.copy(INPUT_FILE, INPUT_FILE_2) + os.system(f"asn_from_list -o {INPUT_ASN} -r DMSLevel2bBase {INPUT_FILE} {INPUT_FILE_2}") + + +@pytest.fixture(scope='module', params=[OUTPUT_FILE]) +def run_spec2_pipeline(make_dummy_rate_file, request): + ''' + Run pipeline, saving one intermediate step + and skipping most of the rest for runtime + ''' + args = ["calwebb_spec2", INPUT_FILE, + "--steps.msa_flagging.skip=true", + "--steps.nsclean.skip=true", + "--steps.flat_field.skip=true", + "--steps.pathloss.skip=true", + "--steps.photom.skip=true", + "--steps.pixel_replace.skip=true", + "--steps.cube_build.save_results=true", + "--steps.extract_1d.skip=true", + f"--output_file={request.param}",] + + Step.from_cmdline(args) + + +@pytest.fixture(scope='module', params=[OUTPUT_FILE_ASN]) +def run_spec2_pipeline_asn(make_dummy_association, request): + ''' + Two-product association passed in. This should trigger a warning + and the output_file parameter should be ignored. + ''' + # save warnings to logfile so can be checked later + logcfg_content = f"[*] \n \ + level = INFO \n \ + handler = file:{LOGFILE}" + with open(LOGCFG, 'w') as f: + f.write(logcfg_content) + + args = ["calwebb_spec2", INPUT_ASN, + f"--logcfg={LOGCFG}", + "--steps.msa_flagging.skip=true", + "--steps.nsclean.skip=true", + "--steps.flat_field.skip=true", + "--steps.pathloss.skip=true", + "--steps.photom.skip=true", + "--steps.pixel_replace.skip=true", + "--steps.cube_build.save_results=true", + "--steps.extract_1d.skip=true", + f"--output_file={request.param}",] + + Step.from_cmdline(args) + + +def test_output_file_rename(run_spec2_pipeline): + ''' + Covers a bug where the output_file parameter was not being + respected in calls to Spec2Pipeline. + _s3d file expected from cube_build save_results=true + _cal file expected from pipeline finishing + ''' + assert os.path.exists(INPUT_FILE) + custom_stem = OUTPUT_FILE.split('.')[0] + for extension in ['s3d', 'cal']: + assert os.path.exists(f'{custom_stem}_{extension}.fits') + + +def test_output_file_norename_asn(run_spec2_pipeline_asn): + ''' + Ensure output_file parameter is ignored, with warning, + when multiple products are in the same association. + ''' + # ensure tmp_cwd_module is successfully keeping all files in cwd + assert os.path.exists(INPUT_ASN) + assert os.path.exists(INPUT_FILE) + assert os.path.exists(INPUT_FILE_2) + + custom_stem = OUTPUT_FILE_ASN.split('.')[0] + input_stem = INPUT_FILE.split('_')[0] + input_stem_2 = INPUT_FILE_2.split('_')[0] + + # ensure default filenames were written, and not the custom one + for extension in ['s3d', 'cal']: + assert not os.path.exists(f'{custom_stem}_{extension}.fits') + assert os.path.exists(f'{input_stem}_{extension}.fits') + assert os.path.exists(f'{input_stem_2}_{extension}.fits') + + # ensure warning goes to log file + assert os.path.exists(LOGFILE) + with open(LOGFILE, 'r') as f: + log_content = f.read() + + assert "Multiple products in input association. Output file name will be ignored." in log_content + + +def test_filenotfounderror_raised(capsys): + # Verify the failure is in the traceback message + with pytest.raises(RuntimeError, match="FileNotFoundError"): + Spec2Pipeline().run('file_does_not_exist.fits') + + # Verify the failure is printed to stderr + captured = capsys.readouterr() + assert 'FileNotFoundError' in captured.err \ No newline at end of file diff --git a/jwst/pipeline/tests/test_calwebspec2.py b/jwst/pipeline/tests/test_calwebspec2.py deleted file mode 100644 index 57483de143..0000000000 --- a/jwst/pipeline/tests/test_calwebspec2.py +++ /dev/null @@ -1,13 +0,0 @@ -import pytest - -from jwst.pipeline.calwebb_spec2 import Spec2Pipeline - - -def test_filenotfounderror_raised(capsys): - # Verify the failure is in the traceback message - with pytest.raises(RuntimeError, match="FileNotFoundError"): - Spec2Pipeline().run('file_does_not_exist.fits') - - # Verify the failure is printed to stderr - captured = capsys.readouterr() - assert 'FileNotFoundError' in captured.err From d6b3e3837611fadfd866f668066d5b4c1316ba26 Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Tue, 2 Apr 2024 13:00:54 -0400 Subject: [PATCH 003/687] JP-3554: Add MSA meta data file docs (#8399) Co-authored-by: Ned Molter --- CHANGES.rst | 6 + docs/jwst/assign_wcs/exp_types.rst | 100 ++++----- docs/jwst/assign_wcs/main.rst | 55 ++--- docs/jwst/assign_wcs/reference_files.rst | 8 +- docs/jwst/data_products/index.rst | 1 + docs/jwst/data_products/msa_metadata.rst | 255 +++++++++++++++++++++++ 6 files changed, 345 insertions(+), 80 deletions(-) create mode 100644 docs/jwst/data_products/msa_metadata.rst diff --git a/CHANGES.rst b/CHANGES.rst index b284e1dc72..baee14669f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,12 @@ 1.14.1 (unreleased) =================== +documentation +------------- + +- Added docs for the NIRSpec MSA metadata file to the data products area of RTD. + [#8399] + pipeline -------- diff --git a/docs/jwst/assign_wcs/exp_types.rst b/docs/jwst/assign_wcs/exp_types.rst index 9264e1f1ff..3502e1e7e8 100644 --- a/docs/jwst/assign_wcs/exp_types.rst +++ b/docs/jwst/assign_wcs/exp_types.rst @@ -2,73 +2,75 @@ WCS reference file information per EXP_TYPE =========================================== -:FGS_IMAGE, FGS_FOCUS, FGS_SKYFLAT, FGS_INTFLAT: +**FGS_IMAGE, FGS_FOCUS, FGS_SKYFLAT, FGS_INTFLAT:** - | reftypes: *distortion* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: reference file provided by NIRISS team + | reftypes: *distortion* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: reference file provided by NIRISS team -:MIR_IMAGE, MIR_TACQ, MIR_LYOT, MIR4QPM, MIR_CORONCAL: +**MIR_IMAGE, MIR_TACQ, MIR_LYOT, MIR4QPM, MIR_CORONCAL:** - | reftypes: *distortion*, *filteroffset* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: CDP6 reference data delivery, MIRI-TN-00070-ATC_Imager_distortion_CDP_Iss5.pdf + | reftypes: *distortion*, *filteroffset* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: CDP6 reference data delivery, MIRI-TN-00070-ATC_Imager_distortion_CDP_Iss5.pdf -:MIR_LRS-FIXEDSLIT, MIR_LRS-SLITLESS: +**MIR_LRS-FIXEDSLIT, MIR_LRS-SLITLESS:** - | reftypes: *specwcs*, *distortion* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: CDP4 reference data delivery, MIRI-TR-10020-MPI-Calibration-Data-Description_LRSPSFDistWave_v4.0.pdf + | reftypes: *specwcs*, *distortion* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: CDP4 reference data delivery, MIRI-TR-10020-MPI-Calibration-Data-Description_LRSPSFDistWave_v4.0.pdf -:MIR_MRS: +**MIR_MRS:** - | reftypes: *distortion*, *specwcs*, *v2v3*, *wavelengthrange*, *regions* - | WCS pipeline coordinate frames: detector, miri_focal, xyan, v2v3, world - | Implements: CDP4 reference data delivery, MIRI-TN-00001-ETH_Iss1-3_Calibrationproduct_MRS_d2c.pdf + | reftypes: *distortion*, *specwcs*, *v2v3*, *wavelengthrange*, *regions* + | WCS pipeline coordinate frames: detector, miri_focal, xyan, v2v3, world + | Implements: CDP4 reference data delivery, MIRI-TN-00001-ETH_Iss1-3_Calibrationproduct_MRS_d2c.pdf -:NRC_IMAGE, NRC_TSIMAGE, NRC_FOCUS, NRC_TACONFIRM, NRC_TACQ: +**NRC_IMAGE, NRC_TSIMAGE, NRC_FOCUS, NRC_TACONFIRM, NRC_TACQ:** - | reftypes: *distortion*, *filteroffset* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: Distortion file created from TEL team data. + | reftypes: *distortion*, *filteroffset* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: Distortion file created from TEL team data. -:NRC_WFSS, NRC_TSGRISM: - | reftypes: *specwcs*, *distortion*, *filteroffset* - | WCS pipeline coordinate frames: grism_detector, detector, v2v3, world - | Implements: reference files provided by NIRCam team +**NRC_WFSS, NRC_TSGRISM:** -:NIS_IMAGE, NIS_TACQ, NIS_TACONFIRM, NIS_FOCUS: + | reftypes: *specwcs*, *distortion*, *filteroffset* + | WCS pipeline coordinate frames: grism_detector, detector, v2v3, world + | Implements: reference files provided by NIRCam team - | reftypes: *distortion*, *filteroffset* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: reference file provided by NIRISS team +**NIS_IMAGE, NIS_TACQ, NIS_TACONFIRM, NIS_FOCUS:** -:NIS_WFSS: - | reftypes: *specwcs*, *distortion*, *filteroffset* - | WCS pipeline coordinate frames: grism_detector, detector, v2v3, world - | Implements: reference files provided by NIRISS team + | reftypes: *distortion*, *filteroffset* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: reference file provided by NIRISS team -:NIS_SOSS: +**NIS_WFSS:** - | reftypes: *distortion*, *specwcs* - | WCS pipeline coordinate frames: detector, v2v3, world - | Implements: reference files provided by NIRISS team + | reftypes: *specwcs*, *distortion*, *filteroffset* + | WCS pipeline coordinate frames: grism_detector, detector, v2v3, world + | Implements: reference files provided by NIRISS team -:NRS_FIXEDSLIT, NRS_MSASPEC, NRS_LAMP, NRS_BRIGHTOBJ: +**NIS_SOSS:** - | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote* - | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world - | Implements: CDP 3 delivery + | reftypes: *distortion*, *specwcs* + | WCS pipeline coordinate frames: detector, v2v3, world + | Implements: reference files provided by NIRISS team -:NRS_IFU: +**NRS_FIXEDSLIT, NRS_MSASPEC, NRS_LAMP, NRS_BRIGHTOBJ:** - | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote*, - | *ifufore*, *ifuslicer*, *ifupost* - | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world - | Implements: CDP 3 delivery + | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote* + | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world + | Implements: CDP 3 delivery -:NRS_IMAGING, NRS_MIMF, NRS_BOTA, NRS_CONFIRM, NRS_TACONFIRM, NRS_TASLIT, NRS_TACQ: +**NRS_IFU:** - | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote* - | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world - | Implements: CDP 3 delivery + | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote*, + | *ifufore*, *ifuslicer*, *ifupost* + | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world + | Implements: CDP 3 delivery + +**NRS_IMAGING, NRS_MIMF, NRS_BOTA, NRS_CONFIRM, NRS_TACONFIRM, NRS_TASLIT, NRS_TACQ:** + + | reftypes: *fpa*, *camera*, *disperser*, *collimator*, *msa*, *wavelengthrange*, *fore*, *ote* + | WCS pipeline coordinate frames: detector, sca, bgwa, slit_frame, msa_frame, ote, v2v3, world + | Implements: CDP 3 delivery diff --git a/docs/jwst/assign_wcs/main.rst b/docs/jwst/assign_wcs/main.rst index c8baf2b742..f2265c9a51 100644 --- a/docs/jwst/assign_wcs/main.rst +++ b/docs/jwst/assign_wcs/main.rst @@ -6,37 +6,45 @@ Description :Alias: assign_wcs -``jwst.assign_wcs`` is run in the beginning of the level 2B JWST pipeline. +The ``assign_wcs`` step is run at the beginning of the Stage 2 pipelines - :ref:`calwebb_image2 ` +and :ref:`calwebb_spec2 ` - for both imaging and spectroscopic exposures. It associates a WCS object with each science exposure. The WCS object transforms positions in the detector frame to positions in a world coordinate frame - ICRS and wavelength. In general there may be intermediate coordinate frames depending on the instrument. The WCS is saved in the ASDF extension of the FITS file. It can be accessed as an attribute of -the meta object when the fits file is opened as a data model. +the meta object when the FITS file is opened as a data model. The forward direction of the transforms is from detector to world coordinates -and the input positions are 0-based. +and the input pixel coordinates are 0-indexed. -``jwst.assign_wcs`` expects to find the basic WCS keywords in the -SCI header. Distortion and spectral models are stored in reference files in the +The ``assign_wcs`` step expects to find the basic WCS keywords in the +"SCI" extension header of the input FITS file. Distortion and spectral models are stored in reference files in the `ASDF `__ format. For each observing mode, determined by the value of ``EXP_TYPE`` in the science header, -assign_wcs retrieves reference files from CRDS and creates a pipeline of transforms from +``assign_wcs`` retrieves reference files from CRDS and creates a pipeline of transforms from input frame ``detector`` to a frame ``v2v3``. This part of the WCS pipeline may include intermediate coordinate frames. The basic WCS keywords are used to create -the transform from frame ``v2v3`` to frame ``world``. +the transform from frame ``v2v3`` to frame ``world``. All of this information is used to +create and populate the WCS object for the exposure. For image display with software like DS9 that relies on specific WCS information, a SIP-based approximation to the WCS is fit. The results are FITS keywords stored in ``model.meta.wcsinfo``. This is not an exact fit, but is accurate to ~0.25 pixel and is sufficient -for display purposes. This step, which occurs for imaging modes early, is performed by default but -can be switched off, and parameters controlling the fit can also be adjusted. +for display purposes. This step, which occurs for imaging modes, is performed by default, but +can be switched off, and parameters controlling the SIP fit can also be adjusted. +The ``assign_wcs`` step can accept either a ``rate`` product, which is the result of averaging +over all integrations in an exposure, or a ``rateints`` product, which is a 3D cube of +per-integration images. -``jwst.assign_wcs`` is based on `gwcs `__ and +The ``assign_wcs`` step is based on `gwcs `__ and uses `asdf `__. - +.. Note:: In addition to CRDS reference files, applying ``assign_wcs`` to NIRSpec MOS + exposures depends critically on an MSA metadata file to provide information + for MOS slitlets in use and their constituent shutters. See :ref:`msa_metadata ` + for detailed information about the MSA metadata files and their contents. Basic WCS keywords and the transform from ``v2v3`` to ``world`` --------------------------------------------------------------- @@ -44,21 +52,21 @@ Basic WCS keywords and the transform from ``v2v3`` to ``world`` All JWST instruments use the following FITS header keywords to define the transform from ``v2v3`` to ``world``: -``RA_REF``, ``DEC_REF`` - a fiducial point on the sky, ICRS, [deg] +``RA_REF``, ``DEC_REF`` - a fiducial point on the sky, ICRS [deg] -``V2_REF``, ``V3_REF`` - a point in the V2V3 system which maps to ``RA_REF``, ``DEC_REF``, [arcsec] +``V2_REF``, ``V3_REF`` - a point in the V2V3 system that maps to ``RA_REF``, ``DEC_REF`` [arcsec] -``ROLL_REF`` - local roll angle associated with each aperture, [deg] +``ROLL_REF`` - local roll angle associated with each aperture [deg] ``RADESYS`` - standard coordinate system [ICRS] These quantities are used to create a 3D Euler angle rotation between the V2V3 spherical system, associated with the telescope, and a standard celestial system. -For spectroscopic data, ``jwst.assign_wcs`` populates keyword ``DISPAXIS`` +For spectroscopic data, ``assign_wcs`` populates the keyword ``DISPAXIS`` with an integer value that indicates whether the dispersion direction is oriented more nearly along the horizontal (DISPAXIS = 1) or vertical -(DISPAXIS = 2) direction. +(DISPAXIS = 2) direction in the image frame. Using the WCS interactively @@ -76,8 +84,8 @@ corresponding world coordinates. Using MIRI LRS fixed slit as an example: >>> print(ra, dec, lam) (329.97260532549336, 372.0242999250267, 5.4176100046836675) -The WFSS modes for NIRCam and NIRISS have a slightly different calling structure, -in addition to the (x, y) coordinate, they need to know other information about the +The WFSS modes for NIRCam and NIRISS have a slightly different calling structure. +In addition to the (x, y) coordinates, they need to know other information about the spectrum or source object. In the JWST backward direction (going from the sky to the detector) the WCS model also looks for the wavelength and order and returns the (x,y) location of that wavelength+order on the dispersed image and the original @@ -95,8 +103,8 @@ source pixel location, as entered, along with the order that was specified: The WCS provides access to intermediate coordinate frames -and transforms between any two frames in the WCS pipeline in forward or -backward direction. For example, for a NIRSpec fixed slits exposure, +and transforms between any two frames in the WCS pipeline in the forward or +backward directions. For example, for a NIRSpec fixed slits exposure, which has been through the extract_2d step: .. doctest-skip:: @@ -108,13 +116,6 @@ which has been through the extract_2d step: >>> msa2detector(0, 0, 2*10**-6) (5042.064255529629, 1119.8937888372516) -For each exposure, assign_wcs uses reference files and WCS header keywords -to create the WCS object. What reference files are retrieved -from CRDS is determined based on EXP_TYPE and other keywords in the science file header. - -The assign_wcs step can accept the single slope image that is the result of averaging -over all integrations or a 3D cube of integrations in the case of TSO exposures. - WCS of slitless grism exposures ------------------------------- diff --git a/docs/jwst/assign_wcs/reference_files.rst b/docs/jwst/assign_wcs/reference_files.rst index 7df12eae1e..c0d4e3f399 100644 --- a/docs/jwst/assign_wcs/reference_files.rst +++ b/docs/jwst/assign_wcs/reference_files.rst @@ -3,11 +3,11 @@ Reference Files WCS Reference files are in the Advanced Scientific Data Format (ASDF). The best way to create the file is to programmatically create the model and then save it to a file. -A tutorial on creating reference files in ASDF format is available at: +A tutorial on creating reference files in ASDF format is available +`here +`_. -https://github.com/spacetelescope/jwreftools/blob/master/docs/notebooks/referece_files_asdf.ipynb - -Transforms are 0-based. The forward direction is from detector to sky. +Transforms are 0-indexed. The forward direction is from detector to sky. Reference file types used by ``assign_wcs`` ------------------------------------------- diff --git a/docs/jwst/data_products/index.rst b/docs/jwst/data_products/index.rst index d9cc684110..c398adca7c 100644 --- a/docs/jwst/data_products/index.rst +++ b/docs/jwst/data_products/index.rst @@ -12,5 +12,6 @@ Data Products Information common_features.rst science_products.rst nonscience_products.rst + msa_metadata.rst guidestar_products.rst migrating.rst diff --git a/docs/jwst/data_products/msa_metadata.rst b/docs/jwst/data_products/msa_metadata.rst new file mode 100644 index 0000000000..edaa703b2f --- /dev/null +++ b/docs/jwst/data_products/msa_metadata.rst @@ -0,0 +1,255 @@ +.. _msa_metadata: + +MSA Metadata File: ``msa`` +^^^^^^^^^^^^^^^^^^^^^^^^^^ +While not containing any actual science data, the NIRSpec MSA metadata file is nonetheless +a crucial component of calibration processing for NIRSpec MOS exposures. +It contains all the slitlet, shutter, and source configuration information that's needed +by the :ref:`calwebb_spec2 ` pipeline to process a MOS exposure. +These metadata are originally generated by the +`MSA Planning Tool `_ +(MPT) in APT and passed through the ground system to populate the +FITS metafile. Details of the metadata and how it is used in calibration processing +are given below. + +File names for MSA metadata files are of the form: + + jw__msa.fits + +where + + - ppppp: program ID number + - ooo: observation number + - vvv: visit number + - cfg: MSA configuration + +such as `jw01180025001_01_msa.fits`. + +Each MOS science exposure contains a primary header keyword `MSAMETFL` that gives +the name of the MSA metadata file to be used when processing the exposure. +The values of the primary header keywords `MSAMETID` and `PATT_NUM` are also used +when retrieving relevant rows of data from the tables in the MSA metadata file. +If any of these keywords is missing from the input science exposure, processing +will not be able to proceed and an error will be raised. + +File Structure +-------------- +The overall structure of the MSA FITS file is as follows: + ++-----+---------------+----------+-----------+--------------------+ +| HDU | EXTNAME | HDU Type | Data Type | Dimensions | ++=====+===============+==========+===========+====================+ +| 0 | N/A | primary | N/A | N/A | ++-----+---------------+----------+-----------+--------------------+ +| 1 | SHUTTER_IMAGE | IMAGE | float32 | 342 x 730 | ++-----+---------------+----------+-----------+--------------------+ +| 2 | SHUTTER_INFO | BINTABLE | N/A | variable x 12 cols | ++-----+---------------+----------+-----------+--------------------+ +| 3 | SOURCE_INFO | BINTABLE | N/A | variable x 8 cols | ++-----+---------------+----------+-----------+--------------------+ + + - PRI: The primary data array is empty. The primary header contains a small amount of + metadata related to the observation and MSA configuration. + - SHUTTER_IMAGE: 2-D data array containing a map of MSA shutters that've been commanded + open for this MSA configuration. + - SHUTTER_INFO: 2-D FITS binary table containing metadata for each shutter + involved in this MSA configuration. One table row per shutter. + - SOURCE_INFO: 2-D FITS binary table containing metadata from the source catalog + used to create the MSA configuration. One table row per source. + + +The SHUTTER_INFO Metadata +------------------------- +The structure of the ``SHUTTER_INFO`` table extension is as follows: + ++-------------------------------+-----------+----------------------+ +| Column Name | Data Type | Contents | ++===============================+===========+======================+ +| SLITLET_ID | int16 | Slitlet ID number | ++-------------------------------+-----------+----------------------+ +| MSA_METADATA_ID | int16 | MSA metadata ID | ++-------------------------------+-----------+----------------------+ +| SHUTTER_QUADRANT | int16 | MSA Quadrant number | ++-------------------------------+-----------+----------------------+ +| SHUTTER_ROW | int16 | Shutter row index | ++-------------------------------+-----------+----------------------+ +| SHUTTER_COLUMN | int16 | Shutter column index | ++-------------------------------+-----------+----------------------+ +| SOURCE_ID | int32 | Source ID number | ++-------------------------------+-----------+----------------------+ +| BACKGROUND | string | Background flag | ++-------------------------------+-----------+----------------------+ +| SHUTTER_STATE | string | Shutter state | ++-------------------------------+-----------+----------------------+ +| ESTIMATED_SOURCE_IN_SHUTTER_X | float32 | Source x position | ++-------------------------------+-----------+----------------------+ +| ESTIMATED_SOURCE_IN_SHUTTER_Y | float32 | Source y position | ++-------------------------------+-----------+----------------------+ +| DITHER_POINT_INDEX | int16 | Dither/nod index | ++-------------------------------+-----------+----------------------+ +| PRIMARY_SOURCE | string | Primary source flag | ++-------------------------------+-----------+----------------------+ + +- SLITLET_ID: integer ID of each slitlet consisting of one or more + open shutters. +- MSA_METADATA_ID: integer ID corresponding to a particular MSA + configuration or MPT plan (typically the same for all rows in a + given metafile). +- SHUTTER_QUADRANT: integer ID of the MSA quadrant in which the open + shutter is located. +- SHUTTER_ROW: integer row index of the shutter within the MSA quadrant. +- SHUTTER_COLUMN: integrer column index of the shutter within the MSA + quadrant. +- SOURCE_ID: unique integer ID for each source in each slitlet, used + for matching to an entry in the ``SOURCE_INFO`` table. +- BACKGROUND: boolean indicating whether the shutter is open to background + only or contains a known source. +- SHUTTER_STATE: OPEN or CLOSED. Generally will always be OPEN, unless + the shutter is part of a long slit that is not contiguous. +- ESTIMATED_SOURCE_IN_SHUTTER_X/Y: the position of the source within the + shutter in relative units, where 0,0 is bottom-left, 0.5,0.5 is center, + and 1,1 is upper-right, as planned in the MPT. +- DITHER_POINT_INDEX: integer index of the nod sequence; matches to + header keyword `PATT_NUM`. +- PRIMARY_SOURCE: boolean indicating whether the shutter contains the + primary science source. + +It is the :ref:`assign_wcs ` step in the +:ref:`calwebb_spec2 ` pipeline that opens and loads all +of the necessary information from the MSA metadata file. For each slitlet that +it identifies as being used in the current exposure, a ``Slit`` object is +created and populated with all the shutter and source information needed in +subsequent processing steps. The list of ``Slit`` objects is attached to the +data model for the exposure being processed and passed along to all subsequent +calibration steps. + +For a given exposure, only the rows of the shutter table +where the values of `MSAMETID` and `PATT_NUM` in the science exposure match +the values of `msa_metdata_id` and `dither_point_index`, respectively, are +loaded. + +To better understand the ways in which these metadata are used, it's useful to +reference a hypothetical example of data within a ``SHUTTER_INFO`` table. +The table below shows the first 9 rows of a ``SHUTTER_INFO`` table for a MOS exposure +that's part of a standard 3-point nod MOS observation, in which the sources +are moved to different shutters within each slitlet from one exposure to +another in the observation. Only the data for slitlet 2 are shown. In this example, +slitlet 2 is comprised of 3 shutters. Because a 3-point nod pattern has been used, +there are 3 different sets of metadata for each slitlet (one set for each dither/nod +position) and hence a total of 9 entries (3 shutters x 3 dithers). + ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| Slit | Meta | | | | Src | | | X | Y | Dith | Pri | +| | | | | | | | | | | | | +| ID | ID | Quad | Row | Col | ID | Bkg | State | pos | pos | Pt | Src | ++======+======+======+=====+=====+========+=====+=======+=======+=======+======+=====+ +| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 1 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 155 | 42 | N | OPEN | 0.399 | 0.702 | 1 | Y | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 1 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 154 | 42 | N | OPEN | 0.410 | 0.710 | 2 | Y | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 2 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 2 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 3 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 3 | N | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ +| 2 | 1 | 2 | 10 | 156 | 42 | N | OPEN | 0.389 | 0.718 | 3 | Y | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ + +The values in the `slitlet_id` column show that we're only looking at table +rows for slitlet 2, all of which come from MSA configuration (`msa_metadata_id`) 1. +The shutters that make up slitlet 2 are all contained in MSA quadrant 2 and are located in +row 10 of that quadrant. The 3 shutters making up slitlet 2 span MSA columns 154, 155, and 156. +The remaining table entries in each row vary depending on whether a source is located in +a given shutter for each dither/nod position. You can see that the source is located in +shutter column 155 in dither position 1, 154 in dither position 2, and 156 in dither position 3. +The `source_id` column shows that source 42 is in those shutter positions for those dithers. +The `background` column, meanwhile, indicates whether a given shutter only has background +signal in it for each dither position. + +When there is a source in a shutter, the `estimated_source_in_shutter_x` and +`estimated_source_in_shutter_y` columns are populated with numerical values that +estimate the source location in the shutter. These are fractional position values that +run from 0,0 at the lower-left corner of a shutter to 1,1 at the upper-right corner +(hence 0.5,0.5 indicates the shutter center). These source positions are used in +calibration steps such as :ref:`wavecorr ` and :ref:`pathloss ` +to compute the appropriate corrections. + +The flags in the `primary_source` column are used to identify which shutter +within each slitlet should be considered the "primary" shutter. This is especially +important for slitlets that contain extended sources and hence the `source_id` and +`background` entries may indicate that the source is present in multiple shutters. + +When a slitlet is found that has no shutters with a primary source (i.e. no shutters +having `primary_source` = "Y"), it is classified as a background slitlet and assigned +a source ID value that's greater than the maximum source ID assigned to other slitlets +(because such slitlets all have a source ID of zero in the MSA metadata coming from +the ground system). +These background slitlets can then be used in :ref:`master background ` +subtraction. + + +The SOURCE_INFO Metadata +------------------------ +The structure of the ``SOURCE_INFO`` table extension is as follows: + ++-------------+-----------+----------------------+ +| Column Name | Data Type | Contents | ++=============+===========+======================+ +| PROGRAM | int32 | Program ID number | ++-------------+-----------+----------------------+ +| SOURCE_ID | int32 | Source ID number | ++-------------+-----------+----------------------+ +| SOURCE_NAME | string | Source name | ++-------------+-----------+----------------------+ +| ALIAS | string | Source alias | ++-------------+-----------+----------------------+ +| RA | float64 | Source RA | ++-------------+-----------+----------------------+ +| DEC | float64 | Source Dec | ++-------------+-----------+----------------------+ +| PREIMAGE_ID | string | Pre-image ID | ++-------------+-----------+----------------------+ +| STELLARITY | float64 | Source stellarity | ++-------------+-----------+----------------------+ + +- PROGRAM: 5-digit JWST program number. +- SOURCE_ID: unique integer identifier for each source in MPT catalog. +- SOURCE_NAME: source name, typically constructed as the concatenation + of program number and source ID number. +- ALIAS: alternate source identifier from the MPT catalog. +- RA/Dec: catalog source coordinates, in decimal degrees. +- PREIMAGE_ID: name of a NIRCam mosaic used to determine the source + catalog, if one was used. +- STELLARITY: DAOphot-style stellarity value for the source, where 0 + is fully extended and 1 is an unresolved point source. + +The table below shows an example of a few rows of hypothetical source metadata, +consisting of the program ID, source ID, source name, source alias, source RA and Dec, +pre-image ID, and source stellarity. + ++------+------+-----------+-------+------------+-------------+--------------+------------+ +| | Src | Src | | | | Pre | | +| | | | | | | | | +| PID | ID | Name | Alias | RA | Dec | Image | Stellarity | ++======+======+===========+=======+============+=============+==============+============+ +| 1180 | 42 | 1180_0042 | Bob | 53.1456291 | -27.7674976 | 95065001_001 | 1.00 | ++------+------+-----------+-------+------------+-------------+--------------+------------+ +| 1180 | 1001 | 1180_1001 | Sue | 53.1435047 | -27.7689669 | 95065001_001 | 0.00 | ++------+------+-----------+-------+------------+-------------+--------------+------------+ +| 1180 | 3333 | 1180_3333 | Erin | 53.1485349 | -27.7696165 | 95065001_001 | 0.23 | ++------+------+-----------+-------+------------+-------------+--------------+------------+ +| 1180 | 9876 | 1180_9876 | Dave | 53.1461433 | -27.7617165 | 95065001_001 | 0.88 | ++------+------+-----------+-------+------------+-------------+--------------+------------+ + +For each slitlet identified as having a source assigned to it in the shutter metadata, +the source name, alias, RA, Dec, and stellarity are retreived from the `SOURCE_INFO` +table and stored with the ``Slit`` object created in the calibration software. +The stellarity values are used in the :ref:`source type ` +step to determine whether the source should be treated as point-like or extended. From 704239bada966c9503ba0230be5c46b506013467 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 2 Apr 2024 13:11:35 -0400 Subject: [PATCH 004/687] JP-3590: Fix config_asdf doc entry (#8406) --- docs/jwst/stpipe/config_asdf.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/stpipe/config_asdf.rst b/docs/jwst/stpipe/config_asdf.rst index b74be053ab..55b7a6ac3f 100644 --- a/docs/jwst/stpipe/config_asdf.rst +++ b/docs/jwst/stpipe/config_asdf.rst @@ -238,7 +238,7 @@ requires: ``meta`` and ``history``. The direct way of creating a parameter reference file is through the ``Step.export_config`` method, just as one would to get a basic parameter file. -The only addition is the argument ``include_meta=True``. For example, to get a +The only addition is the argument ``include_metadata=True``. For example, to get a reference-file ready version of the ``CubeBuildStep``, use the following Python code: @@ -246,7 +246,7 @@ code: >>> from jwst.cube_build import CubeBuildStep >>> step = CubeBuildStep() - >>> step.export_config('pars-cubebuildstep.asdf', include_meta=True) + >>> step.export_config('pars-cubebuildstep.asdf', include_metadata=True) The explanations for the ``meta`` and ``history`` blocks are given below. From 804743f3957c11760a3d681d59ca18a6cae7bc09 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 2 Apr 2024 10:26:00 -0700 Subject: [PATCH 005/687] JP-3494: write tweakreg source catalogs into user-specified output_dir (#8386) Co-authored-by: Howard Bushouse --- CHANGES.rst | 5 ++ jwst/tweakreg/tests/test_tweakreg.py | 27 +++++++++++ jwst/tweakreg/tweakreg_step.py | 72 +++++++++++++++++++++------- 3 files changed, 86 insertions(+), 18 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index baee14669f..de15c485f4 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -20,6 +20,11 @@ ramp_fitting to use uint16 instead of uint8, in order to avoid potential overflow/wraparound problems. [#8377] +tweakreg +-------- + +- Output source catalog file now respects ``output_dir`` parameter. [#8386] + 1.14.0 (2024-03-29) =================== diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index cac427d952..a240fca281 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -3,6 +3,7 @@ import asdf from astropy.modeling.models import Shift +from astropy.table import Table import pytest from jwst.tweakreg import tweakreg_step @@ -10,6 +11,16 @@ from stdatamodels.jwst.datamodels import ImageModel +@pytest.fixture +def dummy_source_catalog(): + + columns = ['id', 'xcentroid', 'ycentroid', 'flux'] + catalog = Table(names=columns, dtype=(int, float, float, float)) + catalog.add_row([1, 100.0, 100.0, 100.0]) + + return catalog + + @pytest.mark.parametrize("offset, is_good", [(1 / 3600, True), (11 / 3600, False)]) def test_is_wcs_correction_small(offset, is_good): path = os.path.join(os.path.dirname(__file__), "mosaic_long_i2d_gwcs.asdf") @@ -63,3 +74,19 @@ def test_expected_failure_bad_starfinder(): model = ImageModel() with pytest.raises(ValueError): tweakreg_catalog.make_tweakreg_catalog(model, 5.0, bkg_boxsize=400, starfinder='bad_value') + + +def test_write_catalog(dummy_source_catalog, tmp_cwd): + ''' + Covers an issue where catalog write did not respect self.output_dir + ''' + + OUTDIR = 'outdir' + model = ImageModel() + step = tweakreg_step.TweakRegStep() + os.mkdir(OUTDIR) + step.output_dir = OUTDIR + expected_outfile = os.path.join(OUTDIR, 'catalog.ecsv') + step._write_catalog(model, dummy_source_catalog, 'catalog.ecsv') + + assert os.path.exists(expected_outfile) \ No newline at end of file diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index fa46c52b02..8971010d45 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -243,23 +243,7 @@ def process(self, input): .format(len(catalog), filename)) if new_cat and self.save_catalogs: - catalog_filename = filename.replace( - '.fits', '_cat.{}'.format(self.catalog_format) - ) - if self.catalog_format == 'ecsv': - fmt = 'ascii.ecsv' - elif self.catalog_format == 'fits': - # NOTE: The catalog must not contain any 'None' values. - # FITS will also not clobber existing files. - fmt = 'fits' - else: - raise ValueError( - '\'catalog_format\' must be "ecsv" or "fits".' - ) - catalog.write(catalog_filename, format=fmt, overwrite=True) - self.log.info('Wrote source catalog: {}' - .format(catalog_filename)) - image_model.meta.tweakreg_catalog = catalog_filename + image_model = self._write_catalog(image_model, catalog, filename) # Temporarily attach catalog to the image model so that it follows # the grouping by exposure, to be removed after use below @@ -412,7 +396,10 @@ def process(self, input): # whatever convention is determined by the JWST Cal Working # Group. if self.save_abs_catalog: - output_name = 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower()) + if self.output_dir is None: + output_name = 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower()) + else: + output_name = path.join(self.output_dir, 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower())) else: output_name = None @@ -524,6 +511,55 @@ def process(self, input): ) return images + + + def _write_catalog(self, image_model, catalog, filename): + ''' + Determine output filename for catalog based on outfile for step + and output dir, then write catalog to file. + + Parameters + ---------- + image_model : jwst.datamodels.ImageModel + Image model containing the source catalog. + catalog : astropy.table.Table + Table containing the source catalog. + filename : str + Output filename for step + + Returns + ------- + image_model : jwst.datamodels.ImageModel + Image model with updated catalog information. + ''' + + catalog_filename = str(filename).replace( + '.fits', '_cat.{}'.format(self.catalog_format) + ) + if self.catalog_format == 'ecsv': + fmt = 'ascii.ecsv' + elif self.catalog_format == 'fits': + # NOTE: The catalog must not contain any 'None' values. + # FITS will also not clobber existing files. + fmt = 'fits' + else: + raise ValueError( + '\'catalog_format\' must be "ecsv" or "fits".' + ) + if self.output_dir is None: + catalog.write(catalog_filename, format=fmt, overwrite=True) + else: + catalog.write( + path.join(self.output_dir, catalog_filename), + format=fmt, + overwrite=True + ) + self.log.info('Wrote source catalog: {}' + .format(catalog_filename)) + image_model.meta.tweakreg_catalog = catalog_filename + + return image_model + def _is_wcs_correction_small(self, wcs, twcs): """Check that the newly tweaked wcs hasn't gone off the rails""" From fa153ccace2da3a7425f82ad9fee4b5b7f080042 Mon Sep 17 00:00:00 2001 From: Robert Jedrzejewski Date: Thu, 4 Apr 2024 08:50:01 -0400 Subject: [PATCH 006/687] JP-3151: Don't make associations for NRS2 IFU if detector not illuminated (#8395) Co-authored-by: Howard Bushouse --- CHANGES.rst | 6 ++++++ jwst/associations/lib/rules_level2b.py | 7 ++++++- jwst/associations/mkpool.py | 3 ++- jwst/associations/tests/test_exposerr.py | 2 +- 4 files changed, 15 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index de15c485f4..226e869f7d 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,12 @@ 1.14.1 (unreleased) =================== +associations +------------ + +- Ensure NRS IFU exposures don't make a spec2 association for grating/filter combinations + where the nrs2 detector isn't illuminated. Remove dupes in mkpool. [#8395] + documentation ------------- diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index 4b652522f9..5217591266 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -320,7 +320,12 @@ def __init__(self, *args, **kwargs): ) ], reduce=Constraint.notany - ) + ), + SimpleConstraint( + value=True, + test=lambda value, item: nrsifu_valid_detector(item), + force_unique=False + ), ]) # Now check and continue initialization. diff --git a/jwst/associations/mkpool.py b/jwst/associations/mkpool.py index 38deb461c0..8cc7a6039f 100644 --- a/jwst/associations/mkpool.py +++ b/jwst/associations/mkpool.py @@ -106,9 +106,10 @@ def mkpool(data, params = params.difference(IGNORE_KEYS) params = [item.lower() for item in params] + # Make sure there's no duplicates + params = list(set(params)) params.sort() defaults = {param: 'null' for param in params} - pool = AssociationPool(names=params, dtype=[object] * len(params)) # Set default values for user-settable non-header parameters diff --git a/jwst/associations/tests/test_exposerr.py b/jwst/associations/tests/test_exposerr.py index e9d6ce8bdd..9d48b8ed3a 100644 --- a/jwst/associations/tests/test_exposerr.py +++ b/jwst/associations/tests/test_exposerr.py @@ -21,7 +21,7 @@ def test_exposerr(): pool=pool ) asns = generated.associations - assert len(asns) > 1 + assert len(asns) == 1 for asn in asns: any_degraded = False for product in asn['products']: From ccdb12d7cb7f12a8ceb4008ab54787f79fc34823 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Fri, 5 Apr 2024 15:42:30 -0400 Subject: [PATCH 007/687] JP-3518: Match imprint and science association members by mosaic and dither position (#8410) --- CHANGES.rst | 3 +++ jwst/associations/lib/rules_level2_base.py | 6 +----- jwst/associations/lib/rules_level2b.py | 24 +++++++++++++++++++-- jwst/regtest/test_associations_sdp_pools.py | 5 +++++ 4 files changed, 31 insertions(+), 7 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 226e869f7d..6899e2c5d3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -7,6 +7,9 @@ associations - Ensure NRS IFU exposures don't make a spec2 association for grating/filter combinations where the nrs2 detector isn't illuminated. Remove dupes in mkpool. [#8395] +- Match NIRSpec imprint observations to science exposures on mosaic tile location + and dither pointing, ``MOSTILNO`` and ``DITHPTIN``. [#8410] + documentation ------------- diff --git a/jwst/associations/lib/rules_level2_base.py b/jwst/associations/lib/rules_level2_base.py index 9243b35537..fe07c1463a 100644 --- a/jwst/associations/lib/rules_level2_base.py +++ b/jwst/associations/lib/rules_level2_base.py @@ -774,11 +774,7 @@ def __init__(self): DMSAttrConstraint( name='imprint', sources=['is_imprt'] - ), - DMSAttrConstraint( - name='mosaic_tile', - sources=['mostilno'], - ), + ) ], reprocess_on_match=True, work_over=ListCategory.EXISTING, diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index 5217591266..a6c2677ccd 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -304,9 +304,29 @@ def __init__(self, *args, **kwargs): ), Constraint( [ + # Allow either any background, or ensure imprint and science members + # match on mosaic tile number and dither pointing position. Constraint_Background(), - Constraint_Imprint(), - Constraint_Single_Science(self.has_science, self.get_exposure_type), + Constraint( + [ + Constraint( + [ + Constraint_Imprint(), + Constraint_Single_Science(self.has_science, self.get_exposure_type), + ], + reduce=Constraint.any + ), + DMSAttrConstraint( + name='mostilno', + sources=['mostilno'] + ), + DMSAttrConstraint( + name='dithptin', + sources=['dithptin'] + ) + ], + reduce=Constraint.all + ), ], reduce=Constraint.any ), diff --git a/jwst/regtest/test_associations_sdp_pools.py b/jwst/regtest/test_associations_sdp_pools.py index ce25e54432..4f02c0692e 100644 --- a/jwst/regtest/test_associations_sdp_pools.py +++ b/jwst/regtest/test_associations_sdp_pools.py @@ -87,6 +87,11 @@ 'xfail': None, 'slow': True, }, + 'jw01192_o008_pool.csv': { + 'args': ['-i', 'o008'], + 'xfail': None, + 'slow': False, + }, 'jw01194_20230115t113819_pool': { 'args': [], 'xfail': None, From 390f1b5e77bde782df9cc70bd7fa921c6c75cb54 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 10 Apr 2024 08:43:56 -0700 Subject: [PATCH 008/687] JP-3589: fixed deprecated code from numpy 2.0 release (#8415) --- CHANGES.rst | 10 ++++++++++ jwst/ami/leastsqnrm.py | 6 +++--- jwst/extract_1d/soss_extract/atoca.py | 2 +- 3 files changed, 14 insertions(+), 4 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 6899e2c5d3..3e55fec513 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ 1.14.1 (unreleased) =================== +ami +--- + +- Replaced deprecated ``np.mat()`` with ``np.asmatrix()``. [#8415] + associations ------------ @@ -16,6 +21,11 @@ documentation - Added docs for the NIRSpec MSA metadata file to the data products area of RTD. [#8399] +extract_1d +---------- + +- Replaced deprecated ``np.trapz`` with ``np.trapezoid()``. [#8415] + pipeline -------- diff --git a/jwst/ami/leastsqnrm.py b/jwst/ami/leastsqnrm.py index 2bc81fce0c..87f011ed63 100644 --- a/jwst/ami/leastsqnrm.py +++ b/jwst/ami/leastsqnrm.py @@ -615,7 +615,7 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): from linearfit import linearfit # dependent variables - M = np.mat(flatimg) + M = np.asmatrix(flatimg) # photon noise noise = np.sqrt(np.abs(flatimg)) @@ -625,9 +625,9 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): # uniform weight wy = weights - S = np.mat(np.diag(wy)) + S = np.asmatrix(np.diag(wy)) # matrix of independent variables - C = np.mat(flatmodeltransp) + C = np.asmatrix(flatmodeltransp) # initialize object result = linearfit.LinearFit(M, S, C) diff --git a/jwst/extract_1d/soss_extract/atoca.py b/jwst/extract_1d/soss_extract/atoca.py index a2afcd81fe..1331c7a7b3 100644 --- a/jwst/extract_1d/soss_extract/atoca.py +++ b/jwst/extract_1d/soss_extract/atoca.py @@ -1485,7 +1485,7 @@ def bin_to_pixel(self, i_order=0, grid_pix=None, grid_f_k=None, convolved_spectr # Integrate integrand = fct_f_k(x_grid) * x_grid - bin_val.append(np.trapz(integrand, x_grid)) + bin_val.append(np.trapezoid(integrand, x_grid)) # Convert to array and return with the pixel centers. return pix_center, np.array(bin_val) From 14c81773863d53561f8e1b08e7d242d9d9572805 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 10 Apr 2024 08:55:03 -0700 Subject: [PATCH 009/687] JP-3585: remove deprecated datamodel types (#8388) Co-authored-by: Howard Bushouse --- CHANGES.rst | 6 ++++++ jwst/datamodels/__init__.py | 4 ++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3e55fec513..a75e237a25 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -26,6 +26,12 @@ extract_1d - Replaced deprecated ``np.trapz`` with ``np.trapezoid()``. [#8415] +general +------- + +- Removed deprecated stdatamodels model types ``DrizProductModel``, + ``MIRIRampModel``, and ``MultiProductModel``. [#8388] + pipeline -------- diff --git a/jwst/datamodels/__init__.py b/jwst/datamodels/__init__.py index 0372570743..2bf017f093 100644 --- a/jwst/datamodels/__init__.py +++ b/jwst/datamodels/__init__.py @@ -29,10 +29,10 @@ _jwst_models = ["ModelContainer", "SourceModelContainer"] # Deprecated modules in stdatamodels -_deprecated_modules = ['drizproduct', 'multiprod', 'schema'] +_deprecated_modules = ['schema'] # Deprecated models in stdatamodels -_deprecated_models = ['DrizProductModel', 'MultiProductModel', 'MIRIRampModel'] +_deprecated_models = [] # Import all submodules from stdatamodels.jwst.datamodels for attr in dir(stdatamodels.jwst.datamodels): From ffc35675f95e10900bf27ad61d58740eaed47a3d Mon Sep 17 00:00:00 2001 From: Jane Morrison Date: Wed, 10 Apr 2024 12:01:37 -0700 Subject: [PATCH 010/687] JP-3288 use DQ plane in residual_fringe code (#8381) Co-authored-by: Howard Bushouse --- CHANGES.rst | 5 +++++ jwst/regtest/test_miri_residual_fringe.py | 2 +- jwst/residual_fringe/residual_fringe.py | 6 ++++-- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index a75e237a25..b5dd6ac022 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -45,6 +45,11 @@ ramp_fitting to use uint16 instead of uint8, in order to avoid potential overflow/wraparound problems. [#8377] +residual_fringe +--------------- + +- Use DQ plane to exclude pixels marked as DO_NOT_USE in correction. [#8381] + tweakreg -------- diff --git a/jwst/regtest/test_miri_residual_fringe.py b/jwst/regtest/test_miri_residual_fringe.py index 282bb4a97f..3d01f15ebc 100644 --- a/jwst/regtest/test_miri_residual_fringe.py +++ b/jwst/regtest/test_miri_residual_fringe.py @@ -10,7 +10,7 @@ def test_residual_fringe_cal(rtdata, fitsdiff_default_kwargs): """Run residual fringe correction on MIRI IFUShort """ - input_file = 'MIRM108-SHORT-6021192005_1_495_MIRIFUSHORT_cal.fits' + input_file = 'jw01523001001_03101_00001_mirifushort_cal.fits' rtdata.get_data(f"miri/mrs/{input_file}") args = [ diff --git a/jwst/residual_fringe/residual_fringe.py b/jwst/residual_fringe/residual_fringe.py index 7fc1f0c08f..09a6597df3 100644 --- a/jwst/residual_fringe/residual_fringe.py +++ b/jwst/residual_fringe/residual_fringe.py @@ -83,11 +83,13 @@ def do_correction(self): raise ErrorNoFringeFlat("The fringe flat step has not been run on file %s", self.input_model.meta.filename) - # Remove any NaN values from the data prior to processing + # Remove any NaN values and flagged DO_NOT_USE pixels from the data prior to processing # Set them to 0 for the residual fringe routine # They will be re-added at the end output_data = self.model.data.copy() - nanval_indx = np.where(~np.isfinite(output_data)) + DO_NOT_USE = datamodels.dqflags.pixel["DO_NOT_USE"] + nanval_indx = np.where(np.logical_or(np.bitwise_and(self.model.dq, DO_NOT_USE).astype(bool), + ~np.isfinite(output_data))) output_data[nanval_indx] = 0 # normalise the output_data to remove units From bc3274ac181a77e06e7bcc967cc3f8a2bbf2ea46 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 11 Apr 2024 06:09:42 -0700 Subject: [PATCH 011/687] JP-3150: skip extract1d for NIRISS SOSS data in F277W filter (#8275) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/extract_1d/extract_1d_step.py | 6 ++--- jwst/extract_1d/tests/test_expected_skips.py | 26 ++++++++++++++++++++ 3 files changed, 31 insertions(+), 4 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b5dd6ac022..f226f0ce55 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -24,6 +24,9 @@ documentation extract_1d ---------- +- Added a hook to bypass the ``extract_1d`` step for NIRISS SOSS data in + the F277W filter with warning. [#8275] + - Replaced deprecated ``np.trapz`` with ``np.trapezoid()``. [#8415] general diff --git a/jwst/extract_1d/extract_1d_step.py b/jwst/extract_1d/extract_1d_step.py index 44c8f71ca2..7cade1a5fa 100644 --- a/jwst/extract_1d/extract_1d_step.py +++ b/jwst/extract_1d/extract_1d_step.py @@ -398,11 +398,9 @@ def process(self, input): if input_model.meta.instrument.filter == 'CLEAR': self.log.info('Exposure is through the GR700XD + CLEAR (science).') soss_filter = 'CLEAR' - elif input_model.meta.instrument.filter == 'F277W': - self.log.info('Exposure is through the GR700XD + F277W (calibration).') - soss_filter = 'F277W' else: - self.log.error('The SOSS extraction is implemented for the CLEAR or F277W filters only.') + self.log.error('The SOSS extraction is implemented for the CLEAR filter only.' + f'Requested filter is {input_model.meta.instrument.filter}.') self.log.error('extract_1d will be skipped.') input_model.meta.cal_step.extract_1d = 'SKIPPED' return input_model diff --git a/jwst/extract_1d/tests/test_expected_skips.py b/jwst/extract_1d/tests/test_expected_skips.py index 86910c037e..2f761107b8 100644 --- a/jwst/extract_1d/tests/test_expected_skips.py +++ b/jwst/extract_1d/tests/test_expected_skips.py @@ -21,6 +21,22 @@ def mock_niriss_full(): return model +@pytest.fixture(scope='module') +def mock_niriss_f277w(): + model = dm.CubeModel((3, 3, 3)) + model.meta.instrument.name = 'NIRISS' + model.meta.instrument.detector = 'NIS' + model.meta.observation.date = '2023-07-22' + model.meta.observation.time = '06:24:45.569' + model.meta.instrument.name = 'NIRISS' + model.meta.instrument.detector = 'NIS' + model.meta.instrument.filter = 'F277W' + model.meta.exposure.type = 'NIS_SOSS' + model.meta.subarray.name = 'FULL' + model.data = np.arange(27).reshape((3, 3, 3)) + return model + + def test_expected_skip_niriss_soss_full(mock_niriss_full): with mock_niriss_full as model: @@ -29,3 +45,13 @@ def test_expected_skip_niriss_soss_full(mock_niriss_full): assert result2.meta.cal_step.photom == 'SKIPPED' assert result2.meta.cal_step.extract_1d == 'SKIPPED' assert np.all(result2.data == model.data) + + +def test_expected_skip_niriss_soss_f277w(mock_niriss_f277w): + + with mock_niriss_f277w as model: + result = Extract1dStep().process(model) + result2 = PhotomStep().process(result) + assert result2.meta.cal_step.photom == 'SKIPPED' + assert result2.meta.cal_step.extract_1d == 'SKIPPED' + assert np.all(result2.data == model.data) From f5e01e9a7a3317528e401633a6c9b58e42eacbf8 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 11 Apr 2024 11:36:13 -0400 Subject: [PATCH 012/687] Revert 8305 (#8419) --- CHANGES.rst | 6 ++++ jwst/resample/resample.py | 68 ++------------------------------------- 2 files changed, 8 insertions(+), 66 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index f226f0ce55..16e6bbd72e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -48,6 +48,12 @@ ramp_fitting to use uint16 instead of uint8, in order to avoid potential overflow/wraparound problems. [#8377] +resample +-------- + +- Remove sleep in median combination added in 8305 as it did not address + the issue in operation [#8419] + residual_fringe --------------- diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 7a8f7b67c4..b168151e8c 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -1,6 +1,5 @@ import logging import os -import time import numpy as np from drizzle import util @@ -290,18 +289,9 @@ def resample_many_to_many(self): if not self.in_memory: # Write out model to disk, then return filename - # This sometimes causes problems in operations because - # the output file isn't always fully written before the - # execution continues output_name = output_model.meta.filename - sleep_min = 1.0 - sleep_max = 40.0 - status = self.write_with_dimension_check(output_model, output_name, - min_wait_time=sleep_min, - max_wait_time=sleep_max) - if status: - log.warning(f"Wait time of {sleep_max}s exceeded.") - log.warning("Continuing with possibly corrupted i2d file") + output_model.save(output_name) + log.info(f"Saved model in {output_name}") self.output_models.append(output_name) else: self.output_models.append(output_model.copy()) @@ -310,60 +300,6 @@ def resample_many_to_many(self): return self.output_models - def write_with_dimension_check(self, output_model, output_name, min_wait_time=1.0, max_wait_time=40.0): - """Write a datamodel to disk after waiting for a short interval, then read back the datamodel - and check if the dimensions match those of the model that was written. If not, delete the - output file, write the datamodel to disk, double the wait time, and check the dimensions - again. Keep iterating like this until either the datamodel data dimensions match, or the wait - wait time exceeds ``max_wait_time`` seconds. - - Parameters - ---------- - - output_model : JWST datamodel - The datamodel that is being written to disk - - output_name : string - The name of the file to be written to - - min_wait_time : float - Initial wait time between writing the file to disk and then reading it back - compare, in seconds - - max_wait_time : float - If the wait time exceeds this, in seconds, exit with a warning - - Returns - ------- - - status : int - Integer status value, 0 = good, 1 = max_wait_time exceeded, so file may be corrupted - - """ - status = 0 - filesavedOK = False - wait_time = min_wait_time - while not filesavedOK: - output_model.save(output_name) - if wait_time > max_wait_time: - log.info(f"Saved model in {output_name}") - status = 1 - break - log.info(f"Sleeping for {wait_time}s and reading back saved data") - time.sleep(wait_time) - readback_model = datamodels.open(output_name) - if output_model.data.shape == readback_model.data.shape: - log.info("Shape of read back model data matches that of input model") - filesavedOK = True - log.info(f"Saved model in {output_name}") - else: - log.info("Shapes of data in datamodel before and after saving don't match") - log.info("Removing output file and re-writing") - os.remove(output_name) - wait_time = wait_time * 2.0 - return status - - def resample_many_to_one(self): """Resample and coadd many inputs to a single output. From 8ac70606d6415346242a0372ba9be898c82340a9 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 11 Apr 2024 13:34:17 -0400 Subject: [PATCH 013/687] JP-2943: add asn_id to outlier_i2d filename (#8418) --- CHANGES.rst | 5 +++++ docs/jwst/outlier_detection/outlier_detection.rst | 4 +++- jwst/outlier_detection/outlier_detection.py | 15 ++++++++++----- jwst/outlier_detection/outlier_detection_step.py | 9 ++++++--- jwst/resample/resample.py | 7 ++++++- jwst/resample/resample_spec.py | 2 ++ 6 files changed, 32 insertions(+), 10 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 16e6bbd72e..e0c94003e0 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -35,6 +35,11 @@ general - Removed deprecated stdatamodels model types ``DrizProductModel``, ``MIRIRampModel``, and ``MultiProductModel``. [#8388] +outlier_detection +----------------- + +- Add association id to ``outlier_i2d`` intermediate filenames. [#8418] + pipeline -------- diff --git a/docs/jwst/outlier_detection/outlier_detection.rst b/docs/jwst/outlier_detection/outlier_detection.rst index ae4d870753..81fa2faa0a 100644 --- a/docs/jwst/outlier_detection/outlier_detection.rst +++ b/docs/jwst/outlier_detection/outlier_detection.rst @@ -58,7 +58,9 @@ Specifically, this routine performs the following operations: should be used when resampling to create the output mosaic. Any pixel with a DQ value not included in this value (or list of values) will be ignored when resampling. - * Resampled images will be written out to disk as `_outlier_i2d.fits` by default. + * Resampled images will be written out to disk with the suffix ``__outlier_i2d.fits`` + if the input model container has an , otherwise the suffix will be ``_outlier_i2d.fits`` + by default. * **If resampling is turned off** through the use of the ``resample_data`` parameter, a copy of the unrectified input images (as a ModelContainer) will be used for subsequent processing. diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index c4eece8f84..fc29873094 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -210,11 +210,16 @@ def do_detection(self): # Perform median combination on set of drizzled mosaics median_model.data = self.create_median(drizzled_models) - median_model_output_path = self.make_output_path( - basepath=median_model.meta.filename.replace(self.resample_suffix, '.fits'), - suffix='median') - median_model.save(median_model_output_path) - log.info(f"Saved model in {median_model_output_path}") + if pars['save_intermediate_results']: + if self.outlierpars.get('asn_id', None) is None: + suffix_to_remove = self.resample_suffix + else: + suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}" + median_model_output_path = self.make_output_path( + basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), + suffix='median') + median_model.save(median_model_output_path) + log.info(f"Saved model in {median_model_output_path}") if pars['resample_data']: # Blot the median image back to recreate each input image specified diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index de7a044df5..077fdcf782 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -165,7 +165,7 @@ def process(self, input_data): reffiles = {} # Set up outlier detection, then do detection - step = detection_step(self.input_models, reffiles=reffiles, **pars) + step = detection_step(self.input_models, reffiles=reffiles, asn_id=asn_id, **pars) step.do_detection() state = 'COMPLETE' @@ -177,8 +177,11 @@ def process(self, input_data): if not self.save_intermediate_results: # Remove unwanted files crf_path = self.make_output_path(basepath=model.meta.filename) - suffix = model.meta.filename.split(sep='_')[-1] - outlr_file = model.meta.filename.replace(suffix, 'outlier_i2d.fits') + if asn_id is None: + suffix = model.meta.filename.split(sep='_')[-1] + outlr_file = model.meta.filename.replace(suffix, 'outlier_i2d.fits') + else: + outlr_file = crf_path.replace('crf', 'outlier_i2d') blot_path = crf_path.replace('crf', 'blot') median_path = blot_path.replace('blot', 'median') diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index b168151e8c..19a38489fa 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -93,6 +93,8 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, crval = kwargs.get('crval', None) rotation = kwargs.get('rotation', None) + self.asn_id = kwargs.get('asn_id', None) + if pscale is not None: log.info(f'Output pixel scale: {pscale} arcsec.') pscale /= 3600.0 @@ -225,7 +227,10 @@ def resample_many_to_many(self): output_type = exposure[0].meta.filename[indx:] output_root = '_'.join(exposure[0].meta.filename.replace( output_type, '').split('_')[:-1]) - output_model.meta.filename = f'{output_root}_outlier_i2d{output_type}' + if self.asn_id is not None: + output_model.meta.filename = f'{output_root}_{self.asn_id}_outlier_i2d{output_type}' + else: + output_model.meta.filename = f'{output_root}_outlier_i2d{output_type}' # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index fa9d46503f..5add89b498 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -80,6 +80,8 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.input_pixscale0 = None # computed pixel scale of the first image (deg) self._recalc_pscale_ratio = pscale is not None + self.asn_id = kwargs.get('asn_id', None) + # Define output WCS based on all inputs, including a reference WCS if output_wcs is None: if resample_utils.is_sky_like( From fb8605c12d9dd6165f95236f2097ecd9cecf4735 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 11 Apr 2024 14:06:58 -0400 Subject: [PATCH 014/687] remove global multiprocessing.set_start_method (#8343) --- jwst/jump/jump_step.py | 3 --- jwst/ramp_fitting/ramp_fit_step.py | 2 -- jwst/wfss_contam/observations.py | 5 +++-- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/jwst/jump/jump_step.py b/jwst/jump/jump_step.py index 7e95f46383..8670a44d31 100755 --- a/jwst/jump/jump_step.py +++ b/jwst/jump/jump_step.py @@ -4,12 +4,9 @@ from ..stpipe import Step from .jump import run_detect_jumps import time -import multiprocessing __all__ = ["JumpStep"] -multiprocessing.set_start_method('forkserver', force=True) - class JumpStep(Step): """ diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index 4bfe500ef2..aa39d1e86d 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -18,12 +18,10 @@ import logging import copy import warnings -import multiprocessing log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -multiprocessing.set_start_method('forkserver', force=True) __all__ = ["RampFitStep"] diff --git a/jwst/wfss_contam/observations.py b/jwst/wfss_contam/observations.py index 19aa05d7f8..62b66dfcf6 100644 --- a/jwst/wfss_contam/observations.py +++ b/jwst/wfss_contam/observations.py @@ -1,6 +1,6 @@ import time +import multiprocessing import numpy as np -from multiprocessing import Pool from scipy import sparse @@ -259,7 +259,8 @@ def disperse_chunk(self, c, order, wmin, wmax, sens_waves, sens_resp): time1 = time.time() if self.max_cpu > 1: - mypool = Pool(self.max_cpu) # Create the pool + ctx = multiprocessing.get_context("forkserver") + mypool = ctx.Pool(self.max_cpu) # Create the pool all_res = mypool.imap_unordered(dispersed_pixel, pars) # Fill the pool mypool.close() # Drain the pool else: From 903cc3c2f05febab2a9dc9938836ac30cbd9317b Mon Sep 17 00:00:00 2001 From: David Law Date: Fri, 12 Apr 2024 09:12:16 -0400 Subject: [PATCH 015/687] JP-3491: Modify MIRI LRS WCS mapping code (#8411) Co-authored-by: Howard Bushouse --- CHANGES.rst | 7 +++ jwst/assign_wcs/miri.py | 103 ++++++++++++++++++---------------------- 2 files changed, 53 insertions(+), 57 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index e0c94003e0..c589dccd29 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -6,6 +6,13 @@ ami - Replaced deprecated ``np.mat()`` with ``np.asmatrix()``. [#8415] +assign_wcs +---------- + +- Change MIRI LRS WCS code to handle the tilted trace via a centroid shift as a function + of pixel row rather than a rotation of the pixel coordinates. The practical impact is + to ensure that iso-lambda is along pixel rows after this change. [#8411] + associations ------------ diff --git a/jwst/assign_wcs/miri.py b/jwst/assign_wcs/miri.py index 4727fea77b..f8f4372687 100644 --- a/jwst/assign_wcs/miri.py +++ b/jwst/assign_wcs/miri.py @@ -289,57 +289,29 @@ def lrs_distortion(input_model, reference_files): # Now deal with the fact that the spectral trace isn't perfectly up and down along detector. # This information is contained in the xcenter/ycenter values in the CDP table, but we'll handle it - # as a simple rotation using a linear fit to this relation provided by the CDP. - - z = np.polyfit(xcen, ycen, 1) - slope = 1. / z[0] - traceangle = np.arctan(slope) * 180. / np.pi # trace angle in degrees - rot = models.Rotation2D(traceangle) # Rotation model - - # Now include this rotation in our overall transform - # First shift to a frame relative to the trace zeropoint, then apply the rotation - # to correct for the curved trace. End in a rotated frame relative to zero at the reference point - # and where yrot is aligned with the spectral trace) - xysubtoxyrot = models.Shift(-zero_point[0]) & models.Shift(-zero_point[1]) | rot - - # Next shift back to the subarray frame, and then map to v2v3 - xyrottov2v3 = models.Shift(zero_point[0]) & models.Shift(zero_point[1]) | det_to_v2v3 - - # The two models together - xysubtov2v3 = xysubtoxyrot | xyrottov2v3 - - # Work out the spectral component of the transform - # First compute the reference trace in the rotated-Y frame - xcenrot, ycenrot = rot(xcen, ycen) - # The input table of wavelengths isn't perfect, and the delta-wavelength - # steps show some unphysical behaviour - # Therefore fit with a spline for the ycenrot->wavelength transform - # Reverse vectors so that yinv is increasing (needed for spline fitting function) - yrev = ycenrot[::-1] - wrev = wavetab[::-1] + # as a simple x shift using a linear fit to this relation provided by the CDP. + # First convert the values in CDP table to subarray x/y + xcen_subarray = xcen + zero_point[0] + ycen_subarray = ycen + zero_point[1] + + # Fit for X shift as a function of Y # Spline fit with enforced smoothness - spl = UnivariateSpline(yrev, wrev, s=0.002) - # Evaluate the fit at the rotated-y reference points - wavereference = spl(yrev) - # wavereference now contains the wavelengths corresponding to regularly-sampled ycenrot, create the model - wavemodel = models.Tabular1D(lookup_table=wavereference, points=yrev, name='waveref', + spl = UnivariateSpline(ycen_subarray[::-1], xcen_subarray[::-1] - zero_point[0], s=0.002) + # Evaluate the fit at the y reference points + xshiftref = spl(ycen_subarray) + # This function will give slit dX as a function of Y subarray pixel value + dxmodel = models.Tabular1D(lookup_table=xshiftref, points=ycen_subarray, name='xshiftref', bounds_error=False, fill_value=np.nan) - # Now construct the inverse spectral transform. - # First we need to create a spline going from wavereference -> ycenrot - spl2 = UnivariateSpline(wavereference[::-1], ycenrot, s=0.002) - # Make a uniform grid of wavelength points from min to max, sampled according - # to the minimum delta in the input table - dw = np.amin(np.absolute(np.diff(wavereference))) - wmin = np.amin(wavereference) - wmax = np.amax(wavereference) - wgrid = np.arange(wmin, wmax, dw) - # Evaluate the rotated y locations of the grid - ygrid = spl2(wgrid) - # ygrid now contains the rotated y pixel locations corresponding to - # regularly-sampled wavelengths, create the model - wavemodel.inverse = models.Tabular1D(lookup_table=ygrid, points=wgrid, name='waverefinv', - bounds_error=False, fill_value=np.nan) + # Fit for the wavelength as a function of Y + # Reverse the vectors so that yinv is increasing (needed for spline fitting function) + # Spline fit with enforced smoothness + spl = UnivariateSpline(ycen_subarray[::-1], wavetab[::-1], s=0.002) + # Evaluate the fit at the y reference points + wavereference = spl(ycen_subarray) + # This model will now give the wavelength corresponding to a given Y subarray pixel value + wavemodel = models.Tabular1D(lookup_table=wavereference, points=ycen_subarray, name='waveref', + bounds_error=False, fill_value=np.nan) # Wavelength barycentric correction try: @@ -352,24 +324,41 @@ def lrs_distortion(input_model, reference_files): wavemodel = wavemodel | velocity_corr log.info("Applied Barycentric velocity correction : {}".format(velocity_corr[1].amplitude.value)) + # What is the effective slit X as a function of subarray x,y? + xmodel = models.Mapping([0], n_inputs=2) - (models.Mapping([1], n_inputs=2) | dxmodel) + # What is the effective Y as a function of subarray x,y? + ymodel = models.Mapping([1], n_inputs=2) + # What is the effective XY as a function of subarray x,y? + xymodel = models.Mapping((0, 1, 0, 1)) | xmodel & ymodel + # Define a shift by the reference point and immediately back again + # This doesn't do anything effectively, but it stores the reference point for later use in pathloss + reftransform = models.Shift(-zero_point[0]) & models.Shift(-zero_point[1]) | models.Shift(+zero_point[0]) & models.Shift(+zero_point[1]) + # Put the transforms together + xytov2v3 = reftransform | xymodel | det_to_v2v3 + # Construct the full distortion model (xsub,ysub -> v2,v3,wavelength) - lrs_wav_model = xysubtoxyrot | models.Mapping([1], n_inputs=2) | wavemodel - dettotel = models.Mapping((0, 1, 0, 1)) | xysubtov2v3 & lrs_wav_model + lrs_wav_model = models.Mapping([1], n_inputs=2) | wavemodel + dettotel = models.Mapping((0, 1, 0, 1)) | xytov2v3 & lrs_wav_model # Construct the inverse distortion model (v2,v3,wavelength -> xsub,ysub) - # Transform to get xrot from v2,v3 - v2v3toxrot = subarray_dist.inverse | xysubtoxyrot | models.Mapping([0], n_inputs=2) - # wavemodel.inverse gives yrot from wavelength - # v2,v3,lambda -> xrot,yrot - xform1 = v2v3toxrot & wavemodel.inverse - dettotel.inverse = xform1 | xysubtoxyrot.inverse + # Go from v2,v3 to slit-x + v2v3_to_xdet = det_to_v2v3.inverse | models.Mapping([0], n_inputs=2) + # Go from lambda to real y + lam_to_y = wavemodel.inverse + # Go from slit-x and real y to real-x + backwards = models.Mapping([0], n_inputs=2) + (models.Mapping([1], n_inputs=2) | dxmodel) + # Go from v2,v3,lam to real x + aa = v2v3_to_xdet & lam_to_y | backwards + # Go from v2,v3,lam to real y + bb = models.Mapping([2], n_inputs=3) | lam_to_y + # Go from v2,v3,lam, to real x,y + dettotel.inverse = models.Mapping((0, 1, 2, 0, 1, 2)) | aa & bb # Bounding box is the subarray bounding box, because we're assuming subarray coordinates passed in dettotel.bounding_box = bb_sub[::-1] return dettotel - def ifu(input_model, reference_files): """ The MIRI MRS WCS pipeline. From 6f412d72301e671974cb5847d4fbe2b00cbf99dd Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 12 Apr 2024 10:04:15 -0400 Subject: [PATCH 016/687] JP-3543: Allow alternate resampling weight type in outlier detection (#8290) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/outlier_detection/outlier_detection.py | 4 ++-- jwst/outlier_detection/outlier_detection_scaled.py | 2 +- jwst/outlier_detection/outlier_detection_spec.py | 2 +- jwst/outlier_detection/outlier_detection_step.py | 3 ++- 5 files changed, 9 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c589dccd29..b912f10ceb 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -47,6 +47,9 @@ outlier_detection - Add association id to ``outlier_i2d`` intermediate filenames. [#8418] +- Pass the ``weight_type`` parameter to all resampling function calls so that + the default weighting can be overridden by the input step parameter. [#8290] + pipeline -------- diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index fc29873094..ef584132ec 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -113,7 +113,7 @@ def _convert_inputs(self): dq=self.inputs.dq[i]) image.meta = self.inputs.meta image.wht = build_driz_weight(image, - weight_type='ivm', + weight_type=self.outlierpars['weight_type'], good_bits=bits) self.input_models.append(image) self.converted = True @@ -199,7 +199,7 @@ def do_detection(self): for i in range(len(self.input_models)): drizzled_models[i].wht = build_driz_weight( self.input_models[i], - weight_type='ivm', + weight_type=pars['weight_type'], good_bits=pars['good_bits']) # Initialize intermediate products used in the outlier detection diff --git a/jwst/outlier_detection/outlier_detection_scaled.py b/jwst/outlier_detection/outlier_detection_scaled.py index a7252bd9af..e3b8c2cfcf 100644 --- a/jwst/outlier_detection/outlier_detection_scaled.py +++ b/jwst/outlier_detection/outlier_detection_scaled.py @@ -102,7 +102,7 @@ def do_detection(self): for image in self.input_models: image.wht = resample_utils.build_driz_weight( image, - weight_type='ivm', + weight_type=pars['weight_type'], good_bits=pars['good_bits'] ) diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 1cc60db1c7..4adf2b4b06 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -88,7 +88,7 @@ def do_detection(self): for i in range(len(self.input_models)): drizzled_models[i].wht = resample_utils.build_driz_weight( self.input_models[i], - weight_type='ivm', + weight_type=pars['weight_type'], good_bits=pars['good_bits']) # Initialize intermediate products used in the outlier detection diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 077fdcf782..2c90435045 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -102,7 +102,8 @@ def process(self, input_data): # Setup outlier detection parameters pars = { - 'weight_type': self.weight_type, + 'weight_type': self.weight_type, # for calling the resample step + 'wht_type': self.weight_type, # for calling the resample class directly 'pixfrac': self.pixfrac, 'kernel': self.kernel, 'fillval': self.fillval, From 9429dc65e509bec07b7912c5e6f228257d1cfacf Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Mon, 15 Apr 2024 08:42:19 -0400 Subject: [PATCH 017/687] Remove configuration for non-existant schema tests (#8366) Co-authored-by: Howard Bushouse --- pyproject.toml | 3 --- 1 file changed, 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 833faa2d64..acccccce70 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -223,9 +223,6 @@ norecursedirs = [ "build", "venv", ] -asdf_schema_tests_enabled = true -asdf_schema_validate_default = false -asdf_schema_root = "jwst/transforms/resources/schemas jwst/datamodels/schemas" junit_family = "xunit2" inputs_root = "jwst-pipeline" results_root = "jwst-pipeline-results" From e4ddbcda96ecf541adb804eabe9a7b32f0f33f17 Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Thu, 18 Apr 2024 22:22:40 -0400 Subject: [PATCH 018/687] Fix a bug in how a image group name is determined (#8426) --- CHANGES.rst | 12 ++++--- jwst/tweakreg/tests/test_tweakreg.py | 31 ----------------- jwst/tweakreg/tweakreg_step.py | 51 ++++++---------------------- 3 files changed, 17 insertions(+), 77 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b912f10ceb..c3a2cd99db 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -16,7 +16,7 @@ assign_wcs associations ------------ -- Ensure NRS IFU exposures don't make a spec2 association for grating/filter combinations +- Ensure NRS IFU exposures don't make a spec2 association for grating/filter combinations where the nrs2 detector isn't illuminated. Remove dupes in mkpool. [#8395] - Match NIRSpec imprint observations to science exposures on mosaic tile location @@ -39,7 +39,7 @@ extract_1d general ------- -- Removed deprecated stdatamodels model types ``DrizProductModel``, +- Removed deprecated stdatamodels model types ``DrizProductModel``, ``MIRIRampModel``, and ``MultiProductModel``. [#8388] outlier_detection @@ -79,6 +79,8 @@ tweakreg - Output source catalog file now respects ``output_dir`` parameter. [#8386] +- Improved how a image group name is determined. [#8426] + 1.14.0 (2024-03-29) =================== @@ -94,7 +96,7 @@ ami - Additional optional input arguments for greater user processing flexibility. See documentation for details. [#7862] -- Bad pixel correction applied to data using new NRM reference file to calculate +- Bad pixel correction applied to data using new NRM reference file to calculate complex visibility support (M. Ireland method implemented by J. Kammerer). [#7862] - Make ``AmiAnalyze`` and ``AmiNormalize`` output conform to the OIFITS standard. [#7862] @@ -129,7 +131,7 @@ charge_migration as DO_NOT_USE. This group, and all subsequent groups, are then flagged as CHARGELOSS and DO_NOT_USE. The four nearest pixel neighbor are then flagged in the same group. [#8336] - + - Added warning handler for expected NaN and inf clipping in the ``sigma_clip`` function. [#8320] @@ -415,7 +417,7 @@ tweakreg - Fixed a bug that caused failures instead of warnings when no GAIA sources were found within the bounding box of the input image. [#8334] - + - Suppress AstropyUserWarnings regarding NaNs in the input data. [#8320] wfs_combine diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index a240fca281..cb5baea3f9 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -38,37 +38,6 @@ def test_is_wcs_correction_small(offset, is_good): assert step._is_wcs_correction_small(wcs, twcs) == is_good -@pytest.mark.parametrize( - "groups, all_group_names, common_name", - [ - ([['abc1_cal.fits', 'abc2_cal.fits']], [], ['abc']), - ( - [ - ['abc1_cal.fits', 'abc2_cal.fits'], - ['abc1_cal.fits', 'abc2_cal.fits'], - ['abc1_cal.fits', 'abc2_cal.fits'], - ['def1_cal.fits', 'def2_cal.fits'], - ], - [], - ["abc", "abc1", "abc2", "def"], - ), - ([['cba1_cal.fits', 'abc2_cal.fits']], [], ['Group #1']), - ([['cba1_cal.fits', 'abc2_cal.fits']], ['Group #1'], ['Group #2']), - ([['cba1_cal.fits', 'abc2_cal.fits']], None, ['Unnamed Group']), - ] -) -def test_common_name(groups, all_group_names, common_name): - for g, cn_truth in zip(groups, common_name): - group = [] - for fname in g: - model = ImageModel() - model.meta.filename = fname - group.append(model) - - cn = tweakreg_step._common_name(group, all_group_names) - assert cn == cn_truth - - def test_expected_failure_bad_starfinder(): model = ImageModel() diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 8971010d45..bddb2c1ff5 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -279,35 +279,30 @@ def process(self, input): g = grp_img[0] if len(g) == 0: raise AssertionError("Logical error in the pipeline code.") - group_name = _common_name(g) imcats = list(map(self._imodel2wcsim, g)) # Remove the attached catalogs for model in g: del model.catalog - self.log.info("* Images in GROUP '{}':".format(group_name)) + self.log.info(f"* Images in GROUP '{imcats[0].meta['group_id']}':") for im in imcats: - im.meta['group_id'] = group_name - self.log.info(" {}".format(im.meta['name'])) + self.log.info(f" {im.meta['name']}") self.log.info('') elif len(grp_img) > 1: # create a list of WCS-Catalog-Images Info and/or their Groups: imcats = [] - all_group_names = [] for g in grp_img: if len(g) == 0: raise AssertionError("Logical error in the pipeline code.") else: - group_name = _common_name(g, all_group_names) wcsimlist = list(map(self._imodel2wcsim, g)) # Remove the attached catalogs for model in g: del model.catalog - self.log.info("* Images in GROUP '{}':".format(group_name)) + self.log.info(f"* Images in GROUP '{wcsimlist[0].meta['group_id']}':") for im in wcsimlist: - im.meta['group_id'] = group_name - self.log.info(" {}".format(im.meta['name'])) + self.log.info(f" {im.meta['name']}") imcats.extend(wcsimlist) self.log.info('') @@ -511,7 +506,6 @@ def process(self, input): ) return images - def _write_catalog(self, image_model, catalog, filename): ''' @@ -560,7 +554,6 @@ def _write_catalog(self, image_model, catalog, filename): return image_model - def _is_wcs_correction_small(self, wcs, twcs): """Check that the newly tweaked wcs hasn't gone off the rails""" if self.use2dhist: @@ -606,41 +599,17 @@ def _imodel2wcsim(self, image_model): wcsinfo={'roll_ref': refang['roll_ref'], 'v2_ref': refang['v2_ref'], 'v3_ref': refang['v3_ref']}, - meta={'image_model': image_model, 'catalog': catalog, - 'name': model_name} + meta={ + 'image_model': image_model, + 'catalog': catalog, + 'name': model_name, + 'group_id': image_model.meta.group_id, + } ) return im -def _common_name(group, all_group_names=None): - file_names = [path.splitext(im.meta.filename)[0].strip('_- ') - for im in group] - - cn = path.commonprefix(file_names) - - if all_group_names is None: - if not cn: - return 'Unnamed Group' - else: - if not cn or cn in all_group_names: - # find the smallest group number to make "Group #..." unique - max_id = 1 - if not cn: - cn = "Group #" - for name in all_group_names: - try: - cid = int(name.lstrip(cn)) - if cid >= max_id: - max_id = cid + 1 - except ValueError: - pass - cn = f"{cn}{max_id}" - all_group_names.append(cn) - - return cn - - def _parse_catfile(catfile): if catfile is None or not catfile.strip(): return None From d57f6337ba1b5a6f14379f236b83f741afffa413 Mon Sep 17 00:00:00 2001 From: David Law Date: Fri, 19 Apr 2024 08:59:49 -0400 Subject: [PATCH 019/687] JP-3587: Clean up NIRSpec IFU flat field SCI/ERR/DQ flagging (#8385) Co-authored-by: Howard Bushouse --- CHANGES.rst | 6 ++++++ jwst/flatfield/flat_field.py | 11 +++++++++-- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c3a2cd99db..73c37648b6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -36,6 +36,12 @@ extract_1d - Replaced deprecated ``np.trapz`` with ``np.trapezoid()``. [#8415] +flat_field +---------- + +- Update the flatfield code for NIRSpec IFU data to ensure that SCI=ERR=NaN and + DQ has the DO_NOT_USE flag set outside the footprint of the IFU slices [#8385] + general ------- diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 3e89c52ab4..3795fd74a4 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -1627,9 +1627,9 @@ def flat_for_nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, """ any_updated = False exposure_type = output_model.meta.exposure.type - flat = np.ones_like(output_model.data) + flat = np.ones_like(output_model.data) * np.nan flat_dq = np.zeros_like(output_model.dq) - flat_err = np.zeros_like(output_model.data) + flat_err = np.zeros_like(output_model.data) * np.nan try: list_of_wcs = nirspec.nrs_ifu_wcs(output_model) @@ -1726,6 +1726,13 @@ def flat_for_nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, any_updated = True + # Ensure consistency between NaN-valued pixels and the DO_NOT_USE flag + indx = np.where((flat_dq & dqflags.pixel['DO_NOT_USE']) != 0) + flat[indx] = np.nan + flat_err[indx] = np.nan + indx = np.where(~ np.isfinite(flat)) + flat_dq[indx] = flat_dq[indx] | dqflags.pixel['DO_NOT_USE'] + # That's all folks return flat, flat_dq, flat_err, any_updated From b4c490bbed3da8baf5ef20e4f5100c0b2a74f26e Mon Sep 17 00:00:00 2001 From: Maria Date: Mon, 22 Apr 2024 12:28:08 -0400 Subject: [PATCH 020/687] JP-2980 Documentation improvement for multiprocessing: a script which spawns processes on import will cause system failure (#8408) Co-authored-by: Ned Molter Co-authored-by: Howard Bushouse Co-authored-by: Brett Graham --- CHANGES.rst | 2 + .../running_pipeline_python.rst | 160 ++++++++++++++++++ 2 files changed, 162 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 73c37648b6..3a08f3e8a3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -27,6 +27,8 @@ documentation - Added docs for the NIRSpec MSA metadata file to the data products area of RTD. [#8399] + +- Added documentation for multiprocessing. [#8408] extract_1d ---------- diff --git a/docs/jwst/user_documentation/running_pipeline_python.rst b/docs/jwst/user_documentation/running_pipeline_python.rst index 1a7ce58128..6d27cd0908 100644 --- a/docs/jwst/user_documentation/running_pipeline_python.rst +++ b/docs/jwst/user_documentation/running_pipeline_python.rst @@ -529,3 +529,163 @@ individual step parameter must be set when using this method, or else the coded defaults will be used, which may be inappropriate for the dataset being processed. See :ref:`call_examples` for more information. + + +.. _multiprocessing: + +Multiprocessing +=============== + +Multiprocessing is supported to speed up certain computationally-intensive steps +in the pipeline, including the :ref:`jump detection `, +:ref:`ramp fitting `, and +:ref:`WFSS contamination correction ` steps. The examples below show how +multiprocessing can be enabled for these steps, as well as how to set up +multiprocessing to simultaneously run the entire pipeline on multiple observations. + +Since the pipeline uses multiprocessing it is critical that any code using the pipeline adhere +to the guidelines described in the +`python multiprocessing documentation `_. +The pipeline uses the `forkserver` start method internally and it is recommended that any +multiprocessing scripts that use the pipline use the same start. As detailed in the +`python documentation `_ +this will require that code be "protected" with a ``if __name__ == '__main__':`` check as follows + +:: + + if __name__ = '__main__': + [code used in multiprocessing] + + +There are a couple of scenarios to use multiprocessing with the pipeline: + +1. Multiprocessing within a pipeline step. At the moment, the steps that +support this are the :ref:`jump `, +:ref:`ramp_fitting `, +and :ref:`wfss_contam ` steps. To enable multiprocessing, the +optional parameter is `maximum_cores` for the ``jump``, ``ramp_fitting``, and +``wfss_contam`` steps. This parameter can be set to a numerical value given +as a string or it can be set to the words `quarter`, `half`, `all`, +or `none`, which is the default value. + +The following example turns on a step's multiprocessing option. Notice only +one of the steps has multiprocessing turned on. We do not recommend +simultaneously enabling both steps to do multiprocessing, as this may likely +lead to running out of system memory. + + + +:: + + # SampleScript1 + + import os, sys + from jwst.pipeline import Detector1Pipeline + + uncal_file = 'jw0000_0000_uncal.fits' + output_dir = '/my_project' + + def main(): + det1 = Detector1Pipeline() + parameter_dict = {"ramp_fit": {"maximum_cores": 'all'}} + det1.call(uncal_file, save_results=True, steps=parameter_dict, output_dir=output_dir) + + if __name__ = '__main__': + sys.exit(main()) + + +2. Calling the pipeline using multiprocessing. The following example uses this +option setting up a log file for each run of the pipeline and a text file with +the full traceback in case there is a crash. Notice that the ``import`` statement +of the pipeline is within the multiprocessing block that gets called by every +worker. This is to avoid a known memory leak. + + +:: + + # SampleScript2 + + import os, sys + import traceback + import configparser + import multiprocessing + from glob import glob + + def mk_stpipe_log_cfg(output_dir, log_name): + """ + Create a configuration file with the name log_name, where + the pipeline will write all output. + Args: + outpur_dir: str, path of the output directory + log_name: str, name of the log to record screen output + Returns: + nothing + """ + config = configparser.ConfigParser() + config.add_section("*") + config.set("*", "handler", "file:" + log_name) + config.set("*", "level", "INFO") + pipe_log_config = os.path.join(output_dir, "pipeline-log.cfg") + config.write(open(pipe_log_config, "w")) + + def run_det1(uncal_file, output_dir): + """ + Run the Detector1 pipeline on the given file. + Args: + uncal_file: str, name of uncalibrated file to run + outpur_dir: str, path of the output directory + Returns: + nothing + """ + log_name = os.path.basename(uncal_file).replace('.fits', '') + mk_stpipe_log_cfg(output_dir, log_name+'.log') + from jwst.pipeline.calwebb_detector1 import Detector1Pipeline + pipe_success = False + try: + det1 = Detector1Pipeline() + det1.call(uncal_file, output_dir=output_dir, logcfg="pipeline-log.cfg", save_results=True) + pipe_success = True + print('\n * Pipeline finished for file: ', uncal_file, ' \n') + except Exception: + print('\n *** OH NO! The detector1 pipeline crashed! *** \n') + pipe_crash_msg = traceback.print_exc() + if not pipe_success: + crashfile = open(log_name+'_pipecrash.txt', 'w') + print('Printing file with full traceback') + print(pipe_crash_msg, file=crashfile) + + def main(): + input_data_dir = '/my_project_dir' + output_dir = input_data_dir + + # get the files to run + files_to_run = glob(os.path.join(input_data_dir, '*_uncal.fits')) + print('Will run the pipeline on {} files'.format(len(files_to_run))) + + # the output list should be the same length as the files to run + outptd = [output_dir for _ in range(len(files_to_run))] + + # get the cores to use + cores2use = int(os.cpu_count()/2) # half of all available cores + print('* Using ', cores2use, ' cores for multiprocessing.') + + # set the pool and run multiprocess + with multiprocessing.Pool(cores2use) as pool: + pool.starmap(run_det1, zip(files_to_run, outptd)) + + print('\n * Finished multiprocessing! \n') + + if __name__ == '__main__': + sys.exit(main()) + + +.. warning:: + Although it is technically possible to call the pipeline with + multiprocessing while also enabling this option in a step, we + strongly recommend not to do this. This scenario would be the same as + `SampleScript2` except with adding and calling the parameter dictionary + `parameter_dict` in `SampleScript1`. However, Python will crash + if both multiprocessing options are set to use all the cores or even + less, because it is not permitted that a worker has children processes. + We recommend not enabling step multiprocessing for parallel pipeline + runs to avoid potentially running out of memory. From 13e092743d6ccf9ce5352ea4d8899b726532aa4a Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 23 Apr 2024 09:11:00 -0400 Subject: [PATCH 021/687] JP-3463: Add log message to dark step (#8425) --- CHANGES.rst | 6 ++++++ jwst/dark_current/dark_current_step.py | 1 + 2 files changed, 7 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 3a08f3e8a3..96a59a092c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -22,6 +22,12 @@ associations - Match NIRSpec imprint observations to science exposures on mosaic tile location and dither pointing, ``MOSTILNO`` and ``DITHPTIN``. [#8410] +dark_current +------------ + +- Add log info message when specifying an average_dark_current for noise calculations. + [#8425] + documentation ------------- diff --git a/jwst/dark_current/dark_current_step.py b/jwst/dark_current/dark_current_step.py index e5af9af395..2a0eddfc80 100755 --- a/jwst/dark_current/dark_current_step.py +++ b/jwst/dark_current/dark_current_step.py @@ -93,6 +93,7 @@ def set_average_dark_current(self, input_model, dark_model): """ if self.average_dark_current is not None: input_model.average_dark_current[:, :] = self.average_dark_current + self.log.info('Using Poisson noise from average dark current %s e-/sec', self.average_dark_current) else: # First prioritize a 2D average_dark_current, if defined in dark. # If not present, apply scalar to datamodel array, if scalar is present. From f4170dc712ae3b5a1b77883e54016e860f3ce7cb Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 13:37:10 -0400 Subject: [PATCH 022/687] bump scipy lower pin to fix oldest deps --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index acccccce70..c513b7153f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ dependencies = [ "pyparsing>=2.2.1", "requests>=2.22", "scikit-image>=0.19", - "scipy>=1.7.2", + "scipy>=1.9.3", "spherical-geometry>=1.2.22", "stcal>=1.7.0,<1.8.0", "stdatamodels>=1.10.1,<1.11.0", From 70d4eee0a9702ee2f3b1afdbae2ed3fa42580b92 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 13:38:40 -0400 Subject: [PATCH 023/687] add changelog --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 96a59a092c..db005cd549 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -56,6 +56,8 @@ general - Removed deprecated stdatamodels model types ``DrizProductModel``, ``MIRIRampModel``, and ``MultiProductModel``. [#8388] +- Increase minimum required scipy. [#8441] + outlier_detection ----------------- From 10cb7cdee2130a711543ac6a1a9837fef445d1d6 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 09:19:56 -0400 Subject: [PATCH 024/687] remove OutlierDetectScaledStep and OutlierDetectionStackStep --- docs/jwst/outlier_detection/main.rst | 4 +- jwst/lib/suffix.py | 6 +- jwst/outlier_detection/__init__.py | 5 +- .../outlier_detection_scaled.py | 170 ------------------ .../outlier_detection_scaled_step.py | 80 --------- .../outlier_detection_stack_step.py | 93 ---------- jwst/pipeline/outlier_detection_scaled.cfg | 2 - jwst/pipeline/outlier_detection_stack.cfg | 2 - jwst/step.py | 4 - jwst/stpipe/integration.py | 2 - 10 files changed, 4 insertions(+), 364 deletions(-) delete mode 100644 jwst/outlier_detection/outlier_detection_scaled.py delete mode 100755 jwst/outlier_detection/outlier_detection_scaled_step.py delete mode 100644 jwst/outlier_detection/outlier_detection_stack_step.py delete mode 100644 jwst/pipeline/outlier_detection_scaled.cfg delete mode 100644 jwst/pipeline/outlier_detection_stack.cfg diff --git a/docs/jwst/outlier_detection/main.rst b/docs/jwst/outlier_detection/main.rst index a0f8291f83..4e53369f27 100644 --- a/docs/jwst/outlier_detection/main.rst +++ b/docs/jwst/outlier_detection/main.rst @@ -1,8 +1,8 @@ Description =========== -:Classes: `jwst.outlier_detection.OutlierDetectionStep`, `jwst.outlier_detection.OutlierDetectionScaledStep`, `jwst.outlier_detection.OutlierDetectionStackStep` -:Aliases: outlier_detection, outlier_detection_scaled, outlier_detection_stack +:Classes: `jwst.outlier_detection.OutlierDetectionStep` +:Aliases: outlier_detection Processing multiple datasets together allows for the identification of bad pixels or cosmic-rays that remain in each of the input images, many times at levels which diff --git a/jwst/lib/suffix.py b/jwst/lib/suffix.py index 559fbd8b64..cab5be43a9 100644 --- a/jwst/lib/suffix.py +++ b/jwst/lib/suffix.py @@ -78,7 +78,6 @@ 'guider_cds', 'gain_scale', 'background', - 'outlier_detection_stack', 'resetstep', 'imprintstep', 'extract1dstep', @@ -104,7 +103,6 @@ 'whtlt', 'assignmtwcsstep', 'engdblog', - 'outlierdetectionstackstep', 'align_refs', 'master_background', 'tso3pipeline', @@ -142,7 +140,7 @@ 'wfsscontamstep', 'straylightstep', 'spectral_leak', - 'spectralleakstep', + 'spectralleakstep', 'rscdstep', 'barshadowstep', 'msaflagopenstep', @@ -172,8 +170,6 @@ 'ramp_fit', 'firstframe', 'srctype', - 'outlierdetectionscaledstep', - 'outlier_detection_scaled', 'straylight', 'spec2nrslamp', 'pathloss', diff --git a/jwst/outlier_detection/__init__.py b/jwst/outlier_detection/__init__.py index bcffe37b32..c012c72cbf 100644 --- a/jwst/outlier_detection/__init__.py +++ b/jwst/outlier_detection/__init__.py @@ -1,6 +1,3 @@ from .outlier_detection_step import OutlierDetectionStep -from .outlier_detection_scaled_step import OutlierDetectionScaledStep -from .outlier_detection_stack_step import OutlierDetectionStackStep -__all__ = ['OutlierDetectionStep', 'OutlierDetectionScaledStep', - 'OutlierDetectionStackStep'] +__all__ = ['OutlierDetectionStep'] diff --git a/jwst/outlier_detection/outlier_detection_scaled.py b/jwst/outlier_detection/outlier_detection_scaled.py deleted file mode 100644 index e3b8c2cfcf..0000000000 --- a/jwst/outlier_detection/outlier_detection_scaled.py +++ /dev/null @@ -1,170 +0,0 @@ -"""Class definition for performing outlier detection with scaling.""" - -from copy import deepcopy -import numpy as np - -from photutils.aperture import (aperture_photometry, CircularAperture, CircularAnnulus) -import astropy.units as u - -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer - -from ..resample import resample_utils -from ..tso_photometry.tso_photometry import tso_aperture_photometry -from .outlier_detection import OutlierDetection - -import logging -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -class OutlierDetectionScaled(OutlierDetection): - """Class definition for applying scaled outlier detection. - - This is the controlling routine for the outlier detection process. - It loads and sets the various input data and parameters needed by - the various functions and then controls the operation of this process - through all the steps used for the detection. - - Notes - ----- - This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Performs photometry on single source from each input observations - 3. Creates a normalized median image from all grouped observations - 4. Perform statistical comparison between scaled,normalized median image - and original image to identify outliers. - 5. Updates input data model DQ arrays with mask of detected outliers. - - """ - - def __init__(self, input_models, reffiles=None, **pars): - """Initialize class with input_models. - - Parameters - ---------- - input_models : list of DataModels, str - list of data models as ModelContainer or ASN file, - one data model for each input image - - reffiles : dict of `stdatamodels.jwst.datamodels.JwstDataModel` - Dictionary of datamodels. Keys are reffile_types. - - """ - super().__init__(self, input_models, reffiles=reffiles, **pars) - - def do_detection(self): - """Flag outlier pixels in DQ of input images.""" - self.build_suffix(**self.outlierpars) - self._convert_inputs() - - pars = self.outlierpars - save_intermediate_results = pars['save_intermediate_results'] - - # Start by performing initial TSO Photometry on stack of DataModels - # TODO: need information about the actual source position in - # TSO imaging mode (for all subarrays). - # Meanwhile, this is a placeholder representing the geometric - # center of the image. - nints, ny, nx = self.inputs.data.shape - xcenter = (ny - 1) / 2. - ycenter = (ny - 1) / 2. - - # all radii are in pixel units - if self.inputs.meta.instrument.pupil == 'WLP8': - radius = 50 - radius_inner = 60 - radius_outer = 70 - else: - radius = 3 - radius_inner = 4 - radius_outer = 5 - - apertures = CircularAperture((xcenter, ycenter), r=radius) - aperture_mask = apertures.to_mask(method='center') - # This mask has 1 for mask region, 0 for outside of mask - median_mask = aperture_mask.to_image((ny, nx)) - inv_median_mask = np.abs(median_mask - 1) - # Perform photometry - catalog = tso_aperture_photometry(self.inputs, xcenter, ycenter, - radius, radius_inner, - radius_outer) - - # Extract net photometry for the source - # This will be the value used for scaling the median image within - # the aperture region - phot_values = catalog['net_aperture_sum'] - - # Convert CubeModel into ModelContainer of 2-D DataModels - for image in self.input_models: - image.wht = resample_utils.build_driz_weight( - image, - weight_type=pars['weight_type'], - good_bits=pars['good_bits'] - ) - - # Initialize intermediate products used in the outlier detection - input_shape = self.input_models[0].data.shape - median_model = datamodels.ImageModel(init=input_shape) - median_model.meta = deepcopy(self.input_models[0].meta) - base_filename = self.inputs.meta.filename - median_model.meta.filename = self.make_output_path( - basepath=base_filename, suffix='median' - ) - - # Perform median combination on set of drizzled mosaics - median_model.data = self.create_median(self.input_models) - aper2 = CircularAnnulus((xcenter, ycenter), r_in=radius_inner, - r_out=radius_outer) - - tbl1 = aperture_photometry(median_model.data, apertures, - error=median_model.data * 0.0 + 1.0) - tbl2 = aperture_photometry(median_model.data, aper2, - error=median_model.data * 0.0 + 1.0) - - aperture_sum = u.Quantity(tbl1['aperture_sum'][0]) - annulus_sum = u.Quantity(tbl2['aperture_sum'][0]) - annulus_mean = annulus_sum / aper2.area - aperture_bkg = annulus_mean * apertures.area - median_phot_value = aperture_sum - aperture_bkg - - if save_intermediate_results: - log.info("Writing out MEDIAN image to: {}".format( - median_model.meta.filename)) - median_model.save(median_model.meta.filename) - - # Scale the median image by the initial photometry (only in aperture) - # to create equivalent of 'blot' images - # Area outside of aperture in median will remain unchanged - blot_models = ModelContainer() - for i in range(nints): - scale_factor = float(phot_values[i] / median_phot_value) - scaled_image = datamodels.ImageModel(init=median_model.data.shape) - scaled_image.meta = deepcopy(median_model.meta) - scaled_data = (median_model.data * (scale_factor * median_mask) + ( - median_model.data * inv_median_mask)) - scaled_image.data = scaled_data - blot_models.append(scaled_image) - - if save_intermediate_results: - log.info("Writing out Scaled Median images...") - - def make_output_path(ignored, idx=None): - output_path = self.make_output_path( - basepath=base_filename, suffix='blot', idx=idx, - component_format='_{asn_id}_{idx}' - ) - return output_path - - blot_models.save(make_output_path) - - # Perform outlier detection using statistical comparisons between - # each original input image and its blotted version of the median image - self.detect_outliers(blot_models) - - # clean-up (just to be explicit about being finished - # with these results) - del median_model, blot_models diff --git a/jwst/outlier_detection/outlier_detection_scaled_step.py b/jwst/outlier_detection/outlier_detection_scaled_step.py deleted file mode 100755 index 62468ec679..0000000000 --- a/jwst/outlier_detection/outlier_detection_scaled_step.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Step interface for performing scaled outlier_detection.""" -from stdatamodels.jwst import datamodels - -from ..stpipe import Step -from . import outlier_detection_scaled - - -class OutlierDetectionScaledStep(Step): - """Flag outlier bad pixels and cosmic rays in DQ array of each input image. - - Input images can listed in an input association file or already opened - with a ModelContainer. DQ arrays are modified in place. - - Parameters - ----------- - input : asn file or ~jwst.datamodels.ModelContainer - Single filename association table, or a datamodels.ModelContainer. - - """ - - class_alias = "outlier_detection_scaled" - - spec = """ - weight_type = option('ivm','exptime',default='ivm') - pixfrac = float(default=1.0) - kernel = string(default='square') # drizzle kernel - fillval = string(default='INDEF') - nlow = integer(default=0) - nhigh = integer(default=0) - maskpt = float(default=0.7) - snr = string(default='4.0 3.0') - scale = string(default='0.5 0.4') - backg = float(default=0.0) - save_intermediate_results = boolean(default=False) - good_bits = string(default="~DO_NOT_USE") # DQ flags to allow - """ - - def process(self, input): - """Step interface to running outlier_detection.""" - with datamodels.open(input) as input_models: - - if not isinstance(input_models, datamodels.CubeModel): - self.log.warning("Input is not a CubeModel.") - self.log.warning("Outlier detection step will be skipped.") - result = input_models.copy() - result.meta.cal_step.outlier_detection = "SKIPPED" - return result - - self.input_models = input_models - - reffiles = {} - - pars = { - 'weight_type': self.weight_type, - 'pixfrac': self.pixfrac, - 'kernel': self.kernel, - 'fillval': self.fillval, - 'nlow': self.nlow, - 'nhigh': self.nhigh, - 'maskpt': self.maskpt, - 'snr': self.snr, - 'scale': self.scale, - 'backg': self.backg, - 'save_intermediate_results': self.save_intermediate_results, - 'good_bits': self.good_bits - } - - # Setup for creating file names - pars['make_output_path'] = self.make_output_path - - # Set up outlier detection, then do detection - step = outlier_detection_scaled.OutlierDetectionScaled( - self.input_models, - reffiles=reffiles, - **pars) - step.do_detection() - - self.input_models.meta.cal_step.outlier_detection = 'COMPLETE' - - return self.input_models diff --git a/jwst/outlier_detection/outlier_detection_stack_step.py b/jwst/outlier_detection/outlier_detection_stack_step.py deleted file mode 100644 index 099cbac033..0000000000 --- a/jwst/outlier_detection/outlier_detection_stack_step.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Step defined for outlier detection for stacked observations.""" - -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer - -from ..stpipe import Step -from . import outlier_detection - - -class OutlierDetectionStackStep(Step): - """Class definition for stacked outlier detection. - - Flag outlier bad pixels and cosmic rays in the DQ array of each input image - of a stack of exposures, which in the case of TSO data are from the same - data cube. - - Input images can listed in an input association file or already opened - with a ModelContainer. - - DQ arrays are modified in place. - - By default, resampling has been disabled. The 'resample_data' attribute - can be reset to 'True' to turn on resampling if desired for the data. - - Parameters - ----------- - input : asn file or ~jwst.datamodels.ModelContainer - Single filename association table, or a datamodels.ModelContainer. - - """ - - class_alias = "outlier_detection_stack" - - spec = """ - weight_type = option('ivm','exptime',default='ivm') - pixfrac = float(default=1.0) - kernel = string(default='square') # drizzle kernel - fillval = string(default='INDEF') - nlow = integer(default=0) - nhigh = integer(default=0) - maskpt = float(default=0.7) - snr = string(default='4.0 3.0') - scale = string(default='0.5 0.4') - backg = float(default=0.0) - save_intermediate_results = boolean(default=False) - resample_data = boolean(default=False) - good_bits = string(default="~DO_NOT_USE") # DQ flags to allow - """ - - def process(self, input): - """Step interface for performing outlier_detection processing.""" - with datamodels.open(input) as input_models: - - if not isinstance(input_models, ModelContainer): - self.log.warning("Input is not a ModelContainer.") - self.log.warning("Outlier detection stack step will \ - be skipped.") - result = input_models.copy() - result.meta.cal_step.outlier_detection = "SKIPPED" - return result - - self.log.info("Performing outlier detection on stack of \ - {} inputs".format(len(input_models))) - self.input_models = input_models - reffiles = {} - - pars = { - 'weight_type': self.weight_type, - 'pixfrac': self.pixfrac, - 'kernel': self.kernel, - 'fillval': self.fillval, - 'nlow': self.nlow, - 'nhigh': self.nhigh, - 'maskpt': self.maskpt, - 'snr': self.snr, - 'scale': self.scale, - 'backg': self.backg, - 'save_intermediate_results': self.save_intermediate_results, - 'resample_data': self.resample_data, - 'good_bits': self.good_bits - } - - # Set up outlier detection, then do detection - step = outlier_detection.OutlierDetection(self.input_models, - reffiles=reffiles, - **pars) - step.do_detection() - - for model in self.input_models: - model.meta.cal_step.outlier_detection = 'COMPLETE' - - return self.input_models diff --git a/jwst/pipeline/outlier_detection_scaled.cfg b/jwst/pipeline/outlier_detection_scaled.cfg deleted file mode 100644 index 86e6dba729..0000000000 --- a/jwst/pipeline/outlier_detection_scaled.cfg +++ /dev/null @@ -1,2 +0,0 @@ -name = "outlier_detection_scaled" -class = "jwst.outlier_detection.OutlierDetectionScaledStep" diff --git a/jwst/pipeline/outlier_detection_stack.cfg b/jwst/pipeline/outlier_detection_stack.cfg deleted file mode 100644 index 4e19586f28..0000000000 --- a/jwst/pipeline/outlier_detection_stack.cfg +++ /dev/null @@ -1,2 +0,0 @@ -name = "outlier_detection_stack" -class = "jwst.outlier_detection.OutlierDetectionStackStep" diff --git a/jwst/step.py b/jwst/step.py index d50f92a6f5..7a5d124b59 100644 --- a/jwst/step.py +++ b/jwst/step.py @@ -35,8 +35,6 @@ from .msaflagopen.msaflagopen_step import MSAFlagOpenStep from .nsclean.nsclean_step import NSCleanStep from .outlier_detection.outlier_detection_step import OutlierDetectionStep -from .outlier_detection.outlier_detection_scaled_step import OutlierDetectionScaledStep -from .outlier_detection.outlier_detection_stack_step import OutlierDetectionStackStep from .pathloss.pathloss_step import PathLossStep from .persistence.persistence_step import PersistenceStep from .photom.photom_step import PhotomStep @@ -100,8 +98,6 @@ "MSAFlagOpenStep", "NSCleanStep", "OutlierDetectionStep", - "OutlierDetectionScaledStep", - "OutlierDetectionStackStep", "PathLossStep", "PersistenceStep", "PhotomStep", diff --git a/jwst/stpipe/integration.py b/jwst/stpipe/integration.py index 736c550f0d..b43f4abff5 100644 --- a/jwst/stpipe/integration.py +++ b/jwst/stpipe/integration.py @@ -67,8 +67,6 @@ def get_steps(): ("jwst.step.MSAFlagOpenStep", 'msa_flagging', False), ("jwst.step.NSCleanStep", 'nsclean', False), ("jwst.step.OutlierDetectionStep", 'outlier_detection', False), - ("jwst.step.OutlierDetectionScaledStep", 'outlier_detection_scaled', False), - ("jwst.step.OutlierDetectionStackStep", 'outlier_detection_stack', False), ("jwst.step.PathLossStep", 'pathloss', False), ("jwst.step.PersistenceStep", 'persistence', False), ("jwst.step.PhotomStep", 'photom', False), From 95e7822ba46a01213ef1e04faefd8769674dae89 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 09:29:50 -0400 Subject: [PATCH 025/687] remove unused outlierpars reference file --- jwst/outlier_detection/outlier_detection.py | 51 +------------------ .../outlier_detection_ifu.py | 7 +-- .../outlier_detection_spec.py | 8 +-- .../outlier_detection_step.py | 3 +- 4 files changed, 6 insertions(+), 63 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index ef584132ec..0758e8c109 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -52,7 +52,7 @@ class OutlierDetection: default_suffix = 'i2d' - def __init__(self, input_models, reffiles=None, **pars): + def __init__(self, input_models, **pars): """ Initialize the class with input ModelContainers. @@ -69,11 +69,8 @@ def __init__(self, input_models, reffiles=None, **pars): """ self.inputs = input_models - self.reffiles = reffiles self.outlierpars = {} - if 'outlierpars' in reffiles: - self._get_outlier_pars() self.outlierpars.update(pars) # Insure that self.input_models always refers to a ModelContainer # representation of the inputs @@ -118,52 +115,6 @@ def _convert_inputs(self): self.input_models.append(image) self.converted = True - def _get_outlier_pars(self): - """Extract outlier detection parameters from reference file.""" - # start by interpreting input data models to define selection criteria - input_dm = self.input_models[0] - filtname = input_dm.meta.instrument.filter - if hasattr(self.input_models, 'group_names'): - num_groups = len(self.input_models.group_names) - else: - num_groups = 1 - - ref_model = datamodels.OutlierParsModel(self.reffiles['outlierpars']) - - # look for row that applies to this set of input data models - # NOTE: - # This logic could be replaced by a method added to the DrizParsModel - # object to select the correct row based on a set of selection - # parameters - row = None - outlierpars = ref_model.outlierpars_table - - # flag to support wild-card rows in outlierpars table - filter_match = False - for n, filt, num in zip(range(1, outlierpars.numimages.shape[0] + 1), - outlierpars.filter, outlierpars.numimages): - # only remember this row if no exact match has already been made - # for the filter. This allows the wild-card row to be anywhere in - # the table; since it may be placed at beginning or end of table. - - if filt == "ANY" and not filter_match and num_groups >= num: - row = n - # always go for an exact match if present, though... - if filtname == filt and num_groups >= num: - row = n - filter_match = True - - # With presence of wild-card rows, code should never trigger this logic - if row is None: - log.error("No row found in %s that matches input data.", - self.reffiles) - raise ValueError - - # read in values from that row for each parameter - for kw in list(self.outlierpars.keys()): - self.outlierpars[kw] = \ - ref_model['outlierpars_table.{0}'.format(kw)] - def build_suffix(self, **pars): """Build suffix. diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index 48f1f536aa..2dc00336ad 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -53,7 +53,7 @@ class OutlierDetectionIFU(OutlierDetection): """ - def __init__(self, input_models, reffiles=None, **pars): + def __init__(self, input_models, **pars): """Initialize class for IFU data processing. Parameters @@ -62,11 +62,8 @@ def __init__(self, input_models, reffiles=None, **pars): list of data models as ModelContainer or ASN file, one data model for each input 2-D ImageModel - reffiles : dict of `~stdatamodels.jwst.datamodels.JwstDataModel` - Dictionary of datamodels. Keys are reffile_types. - """ - OutlierDetection.__init__(self, input_models, reffiles=reffiles, **pars) + OutlierDetection.__init__(self, input_models, **pars) def create_optional_results_model(self, opt_info): """ diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 4adf2b4b06..66885e5947 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -41,7 +41,7 @@ class OutlierDetectionSpec(OutlierDetection): default_suffix = 's2d' - def __init__(self, input_models, reffiles=None, **pars): + def __init__(self, input_models, **pars): """Initialize class with input_models. Parameters @@ -50,17 +50,13 @@ def __init__(self, input_models, reffiles=None, **pars): list of data models as ModelContainer or ASN file, one data model for each input image - reffiles : dict of `stdatamodels.jwst.datamodels.JwstDataModel` - Dictionary of datamodels. Keys are reffile_types. - pars : dict, optional Optional user-specified parameters to modify how outlier_detection will operate. Valid parameters include: - resample_suffix """ - OutlierDetection.__init__(self, input_models, - reffiles=reffiles, **pars) + OutlierDetection.__init__(self, input_models, **pars) def do_detection(self): """Flag outlier pixels in DQ of input images.""" diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 2c90435045..6dd04498e3 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -163,10 +163,9 @@ def process(self, input_data): return self.input_models self.log.debug(f"Using {detection_step.__name__} class for outlier_detection") - reffiles = {} # Set up outlier detection, then do detection - step = detection_step(self.input_models, reffiles=reffiles, asn_id=asn_id, **pars) + step = detection_step(self.input_models, asn_id=asn_id, **pars) step.do_detection() state = 'COMPLETE' From 2b0a4a2111a1b95ff09ae436b696010229e686ce Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 11:47:43 -0400 Subject: [PATCH 026/687] remove scale_detection from OutlierDetectionStep spec --- jwst/outlier_detection/outlier_detection_step.py | 1 - jwst/pipeline/calwebb_tso3.py | 1 - 2 files changed, 2 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 6dd04498e3..86a0e49638 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -67,7 +67,6 @@ class OutlierDetectionStep(Step): save_intermediate_results = boolean(default=False) resample_data = boolean(default=True) good_bits = string(default="~DO_NOT_USE") # DQ flags to allow - scale_detection = boolean(default=False) search_output_file = boolean(default=False) allowed_memory = float(default=None) # Fraction of memory to use for the combined image in_memory = boolean(default=False) diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index f6a6f1b874..f2baf07af6 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -116,7 +116,6 @@ def process(self, input): else: self.log.info("Performing scaled outlier detection on input images ...") - self.outlier_detection.scale_detection = True cube = self.outlier_detection(cube) # Save crfints products From 92686ec0a44b4e9d8bce3cb3d5bae34bce827d9a Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 11:56:51 -0400 Subject: [PATCH 027/687] remove scale_detection for outlier detection docs --- docs/jwst/outlier_detection/arguments.rst | 4 ---- 1 file changed, 4 deletions(-) diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index b5e732e5cf..a528218253 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -70,10 +70,6 @@ that control the behavior of the processing: that should be considered 'good' when building the weight mask. See DQ flag :ref:`dq_parameter_specification` for details. -``--scale_detection`` (bool, default=False) - Specifies whether or not to rescale the individual input images - to match total signal when doing comparisons. - ``--allowed_memory`` (float, default=None) Specifies the fractional amount of free memory to allow when creating the resampled image. If ``None``, the From cab6c2f87a37c757fc7bfc751d282169b9bc8f33 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 25 Apr 2024 11:57:10 -0400 Subject: [PATCH 028/687] remove scale_detection from calwebb_tso3 --- jwst/pipeline/calwebb_tso3.py | 65 ++++++++++++++++++----------------- 1 file changed, 33 insertions(+), 32 deletions(-) diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index f2baf07af6..7420f839b7 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -37,9 +37,7 @@ class Tso3Pipeline(Pipeline): class_alias = "calwebb_tso3" - spec = """ - scale_detection = boolean(default=False) - """ + spec = "" # Define alias to steps step_defs = {'outlier_detection': @@ -87,36 +85,39 @@ def process(self, input): self.log.warning('Input data are 2D; skipping outlier_detection') break + # FIXME OutlierDetectionStep can accept a cube input and will + # unpack it as a container which looks almost identical to the below + # code. There are some differences: + # - the inputs here are SlitModels + # - outlier detection will generate drizzle weight during the unpack + # the below code does not + # - the code below takes a "bitwise or" whereas outlier detection + # assigns over the dq array (I think this is the same as I expect + # outlier detection to only add dq flags). + # Perform regular outlier detection - if not self.scale_detection: - - # Convert CubeModel into ModelContainer of 2-D DataModels to - # use as input to outlier detection step - input_2dmodels = ModelContainer() - for i in range(cube.data.shape[0]): - # convert each plane of data cube into its own array - image = datamodels.ImageModel(data=cube.data[i], - err=cube.err[i], dq=cube.dq[i]) - image.update(cube) - image.meta.wcs = cube.meta.wcs - input_2dmodels.append(image) - - self.log.info("Performing outlier detection on input images ...") - input_2dmodels = self.outlier_detection(input_2dmodels) - - # Transfer updated DQ values to original input observation - for i in range(cube.data.shape[0]): - # Update DQ arrays with those from outlier_detection step - cube.dq[i] = np.bitwise_or(cube.dq[i], input_2dmodels[i].dq) - - cube.meta.cal_step.outlier_detection = \ - input_2dmodels[0].meta.cal_step.outlier_detection - - del input_2dmodels - - else: - self.log.info("Performing scaled outlier detection on input images ...") - cube = self.outlier_detection(cube) + + input_2dmodels = ModelContainer() + for i in range(cube.data.shape[0]): + # convert each plane of data cube into its own array + image = datamodels.ImageModel(data=cube.data[i], + err=cube.err[i], dq=cube.dq[i]) + image.update(cube) + image.meta.wcs = cube.meta.wcs + input_2dmodels.append(image) + + self.log.info("Performing outlier detection on input images ...") + input_2dmodels = self.outlier_detection(input_2dmodels) + + # Transfer updated DQ values to original input observation + for i in range(cube.data.shape[0]): + # Update DQ arrays with those from outlier_detection step + cube.dq[i] = np.bitwise_or(cube.dq[i], input_2dmodels[i].dq) + + cube.meta.cal_step.outlier_detection = \ + input_2dmodels[0].meta.cal_step.outlier_detection + + del input_2dmodels # Save crfints products if input_models[0].meta.cal_step.outlier_detection == 'COMPLETE': From 51a5f4eca1e835e4c007966a2052854d9fdc3041 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Apr 2024 14:30:15 -0400 Subject: [PATCH 029/687] add changelog --- CHANGES.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index db005cd549..74a1a38f0a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -66,12 +66,19 @@ outlier_detection - Pass the ``weight_type`` parameter to all resampling function calls so that the default weighting can be overridden by the input step parameter. [#8290] +- Remove unused ``OutlierDetectionScaledStep``, + ``OutlierDetectionStackStep``, ``outlierpars`` reference file handling, + and ``scale_detection`` (an unused argument). [#8438] + pipeline -------- - Fixed a bug in the ``calwebb_spec2`` and ``calwebb_image2`` pipelines that was causing them not to respect the ``output_file`` parameter. [#8368] +- Removed unused ``scale_detection`` argument from ``calwebb_tso3`` + pipeline. [#8438] + ramp_fitting ------------ From fc2b670acf60a87dc67c8dfdb3fa4c1fd69c8b8f Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Fri, 26 Apr 2024 17:45:56 -0400 Subject: [PATCH 030/687] Fix miri avg_dark regtest (#8446) --- jwst/regtest/test_miri_image.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index c9630dbd03..c8aa65dd67 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -36,7 +36,7 @@ def run_detector1_with_average_dark_current(rtdata_module): estimate of the average dark current for inclusion in ramp_fitting poisson variance estimation.""" rtdata = rtdata_module - rtdata.get_data("miri/image/jw01024001001_04101_00002_mirimage_uncal.fits") + rtdata.get_data("miri/image/jw01024002001_02101_00001_mirimage_uncal.fits") # Run detector1 pipeline only on one of the _uncal files args = ["jwst.pipeline.Detector1Pipeline", rtdata.input, @@ -100,8 +100,8 @@ def test_miri_image_detector1_with_avg_dark_current(run_detector1_with_average_d """Regression test of detector1 pipeline performed on MIRI imaging data with a specified average dark current.""" rtdata = rtdata_module - rtdata.input = "jw01024001001_04101_00002_mirimage_uncal.fits" - output = f"jw01024001001_04101_00002_mirimage_{suffix}.fits" + rtdata.input = "jw01024002001_02101_00001_mirimage_uncal.fits" + output = f"jw01024002001_02101_00001_mirimage_{suffix}.fits" rtdata.output = output rtdata.get_truth(f"truth/test_miri_image_stages/{output}") From 793408435435c570adcc7743d3369054b91eca00 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 2 May 2024 10:13:08 -0400 Subject: [PATCH 031/687] limit rotation test to 10x the angular repeatability (#8456) --- jwst/assign_wcs/tests/test_niriss.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/assign_wcs/tests/test_niriss.py b/jwst/assign_wcs/tests/test_niriss.py index cd72b306c2..8f6fa7ab69 100644 --- a/jwst/assign_wcs/tests/test_niriss.py +++ b/jwst/assign_wcs/tests/test_niriss.py @@ -131,7 +131,8 @@ def test_traverse_wfss_grisms(): traverse_wfss_trace(f) -def test_filter_rotation(theta=[-0.1, 0, 0.5, 20]): +# 1.585 is 10x the angular repeatability of the niriss pupil wheel +def test_filter_rotation(theta=[-0.1, 0, 0.5, 1.585]): """Make sure that the filter rotation is reversable.""" for f in niriss_grisms: wcsobj = create_wfss_wcs(f) From 65809147c8caca44f61cbd7b7e46bd3ed24dbe0e Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 1 May 2024 10:22:50 -0400 Subject: [PATCH 032/687] add workflow dispatch trigger to ci --- .github/workflows/ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 598e193576..4d67ea3583 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -13,6 +13,7 @@ on: schedule: # Weekly Monday 9AM build - cron: "0 9 * * 1" + workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.ref }} From a833633479c30451bc332bd08865dc8ae3342272 Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Thu, 2 May 2024 20:17:26 +0300 Subject: [PATCH 033/687] JP-3605: Change default value for abs_separation in the tweakreg step (#8445) Co-authored-by: Nadia Dencheva --- CHANGES.rst | 7 +++++-- docs/jwst/tweakreg/README.rst | 30 ++++++++++++++---------------- jwst/tweakreg/tweakreg_step.py | 2 +- 3 files changed, 20 insertions(+), 19 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 74a1a38f0a..b579c289db 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -23,7 +23,7 @@ associations and dither pointing, ``MOSTILNO`` and ``DITHPTIN``. [#8410] dark_current ------------- +------------ - Add log info message when specifying an average_dark_current for noise calculations. [#8425] @@ -33,7 +33,7 @@ documentation - Added docs for the NIRSpec MSA metadata file to the data products area of RTD. [#8399] - + - Added documentation for multiprocessing. [#8408] extract_1d @@ -104,6 +104,9 @@ tweakreg - Improved how a image group name is determined. [#8426] +- Changed default settings for ``abs_separation`` parameter for the ``tweakreg`` + step to have a value compatible with the ``abs_tolerance`` parameter. [#8445] + 1.14.0 (2024-03-29) =================== diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index ac0c0588fb..30e39ba0b5 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -21,8 +21,8 @@ format automatically recognized by :py:meth:`~astropy.table.Table.read`. When the ``meta.tweakreg_catalog`` attribute of input data models is `None` or an empty string, then the ``tweakreg`` step will attempt to detect sources in the input images. Stars are detected in the image with one of the following source -detection algorithms: ``photutils.detection.DAOStarFinder`` (default), -``photutils.detection.IRAFStarFinder``, or ``photutils.segmentation.SourceFinder`` +detection algorithms: ``photutils.detection.DAOStarFinder`` (default), +``photutils.detection.IRAFStarFinder``, or ``photutils.segmentation.SourceFinder`` in conjunction with ``photutils.segmentation.SourceCatalog``. DAOStarFinder is an implementation of the `DAOFIND`_ algorithm @@ -38,26 +38,26 @@ specified. IRAFStarFinder is a Python implementation of the IRAF star finding algorithm, which also calculates the objects' centroids, roundness, and sharpness. However, IRAFStarFinder uses image moments -instead of 1-D Gaussian fits to projected light distributions like +instead of 1-D Gaussian fits to projected light distributions like DAOStarFinder. SourceFinder implements a segmentation algorithm that identifies -sources in an image based on a number of connected pixels above a -specified threshold value. The sources are deblended using a -combination of multi-thresholding and watershed segmentation. +sources in an image based on a number of connected pixels above a +specified threshold value. The sources are deblended using a +combination of multi-thresholding and watershed segmentation. SourceCatalog finds the centroids of these sources, which are used -as the retrieved star positions. +as the retrieved star positions. .. warning:: - It has been shown (`STScI Technical Report JWST-STScI-008116, SM-12 - `_) + It has been shown (`STScI Technical Report JWST-STScI-008116, SM-12 + `_) that for undersampled PSFs, e.g. for short-wavelength NIRISS imaging data, ``DAOStarFinder`` gives bad results no matter the input parameters due to its use of 1-D Gaussian fits. ``IRAFStarFinder`` or ``SourceFinder`` should be used instead. -.. note:: - ``SourceFinder`` is likely to detect non-stellar sources +.. note:: + ``SourceFinder`` is likely to detect non-stellar sources such as galaxies because sources are not assumed to be point-source-like. This may lead to mismatches between the derived source catalog and the reference catalog during the @@ -254,7 +254,7 @@ The ``tweakreg`` step has the following optional arguments: **Additional source finding parameters for segmentation:** -* ``npixels``: An `int` value indicating the minimum number of +* ``npixels``: An `int` value indicating the minimum number of connected pixels that comprises a segment (Default=10) * ``connectivity``: An `int` value indicating the connectivity defining the @@ -280,7 +280,7 @@ The ``tweakreg`` step has the following optional arguments: Allowed values: `'correct'`, `'mask'`, `'none'`. (Default= `'correct'`) * ``kron_params``: A tuple of `float` values indicating the - parameters defining Kron aperture. If None, + parameters defining Kron aperture. If None, the parameters `(2.5, 1.4, 0.0)` are used. (Default=None) **Optimize alignment order:** @@ -374,9 +374,7 @@ Parameters used for absolute astrometry to a reference catalog. Otherwise the initial guess for the offsets will be set to zero (Default=True) -* ``abs_separation``: Minimum object separation in arcsec. It is recommended - that a value smaller than ``separation`` be used for this parameter - (e.g. 10 times smaller) (Default=0.1) +* ``abs_separation``: Minimum object separation in arcsec. (Default=1.0) * ``abs_tolerance``: Matching tolerance for ``xyxymatch`` in arcsec. (Default=0.7) diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index bddb2c1ff5..cb86e69054 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -97,7 +97,7 @@ class TweakRegStep(Step): abs_searchrad = float(default=6.0) # The search radius in arcsec for a match when performing absolute astrometry # We encourage setting this parameter to True. Otherwise, xoffset and yoffset will be set to zero. abs_use2dhist = boolean(default=True) # Use 2D histogram to find initial offset when performing absolute astrometry? - abs_separation = float(default=0.1) # Minimum object separation in arcsec when performing absolute astrometry + abs_separation = float(default=1) # Minimum object separation in arcsec when performing absolute astrometry abs_tolerance = float(default=0.7) # Matching tolerance for xyxymatch in arcsec when performing absolute astrometry # Fitting geometry when performing absolute astrometry abs_fitgeometry = option('shift', 'rshift', 'rscale', 'general', default='rshift') From 9f70097527e649d21e8d0441f35f948a486267b7 Mon Sep 17 00:00:00 2001 From: Maria Date: Mon, 6 May 2024 13:06:22 -0400 Subject: [PATCH 034/687] JP-3436 Empty wavelength array in s2d products (#8374) Co-authored-by: Howard Bushouse --- CHANGES.rst | 5 +++++ jwst/regtest/test_miri_lrs_slit_spec3.py | 25 ++++++++++++++++++++++++ jwst/regtest/test_nirspec_fs_spec3.py | 25 ++++++++++++++++++++++++ jwst/regtest/test_nirspec_mos_spec3.py | 17 ++++++++++++++++ jwst/resample/resample_spec_step.py | 12 ++++++++++++ 5 files changed, 84 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index b579c289db..7c45d080d1 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -92,6 +92,11 @@ resample - Remove sleep in median combination added in 8305 as it did not address the issue in operation [#8419] +resample_spec +------------- + +- Populate the wavelength array in resampled `Slit` and `MultiSlit` models. [#8374] + residual_fringe --------------- diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index 9ea82fb64a..513cc5740b 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -1,6 +1,8 @@ """ Test of the spec3 pipeline using MIRI LRS fixed-slit exposures. This takes an association and generates the level 3 products.""" import pytest +import numpy as np +from gwcs import wcstools import asdf from astropy.io.fits.diff import FITSDiff @@ -76,3 +78,26 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + + if "s2d" in output: + # Compare the calculated wavelengths + tolerance = 1e-03 + dmt = datamodels.open(rtdata.truth) + dmr = datamodels.open(rtdata.output) + if isinstance(dmt, datamodels.MultiSlitModel): + names = [s.name for s in dmt.slits] + for name in names: + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] + w = dmt.slits[st_idx].meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] + wlr = dmr.slits[sr_idx].wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) + else: + w = dmt.meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) + diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index 10e205e870..ac15e6a1fd 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -1,7 +1,10 @@ from astropy.io.fits.diff import FITSDiff import pytest +import numpy as np +from gwcs import wcstools from jwst.stpipe import Step +from stdatamodels.jwst import datamodels @pytest.fixture(scope="module") @@ -42,3 +45,25 @@ def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + + if "s2d" in output: + # Compare the calculated wavelengths + tolerance = 1e-03 + dmt = datamodels.open(rtdata.truth) + dmr = datamodels.open(rtdata.output) + if isinstance(dmt, datamodels.MultiSlitModel): + names = [s.name for s in dmt.slits] + for name in names: + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] + w = dmt.slits[st_idx].meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] + wlr = dmr.slits[sr_idx].wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) + else: + w = dmt.meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) diff --git a/jwst/regtest/test_nirspec_mos_spec3.py b/jwst/regtest/test_nirspec_mos_spec3.py index 59d9049837..abe6384f1c 100644 --- a/jwst/regtest/test_nirspec_mos_spec3.py +++ b/jwst/regtest/test_nirspec_mos_spec3.py @@ -1,7 +1,10 @@ import pytest from astropy.io.fits.diff import FITSDiff +import numpy as np +from gwcs import wcstools from jwst.stpipe import Step +from stdatamodels.jwst import datamodels @pytest.fixture(scope="module") @@ -36,3 +39,17 @@ def test_nirspec_mos_spec3(run_pipeline, suffix, source_id, fitsdiff_default_kwa diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + + if "s2d" in output: + # Compare the calculated wavelengths + dmt = datamodels.open(rtdata.truth) + dmr = datamodels.open(rtdata.output) + names = [s.name for s in dmt.slits] + for name in names: + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] + w = dmt.slits[st_idx].meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] + wlr = dmr.slits[sr_idx].wavelength + assert np.all(np.isclose(wave, wlr, atol=1e-03)) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index 921edaee1c..cf00b31692 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -7,6 +7,8 @@ from . import resample_spec, ResampleStep from ..exp_to_source import multislit_to_container from ..assign_wcs.util import update_s_region_spectral +from jwst.lib.wcs_utils import get_wavelengths + # Force use of all DQ flagged data except for DO_NOT_USE and NON_SCIENCE GOOD_BITS = '~DO_NOT_USE+NON_SCIENCE' @@ -109,6 +111,16 @@ def process(self, input): result.meta.asn.table_name = input_models[0].meta.asn.table_name result.meta.asn.pool_name = input_models[0].meta.asn.pool_name + # populate the result wavelength attribute for MultiSlitModel + if isinstance(result, MultiSlitModel): + for slit_idx, slit in enumerate(result.slits): + wl_array = get_wavelengths(result.slits[slit_idx]) + result.slits[slit_idx].wavelength = wl_array + else: + # populate the result wavelength attribute for SlitModel + wl_array = get_wavelengths(result) + result.wavelength = wl_array + return result def _process_multislit(self, input_models): From 4342988027ee0811b57d3641bda4c8486d7da1f5 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Mon, 6 May 2024 13:44:21 -0400 Subject: [PATCH 035/687] ensure extension modules are present in built wheel (#8462) --- .github/workflows/build.yml | 1 + setup.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ca5fb0d53e..f94367e4b1 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -32,5 +32,6 @@ jobs: # Until we have arm64 runners, we can't automatically test arm64 wheels - cp3*-macosx_arm64 sdist: true + test_command: python -c "from jwst.lib import winclip; from jwst.cube_build import cube_match_internal, cube_match_sky_pointcloud, cube_match_sky_driz, blot_median; from jwst.straylight import calc_xart" secrets: pypi_token: ${{ secrets.PYPI_PASSWORD_STSCI_MAINTAINER }} diff --git a/setup.py b/setup.py index 245ed5f6aa..d99ad0adcc 100644 --- a/setup.py +++ b/setup.py @@ -8,6 +8,8 @@ define_macros = [("NUMPY", "1")] setup( + # importing these extension modules is tested in `.github/workflows/build.yml`; + # when adding new modules here, make sure to add them to the `test_command` entry there ext_modules=[ Extension( "jwst.lib.winclip", From 9d96bfdeb0b84b779abed0ceb0d2af7c9c943fe8 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 7 May 2024 10:01:06 -0400 Subject: [PATCH 036/687] JP-3601: Fix background interval edge case (#8433) Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 + jwst/extract_1d/extract1d.py | 18 +++- .../extract_1d/tests/test_extract_src_flux.py | 58 +++++++++++- .../tests/test_fit_background_model.py | 88 +++++++++++++++++++ 4 files changed, 162 insertions(+), 6 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 7c45d080d1..ca6f6531e4 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -44,6 +44,10 @@ extract_1d - Replaced deprecated ``np.trapz`` with ``np.trapezoid()``. [#8415] +- Fix a crash in ``extract_1d`` encountered when multiple background or source + regions are specified and the lower and upper limits for one of them are + outside the valid area for some data range. [#8433] + flat_field ---------- diff --git a/jwst/extract_1d/extract1d.py b/jwst/extract_1d/extract1d.py index 83400a0236..979a3c00eb 100644 --- a/jwst/extract_1d/extract1d.py +++ b/jwst/extract_1d/extract1d.py @@ -676,10 +676,17 @@ def _extract_colpix(image_data, x, j, limits): # compute number of data points: ns = image_data.shape[0] - 1 ns12 = ns + 0.5 - - npts = sum(map(lambda x: min(ns, int(math.floor(x[1] + 0.5))) - - max(0, int(math.floor(x[0] + 0.5))) + 1, intervals)) - npts = max(npts, 1) + npts = 0 + for interval in intervals: + if interval[0] == interval[1]: + # no data between limits + continue + maxval = min(ns, int(math.floor(interval[1] + 0.5))) + minval = max(0, int(math.floor(interval[0] + 0.5))) + if maxval - minval + 1 > 0: + npts += maxval - minval + 1 + if npts == 0: + return [], [], [] # pre-allocate data arrays: y = np.empty(npts, dtype=np.float64) @@ -689,6 +696,8 @@ def _extract_colpix(image_data, x, j, limits): # populate data and weights: k = 0 for i in intervals: + if i[0] == i[1]: + continue i1 = i[0] if i[0] >= -0.5 else -0.5 i2 = i[1] if i[1] <= ns12 else ns12 @@ -697,6 +706,7 @@ def _extract_colpix(image_data, x, j, limits): ii2 = min(ns, int(math.floor(i2 + 0.5))) # special case: ii1 == ii2: + # take the value at the pixel if ii1 == ii2: v = image_data[ii1, x] val[k] = v diff --git a/jwst/extract_1d/tests/test_extract_src_flux.py b/jwst/extract_1d/tests/test_extract_src_flux.py index e3952745fa..cfab06f553 100644 --- a/jwst/extract_1d/tests/test_extract_src_flux.py +++ b/jwst/extract_1d/tests/test_extract_src_flux.py @@ -4,12 +4,13 @@ import math import numpy as np +import pytest from jwst.extract_1d import extract1d -def test_extract_src_flux(): - +@pytest.fixture +def inputs_constant(): shape = (9, 5) image = np.arange(shape[0] * shape[1], dtype=np.float32).reshape(shape) var_poisson = image.copy() @@ -24,6 +25,14 @@ def test_extract_src_flux(): upper = np.zeros(shape[1], dtype=np.float64) + 7. # middle of pixel 7 srclim = [[lower, upper]] + return (image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) + + +def test_extract_src_flux(inputs_constant): + (image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) = inputs_constant + (total_flux, f_var_poisson, f_var_rnoise, f_var_flat, bkg_flux, b_var_poisson, b_var_rnoise, b_var_flat, tarea, twht) = extract1d._extract_src_flux( @@ -61,3 +70,48 @@ def test_extract_src_flux(): assert np.isnan(total_flux) assert tarea == 0. + + +@pytest.mark.parametrize('test_type', ['all_empty', 'all_equal']) +def test_extract_src_flux_empty_interval(inputs_constant, test_type): + (image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) = inputs_constant + + if test_type == 'all_empty': + # no limits provided + srclim = [] + else: + # empty extraction range: upper equals lower + srclim[0][1] = srclim[0][0].copy() + + (total_flux, f_var_poisson, f_var_rnoise, f_var_flat, + bkg_flux, b_var_poisson, b_var_rnoise, b_var_flat, + tarea, twht) = extract1d._extract_src_flux( + image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) + + # empty interval, so no flux returned + assert np.isnan(total_flux) + assert bkg_flux == 0. + assert tarea == 0. + + +@pytest.mark.parametrize('offset', [-100, 100]) +def test_extract_src_flux_interval_out_of_range(inputs_constant, offset): + (image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) = inputs_constant + + # extraction limits out of range + srclim[0][0] += offset + srclim[0][1] += offset + + (total_flux, f_var_poisson, f_var_rnoise, f_var_flat, + bkg_flux, b_var_poisson, b_var_rnoise, b_var_flat, + tarea, twht) = extract1d._extract_src_flux( + image, var_poisson, var_rnoise, var_rflat, + x, j, lam, srclim, weights, bkgmodels) + + # empty interval, so no flux returned + assert np.isnan(total_flux) + assert bkg_flux == 0. + assert tarea == 0. diff --git a/jwst/extract_1d/tests/test_fit_background_model.py b/jwst/extract_1d/tests/test_fit_background_model.py index 3758564f46..5aeb596954 100644 --- a/jwst/extract_1d/tests/test_fit_background_model.py +++ b/jwst/extract_1d/tests/test_fit_background_model.py @@ -2,6 +2,8 @@ Test for extract_1d._fit_background_model """ import math +from copy import deepcopy + import numpy as np import pytest @@ -138,3 +140,89 @@ def test_handles_one_value(inputs_constant): assert math.isclose(b_var_flat_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) assert npts == 0 + + +@pytest.mark.parametrize('test_type', ['all_empty', 'all_equal']) +def test_handles_empty_interval(inputs_constant, test_type): + image, var_poisson, var_rnoise, var_rflat, x, j, bkglim, bkg_fit, bkg_order = inputs_constant + + if test_type == 'all_empty': + # no limits provided + bkglim = [] + else: + # empty extraction range: upper equals lower + bkglim[0][1] = bkglim[0][0].copy() + + # No data available: background model is 0 + (bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \ + extract1d._fit_background_model(image, var_poisson, var_rnoise, var_rflat, + x, j, bkglim, bkg_fit, bkg_order) + + assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_poisson_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_poisson_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_rnoise_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_rnoise_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_flat_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_flat_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert npts == 0 + + +@pytest.mark.parametrize('offset', [-100, 100]) +def test_handles_interval_out_of_range(inputs_constant, offset): + image, var_poisson, var_rnoise, var_rflat, x, j, bkglim, bkg_fit, bkg_order = inputs_constant + bkglim[0][0] += offset + bkglim[0][1] += offset + + # No data available: background model is 0 + (bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \ + extract1d._fit_background_model(image, var_poisson, var_rnoise, var_rflat, + x, j, bkglim, bkg_fit, bkg_order) + + assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_poisson_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_poisson_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_rnoise_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_rnoise_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_flat_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_flat_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert npts == 0 + + +def test_handles_one_empty_interval(inputs_constant): + image, var_poisson, var_rnoise, var_rflat, x, j, bkglim, bkg_fit, bkg_order = inputs_constant + + # add an extra interval that is empty + bkglim.append(deepcopy(bkglim[0])) + bkglim[1][0] += 2.0 + bkglim[1][1] = bkglim[1][0].copy() + print(bkglim) + + # should ignore the second interval and return a valid answer for the first + (bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \ + extract1d._fit_background_model(image, var_poisson, var_rnoise, var_rflat, + x, j, bkglim, bkg_fit, bkg_order) + + assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_poisson_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_poisson_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_rnoise_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_rnoise_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert math.isclose(b_var_flat_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + assert math.isclose(b_var_flat_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8) + + assert npts == 2 From f50a5b15275bf98e24a15c9935b777baa1aeafb6 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 7 May 2024 10:03:27 -0400 Subject: [PATCH 037/687] JP-3569: Update variance handling in resample step (#8437) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 + docs/jwst/resample/main.rst | 55 +++++ jwst/resample/resample.py | 237 ++++++++++++++-------- jwst/resample/resample_utils.py | 4 +- jwst/resample/tests/test_resample_step.py | 16 +- 5 files changed, 228 insertions(+), 87 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index ca6f6531e4..3f7018eb61 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -96,6 +96,9 @@ resample - Remove sleep in median combination added in 8305 as it did not address the issue in operation [#8419] +- Update variance handling to propagate resampled variance components with + weights that match the science `weight_type`. [#8437] + resample_spec ------------- diff --git a/docs/jwst/resample/main.rst b/docs/jwst/resample/main.rst index e0008f0cd6..08e0069f2d 100644 --- a/docs/jwst/resample/main.rst +++ b/docs/jwst/resample/main.rst @@ -34,6 +34,60 @@ WCS of each input image and the WCS of the defined output product. This mapping function gets passed to cdriz to drive the actual drizzling to create the output product. + +Error Propagation +----------------- + +The error associated with each resampled pixel can in principle be derived +from the variance components associated with each input pixel, weighted by +the square of the input user weights and the square of the overlap between +the input and output pixels. In practice, the cdriz routine does not currently +support propagating variance data alongside science images, so the output +error cannot be precisely calculated. + +To approximate the error on a resampled pixel, the variance arrays associated +with each input image are resampled individually, then combined with a weighted +sum. The process is: + +#. For each input image, take the square root of each of the read noise variance + array to make an error image. + +#. Drizzle the read noise error image onto the output WCS, with drizzle + parameters matching those used for the science data. + +#. Square the resampled read noise to make a variance array. + + a. If the resampling `weight_type` is an inverse variance map (`ivm`), weight + the resampled variance by the square of its own inverse. + + #. If the `weight_type` is the exposure time (`exptime`), weight the + resampled variance by the square of the exposure time for the image. + +#. Add the weighted, resampled read noise variance to a running sum across all + images. Add the weights (unsquared) to a separate running sum across + all images. + +#. Perform the same steps for the Poisson noise variance and the flat variance. + For these components, the weight for the sum is either the resampled read + noise variance or else the exposure time. + +#. For each variance component (read noise, Poisson, and flat), divide the + weighted variance sum by the total weight, squared. + +After each variance component is resampled and summed, the final error +array is computed as the square root of the sum of the three independent +variance components. This error image is stored in the ``err`` attribute +in the output data model or the ``'ERR'`` extension of the FITS file. + +It is expected that the output errors computed in this way will +generally overestimate the true error on the resampled data. The magnitude +of the overestimation depends on the details of the pixel weights +and error images. Note, however, that drizzling error images produces +a much better estimate of the output error than directly drizzling +the variance images, since the kernel overlap weights do not need to be +squared for combining error values. + + Context Image ------------- @@ -93,6 +147,7 @@ context array ``con``, one can do something like this: For convenience, this functionality was implemented in the :py:func:`~jwst.resample.resample_utils.decode_context` function. + Spectroscopic Data ------------------ diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 19a38489fa..e33b5305ae 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -1,5 +1,6 @@ import logging import os +import warnings import numpy as np from drizzle import util @@ -373,20 +374,19 @@ def resample_many_to_one(self): ) del data, inwht - # Resample variances array in self.input_models to output_model - self.resample_variance_array("var_rnoise", output_model) - self.resample_variance_array("var_poisson", output_model) - self.resample_variance_array("var_flat", output_model) - output_model.err = np.sqrt( - np.nansum( - [ - output_model.var_rnoise, - output_model.var_poisson, - output_model.var_flat - ], - axis=0 - ) - ) + # Resample variance arrays in self.input_models to output_model + self.resample_variance_arrays(output_model) + var_components = [ + output_model.var_rnoise, + output_model.var_poisson, + output_model.var_flat + ] + output_model.err = np.sqrt(np.nansum(var_components,axis=0)) + + # nansum returns zero for input that is all NaN - + # set those values to NaN instead + all_nan = np.all(np.isnan(var_components), axis=0) + output_model.err[all_nan] = np.nan self.update_exposure_times(output_model) self.output_models.append(output_model) @@ -396,85 +396,160 @@ def resample_many_to_one(self): return self.output_models - def resample_variance_array(self, name, output_model): - """Resample variance arrays from self.input_models to the output_model + def resample_variance_arrays(self, output_model): + """Resample variance arrays from self.input_models to the output_model. - Resample the ``name`` variance array to the same name in output_model, - using a cumulative sum. + Variance images from each input model are resampled individually and + added to a weighted sum. If weight_type is 'ivm', the inverse of the + resampled read noise variance is used as the weight for all the variance + components. If weight_type is 'exptime', the exposure time is used. - This modifies output_model in-place. + The output_model is modified in place. """ - output_wcs = output_model.meta.wcs - inverse_variance_sum = np.full_like(output_model.data, np.nan) - - log.info(f"Resampling {name}") + log.info("Resampling variance components") + weighted_rn_var = np.full_like(output_model.data, np.nan) + weighted_pn_var = np.full_like(output_model.data, np.nan) + weighted_flat_var = np.full_like(output_model.data, np.nan) + total_weight_rn_var = np.zeros_like(output_model.data) + total_weight_pn_var = np.zeros_like(output_model.data) + total_weight_flat_var = np.zeros_like(output_model.data) for model in self.input_models: - variance = getattr(model, name) - if variance is None or variance.size == 0: - log.debug( - f"No data for '{name}' for model " - f"{repr(model.meta.filename)}. Skipping ..." + # Do the read noise variance first, so it can be + # used for weights if needed + rn_var = self._resample_one_variance_array( + "var_rnoise", model, output_model) + + # Find valid weighting values in the variance + if rn_var is not None: + mask = (rn_var > 0) & np.isfinite(rn_var) + else: + mask = np.full_like(rn_var, False) + + # Set the weight for the image from the weight type + weight = np.ones(output_model.data.shape) + if self.weight_type == "ivm" and rn_var is not None: + weight[mask] = rn_var[mask] ** -1 + elif self.weight_type == "exptime": + if resample_utils.check_for_tmeasure(model): + weight[:] = model.meta.exposure.measurement_time + else: + weight[:] = model.meta.exposure.exposure_time + + # Weight and add the readnoise variance + # Note: floating point overflow is an issue if variance weights + # are used - it can't be squared before multiplication + if rn_var is not None: + mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0) + weighted_rn_var[mask] = np.nansum( + [weighted_rn_var[mask], + rn_var[mask] * weight[mask] * weight[mask]], + axis=0 ) - continue - - elif variance.shape != model.data.shape: - log.warning( - f"Data shape mismatch for '{name}' for model " - f"{repr(model.meta.filename)}. Skipping ..." + total_weight_rn_var[mask] += weight[mask] + + # Now do poisson and flat variance, updating only valid new values + # (zero is a valid value; negative, inf, or NaN are not) + pn_var = self._resample_one_variance_array( + "var_poisson", model, output_model) + if pn_var is not None: + mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0) + weighted_pn_var[mask] = np.nansum( + [weighted_pn_var[mask], + pn_var[mask] * weight[mask] * weight[mask]], + axis=0 ) - continue - - # Make input weight map of unity where there is science data - inwht = resample_utils.build_driz_weight( - model, - weight_type=None, - good_bits=self.good_bits - ) - - resampled_variance = np.zeros_like(output_model.data) - outwht = np.zeros_like(output_model.data) - outcon = np.zeros_like(output_model.con) - - xmin, xmax, ymin, ymax = resample_utils._resample_range( - variance.shape, - model.meta.wcs.bounding_box + total_weight_pn_var[mask] += weight[mask] + + flat_var = self._resample_one_variance_array( + "var_flat", model, output_model) + if flat_var is not None: + mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0) + weighted_flat_var[mask] = np.nansum( + [weighted_flat_var[mask], + flat_var[mask] * weight[mask] * weight[mask]], + axis=0 + ) + total_weight_flat_var[mask] += weight[mask] + + # We now have a sum of the weighted resampled variances. + # Divide by the total weights, squared, and set in the output model. + # Zero weight and missing values are NaN in the output. + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) + warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) + + output_variance = (weighted_rn_var + / total_weight_rn_var / total_weight_rn_var) + setattr(output_model, "var_rnoise", output_variance) + + output_variance = (weighted_pn_var + / total_weight_pn_var / total_weight_pn_var) + setattr(output_model, "var_poisson", output_variance) + + output_variance = (weighted_flat_var + / total_weight_flat_var / total_weight_flat_var) + setattr(output_model, "var_flat", output_variance) + + def _resample_one_variance_array(self, name, input_model, output_model): + """Resample one variance image from an input model. + + The error image is passed to drizzle instead of the variance, to + better match kernel overlap and user weights to the data, in the + pixel averaging process. The drizzled error image is squared before + returning. + """ + variance = getattr(input_model, name) + if variance is None or variance.size == 0: + log.debug( + f"No data for '{name}' for model " + f"{repr(input_model.meta.filename)}. Skipping ..." ) + return - iscale = model.meta.iscale - - # Resample the variance array. Fill "unpopulated" pixels with NaNs. - self.drizzle_arrays( - variance, - inwht, - model.meta.wcs, - output_wcs, - resampled_variance, - outwht, - outcon, - iscale=iscale**2, - pixfrac=self.pixfrac, - kernel=self.kernel, - fillval=np.nan, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax + elif variance.shape != input_model.data.shape: + log.warning( + f"Data shape mismatch for '{name}' for model " + f"{repr(input_model.meta.filename)}. Skipping ..." ) + return - # Add the inverse of the resampled variance to a running sum. - # Update only pixels (in the running sum) with valid new values: - mask = resampled_variance > 0 + # Make input weight map + inwht = resample_utils.build_driz_weight( + input_model, + weight_type=self.weight_type, # weights match science + good_bits=self.good_bits + ) - inverse_variance_sum[mask] = np.nansum( - [inverse_variance_sum[mask], np.reciprocal(resampled_variance[mask])], - axis=0 - ) + resampled_error = np.zeros_like(output_model.data) + outwht = np.zeros_like(output_model.data) + outcon = np.zeros_like(output_model.con) - # We now have a sum of the inverse resampled variances. We need the - # inverse of that to get back to units of variance. - output_variance = np.reciprocal(inverse_variance_sum) + xmin, xmax, ymin, ymax = resample_utils._resample_range( + variance.shape, + input_model.meta.wcs.bounding_box + ) - setattr(output_model, name, output_variance) + iscale = input_model.meta.iscale + + # Resample the error array. Fill "unpopulated" pixels with NaNs. + self.drizzle_arrays( + np.sqrt(variance), + inwht, + input_model.meta.wcs, + output_model.meta.wcs, + resampled_error, + outwht, + outcon, + iscale=iscale, + pixfrac=self.pixfrac, + kernel=self.kernel, + fillval=np.nan, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax + ) + return resampled_error ** 2 def update_exposure_times(self, output_model): """Modify exposure time metadata in-place""" @@ -485,7 +560,7 @@ def update_exposure_times(self, output_model): measurement_time_failures = [] for exposure in self.input_models.models_grouped: total_exposure_time += exposure[0].meta.exposure.exposure_time - if not resample_utils._check_for_tmeasure(exposure[0]): + if not resample_utils.check_for_tmeasure(exposure[0]): measurement_time_failures.append(1) else: total_measurement_time += exposure[0].meta.exposure.measurement_time diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index 08572108de..ed4447d8b5 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -182,7 +182,7 @@ def build_driz_weight(model, weight_type=None, good_bits=None): inv_variance = 1.0 result = inv_variance * dqmask elif weight_type == 'exptime': - if _check_for_tmeasure(model): + if check_for_tmeasure(model): exptime = model.meta.exposure.measurement_time else: exptime = model.meta.exposure.exposure_time @@ -311,7 +311,7 @@ def _resample_range(data_shape, bbox=None): return xmin, xmax, ymin, ymax -def _check_for_tmeasure(model): +def check_for_tmeasure(model): ''' Check if the measurement_time keyword is present in the datamodel for use in exptime weighting. If not, revert to using exposure_time. diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 08e1fd436b..73f914181a 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -457,8 +457,10 @@ def test_wcs_keywords(nircam_rate): assert result.meta.wcsinfo.vparity is None -@pytest.mark.parametrize("n_images", [1, 2, 3, 9]) -def test_resample_variance(nircam_rate, n_images): +@pytest.mark.parametrize("n_images,weight_type", + [(1, 'ivm'), (2, 'ivm'), (3, 'ivm'), (9, 'ivm'), + (1, 'exptime'), (2, 'exptime'), (3, 'exptime'), (9, 'exptime')]) +def test_resample_variance(nircam_rate, n_images, weight_type): """Test that resampled variance and error arrays are computed properly""" err = 0.02429 var_rnoise = 0.00034 @@ -474,7 +476,7 @@ def test_resample_variance(nircam_rate, n_images): for n in range(n_images): c.append(im.copy()) - result = ResampleStep.call(c, blendheaders=False) + result = ResampleStep.call(c, blendheaders=False, weight_type=weight_type) # Verify that the combined uncertainty goes as 1 / sqrt(N) assert_allclose(result.err[5:-5, 5:-5].mean(), err / np.sqrt(n_images), atol=1e-5) @@ -493,7 +495,13 @@ def test_resample_undefined_variance(nircam_rate, shape): c = ModelContainer([im]) with pytest.warns(RuntimeWarning, match="var_rnoise array not available"): - ResampleStep.call(c, blendheaders=False) + result = ResampleStep.call(c, blendheaders=False) + + # no valid variance - output error and variance are all NaN + assert_allclose(result.err, np.nan) + assert_allclose(result.var_rnoise, np.nan) + assert_allclose(result.var_poisson, np.nan) + assert_allclose(result.var_flat, np.nan) @pytest.mark.parametrize('ratio', [0.7, 1.2]) From 080d7855611758c308210640108716405d3d7e7a Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 7 May 2024 11:18:43 -0400 Subject: [PATCH 038/687] allow ami_analyze step to run on cal files (#8451) --- CHANGES.rst | 2 ++ jwst/ami/ami_analyze.py | 4 ++++ jwst/regtest/test_niriss_ami3.py | 25 +++++++++++++++++++++++++ 3 files changed, 31 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 3f7018eb61..213f7eefe6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -6,6 +6,8 @@ ami - Replaced deprecated ``np.mat()`` with ``np.asmatrix()``. [#8415] +- Allow ``ami_analyze`` to run on ``cal`` files. [#8451] + assign_wcs ---------- diff --git a/jwst/ami/ami_analyze.py b/jwst/ami/ami_analyze.py index 06d31eab03..dd7e24132c 100644 --- a/jwst/ami/ami_analyze.py +++ b/jwst/ami/ami_analyze.py @@ -3,6 +3,8 @@ import numpy as np import copy +from jwst.datamodels import CubeModel, ImageModel + from .find_affine2d_parameters import find_rotation from . import instrument_data from . import nrm_core @@ -73,6 +75,8 @@ def apply_LG_plus( # If the input image is 2D, expand all relevant extensions to be 3D # Incl. those not currently used? if len(input_model.data.shape) == 2: + if isinstance(input_copy, ImageModel): + input_copy = CubeModel(input_copy) input_copy.data = np.expand_dims(input_copy.data, axis=0) input_copy.dq = np.expand_dims(input_copy.dq, axis=0) # input_copy.err = np.expand_dims(input_copy.err, axis=0) diff --git a/jwst/regtest/test_niriss_ami3.py b/jwst/regtest/test_niriss_ami3.py index b7b8630e9c..0ec0f28e8e 100644 --- a/jwst/regtest/test_niriss_ami3.py +++ b/jwst/regtest/test_niriss_ami3.py @@ -17,6 +17,19 @@ def run_pipeline(rtdata_module): return rtdata +@pytest.fixture(scope="module") +def run_step_with_cal(rtdata_module): + rtdata = rtdata_module + # run step? + rtdata.get_data("niriss/ami/jw04478001001_03102_00001_nis_cal.fits") + args = [ + 'ami_analyze', + rtdata.input, + ] + Step.from_cmdline(args) + return rtdata + + @pytest.mark.bigdata @pytest.mark.parametrize("obs, suffix", [("012", "ami-oi"), ("015", "psf-ami-oi")]) def test_niriss_ami3_exp(run_pipeline, obs, suffix, fitsdiff_default_kwargs): @@ -45,3 +58,15 @@ def test_niriss_ami3_product(run_pipeline, fitsdiff_default_kwargs): diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ("ami-oi", "amimulti-oi", "amilg")) +def test_niriss_ami3_cal(run_step_with_cal, suffix, fitsdiff_default_kwargs): + rtdata = run_step_with_cal + + output = f"jw04478001001_03102_00001_nis_{suffix}.fits" + rtdata.output = output + rtdata.get_truth("truth/test_niriss_ami3/" + output) + + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() From 53b17145c66d0b1aa94c18e5f97a72a31994a684 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 7 May 2024 11:22:46 -0700 Subject: [PATCH 039/687] JP-2075: fixed flux scaling issues in wfss_contam (#8416) --- CHANGES.rst | 5 + jwst/wfss_contam/disperse.py | 12 +- jwst/wfss_contam/observations.py | 44 + jwst/wfss_contam/tests/__init__.py | 0 jwst/wfss_contam/tests/data/__init__.py | 0 jwst/wfss_contam/tests/data/grism_wcs.asdf | 1180 +++++++++++++++++ .../tests/data/segmentation_wcs.asdf | Bin 0 -> 4767 bytes jwst/wfss_contam/tests/test_observations.py | 112 ++ 8 files changed, 1349 insertions(+), 4 deletions(-) create mode 100644 jwst/wfss_contam/tests/__init__.py create mode 100644 jwst/wfss_contam/tests/data/__init__.py create mode 100644 jwst/wfss_contam/tests/data/grism_wcs.asdf create mode 100644 jwst/wfss_contam/tests/data/segmentation_wcs.asdf create mode 100644 jwst/wfss_contam/tests/test_observations.py diff --git a/CHANGES.rst b/CHANGES.rst index 213f7eefe6..04b3261c5d 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -121,6 +121,11 @@ tweakreg - Changed default settings for ``abs_separation`` parameter for the ``tweakreg`` step to have a value compatible with the ``abs_tolerance`` parameter. [#8445] +wfss_contam +----------- + +- Fixed flux scaling issue in model contamination image by adding background + subtraction and re-scaling fluxes to respect wavelength oversampling. [#8416] 1.14.0 (2024-03-29) =================== diff --git a/jwst/wfss_contam/disperse.py b/jwst/wfss_contam/disperse.py index db05c81a95..ef5476215a 100644 --- a/jwst/wfss_contam/disperse.py +++ b/jwst/wfss_contam/disperse.py @@ -1,4 +1,5 @@ import numpy as np +import warnings from scipy.interpolate import interp1d @@ -115,9 +116,10 @@ def flux(x): # Use a natural wavelength scale or the wavelength scale of the input SED/spectrum, # whichever is smaller, divided by oversampling requested - input_dlam = np.median(lams[1:] - lams[:-1]) - if input_dlam < dw: - dlam = input_dlam / oversample_factor + if len(lams) > 1: + input_dlam = np.median(lams[1:] - lams[:-1]) + if input_dlam < dw: + dlam = input_dlam / oversample_factor else: # this value gets used when we only have 1 direct image wavelength dlam = dw / oversample_factor @@ -161,7 +163,9 @@ def flux(x): # values are naturally in units of physical fluxes, so we divide out # the sensitivity (flux calibration) values to convert to units of # countrate (DN/s). - counts = flux(lams) * areas / sens + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message="divide by zero") + counts = flux(lams) * areas / (sens * oversample_factor) counts[no_cal] = 0. # set to zero where no flux cal info available return xs, ys, areas, lams, counts, ID diff --git a/jwst/wfss_contam/observations.py b/jwst/wfss_contam/observations.py index 62b66dfcf6..d500ed4b55 100644 --- a/jwst/wfss_contam/observations.py +++ b/jwst/wfss_contam/observations.py @@ -10,10 +10,53 @@ import logging +from photutils.background import Background2D, MedianBackground +from astropy.stats import SigmaClip + log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) +def background_subtract(data, box_size=None, filter_size=(3,3), sigma=3.0, exclude_percentile=30.0): + """ + Simple astropy background subtraction + + Parameters + ---------- + data : np.ndarray + 2D array of pixel values + box_size : tuple + Size of box in pixels to use for background estimation. + If not set, defaults to 1/5 of the image size. + filter_size : tuple + Size of filter to use for background estimation + sigma : float + Sigma threshold for background clipping + exclude_percentile : float + Percentage of masked pixels above which box is excluded from background estimation + + Returns + ------- + data : np.ndarray + 2D array of pixel values with background subtracted + + Notes + ----- + Improper background subtraction in input _i2d image leads to extra flux + in the simulated dispersed image, and was one cause of flux scaling issues + in a previous version. + """ + if box_size is None: + box_size = (int(data.shape[0]/5), int(data.shape[1]/5)) + sigma_clip = SigmaClip(sigma=sigma) + bkg_estimator = MedianBackground() + bkg = Background2D(data, box_size, filter_size=filter_size, + sigma_clip=sigma_clip, bkg_estimator=bkg_estimator, + exclude_percentile=exclude_percentile) + + return data - bkg.background + + class Observation: """This class defines an actual observation. It is tied to a single grism image.""" @@ -120,6 +163,7 @@ def create_pixel_list(self): log.info(f"Using direct image {dir_image_name}") with datamodels.open(dir_image_name) as model: dimage = model.data + dimage = background_subtract(dimage) if self.sed_file is None: # Default pipeline will use sed_file=None, so we need to compute diff --git a/jwst/wfss_contam/tests/__init__.py b/jwst/wfss_contam/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/jwst/wfss_contam/tests/data/__init__.py b/jwst/wfss_contam/tests/data/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/jwst/wfss_contam/tests/data/grism_wcs.asdf b/jwst/wfss_contam/tests/data/grism_wcs.asdf new file mode 100644 index 0000000000..8334394f81 --- /dev/null +++ b/jwst/wfss_contam/tests/data/grism_wcs.asdf @@ -0,0 +1,1180 @@ +#ASDF 1.0.0 +#ASDF_STANDARD 1.5.0 +%YAML 1.1 +%TAG ! tag:stsci.edu:asdf/ +--- !core/asdf-1.1.0 +asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf', + name: asdf, version: 3.1.1.dev2+g15e830d} +history: + extensions: + - !core/extension_metadata-1.0.0 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/astronomy/gwcs/extensions/gwcs-1.2.0 + software: !core/software-1.0.0 {name: gwcs, version: 0.21.0} + - !core/extension_metadata-1.0.0 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/astronomy/coordinates/extensions/coordinates-1.0.0 + software: !core/software-1.0.0 {name: asdf-astropy, version: 0.5.0} + - !core/extension_metadata-1.0.0 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0 + software: !core/software-1.0.0 {name: asdf, version: 3.1.1.dev2+g15e830d} + - !core/extension_metadata-1.0.0 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/transform/extensions/transform-1.5.0 + software: !core/software-1.0.0 {name: asdf-astropy, version: 0.5.0} + - !core/extension_metadata-1.0.0 + extension_class: asdf_astropy._manifest.CompoundManifestExtension + extension_uri: asdf://astropy.org/core/extensions/core-1.5.0 + software: !core/software-1.0.0 {name: asdf-astropy, version: 0.5.0} + - !core/extension_metadata-1.0.0 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://stsci.edu/jwst_pipeline/extensions/jwst_transforms-1.0.0 + software: !core/software-1.0.0 {name: stdatamodels, version: 1.10.1} +wcs: ! + name: '' + pixel_shape: null + steps: + - ! + frame: ! + axes_names: [x_grism, y_grism] + axes_order: [0, 1] + axis_physical_types: ['custom:x_grism', 'custom:y_grism'] + name: grism_detector + unit: [!unit/unit-1.0.0 pixel, !unit/unit-1.0.0 pixel] + transform: !transform/compose-1.2.0 + bounding_box: !transform/property/bounding_box-1.0.0 + ignore: [] + intervals: + x0: [-0.5, 319.5] + x1: [-0.5, 340.5] + order: C + forward: + - !transform/compose-1.2.0 + forward: + - !transform/remap_axes-1.3.0 + inputs: [x0, x1] + mapping: [0, 1, 0, 0, 0] + outputs: [x0, x1, x2, x3, x4] + - !transform/concatenate-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/shift-1.2.0 + inputs: [x] + offset: 122.0 + outputs: [y] + - !transform/shift-1.2.0 + inputs: [x] + offset: 1031.0 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + - !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + inverse: !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + outputs: [y] + value: 482.26565028841355 + outputs: [y] + value: 482.26565028841355 + inputs: [x0, x1, x] + outputs: [y0, y1, y] + - !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + inverse: !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + outputs: [y] + value: 1205.025009833007 + outputs: [y] + value: 1205.025009833007 + inputs: [x00, x10, x0, x1] + outputs: [y00, y10, y0, y1] + - !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + inverse: !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + outputs: [y] + value: 1.0 + outputs: [y] + value: 1.0 + inputs: [x00, x10, x0, x1, x] + outputs: [y00, y10, y0, y1, y] + inputs: [x0, x1] + outputs: [y00, y10, y0, y1, y] + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - ! + inputs: [x, y, x0, y0, order] + inverse: ! + inputs: [x, y, wavelength, order] + lmodels: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 35 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 36 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 37 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 38 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 39 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + model_type: NIRISSBackwardGrismDispersion + name: niriss_backward_grism_dispersion + orders: [1, 2, 3, -1, 0] + outputs: [x, y, x0, y0, order] + theta: 0.006000000000653927 + xmodels: + - - &id001 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 0 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id002 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 1 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id003 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 2 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id004 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 3 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id005 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 4 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id006 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 5 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id007 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 6 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id008 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 7 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id009 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 8 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id010 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 9 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id011 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 10 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id012 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 11 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id013 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 12 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id014 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 13 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id015 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 14 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + ymodels: + - - &id016 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 15 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id017 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 16 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id018 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 17 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id019 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 18 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id020 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 19 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id021 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 20 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id022 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 21 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id023 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 22 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id024 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 23 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id025 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 24 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id026 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 25 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id027 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 26 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - - &id028 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 27 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id029 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 28 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - &id030 !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 29 + datatype: float64 + byteorder: little + shape: [3, 3] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + lmodels: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 30 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 31 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 32 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 33 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 34 + datatype: float64 + byteorder: little + shape: [2] + domain: [-1, 1] + inputs: [x] + outputs: [y] + window: [-1, 1] + model_type: NIRISSForwardRowGrismDispersion + name: niriss_forward_row_grism_dispersion + orders: [1, 2, 3, -1, 0] + outputs: [x, y, wavelength, order] + theta: -0.006000000000653927 + xmodels: + - - *id001 + - *id002 + - *id003 + - - *id004 + - *id005 + - *id006 + - - *id007 + - *id008 + - *id009 + - - *id010 + - *id011 + - *id012 + - - *id013 + - *id014 + - *id015 + ymodels: + - - *id016 + - *id017 + - *id018 + - - *id019 + - *id020 + - *id021 + - - *id022 + - *id023 + - *id024 + - - *id025 + - *id026 + - *id027 + - - *id028 + - *id029 + - *id030 + - !transform/remap_axes-1.3.0 + inputs: [x0, x1, x2, x3] + mapping: [0, 1, 2, 3] + outputs: [x0, x1, x2, x3] + inputs: [x, y, x0, y0, order] + outputs: [x0, x1, x2, x3] + - !transform/concatenate-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/identity-1.2.0 + inputs: [x0, x1] + n_dims: 2 + outputs: [x0, x1] + - !transform/multiply-1.2.0 + forward: + - !transform/identity-1.2.0 + inputs: [x0] + outputs: [x0] + - !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + name: velocity_correction + outputs: [y] + value: 0.99999381609348 + inputs: [x0] + inverse: !transform/divide-1.2.0 + forward: + - !transform/identity-1.2.0 + inputs: [x0] + outputs: [x0] + - !transform/constant-1.4.0 + dimensions: 1 + inputs: [x] + name: inv_vel_correction + outputs: [y] + value: 0.99999381609348 + inputs: [x0] + outputs: [x0] + outputs: [x0] + inputs: [x00, x10, x01] + outputs: [x00, x10, x01] + - !transform/identity-1.2.0 + inputs: [x0] + outputs: [x0] + inputs: [x00, x10, x01, x0] + outputs: [x00, x10, x01, x0] + inputs: [x, y, x0, y0, order] + outputs: [x00, x10, x01, x0] + inputs: [x0, x1] + outputs: [x00, x10, x01, x0] + - ! + frame: ! + frames: + - ! + axes_names: [x, y] + axes_order: [0, 1] + axis_physical_types: ['custom:x', 'custom:y'] + name: detectorspatial + unit: [!unit/unit-1.0.0 pixel, !unit/unit-1.0.0 pixel] + - &id033 ! + axes_names: [wavelength] + axes_order: [2] + axis_physical_types: [em.wl] + name: spectral + unit: [!unit/unit-1.0.0 um] + name: detector + transform: !transform/concatenate-1.2.0 + forward: + - !transform/compose-1.2.0 + bounding_box: !transform/property/bounding_box-1.0.0 + ignore: [] + intervals: + x0: [-0.5, 2047.5] + x1: [-0.5, 2047.5] + order: C + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/shift-1.2.0 + inputs: [x] + offset: 2.119 + outputs: [y] + - !transform/shift-1.2.0 + inputs: [x] + offset: -1.0476 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/shift-1.2.0 + inputs: [x] + offset: -1023.5 + outputs: [y] + - !transform/shift-1.2.0 + inputs: [x] + offset: -1023.5 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + - &id031 !transform/remap_axes-1.3.0 + inputs: [x0, x1] + inverse: !transform/identity-1.2.0 + inputs: [x0, x1] + n_dims: 2 + outputs: [x0, x1] + mapping: [0, 1, 0, 1] + outputs: [x0, x1, x2, x3] + inputs: [x0, x1] + outputs: [x0, x1, x2, x3] + - !transform/concatenate-1.2.0 + forward: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 40 + datatype: float64 + byteorder: little + shape: [6, 6] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 41 + datatype: float64 + byteorder: little + shape: [6, 6] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + inputs: [x0, y0, x1, y1] + inverse: !transform/concatenate-1.2.0 + forward: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 42 + datatype: float64 + byteorder: little + shape: [6, 6] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 43 + datatype: float64 + byteorder: little + shape: [6, 6] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + inputs: [x0, y0, x1, y1] + outputs: [z0, z1] + outputs: [z0, z1] + inputs: [x0, x1] + outputs: [z0, z1] + - &id032 !transform/identity-1.2.0 + inputs: [x0, x1] + inverse: !transform/remap_axes-1.3.0 + inputs: [x0, x1] + mapping: [0, 1, 0, 1] + outputs: [x0, x1, x2, x3] + n_dims: 2 + outputs: [x0, x1] + inputs: [x0, x1] + outputs: [x0, x1] + - *id031 + inputs: [x0, x1] + outputs: [x0, x1, x2, x3] + - !transform/concatenate-1.2.0 + forward: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 44 + datatype: float64 + byteorder: little + shape: [2, 2] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 45 + datatype: float64 + byteorder: little + shape: [2, 2] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + inputs: [x0, y0, x1, y1] + inverse: !transform/concatenate-1.2.0 + forward: + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 46 + datatype: float64 + byteorder: little + shape: [2, 2] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + - !transform/polynomial-1.2.0 + coefficients: !core/ndarray-1.0.0 + source: 47 + datatype: float64 + byteorder: little + shape: [2, 2] + domain: + - [-1, 1] + - [-1, 1] + inputs: [x, y] + outputs: [z] + window: + - [-1, 1] + - [-1, 1] + inputs: [x0, y0, x1, y1] + outputs: [z0, z1] + outputs: [z0, z1] + inputs: [x0, x1] + outputs: [z0, z1] + - *id032 + inputs: [x0, x1] + outputs: [x0, x1] + - !transform/concatenate-1.2.0 + forward: + - !transform/shift-1.2.0 + inputs: [x] + offset: -291.141 + outputs: [y] + - !transform/shift-1.2.0 + inputs: [x] + offset: -698.015 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + inputs: [x0, x1] + outputs: [y0, y1] + inputs: [x0, x1] + outputs: [y0, y1] + - !transform/identity-1.2.0 + inputs: [x0, x1] + n_dims: 2 + outputs: [x0, x1] + inputs: [x00, x10, x01, x11] + outputs: [y0, y1, x0, x1] + - ! + frame: ! + frames: + - ! + axes_names: [v2, v3] + axes_order: [0, 1] + axis_physical_types: ['custom:v2', 'custom:v3'] + name: v2v3spatial + unit: [!unit/unit-1.0.0 arcsec, !unit/unit-1.0.0 arcsec] + - *id033 + name: v2v3 + transform: !transform/concatenate-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/scale-1.2.0 + factor: 0.9999939001894596 + inputs: [x] + name: dva_scale_v2 + outputs: [y] + - !transform/scale-1.2.0 + factor: 0.9999939001894596 + inputs: [x] + name: dva_scale_v3 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + - !transform/concatenate-1.2.0 + forward: + - !transform/shift-1.2.0 + inputs: [x] + name: dva_v2_shift + offset: -0.0017759049405570732 + outputs: [y] + - !transform/shift-1.2.0 + inputs: [x] + name: dva_v3_shift + offset: -0.004257759254392014 + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + inputs: [x0, x1] + name: DVA_Correction + outputs: [y0, y1] + - !transform/identity-1.2.0 + inputs: [x0, x1] + n_dims: 2 + outputs: [x0, x1] + inputs: [x00, x10, x01, x11] + outputs: [y0, y1, x0, x1] + - ! + frame: ! + frames: + - ! + axes_names: [v2, v3] + axes_order: [0, 1] + axis_physical_types: ['custom:v2', 'custom:v3'] + name: v2v3vacorrspatial + unit: [!unit/unit-1.0.0 arcsec, !unit/unit-1.0.0 arcsec] + - *id033 + name: v2v3vacorr + transform: !transform/concatenate-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/compose-1.2.0 + forward: + - !transform/concatenate-1.2.0 + forward: + - !transform/scale-1.2.0 + factor: 0.0002777777777777778 + inputs: [x] + outputs: [y] + - !transform/scale-1.2.0 + factor: 0.0002777777777777778 + inputs: [x] + outputs: [y] + inputs: [x0, x1] + outputs: [y0, y1] + - ! + inputs: [lon, lat] + outputs: [x, y, z] + transform_type: spherical_to_cartesian + wrap_lon_at: 180 + inputs: [x0, x1] + outputs: [x, y, z] + - !transform/rotate_sequence_3d-1.0.0 + angles: [-0.0808725, 0.19389305555555555, 196.1037680531535, 65.83768326569226, + -260.82488865179715] + axes_order: zyxyz + inputs: [x, y, z] + outputs: [x, y, z] + rotation_type: cartesian + inputs: [x0, x1] + outputs: [x, y, z] + - ! + inputs: [x, y, z] + outputs: [lon, lat] + transform_type: cartesian_to_spherical + wrap_lon_at: 360 + inputs: [x0, x1] + name: v23tosky + outputs: [lon, lat] + - !transform/identity-1.2.0 + inputs: [x0, x1] + n_dims: 2 + outputs: [x0, x1] + inputs: [x00, x10, x01, x11] + outputs: [lon, lat, x0, x1] + - ! + frame: ! + frames: + - ! + axes_names: [lon, lat] + axes_order: [0, 1] + axis_physical_types: [pos.eq.ra, pos.eq.dec] + name: sky + reference_frame: ! + frame_attributes: {} + unit: [!unit/unit-1.0.0 deg, !unit/unit-1.0.0 deg] + - *id033 + name: world + transform: null +... +ÓBLK0HHH°_ÏnHÈÂØä¢LdÐîcÙ=yŒO@Íîuµ‰Q?*œŸÎþŒš¾,U ÑÍpf¿=)Ðc{ïŒ>uF.ìZµ>ÓBLK0HHHï.ö+•`1Ë›XMVäñŸOjMtÀ«ž¿ø¦s¿áÿ+†i–§>ʳDÇ rr?ÒàÓb¾“¥>Jý…¹òžÓBLK0HHH—ìêZĬFžõõ$}O‘X +7ÏÇëÒ?=î AïÎH?V§®Sto¾uf%'¿·\¿tÄÄqñó¡¾Ü™Ë˜çZ²>ÓBLK0HHHx¸"ùü~q'oàFûÑ 'kÔC4ÚVÀ<±Ì÷Ü-¿¥æ“ª¥;©¾ÞQTŸëc?MŽb”¶‡>˜êþ¼· ¿¾ÓBLK0HHHù8Þ0à$zÝ—þYxJ¹ªSDúíë@P„ÀsëjxÄ ¿Õž°Úl¦>EapýV}¿+ŽÍÿÀ§Ã¾È‰¸ + Û>ÓBLK0HHHÒ9æù×8óVØI€ãi˜kÑ´-þ¿ÇSkn@Üq?ç,°zþDîøL.z?­GˆÃå:¬¾åùø# ϾÓBLK0HHH€²`Ôl^vÍCq`ròó‘ˆc]ÜFçjÀˆ”°ã‘?¡‡ça;ݾoÀBA„º¿ž2溾ԣ¼]Žš?ÓBLK0HHHúЂ£ßï‡#ô`¨xœ#ÑóD‹lçû÷À²á]쳿¬_³2ñø>žY·æ3ÅÒ?óÒt¹Ué>Ò¼¬Øs"¿ÓBLK0HHHžôî°ðÃ÷y­Ë³ßy¸éÏ~¤ZQ@—×øª?h¼$º]¢ð¾~ìäÂ7Ê¿‰cħÖë¾[\¿¶€D?ÓBLK0HHHO8,òµnÄHðêï& ŠO3f÷äa¡—w@J_ý¡VæO?)ÃMÿ¦ª>¨ÃQŒÓs?Q"‰È—s—¾q²P˜T3½¾ÓBLK0HHHŒkh.¿Ž )½ ]AóÞÝ$•Út@P0Jm5-‘?i¡6m½Û¾}‰Âx9¿ëµÿ² V®> +$~±×šë>ÓBLK0HHH/¢l{ýk²÷5Ò•ûy4¸­-ÜÀc»V3cˆ¿°˜ñ31UÔ>&JJ“µ–?%¿"Q°>¡ã¡2°\ç¾ÓBLK0HHHp¤zó©¡kÏìðc–PtY\Âõ¸ŠÑÀR·éS·p?jXNu ë>žuðÀ¾¥¿XY‘$ù¾!—Ѿ·I?ÓBLK0HHH ¹êQ4ðNf½õùR¸…ëÊyÀWÑÝ}§,?T !° +… ?ÛÆ]1á­¿©)¬”­Ÿ¿ìý¦­š²&?ÓBLK0HHH¾…R¥„q3:IWw7F5š™™™ýKò@CÁR~ÎŒ?®¶6ep ? 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[#8438] +photom +------ + +- Ensure that NaNs in MRS photom files are not replaced with ones by + pipeline code for consistency with other modes [#8453] + pipeline -------- diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py index 824f5594a7..7f7a6cc4da 100644 --- a/jwst/photom/photom.py +++ b/jwst/photom/photom.py @@ -508,18 +508,10 @@ def calc_miri(self, ftab): # MRS detectors elif self.detector == 'MIRIFUSHORT' or self.detector == 'MIRIFULONG': - # Reset conversion and pixel size values with DQ=NON_SCIENCE to 1, - # so no conversion is applied - where_dq = np.bitwise_and(ftab.dq, dqflags.pixel['NON_SCIENCE']) - ftab.data[where_dq > 0] = 1.0 - - # Reset NaN's in conversion array to 1 + # Make sure all NaN's have DO_NOT_USE flag set where_nan = np.isnan(ftab.data) - ftab.data[where_nan] = 1.0 - - # Make sure all NaN's and zeros have DQ flags set ftab.dq[where_nan] = np.bitwise_or(ftab.dq[where_nan], - dqflags.pixel['NON_SCIENCE']) + dqflags.pixel['DO_NOT_USE']) # Compute the combined 2D sensitivity factors sens2d = ftab.data From c9e8275624f2d2e4a58004e06ade9b5f23089c65 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 8 May 2024 08:10:06 -0400 Subject: [PATCH 041/687] JP-3612: Fix MOS background slit source position (#8461) Co-authored-by: Nadia Dencheva --- CHANGES.rst | 3 +++ jwst/assign_wcs/nirspec.py | 4 ++-- jwst/assign_wcs/tests/test_nirspec.py | 2 +- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index cffcd7166f..20081f4a78 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -15,6 +15,9 @@ assign_wcs of pixel row rather than a rotation of the pixel coordinates. The practical impact is to ensure that iso-lambda is along pixel rows after this change. [#8411] +- Move the assigned source position for dedicated NIRSpec MOS background slits from the + lower left corner of the slit to the middle of the slit. [#8461] + associations ------------ diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index f106dc4c96..c6e3e8c2ef 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -662,8 +662,8 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, quadrant = slitlets_sid[0]['shutter_quadrant'] ycen = j xcen = slitlets_sid[0]['shutter_row'] # grab the first as they are all the same - source_xpos = 0.0 - source_ypos = 0.0 + source_xpos = 0.5 + source_ypos = 0.5 source_id = _get_bkg_source_id(bkg_counter, max_source_id) log.info(f'Slitlet_id {slitlet_id} is background only; assigned source_id = {source_id}') bkg_counter += 1 diff --git a/jwst/assign_wcs/tests/test_nirspec.py b/jwst/assign_wcs/tests/test_nirspec.py index 7fdbaffd1e..a517916835 100644 --- a/jwst/assign_wcs/tests/test_nirspec.py +++ b/jwst/assign_wcs/tests/test_nirspec.py @@ -311,7 +311,7 @@ def test_msa_configuration_all_background(): slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) ref_slit = trmodels.Slit(57, 8281, 1, 251, 23, -2.15, 2.15, 4, 0, '1x1', 'background_57', 'bkg_57', - 0, -0.5, -0.5) + 0, 0.0, 0.0) _compare_slits(slitlet_info[0], ref_slit) From 4e91a1c392aa1c917d8ade745a37e5aacd5bb64b Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 8 May 2024 14:55:08 -0400 Subject: [PATCH 042/687] clarify checklist in PR template (#8466) --- .github/pull_request_template.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 56bee3d2d2..39941f867a 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -8,7 +8,7 @@ Closes # This PR addresses ... -**Checklist for maintainers** +**Checklist for PR authors (skip items if you don't have permissions or they are not applicable)** - [ ] added entry in `CHANGES.rst` within the relevant release section - [ ] updated or added relevant tests - [ ] updated relevant documentation @@ -16,4 +16,5 @@ This PR addresses ... - [ ] added relevant label(s) - [ ] ran regression tests, post a link to the Jenkins job below. [How to run regression tests on a PR](https://github.com/spacetelescope/jwst/wiki/Running-Regression-Tests-Against-PR-Branches) +- [ ] All comments are resolved - [ ] Make sure the JIRA ticket is [resolved properly](https://github.com/spacetelescope/jwst/wiki/How-to-resolve-JIRA-issues) From 241c5c004a51d758f4ddc16a9808b1bc615289e2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 8 May 2024 15:36:50 -0400 Subject: [PATCH 043/687] JP-3613: Update error arrays to match NaN in data (#8463) --- CHANGES.rst | 3 +++ jwst/flatfield/flat_field.py | 21 ++++++++++++++++++--- jwst/regtest/test_nirspec_steps_spec2.py | 7 +++++++ 3 files changed, 28 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 20081f4a78..06cf8cbcff 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -59,6 +59,9 @@ flat_field - Update the flatfield code for NIRSpec IFU data to ensure that SCI=ERR=NaN and DQ has the DO_NOT_USE flag set outside the footprint of the IFU slices [#8385] +- Update NIRSpec flatfield code for all modes to ensure SCI=ERR=NaN wherever the + DO_NOT_USE flag is set in the DQ array. [#8463] + general ------- diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 3795fd74a4..1c4c1db6ea 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -442,7 +442,12 @@ def nirspec_fs_msa(output_model, f_flat_model, s_flat_model, d_flat_model, dispa # Make sure all DO_NOT_USE pixels are set to NaN, # including those flagged by this step - slit.data[np.where(slit.dq & dqflags.pixel['DO_NOT_USE'])] = np.nan + dnu = np.where(slit.dq & dqflags.pixel['DO_NOT_USE']) + slit.data[dnu] = np.nan + slit.err[dnu] = np.nan + slit.var_poisson[dnu] = np.nan + slit.var_rnoise[dnu] = np.nan + slit.var_flat[dnu] = np.nan any_updated = True @@ -521,7 +526,12 @@ def nirspec_brightobj(output_model, f_flat_model, s_flat_model, d_flat_model, di # Make sure all DO_NOT_USE pixels are set to NaN, # including those flagged by this step - output_model.data[np.where(output_model.dq & dqflags.pixel['DO_NOT_USE'])] = np.nan + dnu = np.where(output_model.dq & dqflags.pixel['DO_NOT_USE']) + output_model.data[dnu] = np.nan + output_model.err[dnu] = np.nan + output_model.var_poisson[dnu] = np.nan + output_model.var_rnoise[dnu] = np.nan + output_model.var_flat[dnu] = np.nan output_model.meta.cal_step.flat_field = 'COMPLETE' @@ -591,7 +601,12 @@ def nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, dispaxis # Make sure all DO_NOT_USE pixels are set to NaN, # including those flagged by this step - output_model.data[np.where(output_model.dq & dqflags.pixel['DO_NOT_USE'])] = np.nan + dnu = np.where(output_model.dq & dqflags.pixel['DO_NOT_USE']) + output_model.data[dnu] = np.nan + output_model.err[dnu] = np.nan + output_model.var_poisson[dnu] = np.nan + output_model.var_rnoise[dnu] = np.nan + output_model.var_flat[dnu] = np.nan output_model.meta.cal_step.flat_field = 'COMPLETE' diff --git a/jwst/regtest/test_nirspec_steps_spec2.py b/jwst/regtest/test_nirspec_steps_spec2.py index d51c685404..0314d77a1e 100644 --- a/jwst/regtest/test_nirspec_steps_spec2.py +++ b/jwst/regtest/test_nirspec_steps_spec2.py @@ -60,6 +60,13 @@ def test_ff_inv(rtdata, fitsdiff_default_kwargs): # make sure NaNs are only at do_not_use pixels assert np.all(unflatted.dq[is_nan] & dm.dqflags.pixel['DO_NOT_USE']) + # make sure NaNs at science pixels are also NaN in error and var arrays + assert np.all(np.isnan(unflatted.err[is_nan])) + assert np.all(np.isnan(unflatted.var_poisson[is_nan])) + assert np.all(np.isnan(unflatted.var_rnoise[is_nan])) + assert np.all(np.isnan(unflatted.var_flat[is_nan])) + + @pytest.mark.slow @pytest.mark.bigdata def test_pathloss_corrpars(rtdata): From 9e857a52f8cd2dd58a04e6b71751fa15fca32556 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 8 May 2024 13:11:56 -0700 Subject: [PATCH 044/687] JP-3595: handling for NIRCam GRISM Time Series Pointing offset SR (#8449) --- CHANGES.rst | 9 ++++ docs/jwst/extract_2d/main.rst | 7 +++ jwst/assign_wcs/nircam.py | 19 ++++---- jwst/assign_wcs/tests/test_nircam.py | 37 +++++++++------ jwst/extract_2d/grisms.py | 67 ++++++++++++++++++++++------ jwst/extract_2d/tests/test_grisms.py | 14 +++++- jwst/regtest/test_nircam_tsgrism.py | 29 ++++++++++++ 7 files changed, 146 insertions(+), 36 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 06cf8cbcff..6ccd14ff75 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -15,6 +15,8 @@ assign_wcs of pixel row rather than a rotation of the pixel coordinates. The practical impact is to ensure that iso-lambda is along pixel rows after this change. [#8411] +- Fixed test for NIRCam TSGRISM mode. [#8449] + - Move the assigned source position for dedicated NIRSpec MOS background slits from the lower left corner of the slit to the middle of the slit. [#8461] @@ -41,6 +43,8 @@ documentation - Added documentation for multiprocessing. [#8408] +- Added documentation for NIRCam GRISM time series pointing offsets. [#8449] + extract_1d ---------- @@ -53,6 +57,11 @@ extract_1d regions are specified and the lower and upper limits for one of them are outside the valid area for some data range. [#8433] +extract_2d +---------- + +- Added handling for NIRCam GRISM time series pointing offsets. [#8449] + flat_field ---------- diff --git a/docs/jwst/extract_2d/main.rst b/docs/jwst/extract_2d/main.rst index fc2d3583e7..e138f6271a 100644 --- a/docs/jwst/extract_2d/main.rst +++ b/docs/jwst/extract_2d/main.rst @@ -245,6 +245,13 @@ extension header of the input image. These values are used to set the source loc for all computations involving the extent of the spectral trace and pixel wavelength assignments. +In rare cases, it may be desirable to shift the source location in the X-direction, e.g. +for a custom noise suppression scheme. This is achieved in the APT by specifying an +offset special requirement, and shows up in the header keyword "XOFFSET". The +``extract_2d`` step accounts for this offset by simply shifting the wavelength array by +the appropriate amount. The WCS information remains unchanged. Note that offsets in the +Y-direction (cross-dispersion direction) are not supported and should not be attempted. + NIRCam subarrays used for TSGRISM observations always have their "bottom" edge located at the physical bottom edge of the detector and vary in size vertically. The source spectrum trace will always be centered somewhere near row 34 in the vertical diff --git a/jwst/assign_wcs/nircam.py b/jwst/assign_wcs/nircam.py index 23f202e6e5..676a4b64f6 100644 --- a/jwst/assign_wcs/nircam.py +++ b/jwst/assign_wcs/nircam.py @@ -178,6 +178,11 @@ def tsgrism(input_model, reference_files): frame coordinates around the trace transform. TSGRISM is only slated to work with GRISMR and Mod A + + For this mode, the source is typically at crpix1 x crpix2, which + are stored in keywords XREF_SCI, YREF_SCI. + offset special requirements may be encoded in the X_OFFSET parameter, + but those are handled in extract_2d. """ # make sure this is a grism image @@ -230,14 +235,12 @@ def tsgrism(input_model, reference_files): # input into the forward transform is x,y,x0,y0,order # where x,y is the pixel location in the grism image - # and x0,y0 is the source location in the "direct" image - # For this mode, the source is always at crpix1 x crpix2, which - # are stored in keywords XREF_SCI, YREF_SCI. - # Discussion with nadia that wcsinfo might not be available - # here but crpix info could be in wcs.source_location or similar - # TSGRISM mode places the sources at crpix, and all subarrays - # begin at 0,0, so no need to translate the crpix to full frame - # because they already are in full frame coordinates. + # and x0,y0 is the source location in the "direct" image. + # For this mode (tsgrism), it is assumed that the source is + # at the nominal aperture reference point, i.e., + # crpix1 <--> xref_sci and crpix2 <--> yref_sci + # offsets in X are handled in extract_2d, e.g. if an offset + # special requirement was specified in the APT. xc, yc = (input_model.meta.wcsinfo.siaf_xref_sci, input_model.meta.wcsinfo.siaf_yref_sci) if xc is None: diff --git a/jwst/assign_wcs/tests/test_nircam.py b/jwst/assign_wcs/tests/test_nircam.py index 5957226ed5..3ee141f0f9 100644 --- a/jwst/assign_wcs/tests/test_nircam.py +++ b/jwst/assign_wcs/tests/test_nircam.py @@ -8,7 +8,7 @@ file. """ -from numpy.testing import assert_allclose +import numpy as np import pytest @@ -99,6 +99,7 @@ def create_imaging_wcs(): return wcsobj +@pytest.fixture def create_tso_wcs(filtername=tsgrism_filters[0], subarray="SUBGRISM256"): """Help create tsgrism GWCS object.""" hdul = create_hdul(exptype='NRC_TSGRISM', pupil='GRISMR', @@ -159,16 +160,20 @@ def test_nircam_wfss_available_frames(): assert all([a == b for a, b in zip(nircam_wfss_frames, available_frames)]) -def test_nircam_tso_available_frames(): +def test_nircam_tso_available_frames(create_tso_wcs): """Make sure that the expected GWCS reference frames for TSO are created.""" - wcsobj = create_tso_wcs() + wcsobj = create_tso_wcs available_frames = wcsobj.available_frames assert all([a == b for a, b in zip(nircam_tsgrism_frames, available_frames)]) @pytest.mark.parametrize('key', ['xref_sci', 'yref_sci']) def test_extract_tso_object_fails_without_xref_yref(tsgrism_inputs, key): - with pytest.raises(ValueError): + ''' + TypeError for xref_sci because computing dither offset is attempted, get float - NoneType + ValueError for yref_sci because computing dither offset is not attempted + ''' + with pytest.raises((TypeError, ValueError)): nircam.tsgrism(*tsgrism_inputs(missing_key=key)) @@ -197,25 +202,29 @@ def test_traverse_wfss_grisms(): traverse_wfss_trace(pupil) -@pytest.mark.xfail(reason="Fails due to V2 NIRCam specwcs ref files delivered to CRDS") -def test_traverse_tso_grism(): - """Make sure that the TSO dispersion polynomials are reversable.""" - wcsobj = create_tso_wcs() +def test_traverse_tso_grism(create_tso_wcs): + """Make sure that the TSO dispersion polynomials are reversable. + All assert statements are in pixel space so 1/1000 px seems easily acceptable""" + wcsobj = create_tso_wcs detector_to_grism = wcsobj.get_transform('direct_image', 'grism_detector') grism_to_detector = wcsobj.get_transform('grism_detector', 'direct_image') # TSGRISM always has same source locations # takes x,y,order -> ra, dec, wave, order - xin, yin, order = (100, 100, 1) - + xin, yin, order = (100, 35, 1) x0, y0, lam, orderdet = grism_to_detector(xin, yin, order) x, y, orderdet = detector_to_grism(x0, y0, lam, order) - assert x0 == wcs_tso_kw['xref_sci'] - assert y0 == wcs_tso_kw['yref_sci'] + assert np.isclose(x0, wcs_tso_kw['xref_sci'], atol = 1e-3) + assert np.isclose(y0, wcs_tso_kw['yref_sci'], atol = 1e-3) assert order == orderdet - assert_allclose(x, xin) - assert y == wcs_tso_kw['yref_sci'] + assert np.isclose(x, xin, atol=1e-3) + + # this roundtrip fails to account for the ~22.5 pixel shift from target acquisition + # to science position; grism shifts spectrum wrt direct image by that amount. + # as of 29 April 2024, how to properly store this in the header and propagate + # it through assign_wcs is being discussed + # assert np.isclose(y, wcs_tso_kw['yref_sci']) def test_imaging_frames(): diff --git a/jwst/extract_2d/grisms.py b/jwst/extract_2d/grisms.py index fe77196b55..f8dbe35f3c 100644 --- a/jwst/extract_2d/grisms.py +++ b/jwst/extract_2d/grisms.py @@ -12,7 +12,9 @@ from gwcs.utils import _toindex from stdatamodels.jwst import datamodels -from stdatamodels.jwst.datamodels import WavelengthrangeModel +from stdatamodels.jwst.datamodels import WavelengthrangeModel, ImageModel +from stdatamodels.jwst.transforms.models import IdealToV2V3 +from astropy.modeling import CompoundModel from ..assign_wcs import util @@ -133,7 +135,8 @@ def extract_tso_object(input_model, lmin, lmax = range_select.pop() # Create the order bounding box - source_xpos = input_model.meta.wcsinfo.siaf_xref_sci - 1 # remove FITS 1-indexed offset + distortion = subwcs.get_transform("v2v3", "direct_image") + source_xpos, _ = compute_tso_offset_center(input_model, distortion) # 1-indexing already handled source_ypos = input_model.meta.wcsinfo.siaf_yref_sci - 1 # remove FITS 1-indexed offset transform = input_model.meta.wcs.get_transform('direct_image', 'grism_detector') xmin, ymin, _ = transform(source_xpos, @@ -151,9 +154,6 @@ def extract_tso_object(input_model, # model with the known object center and order information (in pixels of direct image) # This changes the user input to the model from (x,y,x0,y0,order) -> (x,y) # - # The bounding box is limited to the size of the detector in the dispersion direction - # and 64 pixels in the cross-dispersion direction (at request of instrument team). - # # The team wants the object to fall near row 34 for all cutouts, but the default cutout # height is 64 pixels (32 on either side). So bump the extraction ycenter, when necessary, # so that the height is 30 above and 34 below (in full frame) the object center. @@ -177,6 +177,8 @@ def extract_tso_object(input_model, raise ValueError("Something bad happened calculating extraction y-size") # Limit the bounding box to the detector edges + # The bounding box is limited to the size of the detector in the dispersion direction + # and 64 pixels in the cross-dispersion direction (at request of instrument team). ymin, ymax = (max(extract_y_min, 0), min(extract_y_max, input_model.meta.subarray.ysize)) xmin, xmax = (max(xmin, 0), min(xmax, input_model.meta.subarray.xsize)) @@ -240,14 +242,15 @@ def extract_tso_object(input_model, output_model.var_flat = var_flat output_model.meta.wcs = subwcs output_model.meta.wcs.bounding_box = util.wcs_bbox_from_shape(ext_data.shape) + if compute_wavelength: + # this seems to happen near-instantly, suggest remove this message + #log.debug("Computing wavelengths (this takes a while ...)") + output_model.wavelength = compute_tso_wavelength_array(output_model) output_model.meta.wcsinfo.siaf_yref_sci = 34 # update for the move, vals are the same - output_model.meta.wcsinfo.siaf_xref_sci = input_model.meta.wcsinfo.siaf_xref_sci + output_model.meta.wcsinfo.siaf_xref_sci = source_xpos + 1 # back to 1-indexed output_model.meta.wcsinfo.spectral_order = order output_model.meta.wcsinfo.dispersion_direction = \ input_model.meta.wcsinfo.dispersion_direction - if compute_wavelength: - log.debug("Computing wavelengths (this takes a while ...)") - output_model.wavelength = compute_wavelength_array(output_model) output_model.name = '1' output_model.source_type = input_model.meta.target.source_type output_model.source_name = input_model.meta.target.catalog_name @@ -562,7 +565,7 @@ def compute_dispersion(wcs): raise NotImplementedError -def compute_wavelength_array(slit): +def compute_tso_wavelength_array(slit): """ Compute the wavelength array for a slit with gwcs object @@ -576,12 +579,50 @@ def compute_wavelength_array(slit): wavelength : numpy.array The wavelength array """ - transform = slit.meta.wcs.forward_transform - x, y = grid_from_bounding_box(slit.meta.wcs.bounding_box) - wavelength = transform(x, y)[2] + wcs = slit.meta.wcs + full_transform = slit.meta.wcs.forward_transform + x, y = grid_from_bounding_box(wcs.bounding_box) + wavelength = full_transform(x, y)[2] return wavelength +def compute_tso_offset_center(input_model: ImageModel, distortion: CompoundModel) -> tuple[float, float]: + """ + In the case that an Offset Special Requirement is requested in the APT, + the source is no longer at the aperture reference point. + The dither.x_offset and dither.y_offset values encode the offset + in units of arcseconds. They need to be translated from Ideal to + detector coordinates and into pixel units. + + Parameters + ---------- + input_model : ImageModel + The input data model + distortion : DistortionModel + The distortion model + + Returns + ------- + xc, yc : tuple + The x and y center of the image in direct image coordinates + + Notes + ----- + The wavelength is not used for the distortion calculation between + v2v3 and direct image coordinates, so this can be hardcoded to NaN. + """ + + idltov23 = IdealToV2V3(input_model.meta.wcsinfo.v3yangle, + input_model.meta.wcsinfo.v2_ref, + input_model.meta.wcsinfo.v3_ref, + input_model.meta.wcsinfo.vparity) + v2_offset, v3_offset = idltov23(input_model.meta.dither.x_offset, input_model.meta.dither.y_offset) + wavelength = np.nan + xc, yc, _, _ = distortion(v2_offset, v3_offset, wavelength, 1) + + return xc, yc + + def compute_wfss_wavelength(slit): """ Compute the wavelength array for a slit with gwcs object diff --git a/jwst/extract_2d/tests/test_grisms.py b/jwst/extract_2d/tests/test_grisms.py index f3a2e89362..0da23a2ca1 100644 --- a/jwst/extract_2d/tests/test_grisms.py +++ b/jwst/extract_2d/tests/test_grisms.py @@ -27,7 +27,7 @@ from jwst.assign_wcs import AssignWcsStep, nircam from jwst.extract_2d.extract_2d_step import Extract2dStep -from jwst.extract_2d.grisms import extract_tso_object, extract_grism_objects +from jwst.extract_2d.grisms import extract_tso_object, extract_grism_objects, compute_tso_offset_center from jwst.extract_2d.tests import data @@ -66,6 +66,7 @@ wcs_tso_kw = {'wcsaxes': 2, 'ra_ref': 86.9875, 'dec_ref': 23.423, 'v2_ref': 95.043034, 'v3_ref': -556.150466, 'roll_ref': 359.9521, + 'v3i_yang': -0.37562675, 'vparity': -1, } @@ -97,6 +98,8 @@ def create_hdul(detector='NRCALONG', channel='LONG', module='A', phdu.header['SUBSIZE2'] = 2048 phdu.header['SUBSTRT1'] = 1 phdu.header['SUBSTRT2'] = 1 + phdu.header['XOFFSET'] = 5.0 # random offset for testing + phdu.header['YOFFSET'] = 1.45 scihdu = fits.ImageHDU() scihdu.header['EXTNAME'] = "SCI" scihdu.header.update(wcskeys) @@ -373,6 +376,15 @@ def test_extract_tso_height(): del outmodel +def test_compute_tso_offset_center(): + + image_model = create_tso_wcsimage(filtername="F444W", subarray=False) + distortion = image_model.meta.wcs.get_transform("v2v3", "direct_image") + xc, yc = compute_tso_offset_center(image_model, distortion) + assert np.isclose(yc, 59.929, atol=1e-3) + assert np.isclose(xc, 961.355, atol=1e-3) + + @pytest.mark.filterwarnings("ignore: Card is too long") def test_extract_wfss_object(): """Test extraction of a WFSS object. diff --git a/jwst/regtest/test_nircam_tsgrism.py b/jwst/regtest/test_nircam_tsgrism.py index efd09185c6..de92f1015b 100644 --- a/jwst/regtest/test_nircam_tsgrism.py +++ b/jwst/regtest/test_nircam_tsgrism.py @@ -29,6 +29,35 @@ def run_pipelines(rtdata_module): return rtdata +@pytest.fixture(scope="module", + params=["jw01185015001_03104_00001-seg004_nrcalong_rate.fits", + "jw01185013001_04103_00001-seg003_nrcalong_rate.fits"]) +def run_pipeline_offsetSR(request, rtdata_module): + '''''' + rtdata = rtdata_module + rtdata.get_data("nircam/tsgrism/" + request.param) + args = ["calwebb_spec2", rtdata.input, + "--steps.extract_1d.save_results=True",] + Step.from_cmdline(args) + return rtdata + + +@pytest.mark.bigdata +def test_nircam_tsgrism_stage2_offsetSR(run_pipeline_offsetSR, fitsdiff_default_kwargs): + ''' + Test coverage for offset special requirement specifying nonzero offset in X. + Test data are two observations of Gliese 436, one with offset specified and one without. + Quantitatively we just ensure that the outputs are identical to the inputs, but qualitatively + can check that the spectral lines fall at the same wavelengths in both cases, + which is why the zero-offset case is also included here.''' + rtdata = run_pipeline_offsetSR + rtdata.output = rtdata.input.replace("rate", "x1d") + rtdata.get_truth("truth/test_nircam_tsgrism_stages/" + rtdata.output.split('/')[-1]) + + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["calints", "extract_2d", "flat_field", "o012_crfints", "srctype", "x1dints"]) From 55098d82f6d30a64bcb56b05628cae907f2c824f Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Thu, 9 May 2024 13:04:53 -0400 Subject: [PATCH 045/687] JP-3604: Fix possible crashes in the alignment to abs catalog (#8450) --- CHANGES.rst | 3 ++ jwst/tweakreg/tweakreg_step.py | 72 ++++++++++++++++++++++++++++------ 2 files changed, 64 insertions(+), 11 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 6ccd14ff75..06e74e71da 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -142,12 +142,15 @@ tweakreg - Changed default settings for ``abs_separation`` parameter for the ``tweakreg`` step to have a value compatible with the ``abs_tolerance`` parameter. [#8445] +- Improve error handling in the absolute alignment. [#8450] + wfss_contam ----------- - Fixed flux scaling issue in model contamination image by adding background subtraction and re-scaling fluxes to respect wavelength oversampling. [#8416] + 1.14.0 (2024-03-29) =================== diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index cb86e69054..519a200ab2 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -173,6 +173,8 @@ def process(self, input): if len(images) == 0: raise ValueError("Input must contain at least one image model.") + rel_outcomes = set() + # Build the catalogs for input images for image_model in images: if use_custom_catalogs and image_model.meta.tweakreg_catalog: @@ -365,6 +367,7 @@ def process(self, input): for imcat in imcats: model = imcat.meta['image_model'] + rel_outcomes.add(model.meta.cal_step.tweakreg) if model.meta.cal_step.tweakreg == "SKIPPED": continue wcs = model.meta.wcs @@ -390,6 +393,7 @@ def process(self, input): # catalog as a separate product with a name based on # whatever convention is determined by the JWST Cal Working # Group. + if self.save_abs_catalog: if self.output_dir is None: output_name = 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower()) @@ -398,6 +402,11 @@ def process(self, input): else: output_name = None + rel_ok = ( + len(rel_outcomes) > 1 or + (rel_outcomes and rel_outcomes.pop() != "SKIPPED") + ) + # initial shift to be used with absolute astrometry self.abs_xoffset = 0 self.abs_yoffset = 0 @@ -455,17 +464,58 @@ def process(self, input): del imcat.meta['fit_info'] # Perform fit - align_wcs( - imcats, - refcat=ref_cat, - enforce_user_order=True, - expand_refcat=False, - minobj=self.abs_minobj, - match=xyxymatch_gaia, - fitgeom=self.abs_fitgeometry, - nclip=self.abs_nclip, - sigma=(self.abs_sigma, 'rmse') - ) + try: + align_wcs( + imcats, + refcat=ref_cat, + enforce_user_order=True, + expand_refcat=False, + minobj=self.abs_minobj, + match=xyxymatch_gaia, + fitgeom=self.abs_fitgeometry, + nclip=self.abs_nclip, + sigma=(self.abs_sigma, 'rmse') + ) + except ValueError as e: + msg = e.args[0] + if (msg == "Too few input images (or groups of images) with " + "non-empty catalogs."): + # we need at least two exposures to perform image alignment + self.log.warning(msg) + self.log.warning( + "At least one exposure is required to align images " + "to an absolute reference catalog. Alignment to an " + "absolute reference catalog will not be performed." + ) + if not rel_ok: + self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + self.skip = True + return images + else: + raise e + + except RuntimeError as e: + msg = e.args[0] + if msg.startswith("Number of output coordinates exceeded allocation"): + # we need at least two exposures to perform image alignment + self.log.error(msg) + self.log.error( + "Multiple sources within specified tolerance " + "matched to a single reference source. Try to " + "adjust 'tolerance' and/or 'separation' parameters." + "Alignment to an absolute reference catalog will " + "not be performed." + ) + if not rel_ok: + self.log.warning("Skipping 'TweakRegStep'...") + self.skip = True + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + return images + else: + raise e for imcat in imcats: image_model = imcat.meta['image_model'] From b5512949472cc2fa96ad32966354ffe402b9fc65 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 9 May 2024 15:33:34 -0400 Subject: [PATCH 046/687] JP-3616: Fix NIRSpec FS spec3 slit name (#8470) --- CHANGES.rst | 4 + jwst/extract_1d/extract.py | 5 +- jwst/extract_1d/tests/test_extract_1d_step.py | 118 ++++++++++++++++++ 3 files changed, 126 insertions(+), 1 deletion(-) create mode 100644 jwst/extract_1d/tests/test_extract_1d_step.py diff --git a/CHANGES.rst b/CHANGES.rst index 06e74e71da..b5f61a40dc 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -57,11 +57,15 @@ extract_1d regions are specified and the lower and upper limits for one of them are outside the valid area for some data range. [#8433] +- Correct the output slit name for non-primary slit extractions in the + spec3 pipeline, for NIRSpec fixed slit mode. [#8470] + extract_2d ---------- - Added handling for NIRCam GRISM time series pointing offsets. [#8449] + flat_field ---------- diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 0f7e1e147a..56f81e52f0 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -2986,7 +2986,10 @@ def do_extract1d( # For NRS_FIXEDSLIT, the slit name comes from the FXD_SLIT keyword in the model meta if input_model.meta.exposure.type == 'NRS_FIXEDSLIT': - slitname = input_model.meta.instrument.fixed_slit + if hasattr(input_model, "name") and input_model.name is not None: + slitname = input_model.name + else: + slitname = input_model.meta.instrument.fixed_slit # Loop over all spectral orders available for extraction prev_offset = OFFSET_NOT_ASSIGNED_YET diff --git a/jwst/extract_1d/tests/test_extract_1d_step.py b/jwst/extract_1d/tests/test_extract_1d_step.py new file mode 100644 index 0000000000..3e096b38b0 --- /dev/null +++ b/jwst/extract_1d/tests/test_extract_1d_step.py @@ -0,0 +1,118 @@ +import numpy as np +import pytest +import stdatamodels.jwst.datamodels as dm + +from jwst.assign_wcs.util import wcs_bbox_from_shape +from jwst.extract_1d.extract_1d_step import Extract1dStep + + +@pytest.fixture +def simple_wcs(): + shape = (50, 50) + xcenter = shape[1] // 2.0 + + def simple_wcs_function(x, y): + """ Simple WCS for testing """ + crpix1 = xcenter + crpix3 = 1.0 + cdelt1 = 0.1 + cdelt2 = 0.1 + cdelt3 = 0.01 + + crval1 = 45.0 + crval2 = 45.0 + crval3 = 7.5 + + wave = (x + 1 - crpix3) * cdelt3 + crval3 + ra = (x + 1 - crpix1) * cdelt1 + crval1 + dec = np.full_like(ra, crval2 + 1 * cdelt2) + + return ra, dec, wave + + simple_wcs_function.bounding_box = wcs_bbox_from_shape(shape) + + return simple_wcs_function + + +@pytest.fixture() +def mock_nirspec_fs_one_slit(simple_wcs): + model = dm.SlitModel() + model.meta.instrument.name = 'NIRSPEC' + model.meta.instrument.detector = 'NRS1' + model.meta.observation.date = '2023-07-22' + model.meta.observation.time = '06:24:45.569' + model.meta.instrument.fixed_slit = 'S200A1' + model.meta.exposure.type = 'NRS_FIXEDSLIT' + model.meta.subarray.name = 'ALLSLITS' + + model.source_type = 'EXTENDED' + model.meta.wcsinfo.dispersion_direction = 1 + model.meta.wcs = simple_wcs + + model.data = np.arange(50 * 50, dtype=float).reshape((50, 50)) + model.var_poisson = model.data * 0.02 + model.var_rnoise = model.data * 0.02 + model.var_flat = model.data * 0.05 + yield model + model.close() + + +@pytest.fixture() +def mock_nirspec_mos(mock_nirspec_fs_one_slit): + model = dm.MultiSlitModel() + model.meta.instrument.name = 'NIRSPEC' + model.meta.instrument.detector = 'NRS1' + model.meta.observation.date = '2023-07-22' + model.meta.observation.time = '06:24:45.569' + model.meta.exposure.type = 'NRS_MSASPEC' + + nslit = 3 + for i in range(nslit): + slit = mock_nirspec_fs_one_slit.copy() + slit.name = str(i + 1) + model.slits.append(slit) + + yield model + model.close() + + +@pytest.mark.parametrize('slit_name', [None, 'S200A1', 'S1600A1']) +def test_extract_nirspec_fs_slit(mock_nirspec_fs_one_slit, simple_wcs, slit_name): + mock_nirspec_fs_one_slit.name = slit_name + result = Extract1dStep.call(mock_nirspec_fs_one_slit) + assert result.meta.cal_step.extract_1d == 'COMPLETE' + + if slit_name is None: + assert (result.spec[0].name + == mock_nirspec_fs_one_slit.meta.instrument.fixed_slit) + else: + assert result.spec[0].name == slit_name + + # output wavelength is the same as input + _, _, expected_wave = simple_wcs(np.arange(50), np.arange(50)) + assert np.allclose(result.spec[0].spec_table['WAVELENGTH'], expected_wave) + + # output flux and errors are non-zero, exact values will depend + # on extraction parameters + assert np.all(result.spec[0].spec_table['FLUX'] > 0) + assert np.all(result.spec[0].spec_table['FLUX_ERROR'] > 0) + result.close() + + +def test_extract_nirspec_mos_multi_slit(mock_nirspec_mos, simple_wcs): + result = Extract1dStep.call(mock_nirspec_mos) + assert result.meta.cal_step.extract_1d == 'COMPLETE' + + for i, spec in enumerate(result.spec): + assert spec.name == str(i + 1) + + # output wavelength is the same as input + _, _, expected_wave = simple_wcs(np.arange(50), np.arange(50)) + assert np.allclose(spec.spec_table['WAVELENGTH'], expected_wave) + + # output flux and errors are non-zero, exact values will depend + # on extraction parameters + assert np.all(spec.spec_table['FLUX'] > 0) + assert np.all(spec.spec_table['FLUX_ERROR'] > 0) + + result.close() From 570a0841b0124c4dcf5f11ecb1b11c3053d5ebc3 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 19 Apr 2024 13:08:21 -0400 Subject: [PATCH 047/687] remove calls to closeout --- jwst/stpipe/tests/test_saving.py | 8 -------- jwst/stpipe/tests/test_step.py | 2 -- 2 files changed, 10 deletions(-) diff --git a/jwst/stpipe/tests/test_saving.py b/jwst/stpipe/tests/test_saving.py index a8380c9819..0d4f8c936c 100644 --- a/jwst/stpipe/tests/test_saving.py +++ b/jwst/stpipe/tests/test_saving.py @@ -44,7 +44,6 @@ def test_save_step_default(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() fname = 'flat_stepwithmodel.fits' assert path.isfile(fname) @@ -63,7 +62,6 @@ def test_save_step_withoutput(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() output_path, output_ext = path.splitext(output_file) assert path.isfile(output_path + '_stepwithmodel' + output_ext) @@ -83,7 +81,6 @@ def test_save_step_withoutputsuffix(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() assert path.isfile(actual_output_file) @@ -99,7 +96,6 @@ def test_save_step_withdir(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() output_fn_path = path.join( tmp_data_path, @@ -121,7 +117,6 @@ def test_save_step_withdir_environment(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() output_fn_path = path.join( tmp_data_path, @@ -144,7 +139,6 @@ def test_save_step_withdir_withoutput(mk_tmp_dirs): ] step = Step.from_cmdline(args) - step.closeout() output_path, output_ext = path.splitext(output_file) output_fn_path = path.join( @@ -200,8 +194,6 @@ def test_save_container_withfile(mk_tmp_dirs): assert path.isfile('tscwf_0_stepwithcontainer.fits') assert path.isfile('tscwf_1_stepwithcontainer.fits') - step.closeout() - def test_save_pipeline_default(mk_tmp_dirs): """Default save should be current working directory""" diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 9706629a1e..3ed43c1101 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -120,8 +120,6 @@ def test_saving_pars(tmp_path): config = StepConfig.from_asdf(af) assert config.parameters == original_config.parameters - step.closeout() - @pytest.mark.parametrize( 'step_obj, expected', From 781e0e0cdb9ccc107e72ed97b7801006bb83083c Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 19 Apr 2024 13:34:16 -0400 Subject: [PATCH 048/687] fix style --- jwst/stpipe/tests/test_saving.py | 14 +++++++------- jwst/stpipe/tests/test_step.py | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/jwst/stpipe/tests/test_saving.py b/jwst/stpipe/tests/test_saving.py index 0d4f8c936c..2ec6fe42da 100644 --- a/jwst/stpipe/tests/test_saving.py +++ b/jwst/stpipe/tests/test_saving.py @@ -43,7 +43,7 @@ def test_save_step_default(mk_tmp_dirs): data_fn_path ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) fname = 'flat_stepwithmodel.fits' assert path.isfile(fname) @@ -61,7 +61,7 @@ def test_save_step_withoutput(mk_tmp_dirs): '--output_file=' + output_file ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) output_path, output_ext = path.splitext(output_file) assert path.isfile(output_path + '_stepwithmodel' + output_ext) @@ -80,7 +80,7 @@ def test_save_step_withoutputsuffix(mk_tmp_dirs): '--output_file=' + output_file ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) assert path.isfile(actual_output_file) @@ -95,7 +95,7 @@ def test_save_step_withdir(mk_tmp_dirs): '--output_dir=' + tmp_data_path ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) output_fn_path = path.join( tmp_data_path, @@ -116,7 +116,7 @@ def test_save_step_withdir_environment(mk_tmp_dirs): '--output_dir=$TSSWE_OUTPATH' ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) output_fn_path = path.join( tmp_data_path, @@ -138,7 +138,7 @@ def test_save_step_withdir_withoutput(mk_tmp_dirs): '--output_file=' + output_file ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) output_path, output_ext = path.splitext(output_file) output_fn_path = path.join( @@ -189,7 +189,7 @@ def test_save_container_withfile(mk_tmp_dirs): '--output_file=tscwf.fits', ] - step = Step.from_cmdline(args) + Step.from_cmdline(args) assert path.isfile('tscwf_0_stepwithcontainer.fits') assert path.isfile('tscwf_1_stepwithcontainer.fits') diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 3ed43c1101..1c1d3a76d2 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -105,7 +105,7 @@ def test_saving_pars(tmp_path): """Save the step parameters from the commandline""" cfg_path = t_path(join('steps', 'jwst_generic_pars-makeliststep_0002.asdf')) saved_path = os.path.join(tmp_path, 'savepars.asdf') - step = Step.from_cmdline([ + Step.from_cmdline([ cfg_path, '--save-parameters', str(saved_path) From 8b254ae2b69fed42c413f4ff7163ff2f983dfa59 Mon Sep 17 00:00:00 2001 From: Maria Date: Fri, 10 May 2024 08:52:56 -0400 Subject: [PATCH 049/687] JP-3311: OutlierDetectionStep producing extraneous outlier_i2d files (#8464) Co-authored-by: Brett --- CHANGES.rst | 4 ++++ jwst/outlier_detection/outlier_detection.py | 21 +++++++++++++++- .../outlier_detection_spec.py | 21 +++++++++------- .../outlier_detection_step.py | 17 ------------- jwst/regtest/test_nirspec_mos_spec3.py | 24 ++++++++++++------- jwst/resample/resample.py | 7 ++++++ jwst/resample/resample_spec.py | 4 ++++ 7 files changed, 64 insertions(+), 34 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b5f61a40dc..b9437ab3e5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -95,6 +95,10 @@ outlier_detection ``OutlierDetectionStackStep``, ``outlierpars`` reference file handling, and ``scale_detection`` (an unused argument). [#8438] +- Intermediate output files are now correctly removed after the step has + finished, unless save_intermediate_results is True. This PR also addressed + the _i2d files not being saved in the specified output directory. [#8464] + photom ------ diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 0758e8c109..22eb508f13 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -3,6 +3,7 @@ from functools import partial import logging import warnings +import os import numpy as np from astropy.stats import sigma_clip @@ -140,7 +141,10 @@ def do_detection(self): if pars['resample_data']: # Start by creating resampled/mosaic images for # each group of exposures - resamp = resample.ResampleData(self.input_models, single=True, + output_path = self.make_output_path(basepath=self.input_models[0].meta.filename, + suffix='') + output_path = os.path.dirname(output_path) + resamp = resample.ResampleData(self.input_models, output=output_path, single=True, blendheaders=False, **pars) drizzled_models = resamp.do_drizzle() @@ -171,6 +175,12 @@ def do_detection(self): suffix='median') median_model.save(median_model_output_path) log.info(f"Saved model in {median_model_output_path}") + else: + # since we're not saving intermediate results if the drizzled models + # were written to disk, remove them + if not pars['in_memory']: + for fn in drizzled_models._models: + _remove_file(fn) if pars['resample_data']: # Blot the median image back to recreate each input image specified @@ -189,6 +199,9 @@ def do_detection(self): # clean-up (just to be explicit about being finished with # these results) + if not pars['save_intermediate_results'] and not pars['in_memory']: + for fn in blot_models._models: + _remove_file(fn) del median_model, blot_models def create_median(self, resampled_models): @@ -335,6 +348,12 @@ def detect_outliers(self, blot_models): self.inputs.dq[i, :, :] = self.input_models[i].dq +def _remove_file(fn): + if isinstance(fn, str) and os.path.isfile(fn): + os.remove(fn) + log.debug(f" {fn}") + + def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data=True, **kwargs): """Masks outliers in science image by updating DQ in-place diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 66885e5947..03f32ac1ea 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -1,12 +1,10 @@ """Class definition for performing outlier detection on spectra.""" -from functools import partial - from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer from ..resample import resample_spec, resample_utils -from .outlier_detection import OutlierDetection +from .outlier_detection import OutlierDetection, _remove_file import logging log = logging.getLogger(__name__) @@ -58,6 +56,9 @@ def __init__(self, input_models, **pars): """ OutlierDetection.__init__(self, input_models, **pars) + # Set up the list of all intermediate output files + self.output_list = [] + def do_detection(self): """Flag outlier pixels in DQ of input images.""" self._convert_inputs() @@ -103,16 +104,17 @@ def do_detection(self): log.info("Writing out MEDIAN image to: {}".format( median_model.meta.filename)) median_model.save(median_model.meta.filename) + else: + # since we're not saving intermediate results if the drizzled models + # were written to disk, remove them + if not pars['in_memory']: + for fn in drizzled_models._models: + _remove_file(fn) if pars['resample_data'] is True: # Blot the median image back to recreate each input image specified # in the original input list/ASN/ModelContainer blot_models = self.blot_median(median_model) - if save_intermediate_results: - log.info("Writing out BLOT images...") - blot_models.save( - partial(self.make_output_path, suffix='blot') - ) else: # Median image will serve as blot image blot_models = ModelContainer() @@ -125,4 +127,7 @@ def do_detection(self): # clean-up (just to be explicit about being finished # with these results) + if not pars['save_intermediate_results']: + for fn in blot_models._models: + _remove_file(fn) del median_model, blot_models diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 86a0e49638..5eebdce717 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -1,6 +1,4 @@ """Public common step definition for OutlierDetection processing.""" -import os - from functools import partial from stdatamodels.jwst import datamodels @@ -173,21 +171,6 @@ def process(self, input_data): self.log.debug("The following files will be deleted since save_intermediate_results=False:") for model in self.input_models: model.meta.cal_step.outlier_detection = state - if not self.save_intermediate_results: - # Remove unwanted files - crf_path = self.make_output_path(basepath=model.meta.filename) - if asn_id is None: - suffix = model.meta.filename.split(sep='_')[-1] - outlr_file = model.meta.filename.replace(suffix, 'outlier_i2d.fits') - else: - outlr_file = crf_path.replace('crf', 'outlier_i2d') - blot_path = crf_path.replace('crf', 'blot') - median_path = blot_path.replace('blot', 'median') - - for fle in [outlr_file, blot_path, median_path]: - if os.path.isfile(fle): - os.remove(fle) - self.log.debug(f" {fle}") else: self.input_models.meta.cal_step.outlier_detection = state return self.input_models diff --git a/jwst/regtest/test_nirspec_mos_spec3.py b/jwst/regtest/test_nirspec_mos_spec3.py index abe6384f1c..507eb5c1c5 100644 --- a/jwst/regtest/test_nirspec_mos_spec3.py +++ b/jwst/regtest/test_nirspec_mos_spec3.py @@ -40,16 +40,24 @@ def test_nirspec_mos_spec3(run_pipeline, suffix, source_id, fitsdiff_default_kwa diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - if "s2d" in output: + if suffix == "s2d": # Compare the calculated wavelengths + tolerance = 1e-03 dmt = datamodels.open(rtdata.truth) dmr = datamodels.open(rtdata.output) - names = [s.name for s in dmt.slits] - for name in names: - st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] - w = dmt.slits[st_idx].meta.wcs + if isinstance(dmt, datamodels.MultiSlitModel): + names = [s.name for s in dmt.slits] + for name in names: + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] + w = dmt.slits[st_idx].meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] + wlr = dmr.slits[sr_idx].wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) + else: + w = dmt.meta.wcs x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) _, _, wave = w(x, y) - sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] - wlr = dmr.slits[sr_idx].wavelength - assert np.all(np.isclose(wave, wlr, atol=1e-03)) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index e33b5305ae..1884e39864 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -70,7 +70,12 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, all products in memory. """ self.input_models = input_models + self.output_dir = None self.output_filename = output + if output is not None and '.fits' not in output: + self.output_dir = output + self.output_filename = None + self.pscale_ratio = pscale_ratio self.single = single self.blendheaders = blendheaders @@ -296,6 +301,8 @@ def resample_many_to_many(self): if not self.in_memory: # Write out model to disk, then return filename output_name = output_model.meta.filename + if self.output_dir is not None: + output_name = os.path.join(self.output_dir, output_name) output_model.save(output_name) log.info(f"Saved model in {output_name}") self.output_models.append(output_name) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 5add89b498..162ec403b4 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -63,6 +63,10 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.input_models = input_models self.output_filename = output + self.output_dir = None + if output is not None and '.fits' not in output: + self.output_dir = output + self.output_filename = None self.pscale_ratio = pscale_ratio self.pscale = pscale self.single = single From 3df35ef10cbfda904a7bfee947d8fd5e87e2092d Mon Sep 17 00:00:00 2001 From: Nadia Dencheva Date: Wed, 15 May 2024 09:04:07 -0400 Subject: [PATCH 050/687] Regression test for JP-3604 (#8477) --- CHANGES.rst | 2 +- jwst/regtest/test_niriss_image.py | 25 +++++++++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index b9437ab3e5..405e502c76 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -150,7 +150,7 @@ tweakreg - Changed default settings for ``abs_separation`` parameter for the ``tweakreg`` step to have a value compatible with the ``abs_tolerance`` parameter. [#8445] -- Improve error handling in the absolute alignment. [#8450] +- Improve error handling in the absolute alignment. [#8450, #8477] wfss_contam ----------- diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 0cc2fd3a3e..eb2a0ad70a 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -6,7 +6,9 @@ import pytest from astropy.io.fits.diff import FITSDiff +from jwst import datamodels from jwst.stpipe import Step +from jwst.tweakreg import TweakRegStep @pytest.fixture(scope="module") @@ -44,6 +46,29 @@ def test_niriss_image_detector1(run_detector1, rtdata_module, fitsdiff_default_k _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) +@pytest.mark.bigdata +def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): + """Make sure tweakreg is skipped when sources are not found. + """ + + rtdata.input = "niriss/imaging/jw01537-o003_20240406t164421_image3_00004_asn.json" + rtdata.get_asn("niriss/imaging/jw01537-o003_20240406t164421_image3_00004_asn.json") + + args = ["jwst.tweakreg.TweakRegStep", rtdata.input, "--abs_refcat='GAIADR3'"] + result = Step.from_cmdline(args) + # Check that the step is skipped + assert result.skip + + # Check the status of the step is set correctly in the files. + result = TweakRegStep.call(rtdata.input) + + for fi in result._models: + with datamodels.open(fi) as model: + assert model.meta.cal_step.tweakreg == 'SKIPPED' + + result.close() + + def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix): """Assertion helper for the above tests""" rtdata = rtdata_module From 4d4f935901374b7febd1805febd0da0241e0a1f4 Mon Sep 17 00:00:00 2001 From: Maria Date: Fri, 17 May 2024 14:02:10 -0400 Subject: [PATCH 051/687] adding tests for changes in JP-3311 and minor fixes (#8481) Co-authored-by: Howard Bushouse --- CHANGES.rst | 2 ++ jwst/outlier_detection/outlier_detection.py | 2 +- .../outlier_detection_spec.py | 2 ++ .../outlier_detection_step.py | 2 -- .../tests/test_outlier_detection.py | 20 ++++++++++++++++++- jwst/regtest/test_miri_lrs_slit_spec3.py | 2 +- jwst/regtest/test_nirspec_fs_spec3.py | 2 +- jwst/resample/resample.py | 2 +- jwst/resample/resample_spec.py | 2 +- 9 files changed, 28 insertions(+), 8 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 405e502c76..90b8dfad21 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -99,6 +99,8 @@ outlier_detection finished, unless save_intermediate_results is True. This PR also addressed the _i2d files not being saved in the specified output directory. [#8464] +- Added tests for changes made in #8464. [#8481] + photom ------ diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 22eb508f13..2a026d8e36 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -351,7 +351,7 @@ def detect_outliers(self, blot_models): def _remove_file(fn): if isinstance(fn, str) and os.path.isfile(fn): os.remove(fn) - log.debug(f" {fn}") + log.info(f"Removing file {fn}") def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 03f32ac1ea..d4f4b799c5 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -110,6 +110,7 @@ def do_detection(self): if not pars['in_memory']: for fn in drizzled_models._models: _remove_file(fn) + log.info(f"Removing file {fn}") if pars['resample_data'] is True: # Blot the median image back to recreate each input image specified @@ -130,4 +131,5 @@ def do_detection(self): if not pars['save_intermediate_results']: for fn in blot_models._models: _remove_file(fn) + log.info(f"Removing file {fn}") del median_model, blot_models diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 5eebdce717..b7798e1aa5 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -167,8 +167,6 @@ def process(self, input_data): state = 'COMPLETE' if self.input_container: - if not self.save_intermediate_results: - self.log.debug("The following files will be deleted since save_intermediate_results=False:") for model in self.input_models: model.meta.cal_step.outlier_detection = state else: diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 354ce57895..00ea0502ad 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -1,6 +1,8 @@ import pytest import numpy as np from scipy.ndimage import gaussian_filter +from glob import glob +import os from stdatamodels.jwst import datamodels @@ -15,7 +17,6 @@ ) from jwst.assign_wcs.pointing import create_fitswcs - OUTLIER_DO_NOT_USE = np.bitwise_or( datamodels.dqflags.pixel["DO_NOT_USE"], datamodels.dqflags.pixel["OUTLIER"] ) @@ -184,6 +185,15 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Drop a CR on the science array container[0].data[12, 12] += 1 + # Verify that intermediary files are removed + OutlierDetectionStep.call(container) + i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) + median_files = glob(os.path.join(tmp_cwd, '*median.fits')) + blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) + assert len(i2d_files) == 0 + assert len(median_files) == 0 + assert len(blot_files) == 0 + result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True ) @@ -199,6 +209,14 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Verify CR is flagged assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE + # Verify that intermediary files are saved at the specified location + i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) + median_files = glob(os.path.join(tmp_cwd, '*median.fits')) + blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) + assert len(i2d_files) != 0 + assert len(median_files) != 0 + assert len(blot_files) != 0 + def test_outlier_step_on_disk(we_three_sci, tmp_cwd): """Test whole step with an outlier including saving intermediate and results files""" diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index 513cc5740b..d8766db97e 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -79,7 +79,7 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - if "s2d" in output: + if output == "s2d": # Compare the calculated wavelengths tolerance = 1e-03 dmt = datamodels.open(rtdata.truth) diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index ac15e6a1fd..869d6c3949 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -46,7 +46,7 @@ def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - if "s2d" in output: + if output == "s2d": # Compare the calculated wavelengths tolerance = 1e-03 dmt = datamodels.open(rtdata.truth) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 1884e39864..70641182f5 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -72,7 +72,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, self.input_models = input_models self.output_dir = None self.output_filename = output - if output is not None and '.fits' not in output: + if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 162ec403b4..fcd49a9302 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -64,7 +64,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.output_filename = output self.output_dir = None - if output is not None and '.fits' not in output: + if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None self.pscale_ratio = pscale_ratio From 70bed1d23f6074267c6bd31943c2dce5f5f50f34 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 17 May 2024 22:50:23 -0400 Subject: [PATCH 052/687] JP-3603: reorder tweakreg to reduce iterations through models (#8424) --- CHANGES.rst | 2 + jwst/assign_wcs/util.py | 5 +- jwst/datamodels/container.py | 17 +- jwst/tweakreg/tests/test_tweakreg.py | 270 ++++++++++++- jwst/tweakreg/tweakreg_step.py | 556 +++++++++++++-------------- 5 files changed, 557 insertions(+), 293 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 90b8dfad21..c8f89457bc 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -149,6 +149,8 @@ tweakreg - Improved how a image group name is determined. [#8426] +- Refactor step to work towards performance improvements. [#8424] + - Changed default settings for ``abs_separation`` parameter for the ``tweakreg`` step to have a value compatible with the ``abs_tolerance`` parameter. [#8445] diff --git a/jwst/assign_wcs/util.py b/jwst/assign_wcs/util.py index 50c8ba31d3..f9f90918b4 100644 --- a/jwst/assign_wcs/util.py +++ b/jwst/assign_wcs/util.py @@ -199,7 +199,7 @@ def calc_rotation_matrix(roll_ref: float, v3i_yang: float, vparity: int = 1) -> def wcs_from_footprints(dmodels, refmodel=None, transform=None, bounding_box=None, pscale_ratio=None, pscale=None, rotation=None, - shape=None, crpix=None, crval=None): + shape=None, crpix=None, crval=None, wcslist=None): """ Create a WCS from a list of input data models. @@ -259,7 +259,8 @@ def wcs_from_footprints(dmodels, refmodel=None, transform=None, bounding_box=Non """ bb = bounding_box - wcslist = [im.meta.wcs for im in dmodels] + if wcslist is None: + wcslist = [im.meta.wcs for im in dmodels] if not isiterable(wcslist): raise ValueError("Expected 'wcslist' to be an iterable of WCS objects.") diff --git a/jwst/datamodels/container.py b/jwst/datamodels/container.py index 6152156224..45440bcb17 100644 --- a/jwst/datamodels/container.py +++ b/jwst/datamodels/container.py @@ -160,6 +160,7 @@ def __init__(self, init=None, asn_exptypes=None, asn_n_members=None, self.asn_table = {} self.asn_table_name = None self.asn_pool_name = None + self.asn_file_path = None self._memmap = kwargs.get("memmap", False) self._return_open = kwargs.get('return_open', True) @@ -196,7 +197,8 @@ def __init__(self, init=None, asn_exptypes=None, asn_n_members=None, self.from_asn(init) elif isinstance(init, str): init_from_asn = self.read_asn(init) - self.from_asn(init_from_asn, asn_file_path=init) + self.asn_file_path = init + self.from_asn(init_from_asn) else: raise TypeError('Input {0!r} is not a list of JwstDataModels or ' 'an ASN file'.format(init)) @@ -275,7 +277,7 @@ def read_asn(filepath): raise IOError("Cannot read ASN file.") from e return asn_data - def from_asn(self, asn_data, asn_file_path=None): + def from_asn(self, asn_data): """ Load fits files from a JWST association file. @@ -283,9 +285,6 @@ def from_asn(self, asn_data, asn_file_path=None): ---------- asn_data : ~jwst.associations.Association An association dictionary - - asn_file_path: str - Filepath of the association, if known. """ # match the asn_exptypes to the exptype in the association and retain # only those file that match, as a list, if asn_exptypes is set to none @@ -303,8 +302,8 @@ def from_asn(self, asn_data, asn_file_path=None): infiles = [member for member in asn_data['products'][0]['members']] - if asn_file_path: - asn_dir = op.dirname(asn_file_path) + if self.asn_file_path: + asn_dir = op.dirname(self.asn_file_path) else: asn_dir = '' @@ -348,8 +347,8 @@ def from_asn(self, asn_data, asn_file_path=None): self.meta.asn_table._instance, asn_data ) - if asn_file_path is not None: - self.asn_table_name = op.basename(asn_file_path) + if self.asn_file_path is not None: + self.asn_table_name = op.basename(self.asn_file_path) self.asn_pool_name = asn_data['asn_pool'] for model in self: try: diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index cb5baea3f9..c6f6e2b596 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -1,14 +1,23 @@ from copy import deepcopy +import json import os import asdf from astropy.modeling.models import Shift from astropy.table import Table +import numpy as np import pytest from jwst.tweakreg import tweakreg_step from jwst.tweakreg import tweakreg_catalog +from jwst.tweakreg.utils import _wcsinfo_from_wcs_transform from stdatamodels.jwst.datamodels import ImageModel +from jwst.datamodels import ModelContainer + + +BKG_LEVEL = 0.001 +N_EXAMPLE_SOURCES = 21 +N_CUSTOM_SOURCES = 15 @pytest.fixture @@ -21,8 +30,55 @@ def dummy_source_catalog(): return catalog +@pytest.mark.parametrize("inplace", [True, False]) +def test_rename_catalog_columns(dummy_source_catalog, inplace): + """ + Test that a catalog with 'xcentroid' and 'ycentroid' columns + passed to _renamed_catalog_columns successfully renames those columns + to 'x' and 'y' (and does so "inplace" modifying the input catalog) + """ + renamed_catalog = tweakreg_step._rename_catalog_columns(dummy_source_catalog) + + # if testing inplace, check the input catalog + if inplace: + catalog = dummy_source_catalog + else: + catalog = renamed_catalog + + assert 'xcentroid' not in catalog.colnames + assert 'ycentroid' not in catalog.colnames + assert 'x' in catalog.colnames + assert 'y' in catalog.colnames + + +@pytest.mark.parametrize("missing", ["x", "y", "xcentroid", "ycentroid"]) +def test_rename_catalog_columns_invalid(dummy_source_catalog, missing): + """ + Test that passing a catalog that is missing either "x" or "y" + (or "xcentroid" and "ycentroid" which is renamed to "x" or "y") + results in an exception indicating that a required column is missing + """ + # if the column we want to remove is not in the table, first run + # rename to rename columns this should add the column we want to remove + if missing not in dummy_source_catalog.colnames: + tweakreg_step._rename_catalog_columns(dummy_source_catalog) + dummy_source_catalog.remove_column(missing) + with pytest.raises(ValueError, match="catalogs must contain"): + tweakreg_step._rename_catalog_columns(dummy_source_catalog) + + @pytest.mark.parametrize("offset, is_good", [(1 / 3600, True), (11 / 3600, False)]) def test_is_wcs_correction_small(offset, is_good): + """ + Test that the _is_wcs_correction_small method returns True for a small + wcs correction and False for a "large" wcs correction. The values in this + test are selected based on the current step default parameters: + - use2dhist + - searchrad + - tolerance + Changes to the defaults for these parameters will likely require updating the + values uses for parametrizing this test. + """ path = os.path.join(os.path.dirname(__file__), "mosaic_long_i2d_gwcs.asdf") with asdf.open(path) as af: wcs = af.tree["wcs"] @@ -35,7 +91,18 @@ def test_is_wcs_correction_small(offset, is_good): step = tweakreg_step.TweakRegStep() - assert step._is_wcs_correction_small(wcs, twcs) == is_good + class FakeCorrector: + def __init__(self, wcs, original_skycoord): + self.wcs = wcs + self._original_skycoord = original_skycoord + + @property + def meta(self): + return {'original_skycoord': self._original_skycoord} + + correctors = [FakeCorrector(twcs, tweakreg_step._wcs_to_skycoord(wcs))] + + assert step._is_wcs_correction_small(correctors) == is_good def test_expected_failure_bad_starfinder(): @@ -51,11 +118,206 @@ def test_write_catalog(dummy_source_catalog, tmp_cwd): ''' OUTDIR = 'outdir' - model = ImageModel() step = tweakreg_step.TweakRegStep() os.mkdir(OUTDIR) step.output_dir = OUTDIR expected_outfile = os.path.join(OUTDIR, 'catalog.ecsv') - step._write_catalog(model, dummy_source_catalog, 'catalog.ecsv') + step._write_catalog(dummy_source_catalog, 'catalog.ecsv') + + assert os.path.exists(expected_outfile) + + +@pytest.fixture() +def example_wcs(): + path = os.path.join( + os.path.dirname(__file__), + "data", + "nrcb1-wcs.asdf") + with asdf.open(path, lazy_load=False) as af: + return af.tree["wcs"] + + +@pytest.fixture() +def example_input(example_wcs): + m0 = ImageModel((512, 512)) + + # add a wcs and wcsinfo + m0.meta.wcs = example_wcs + m0.meta.wcsinfo = _wcsinfo_from_wcs_transform(example_wcs) + + # and a few 'sources' + m0.data[:] = BKG_LEVEL + n_sources = N_EXAMPLE_SOURCES # a few more than default minobj + rng = np.random.default_rng(26) + xs = rng.choice(50, n_sources, replace=False) * 8 + 10 + ys = rng.choice(50, n_sources, replace=False) * 8 + 10 + for y, x in zip(ys, xs): + m0.data[y-1:y+2, x-1:x+2] = [ + [0.1, 0.6, 0.1], + [0.6, 0.8, 0.6], + [0.1, 0.6, 0.1], + ] + + m1 = m0.copy() + # give each a unique filename + m0.meta.filename = 'some_file_0.fits' + m1.meta.filename = 'some_file_1.fits' + c = ModelContainer([m0, m1]) + return c + + +@pytest.mark.parametrize("with_shift", [True, False]) +def test_tweakreg_step(example_input, with_shift): + """ + A simplified unit test for basic operation of the TweakRegStep + when run with or without a small shift in the input image sources + """ + if with_shift: + # shift 9 pixels so that the sources in one of the 2 images + # appear at different locations (resulting in a correct wcs update) + example_input[1].data[:-9] = example_input[1].data[9:] + example_input[1].data[-9:] = BKG_LEVEL + + # assign images to different groups (so they are aligned to each other) + example_input[0].meta.group_id = 'a' + example_input[1].meta.group_id = 'b' + + # make the step with default arguments + step = tweakreg_step.TweakRegStep() + + # run the step on the example input modified above + result = step(example_input) + + # check that step completed + for model in result: + assert model.meta.cal_step.tweakreg == 'COMPLETE' + + # and that the wcses differ by a small amount due to the shift above + # by projecting one point through each wcs and comparing the difference + abs_delta = abs(result[1].meta.wcs(0, 0)[0] - result[0].meta.wcs(0, 0)[0]) + if with_shift: + assert abs_delta > 1E-5 + else: + assert abs_delta < 1E-12 + + +@pytest.fixture() +def custom_catalog_path(tmp_path): + fn = tmp_path / "custom_catalog.ecsv" + + # it's important that the sources here don't match + # those added in example_input but conform to the input + # shape, wcs, etc used in example_input + rng = np.random.default_rng(42) + n_sources = N_CUSTOM_SOURCES + xs = rng.choice(50, n_sources, replace=False) * 8 + 10 + ys = rng.choice(50, n_sources, replace=False) * 8 + 10 + catalog = Table(np.vstack((xs, ys)).T, names=['x', 'y'], dtype=[float, float]) + catalog.write(fn) + return fn + + +@pytest.mark.parametrize( + "catfile", + ["no_catfile", "valid_catfile", "invalid_catfile", "empty_catfile_row"], +) +@pytest.mark.parametrize( + "asn", + ["no_cat_in_asn", "cat_in_asn", "empty_asn_entry"], +) +@pytest.mark.parametrize( + "meta", + ["no_meta", "cat_in_meta", "empty_meta"], +) +@pytest.mark.parametrize("custom", [True, False]) +@pytest.mark.slow +def test_custom_catalog(custom_catalog_path, example_input, catfile, asn, meta, custom, monkeypatch): + """ + Test that TweakRegStep uses a custom catalog provided by the user + when the correct set of options are provided. The combinations here can be confusing + and this test attempts to test all likely combinations of: + - a catalog in a `catfile` + - a catalog in the asn + - a catalog in the metadata + combined with step options: + - `use_custom_catalogs` (True/False) + - a "valid" file passed as `catfile` + """ + example_input[0].meta.group_id = 'a' + example_input[1].meta.group_id = 'b' + + # this worked because if use_custom_catalogs was true but + # catfile was blank tweakreg still uses custom catalogs + # which in this case is defined in model.meta.tweakreg_catalog + if meta == "cat_in_meta": + example_input[0].meta.tweakreg_catalog = str(custom_catalog_path) + elif meta == "empty_meta": + example_input[0].meta.tweakreg_catalog = "" + + # write out the ModelContainer and association (so the association table will be loaded) + example_input.save(dir_path=str(custom_catalog_path.parent)) + asn_data = { + 'asn_id': 'foo', + 'asn_pool': 'bar', + 'products': [ + { + 'members': [{'expname': m.meta.filename, 'exptype': 'science'} for m in example_input], + }, + ], + } + + if asn == "empty_asn_entry": + asn_data['products'][0]['members'][0]['tweakreg_catalog'] = '' + elif asn == "cat_in_asn": + asn_data['products'][0]['members'][0]['tweakreg_catalog'] = str(custom_catalog_path.name) + + asn_path = custom_catalog_path.parent / 'example_input.json' + with open(asn_path, 'w') as f: + json.dump(asn_data, f) + + # write out a catfile + if catfile != "no_catfile": + catfile_path = custom_catalog_path.parent / 'catfile.txt' + with open(catfile_path, 'w') as f: + if catfile == "valid_catfile": + f.write(f"{example_input[0].meta.filename} {custom_catalog_path.name}") + elif catfile == "empty_catfile_row": + f.write(f"{example_input[0].meta.filename}") + elif catfile == "invalid_catfile": + pass + + # figure out how many sources to expect for the model in group 'a' + n_custom_sources = N_EXAMPLE_SOURCES + if custom: + if catfile == "valid_catfile": + # for a 'valid' catfile, expect the custom number + n_custom_sources = N_CUSTOM_SOURCES + elif catfile == "no_catfile": + # since catfile is not defined, now look at asn_ + if asn == "cat_in_asn": + # for a 'valid' asn entry, expect the custom number + n_custom_sources = N_CUSTOM_SOURCES + elif asn == "no_cat_in_asn" and meta == "cat_in_meta": + n_custom_sources = N_CUSTOM_SOURCES + + kwargs = {'use_custom_catalogs': custom} + if catfile != "no_catfile": + kwargs["catfile"] = str(catfile_path) + step = tweakreg_step.TweakRegStep(**kwargs) + + # patch _construct_wcs_corrector to check the correct catalog was loaded + def patched_construct_wcs_corrector(model, catalog, _seen=[]): + # we don't need to continue + if model.meta.group_id == 'a': + assert len(catalog) == n_custom_sources + elif model.meta.group_id == 'b': + assert len(catalog) == N_EXAMPLE_SOURCES + _seen.append(model) + if len(_seen) == 2: + raise ValueError("done testing") + return None + + monkeypatch.setattr(tweakreg_step, "_construct_wcs_corrector", patched_construct_wcs_corrector) - assert os.path.exists(expected_outfile) \ No newline at end of file + with pytest.raises(ValueError, match="done testing"): + step(str(asn_path)) diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 519a200ab2..da530151ca 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -6,22 +6,21 @@ """ from os import path -from astropy.table import Table from astropy import units as u from astropy.coordinates import SkyCoord +from astropy.table import Table +from astropy.time import Time from tweakwcs.imalign import align_wcs from tweakwcs.correctors import JWSTWCSCorrector from tweakwcs.matchutils import XYXYMatch -from stdatamodels.jwst.datamodels.util import is_association - from jwst.datamodels import ModelContainer # LOCAL from ..stpipe import Step -from ..assign_wcs.util import update_fits_wcsinfo, update_s_region_imaging -from . import astrometric_utils as amutils -from . tweakreg_catalog import make_tweakreg_catalog +from ..assign_wcs.util import update_fits_wcsinfo, update_s_region_imaging, wcs_from_footprints +from .astrometric_utils import create_astrometric_catalog +from .tweakreg_catalog import make_tweakreg_catalog def _oxford_or_str_join(str_list): @@ -56,8 +55,12 @@ class TweakRegStep(Step): use_custom_catalogs = boolean(default=False) # Use custom user-provided catalogs? catalog_format = string(default='ecsv') # Catalog output file format catfile = string(default='') # Name of the file with a list of custom user-provided catalogs + + # general starfinder options starfinder = option('dao', 'iraf', 'segmentation', default='dao') # Star finder to use. snr_threshold = float(default=10.0) # SNR threshold above the bkg for star finder + bkg_boxsize = integer(default=400) # The background mesh box size in pixels. + # kwargs for DAOStarFinder and IRAFStarFinder, only used if starfinder is 'dao' or 'iraf' kernel_fwhm = float(default=2.5) # Gaussian kernel FWHM in pixels minsep_fwhm = float(default=0.0) # Minimum separation between detected objects in FWHM @@ -68,6 +71,7 @@ class TweakRegStep(Step): roundhi = float(default=1.0) # The upper bound on roundness for object detection. brightest = integer(default=200) # Keep top ``brightest`` objects peakmax = float(default=None) # Filter out objects with pixel values >= ``peakmax`` + # kwargs for SourceCatalog and SourceFinder, only used if starfinder is 'segmentation' npixels = integer(default=10) # Minimum number of connected pixels connectivity = option(4, 8, default=8) # The connectivity defining the neighborhood of a pixel @@ -77,90 +81,81 @@ class TweakRegStep(Step): localbkg_width = integer(default=0) # Width of rectangular annulus used to compute local background around each source apermask_method = option('correct', 'mask', 'none', default='correct') # How to handle neighboring sources kron_params = float_list(min=2, max=3, default=None) # Parameters defining Kron aperture - # continue args for rest of step - bkg_boxsize = integer(default=400) # The background mesh box size in pixels. + + # align wcs options enforce_user_order = boolean(default=False) # Align images in user specified order? expand_refcat = boolean(default=False) # Expand reference catalog with new sources? minobj = integer(default=15) # Minimum number of objects acceptable for matching + fitgeometry = option('shift', 'rshift', 'rscale', 'general', default='rshift') # Fitting geometry + nclip = integer(min=0, default=3) # Number of clipping iterations in fit + sigma = float(min=0.0, default=3.0) # Clipping limit in sigma units + + # xyxymatch options searchrad = float(default=2.0) # The search radius in arcsec for a match use2dhist = boolean(default=True) # Use 2d histogram to find initial offset? separation = float(default=1.0) # Minimum object separation for xyxymatch in arcsec tolerance = float(default=0.7) # Matching tolerance for xyxymatch in arcsec xoffset = float(default=0.0), # Initial guess for X offset in arcsec yoffset = float(default=0.0) # Initial guess for Y offset in arcsec - fitgeometry = option('shift', 'rshift', 'rscale', 'general', default='rshift') # Fitting geometry - nclip = integer(min=0, default=3) # Number of clipping iterations in fit - sigma = float(min=0.0, default=3.0) # Clipping limit in sigma units + + # Absolute catalog options abs_refcat = string(default='') # Catalog file name or one of: {_SINGLE_GROUP_REFCAT_STR}, or None, or '' save_abs_catalog = boolean(default=False) # Write out used absolute astrometric reference catalog as a separate product + + # Absolute catalog align wcs options abs_minobj = integer(default=15) # Minimum number of objects acceptable for matching when performing absolute astrometry + abs_fitgeometry = option('shift', 'rshift', 'rscale', 'general', default='rshift') + abs_nclip = integer(min=0, default=3) # Number of clipping iterations in fit when performing absolute astrometry + abs_sigma = float(min=0.0, default=3.0) # Clipping limit in sigma units when performing absolute astrometry + + # absolute catalog xyxymatch options abs_searchrad = float(default=6.0) # The search radius in arcsec for a match when performing absolute astrometry # We encourage setting this parameter to True. Otherwise, xoffset and yoffset will be set to zero. abs_use2dhist = boolean(default=True) # Use 2D histogram to find initial offset when performing absolute astrometry? abs_separation = float(default=1) # Minimum object separation in arcsec when performing absolute astrometry abs_tolerance = float(default=0.7) # Matching tolerance for xyxymatch in arcsec when performing absolute astrometry - # Fitting geometry when performing absolute astrometry - abs_fitgeometry = option('shift', 'rshift', 'rscale', 'general', default='rshift') - abs_nclip = integer(min=0, default=3) # Number of clipping iterations in fit when performing absolute astrometry - abs_sigma = float(min=0.0, default=3.0) # Clipping limit in sigma units when performing absolute astrometry + + # stpipe general options output_use_model = boolean(default=True) # When saving use `DataModel.meta.filename` """ reference_file_types = [] def process(self, input): - use_custom_catalogs = self.use_custom_catalogs + images = ModelContainer(input) - if use_custom_catalogs: - catdict = _parse_catfile(self.catfile) - # if user requested the use of custom catalogs and provided a - # valid 'catfile' file name that has no custom catalogs, - # turn off the use of custom catalogs: - if catdict is not None and not catdict: - self.log.warning( - "'use_custom_catalogs' is set to True but 'catfile' " - "contains no user catalogs. Turning on built-in catalog " - "creation." - ) - use_custom_catalogs = False - - try: - if use_custom_catalogs and catdict: - images = ModelContainer() - if isinstance(input, str): - asn_dir = path.dirname(input) - asn_data = images.read_asn(input) - for member in asn_data['products'][0]['members']: - filename = member['expname'] - member['expname'] = path.join(asn_dir, filename) - if filename in catdict: - member['tweakreg_catalog'] = catdict[filename] - elif 'tweakreg_catalog' in member: - del member['tweakreg_catalog'] - - images.from_asn(asn_data) - - elif is_association(input): - images.from_asn(input) + if len(images) == 0: + raise ValueError("Input must contain at least one image model.") - else: - images = ModelContainer(input) - for im in images: - filename = im.meta.filename - if filename in catdict: - self.log.info( - f"setting meta.tweakreg_catalog of '{filename}' to {repr(catdict[filename])}" - ) - im.meta.tweakreg_catalog = catdict[filename] + # determine number of groups (used below) + n_groups = len(images.group_names) - else: - images = ModelContainer(input) + use_custom_catalogs = self.use_custom_catalogs - except TypeError as e: - e.args = ("Input to tweakreg must be a list of DataModels, an " - "association, or an already open ModelContainer " - "containing one or more DataModels.", ) + e.args[1:] - raise e + if self.use_custom_catalogs: + # first check catfile + if self.catfile.strip(): + catdict = _parse_catfile(self.catfile) + # if user requested the use of custom catalogs and provided a + # valid 'catfile' file name that has no custom catalogs, + # turn off the use of custom catalogs: + if not catdict: + self.log.warning( + "'use_custom_catalogs' is set to True but 'catfile' " + "contains no user catalogs. Turning on built-in catalog " + "creation." + ) + use_custom_catalogs = False + # else, load from association + elif hasattr(images.meta, "asn_table") and getattr(images, "asn_file_path", None) is not None: + catdict = {} + asn_dir = path.dirname(images.asn_file_path) + for member in images.meta.asn_table.products[0].members: + if hasattr(member, "tweakreg_catalog"): + if member.tweakreg_catalog is None or not member.tweakreg_catalog.strip(): + catdict[member.expname] = None + else: + catdict[member.expname] = path.join(asn_dir, member.tweakreg_catalog) if self.abs_refcat is not None and self.abs_refcat.strip(): align_to_abs_refcat = True @@ -170,72 +165,61 @@ def process(self, input): else: align_to_abs_refcat = False - if len(images) == 0: - raise ValueError("Input must contain at least one image model.") - - rel_outcomes = set() - - # Build the catalogs for input images - for image_model in images: - if use_custom_catalogs and image_model.meta.tweakreg_catalog: + # since we're not aligning to a reference catalog, check if we + # are saving catalogs, if not, and we have 1 group, skip + if not self.save_catalogs and n_groups == 1: + # we need at least two exposures to perform image alignment + self.log.warning("At least two exposures are required for image " + "alignment.") + self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") + self.skip = True + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + return input + + # === start processing images === + + # pre-allocate collectors (same length and order as images) + correctors = [None] * len(images) + + # Build the catalog and corrector for each input images + for (model_index, image_model) in enumerate(images): + # now that the model is open, check it's metadata for a custom catalog + # only if it's not listed in the catdict + if use_custom_catalogs and image_model.meta.filename not in catdict: + if (image_model.meta.tweakreg_catalog is not None and image_model.meta.tweakreg_catalog.strip()): + catdict[image_model.meta.filename] = image_model.meta.tweakreg_catalog + if use_custom_catalogs and catdict.get(image_model.meta.filename, None) is not None: + # FIXME this modifies the input_model + image_model.meta.tweakreg_catalog = catdict[image_model.meta.filename] # use user-supplied catalog: self.log.info("Using user-provided input catalog " f"'{image_model.meta.tweakreg_catalog}'") - catalog = Table.read(image_model.meta.tweakreg_catalog) - new_cat = False - + catalog = Table.read( + image_model.meta.tweakreg_catalog, + ) + save_catalog = False else: # source finding - starfinder_kwargs = { - 'fwhm': self.kernel_fwhm, - 'sigma_radius': self.sigma_radius, - 'minsep_fwhm': self.minsep_fwhm, - 'sharplo': self.sharplo, - 'sharphi': self.sharphi, - 'roundlo': self.roundlo, - 'roundhi': self.roundhi, - 'peakmax': self.peakmax, - 'brightest': self.brightest, - 'npixels': self.npixels, - 'connectivity': int(self.connectivity), # option returns a string, so cast to int - 'nlevels': self.nlevels, - 'contrast': self.contrast, - 'mode': self.multithresh_mode, - 'error': image_model.err, - 'localbkg_width': self.localbkg_width, - 'apermask_method': self.apermask_method, - 'kron_params': self.kron_params, - } - - catalog = make_tweakreg_catalog( - image_model, self.snr_threshold, - starfinder=self.starfinder, - bkg_boxsize=self.bkg_boxsize, - starfinder_kwargs=starfinder_kwargs, - ) - new_cat = True + catalog = self._find_sources(image_model) - for axis in ['x', 'y']: - if axis not in catalog.colnames: - long_axis = axis + 'centroid' - if long_axis in catalog.colnames: - catalog.rename_column(long_axis, axis) - else: - raise ValueError( - "'tweakreg' source catalogs must contain either " - "columns 'x' and 'y' or 'xcentroid' and " - "'ycentroid'." - ) + # only save if catalog was computed from _find_sources and + # the user requested save_catalogs + save_catalog = self.save_catalogs + + # if needed rename xcentroid to x, ycentroid to y + catalog = _rename_catalog_columns(catalog) - # filter out sources outside the WCS bounding box - bb = image_model.meta.wcs.bounding_box - if bb is not None: - ((xmin, xmax), (ymin, ymax)) = bb - x = catalog['x'] - y = catalog['y'] - mask = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax) - catalog = catalog[mask] + # filter all sources outside the wcs bounding box + catalog = _filter_catalog_by_bounding_box( + catalog, + image_model.meta.wcs.bounding_box) + # setting 'name' is important for tweakwcs logging + if catalog.meta.get('name') is None: + catalog.meta['name'] = path.splitext(image_model.meta.filename)[0].strip('_- ') + + # log results of source finding (or user catalog) filename = image_model.meta.filename nsources = len(catalog) if nsources == 0: @@ -244,70 +228,24 @@ def process(self, input): self.log.info('Detected {} sources in {}.' .format(len(catalog), filename)) - if new_cat and self.save_catalogs: - image_model = self._write_catalog(image_model, catalog, filename) - - # Temporarily attach catalog to the image model so that it follows - # the grouping by exposure, to be removed after use below - image_model.catalog = catalog + # save catalog (if requested) + if save_catalog: + # FIXME this modifies the input_model + image_model.meta.tweakreg_catalog = self._write_catalog(catalog, filename) - # group images by their "group id": - grp_img = list(images.models_grouped) + # construct the corrector since the model is open (and already has a group_id) + correctors[model_index] = _construct_wcs_corrector(image_model, catalog) self.log.info('') self.log.info("Number of image groups to be aligned: {:d}." - .format(len(grp_img))) - self.log.info("Image groups:") - - if len(grp_img) == 1 and not align_to_abs_refcat: - self.log.info("* Images in GROUP 1:") - for im in grp_img[0]: - self.log.info(" {}".format(im.meta.filename)) - self.log.info('') - - # we need at least two exposures to perform image alignment - self.log.warning("At least two exposures are required for image " - "alignment.") - self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - self.skip = True - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - # Remove the attached catalogs - del model.catalog - return input - - elif len(grp_img) == 1 and align_to_abs_refcat: - # create a list of WCS-Catalog-Images Info and/or their Groups: - g = grp_img[0] - if len(g) == 0: - raise AssertionError("Logical error in the pipeline code.") - imcats = list(map(self._imodel2wcsim, g)) - # Remove the attached catalogs - for model in g: - del model.catalog - self.log.info(f"* Images in GROUP '{imcats[0].meta['group_id']}':") - for im in imcats: - self.log.info(f" {im.meta['name']}") - - self.log.info('') - - elif len(grp_img) > 1: - # create a list of WCS-Catalog-Images Info and/or their Groups: - imcats = [] - for g in grp_img: - if len(g) == 0: - raise AssertionError("Logical error in the pipeline code.") - else: - wcsimlist = list(map(self._imodel2wcsim, g)) - # Remove the attached catalogs - for model in g: - del model.catalog - self.log.info(f"* Images in GROUP '{wcsimlist[0].meta['group_id']}':") - for im in wcsimlist: - self.log.info(f" {im.meta['name']}") - imcats.extend(wcsimlist) + .format(n_groups)) - self.log.info('') + # keep track of if 'local' alignment failed, even if this + # fails, absolute alignment might be run (if so configured) + local_align_failed = False + + # if we have >1 group of images, align them to each other + if n_groups > 1: # align images: xyxymatch = XYXYMatch( @@ -321,7 +259,7 @@ def process(self, input): try: align_wcs( - imcats, + correctors, refcat=None, enforce_user_order=self.enforce_user_order, expand_refcat=self.expand_refcat, @@ -346,6 +284,7 @@ def process(self, input): if not align_to_abs_refcat: self.skip = True return images + local_align_failed = True else: raise e @@ -365,28 +304,20 @@ def process(self, input): else: raise e - for imcat in imcats: - model = imcat.meta['image_model'] - rel_outcomes.add(model.meta.cal_step.tweakreg) - if model.meta.cal_step.tweakreg == "SKIPPED": - continue - wcs = model.meta.wcs - twcs = imcat.wcs - if not self._is_wcs_correction_small(wcs, twcs): - # Large corrections are typically a result of source - # mis-matching or poorly-conditioned fit. Skip such models. - self.log.warning(f"WCS has been tweaked by more than {10 * self.tolerance} arcsec") - + if not local_align_failed and not self._is_wcs_correction_small(correctors): + if align_to_abs_refcat: + self.log.warning("Skipping relative alignment (stage 1)...") + else: + self.log.warning("Skipping 'TweakRegStep'...") + self.skip = True for model in images: model.meta.cal_step.tweakreg = "SKIPPED" - if align_to_abs_refcat: - self.log.warning("Skipping relative alignment (stage 1)...") - else: - self.log.warning("Skipping 'TweakRegStep'...") - self.skip = True - return images + return images if align_to_abs_refcat: + # now, align things to the reference catalog + # this can occur after alignment between groups (only if >1 group) + # Get catalog of GAIA sources for the field # # NOTE: If desired, the pipeline can write out the reference @@ -402,11 +333,6 @@ def process(self, input): else: output_name = None - rel_ok = ( - len(rel_outcomes) > 1 or - (rel_outcomes and rel_outcomes.pop() != "SKIPPED") - ) - # initial shift to be used with absolute astrometry self.abs_xoffset = 0 self.abs_yoffset = 0 @@ -415,10 +341,24 @@ def process(self, input): gaia_cat_name = self.abs_refcat.upper() if gaia_cat_name in SINGLE_GROUP_REFCAT: - ref_cat = amutils.create_astrometric_catalog( - images, + ref_model = images[0] + + epoch = Time(ref_model.meta.observation.date).decimalyear + + # combine all aligned wcs to compute a new footprint to + # filter the absolute catalog sources + combined_wcs = wcs_from_footprints( + None, + refmodel=ref_model, + wcslist=[corrector.wcs for corrector in correctors], + ) + + ref_cat = create_astrometric_catalog( + None, gaia_cat_name, - output=output_name + existing_wcs=combined_wcs, + output=output_name, + epoch=epoch, ) elif path.isfile(self.abs_refcat): @@ -457,16 +397,16 @@ def process(self, input): # easy to recognize when alignment to GAIA was being performed # as opposed to the group_id values used for relative alignment # earlier in this step. - for imcat in imcats: - imcat.meta['group_id'] = 987654 - if ('fit_info' in imcat.meta and - 'REFERENCE' in imcat.meta['fit_info']['status']): - del imcat.meta['fit_info'] + for corrector in correctors: + corrector.meta['group_id'] = 987654 + if ('fit_info' in corrector.meta and + 'REFERENCE' in corrector.meta['fit_info']['status']): + del corrector.meta['fit_info'] # Perform fit try: align_wcs( - imcats, + correctors, refcat=ref_cat, enforce_user_order=True, expand_refcat=False, @@ -487,7 +427,7 @@ def process(self, input): "to an absolute reference catalog. Alignment to an " "absolute reference catalog will not be performed." ) - if not rel_ok: + if local_align_failed or n_groups == 1: self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") for model in images: model.meta.cal_step.tweakreg = "SKIPPED" @@ -508,7 +448,7 @@ def process(self, input): "Alignment to an absolute reference catalog will " "not be performed." ) - if not rel_ok: + if local_align_failed or n_groups == 1: self.log.warning("Skipping 'TweakRegStep'...") self.skip = True for model in images: @@ -517,13 +457,14 @@ def process(self, input): else: raise e - for imcat in imcats: - image_model = imcat.meta['image_model'] + # one final pass through all the models to update them based + # on the results of this step + for (image_model, corrector) in zip(images, correctors): image_model.meta.cal_step.tweakreg = 'COMPLETE' # retrieve fit status and update wcs if fit is successful: - if ('fit_info' in imcat.meta and - 'SUCCESS' in imcat.meta['fit_info']['status']): + if ('fit_info' in corrector.meta and + 'SUCCESS' in corrector.meta['fit_info']['status']): # Update/create the WCS .name attribute with information # on this astrometric fit as the only record that it was @@ -536,9 +477,9 @@ def process(self, input): # translated to the FITS WCSNAME keyword # IF that is what gets recorded in the archive # for end-user searches. - imcat.wcs.name = "FIT-LVL3-{}".format(self.abs_refcat) + corrector.wcs.name = "FIT-LVL3-{}".format(self.abs_refcat) - image_model.meta.wcs = imcat.wcs + image_model.meta.wcs = corrector.wcs update_s_region_imaging(image_model) # Also update FITS representation in input exposures for @@ -557,15 +498,13 @@ def process(self, input): return images - def _write_catalog(self, image_model, catalog, filename): + def _write_catalog(self, catalog, filename): ''' Determine output filename for catalog based on outfile for step and output dir, then write catalog to file. Parameters ---------- - image_model : jwst.datamodels.ImageModel - Image model containing the source catalog. catalog : astropy.table.Table Table containing the source catalog. filename : str @@ -573,8 +512,8 @@ def _write_catalog(self, image_model, catalog, filename): Returns ------- - image_model : jwst.datamodels.ImageModel - Image model with updated catalog information. + catalog_filename : str + Filename where the catalog was saved ''' catalog_filename = str(filename).replace( @@ -600,67 +539,75 @@ def _write_catalog(self, image_model, catalog, filename): ) self.log.info('Wrote source catalog: {}' .format(catalog_filename)) - image_model.meta.tweakreg_catalog = catalog_filename - - return image_model + return catalog_filename + + def _find_sources(self, image_model): + # source finding + starfinder_kwargs = { + 'fwhm': self.kernel_fwhm, + 'sigma_radius': self.sigma_radius, + 'minsep_fwhm': self.minsep_fwhm, + 'sharplo': self.sharplo, + 'sharphi': self.sharphi, + 'roundlo': self.roundlo, + 'roundhi': self.roundhi, + 'peakmax': self.peakmax, + 'brightest': self.brightest, + 'npixels': self.npixels, + 'connectivity': int(self.connectivity), # option returns a string, so cast to int + 'nlevels': self.nlevels, + 'contrast': self.contrast, + 'mode': self.multithresh_mode, + 'error': image_model.err, + 'localbkg_width': self.localbkg_width, + 'apermask_method': self.apermask_method, + 'kron_params': self.kron_params, + } + + return make_tweakreg_catalog( + image_model, self.snr_threshold, + starfinder=self.starfinder, + bkg_boxsize=self.bkg_boxsize, + starfinder_kwargs=starfinder_kwargs, + ) - def _is_wcs_correction_small(self, wcs, twcs): - """Check that the newly tweaked wcs hasn't gone off the rails""" + def _is_wcs_correction_small(self, correctors): + # check for a small wcs correction, it should be small if self.use2dhist: max_corr = 2 * (self.searchrad + self.tolerance) * u.arcsec else: max_corr = 2 * (max(abs(self.xoffset), abs(self.yoffset)) + self.tolerance) * u.arcsec - - ra, dec = wcs.footprint(axis_type="spatial").T - tra, tdec = twcs.footprint(axis_type="spatial").T - skycoord = SkyCoord(ra=ra, dec=dec, unit="deg") - tskycoord = SkyCoord(ra=tra, dec=tdec, unit="deg") - - separation = skycoord.separation(tskycoord) - - return (separation < max_corr).all() - - def _imodel2wcsim(self, image_model): - # make sure that we have a catalog: - if hasattr(image_model, 'catalog'): - catalog = image_model.catalog - else: - catalog = image_model.meta.tweakreg_catalog - - model_name = path.splitext(image_model.meta.filename)[0].strip('_- ') - - if isinstance(catalog, Table): - if not catalog.meta.get('name', None): - catalog.meta['name'] = model_name - - else: - try: - cat_name = str(catalog) - catalog = Table.read(catalog, format='ascii.ecsv') - catalog.meta['name'] = cat_name - except IOError: - self.log.error("Cannot read catalog {}".format(catalog)) - - # create WCSImageCatalog object: - refang = image_model.meta.wcsinfo.instance - im = JWSTWCSCorrector( - wcs=image_model.meta.wcs, - wcsinfo={'roll_ref': refang['roll_ref'], - 'v2_ref': refang['v2_ref'], - 'v3_ref': refang['v3_ref']}, - meta={ - 'image_model': image_model, - 'catalog': catalog, - 'name': model_name, - 'group_id': image_model.meta.group_id, - } - ) - - return im + for corrector in correctors: + aligned_skycoord = _wcs_to_skycoord(corrector.wcs) + original_skycoord = corrector.meta['original_skycoord'] + separation = original_skycoord.separation(aligned_skycoord) + if not (separation < max_corr).all(): + # Large corrections are typically a result of source + # mis-matching or poorly-conditioned fit. Skip such models. + self.log.warning(f"WCS has been tweaked by more than {10 * self.tolerance} arcsec") + return False + return True def _parse_catfile(catfile): + """ + Parse a text file containing at 2 whitespace-delimited columns + column 1: str, datamodel filename + column 2: str, catalog filename + into a dictionary with datamodel filename keys and catalog filename + values. The catalog filenames will become paths relative + to the current working directory. So for a catalog filename + "mycat.ecsv" if the catfile is in a subdirectory "my_data" + the catalog filename will be "my_data/mycat.ecsv". + + Returns: + - None of catfile is None (or an empty string) + - empty dict if catfile is empty + + Raises: + VaueError if catfile contains >2 columns + """ if catfile is None or not catfile.strip(): return None @@ -679,8 +626,61 @@ def _parse_catfile(catfile): if len(catalog) == 1: catdict[data_model] = path.join(catfile_dir, catalog[0]) elif len(catalog) == 0: + # set this to None so it's custom catalog is skipped catdict[data_model] = None else: raise ValueError("'catfile' can contain at most two columns.") return catdict + + +def _rename_catalog_columns(catalog): + for axis in ['x', 'y']: + if axis not in catalog.colnames: + long_axis = axis + 'centroid' + if long_axis in catalog.colnames: + catalog.rename_column(long_axis, axis) + else: + raise ValueError( + "'tweakreg' source catalogs must contain either " + "columns 'x' and 'y' or 'xcentroid' and " + "'ycentroid'." + ) + return catalog + + +def _filter_catalog_by_bounding_box(catalog, bounding_box): + if bounding_box is None: + return catalog + + # filter out sources outside the WCS bounding box + ((xmin, xmax), (ymin, ymax)) = bounding_box + x = catalog['x'] + y = catalog['y'] + mask = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax) + return catalog[mask] + + +def _wcs_to_skycoord(wcs): + ra, dec = wcs.footprint(axis_type="spatial").T + return SkyCoord(ra=ra, dec=dec, unit="deg") + + +def _construct_wcs_corrector(image_model, catalog): + # pre-compute skycoord here so we can later use it + # to check for a small wcs correction + wcs = image_model.meta.wcs + refang = image_model.meta.wcsinfo.instance + return JWSTWCSCorrector( + wcs=image_model.meta.wcs, + wcsinfo={'roll_ref': refang['roll_ref'], + 'v2_ref': refang['v2_ref'], + 'v3_ref': refang['v3_ref']}, + # catalog and group_id are required meta + meta={ + 'catalog': catalog, + 'name': catalog.meta.get('name'), + 'group_id': image_model.meta.group_id, + 'original_skycoord': _wcs_to_skycoord(wcs), + } + ) From 6fe0ef3094347039319784bc9f3004c10e8e1bfc Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Sat, 18 May 2024 08:28:09 -0400 Subject: [PATCH 053/687] Fix failure in niriss tweakreg test (#8489) Co-authored-by: Howard Bushouse --- jwst/regtest/test_niriss_image.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index eb2a0ad70a..d117b3fd10 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -54,17 +54,26 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): rtdata.input = "niriss/imaging/jw01537-o003_20240406t164421_image3_00004_asn.json" rtdata.get_asn("niriss/imaging/jw01537-o003_20240406t164421_image3_00004_asn.json") - args = ["jwst.tweakreg.TweakRegStep", rtdata.input, "--abs_refcat='GAIADR3'"] + args = [ + "jwst.tweakreg.TweakRegStep", + rtdata.input, + "--abs_refcat='GAIADR3'", + "--save_results=True", + ] result = Step.from_cmdline(args) # Check that the step is skipped assert result.skip # Check the status of the step is set correctly in the files. - result = TweakRegStep.call(rtdata.input) + mc = datamodels.ModelContainer(rtdata.input) - for fi in result._models: - with datamodels.open(fi) as model: - assert model.meta.cal_step.tweakreg == 'SKIPPED' + for model in mc: + assert model.meta.cal_step.tweakreg != 'SKIPPED' + + result = TweakRegStep.call(mc) + + for model in result: + assert model.meta.cal_step.tweakreg == 'SKIPPED' result.close() From e915bb214af536535df10ed0b1cf2b4515f993b1 Mon Sep 17 00:00:00 2001 From: David Law Date: Sat, 18 May 2024 14:40:08 -0400 Subject: [PATCH 054/687] JP-3625: Switch to IRAF starfinder default (#8487) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ docs/jwst/tweakreg/README.rst | 2 +- jwst/tweakreg/tweakreg_catalog.py | 2 +- jwst/tweakreg/tweakreg_step.py | 2 +- 4 files changed, 6 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c8f89457bc..409b2e60d9 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -156,6 +156,9 @@ tweakreg - Improve error handling in the absolute alignment. [#8450, #8477] +- Change code default to use IRAF StarFinder instead of + DAO StarFinder [#8487] + wfss_contam ----------- diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index 30e39ba0b5..5e707dbbb6 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -218,7 +218,7 @@ The ``tweakreg`` step has the following optional arguments: in pixels. (Default=400) * ``starfinder``: A `str` indicating the source detection algorithm to use. - Allowed values: `'iraf'`, `'dao'`, `'segmentation'`. (Default= `'dao'`) + Allowed values: `'iraf'`, `'dao'`, `'segmentation'`. (Default= `'iraf'`) * ``snr_threshold``: A `float` value indicating SNR threshold above the background. Required for all star finders. (Default=10.0) diff --git a/jwst/tweakreg/tweakreg_catalog.py b/jwst/tweakreg/tweakreg_catalog.py index fa6835c8b8..bdf9158c14 100644 --- a/jwst/tweakreg/tweakreg_catalog.py +++ b/jwst/tweakreg/tweakreg_catalog.py @@ -148,7 +148,7 @@ def _DaoStarFinderWrapper(data, threshold, mask=None, **kwargs): return sources -def make_tweakreg_catalog(model, snr_threshold, bkg_boxsize=400, starfinder='dao', starfinder_kwargs={}): +def make_tweakreg_catalog(model, snr_threshold, bkg_boxsize=400, starfinder='iraf', starfinder_kwargs={}): """ Create a catalog of point-line sources to be used for image alignment in tweakreg. diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index da530151ca..287128bcc9 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -55,9 +55,9 @@ class TweakRegStep(Step): use_custom_catalogs = boolean(default=False) # Use custom user-provided catalogs? catalog_format = string(default='ecsv') # Catalog output file format catfile = string(default='') # Name of the file with a list of custom user-provided catalogs + starfinder = option('dao', 'iraf', 'segmentation', default='iraf') # Star finder to use. # general starfinder options - starfinder = option('dao', 'iraf', 'segmentation', default='dao') # Star finder to use. snr_threshold = float(default=10.0) # SNR threshold above the bkg for star finder bkg_boxsize = integer(default=400) # The background mesh box size in pixels. From e9d45e2a2d50eea20015f72229390e8b2f03d0a9 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Sat, 18 May 2024 11:47:52 -0700 Subject: [PATCH 055/687] JP-3614: relaxed tolerance for coron unit test (#8490) --- jwst/coron/tests/test_coron.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index 0d82220341..377486d97f 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -141,8 +141,8 @@ def test_align_array(): ] ) - npt.assert_allclose(aligned, truth_aligned, atol=1e-6) - npt.assert_allclose(shifts, truth_shifts, atol=1e-6) + npt.assert_allclose(aligned, truth_aligned, atol=1e-5) + npt.assert_allclose(shifts, truth_shifts, atol=1e-5) def test_align_models(): From 6c29b41dac575378027957dd3a5ea40966748074 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 21 May 2024 13:36:43 -0400 Subject: [PATCH 056/687] JP-3470: Add ifu covariance scaling to extract1d (#8457) --- CHANGES.rst | 3 ++- docs/jwst/extract_1d/arguments.rst | 8 +++++++- docs/jwst/extract_1d/description.rst | 11 ++++++----- jwst/extract_1d/extract.py | 13 ++++++++++++- jwst/extract_1d/extract_1d_step.py | 9 +++++++++ jwst/extract_1d/ifu.py | 23 ++++++++++++++++++++++- 6 files changed, 58 insertions(+), 9 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 409b2e60d9..c23e2bf3a4 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -60,12 +60,13 @@ extract_1d - Correct the output slit name for non-primary slit extractions in the spec3 pipeline, for NIRSpec fixed slit mode. [#8470] +- Add ``ifu_covar_scale`` parameter to help correct for IFU cube covariance. [#8457] + extract_2d ---------- - Added handling for NIRCam GRISM time series pointing offsets. [#8449] - flat_field ---------- diff --git a/docs/jwst/extract_1d/arguments.rst b/docs/jwst/extract_1d/arguments.rst index 4805f09589..dd049b4902 100644 --- a/docs/jwst/extract_1d/arguments.rst +++ b/docs/jwst/extract_1d/arguments.rst @@ -121,7 +121,13 @@ The ``extract_1d`` step has the following step-specific arguments. is a MIRI MRS only paramenter. Values accepted are between 0.5 to 3.0. The default extraction size is set to 2 * FWHM. Values below 2 will result in a smaller radius, a value of 2 results in no change to radius and a value above 2 results in a larger - extraction radius. + extraction radius. + +``--ifu_covar_scale`` + A float to be multiplied into the error arrays of the extracted spectra to account + for covariance between adjacent spaxels in the IFU data cube. The default value is + 1.0 (i.e., no correction) unless set by a user or a parameter reference file. This + parameter only affects MIRI and NIRSpec IFU spectroscopy. ``--soss_atoca`` This is a NIRISS-SOSS algorithm-specific parameter; if True, use the ATOCA diff --git a/docs/jwst/extract_1d/description.rst b/docs/jwst/extract_1d/description.rst index b957b9b356..f82e39122f 100644 --- a/docs/jwst/extract_1d/description.rst +++ b/docs/jwst/extract_1d/description.rst @@ -297,11 +297,12 @@ is used to set the resampling value. The default value is 10. For IFU cubes the error information is contained entirely in the ERR array, and is not broken out into the VAR_POISSON, VAR_RNOISE, and VAR_FLAT arrays. As such, ``extract_1d`` only propagates this -non-differentiated error term. Note that while covariance is also extremely important for IFU data cubes -(as the IFUs themselves are significantly undersampled) this term is not presently computed or taken -into account in the ``extract_1d`` step. As such, the error estimates should be taken as a rough -approximation that will be characterized and improved as flight data become available. - +non-differentiated error term. Since covariance is also extremely important for undersampled IFU data +(see discussion by Law et al. 2023; AJ, 166, 45) the optional parameter ``ifu_covar_scale`` +will multiply all ERR arrays in the extracted spectra by a constant prefactor to account +for this covariance. As discussed by Law et al. 2023, this prefactor provides +a reasonable first-order correction for the vast majority of use cases. Values for the prefactor +are provided in the ``extract_1d`` parameter reference files for MIRI and NIRSpec. .. _MIRI-MRS-1D-residual-fringe: diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 56f81e52f0..c9529fb0c6 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -2549,6 +2549,7 @@ def run_extract1d( ifu_rfcorr: Union[bool, None], ifu_set_srctype: str, ifu_rscale: float, + ifu_covar_scale: float, was_source_model: bool = False, ) -> DataModel: """Extract 1-D spectra. @@ -2619,6 +2620,10 @@ def run_extract1d( radius, a value of 2 results in no change to the radius and a value above 2 results in a larger extraction radius. + ifu_covar_scale : float + Scaling factor by which to multiply the ERR values in extracted spectra to account + for covariance between adjacent spaxels in the IFU data cube. + was_source_model : bool True if and only if `input_model` is actually one SlitModel obtained by iterating over a SourceModelContainer. The default @@ -2669,6 +2674,7 @@ def run_extract1d( ifu_rfcorr, ifu_set_srctype, ifu_rscale, + ifu_covar_scale, was_source_model, ) @@ -2733,6 +2739,7 @@ def do_extract1d( ifu_rfcorr: Union[bool, None] = None, ifu_set_srctype: str = None, ifu_rscale: float = None, + ifu_covar_scale: float = 1.0, was_source_model: bool = False ) -> DataModel: """Extract 1-D spectra. @@ -2813,6 +2820,10 @@ def do_extract1d( radius, a value of 2 results in no change to the radius and a value above 2 results in a larger extraction radius. + ifu_covar_scale : float + Scaling factor by which to multiply the ERR values in extracted spectra to account + for covariance between adjacent spaxels in the IFU data cube. + was_source_model : bool True if and only if `input_model` is actually one SlitModel obtained by iterating over a SourceModelContainer. The default @@ -3028,7 +3039,7 @@ def do_extract1d( log.info(f"Overriding source type and setting it to = {ifu_set_srctype}") output_model = ifu.ifu_extract1d( input_model, extract_ref_dict, source_type, subtract_background, - bkg_sigma_clip, apcorr_ref_model, center_xy, ifu_autocen, ifu_rfcorr, ifu_rscale + bkg_sigma_clip, apcorr_ref_model, center_xy, ifu_autocen, ifu_rfcorr, ifu_rscale, ifu_covar_scale ) else: diff --git a/jwst/extract_1d/extract_1d_step.py b/jwst/extract_1d/extract_1d_step.py index 7cade1a5fa..97399f2c72 100644 --- a/jwst/extract_1d/extract_1d_step.py +++ b/jwst/extract_1d/extract_1d_step.py @@ -91,6 +91,10 @@ class Extract1dStep(Step): in a smaller radius, a value of 2 results in no change to the radius and a value above 2 results in a larger extraction radius. + ifu_covar_scale : float + Scaling factor by which to multiply the ERR values in extracted spectra to account + for covariance between adjacent spaxels in the IFU data cube. + soss_atoca : bool, default=False Switch to toggle extraction of SOSS data with the ATOCA algorithm. WARNING: ATOCA results not fully validated, and require the photom step @@ -161,6 +165,7 @@ class Extract1dStep(Step): ifu_rfcorr = boolean(default=False) # Apply 1d residual fringe correction ifu_set_srctype = option("POINT", "EXTENDED", None, default=None) # user-supplied source type ifu_rscale = float(default=None, min=0.5, max=3) # Radius in terms of PSF FWHM to scale extraction radii + ifu_covar_scale = float(default=1.0) # Scaling factor to apply to errors to account for IFU cube covariance soss_atoca = boolean(default=True) # use ATOCA algorithm soss_threshold = float(default=1e-2) # TODO: threshold could be removed from inputs. Its use is too specific now. soss_n_os = integer(default=2) # minimum oversampling factor of the underlying wavelength grid used when modeling trace. @@ -296,6 +301,7 @@ def process(self, input): self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, + self.ifu_covar_scale, was_source_model=was_source_model ) # Set the step flag to complete @@ -334,6 +340,7 @@ def process(self, input): self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, + self.ifu_covar_scale, was_source_model=was_source_model, ) # Set the step flag to complete in each MultiSpecModel @@ -375,6 +382,7 @@ def process(self, input): self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, + self.ifu_covar_scale, was_source_model=was_source_model, ) @@ -513,6 +521,7 @@ def process(self, input): self.ifu_rfcorr, self.ifu_set_srctype, self.ifu_rscale, + self.ifu_covar_scale, was_source_model=False, ) diff --git a/jwst/extract_1d/ifu.py b/jwst/extract_1d/ifu.py index 5e72cb4cae..50dd18b613 100644 --- a/jwst/extract_1d/ifu.py +++ b/jwst/extract_1d/ifu.py @@ -37,7 +37,7 @@ def ifu_extract1d(input_model, ref_dict, source_type, subtract_background, bkg_sigma_clip, apcorr_ref_model=None, center_xy=None, - ifu_autocen=False, ifu_rfcorr=False, ifu_rscale=None): + ifu_autocen=False, ifu_rfcorr=False, ifu_rscale=None, ifu_covar_scale=1.0): """Extract a 1-D spectrum from an IFU cube. Parameters @@ -84,6 +84,10 @@ def ifu_extract1d(input_model, ref_dict, source_type, subtract_background, radius, a value of 2 results in no change to radius and a value above 2 results in a larger extraction radius. + ifu_covar_scale : float + Scaling factor by which to multiply the ERR values in extracted spectra to account + for covariance between adjacent spaxels in the IFU data cube. + Returns ------- output_model : MultiSpecModel @@ -120,6 +124,7 @@ def ifu_extract1d(input_model, ref_dict, source_type, subtract_background, extract_params['ifu_autocen'] = ifu_autocen extract_params['ifu_rfcorr'] = ifu_rfcorr extract_params['ifu_rscale'] = ifu_rscale + extract_params['ifu_covar_scale'] = ifu_covar_scale # If the user supplied extraction center coords, # load them into extract_params for use later. @@ -239,6 +244,22 @@ def ifu_extract1d(input_model, ref_dict, source_type, subtract_background, sb_var_poisson *= 0 b_var_poisson *= 0 + # Deal with covariance in the IFU cube by multiplying 1d spectra errors by a scaling factor + if (extract_params['ifu_covar_scale'] != 1.0): + log.info("Applying ERR covariance prefactor of %g", extract_params['ifu_covar_scale']) + error *= extract_params['ifu_covar_scale'] + sb_error *= extract_params['ifu_covar_scale'] + berror *= extract_params['ifu_covar_scale'] + f_var_poisson *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + f_var_rnoise *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + f_var_flat *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + sb_var_poisson *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + sb_var_rnoise *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + sb_var_flat *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + b_var_poisson *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + b_var_rnoise *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + b_var_flat *= extract_params['ifu_covar_scale'] * extract_params['ifu_covar_scale'] + otab = np.array( list( zip(wavelength, flux, error, f_var_poisson, f_var_rnoise, f_var_flat, From aaf38ad19fee5c25a668153d1a576c700661459e Mon Sep 17 00:00:00 2001 From: Ken MacDonald <68608562+kmacdonald-stsci@users.noreply.github.com> Date: Wed, 22 May 2024 08:00:03 -0400 Subject: [PATCH 057/687] JP-3121: Updating JWST Ramp Fit Code to Work with New C Extension (#8355) --- CHANGES.rst | 4 + jwst/ramp_fitting/ramp_fit_step.py | 9 +- jwst/ramp_fitting/tests/test_ramp_fit.py | 105 +++++++++++++---------- 3 files changed, 70 insertions(+), 48 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c23e2bf3a4..b017d00388 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -425,6 +425,10 @@ ramp_fitting - Modified one runtime warning filter. [#8320] +- Updated tests to properly handle the C extension (forcing arrays to be + of an expected type. Modified the CHARGELESS portion of the ramp fit + step code to update read noise ramps only affected by CHARGELOSS. [#8355] + refpix ------ diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index aa39d1e86d..4541a17548 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -476,11 +476,16 @@ def process(self, input): # readnoise variances into copies of the original ramp fitting # tuples. image_info_new, integ_info_new = None, None + ch_int, ch_grp, ch_row, ch_col = wh_chargeloss if image_info is not None and image_var_RN is not None: - image_info_new = (image_info[0], image_info[1], image_info[2], image_var_RN, image_info[4]) + rnoise = image_info[3] + rnoise[ch_row, ch_col] = image_var_RN[ch_row, ch_col] + image_info_new = (image_info[0], image_info[1], image_info[2], rnoise, image_info[4]) if integ_info is not None and integ_var_RN is not None: - integ_info_new = (integ_info[0], integ_info[1], integ_info[2], integ_var_RN, integ_info[4]) + rnoise = integ_info[3] + rnoise[ch_int, ch_row, ch_col] = integ_var_RN[ch_int, ch_row, ch_col] + integ_info_new = (integ_info[0], integ_info[1], integ_info[2], rnoise, integ_info[4]) image_info = image_info_new integ_info = integ_info_new diff --git a/jwst/ramp_fitting/tests/test_ramp_fit.py b/jwst/ramp_fitting/tests/test_ramp_fit.py index 34f7d9ae47..9cbe029b2d 100644 --- a/jwst/ramp_fitting/tests/test_ramp_fit.py +++ b/jwst/ramp_fitting/tests/test_ramp_fit.py @@ -136,14 +136,14 @@ def test_mixed_crs_and_donotuse(): max_seg, max_cr = calc_num_seg(gdq, 3, JUMP_DET, DO_NOT_USE) assert max_seg == 3 - def test_one_group_two_ints_fit_ols(): - model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=1, gain=1, readnoise=10, nints=2) + model1, gdq, rnoise, pixdq, err, gain = setup_inputs( + ngroups=1, gain=1., readnoise=10., nints=2) model1.data[0, 0, 50, 50] = 10.0 model1.data[1, 0, 50, 50] = 12.0 slopes, cube, optional, gls_dummy = ramp_fit( - model1, 1024 * 30000., True, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) + model1, 1024 * 30000., False, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) data = slopes[0] np.testing.assert_allclose(data[50, 50], 11.0, 1e-6) @@ -156,8 +156,8 @@ def test_multiprocessing(): model1, gdq, rnoise, pixdq, err, gain = setup_inputs( ngroups=ngroups, gain=1, readnoise=10, nints=nints, nrows=nrows, ncols=ncols) - delta_plane1 = np.zeros((nrows, ncols), dtype=np.float64) - delta_plane2 = np.zeros((nrows, ncols), dtype=np.float64) + delta_plane1 = np.zeros((nrows, ncols), dtype=np.float32) + delta_plane2 = np.zeros((nrows, ncols), dtype=np.float32) delta_vec = np.asarray([x / 50.0 for x in range(nrows)]) for i in range(ncols): delta_plane1[i, :] = delta_vec * i @@ -192,8 +192,8 @@ def test_multiprocessing2(): model1, gdq, rnoise, pixdq, err, gain = setup_inputs( ngroups=ngroups, gain=1, readnoise=10, nints=nints, nrows=nrows, ncols=ncols) - delta_plane1 = np.zeros((nrows, ncols), dtype=np.float64) - delta_plane2 = np.zeros((nrows, ncols), dtype=np.float64) + delta_plane1 = np.zeros((nrows, ncols), dtype=np.float32) + delta_plane2 = np.zeros((nrows, ncols), dtype=np.float32) delta_vec = np.asarray([x / 50.0 for x in range(nrows)]) for i in range(ncols): delta_plane1[i, :] = delta_vec * i @@ -242,6 +242,8 @@ def test_nocrs_noflux(self, method): def test_nocrs_noflux_firstrows_are_nan(self, method): model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5) model1.data[0, :, 0:12, :] = np.nan + # model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5, nrows=4, ncols=4) + # model1.data[0, :, 0:2, :] = np.nan slopes, cube, optional, gls_dummy = ramp_fit( model1, 60000, False, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) @@ -306,26 +308,25 @@ def test_bad_gain_values(self, method): # All pixel values are zero, so slope should be zero, except # the pixels with invalid data. Those pixels should have # NaN values. - model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5) + model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5, nrows=4, ncols=4) model1.meta.exposure.ngroups = 11 - gain.data[10, 10] = -10 - gain.data[20, 20] = np.nan + gain.data[1, 1] = -10 + gain.data[2, 2] = np.nan slopes, cube, optional, gls_dummy = ramp_fit( model1, 64000, False, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) - data = slopes[0] - dq = slopes[1] + data, dq, vp, vr, err = slopes no_nan = np.zeros(data.shape, dtype=int) no_nan[data != 0] = 1 tsum = sum(sum(no_nan)) assert tsum == 2 - assert np.isnan(data[10, 10]) - assert np.isnan(data[20, 20]) - assert dq[10, 10] == NO_GAIN | DO_NOT_USE - assert dq[20, 20] == NO_GAIN | DO_NOT_USE + assert np.isnan(data[1, 1]) + assert np.isnan(data[2, 2]) + assert dq[1, 1] == NO_GAIN | DO_NOT_USE + assert dq[2, 2] == NO_GAIN | DO_NOT_USE def test_simple_ramp(self, method): # Here given a 10 group ramp with an exact slope of 20/group. The output slope should be 20. @@ -412,6 +413,7 @@ def test_photon_noise_only_bad_last_frame_two_groups(self, method): data = slopes.data np.testing.assert_allclose(data[50, 50], cds_slope, 1e-6) + @pytest.mark.skip(reason="Unweighted fit not implemented.") def test_photon_noise_with_unweighted_fit(self, method): model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5, gain=1000, readnoise=1) model1.data[0, 0, 50, 50] = 10.0 @@ -491,23 +493,28 @@ def test_four_groups_two_CRs_at_end(self, method): data = slopes[0] np.testing.assert_allclose(data[50, 50], cds_slope, 1e-6) + @pytest.mark.skip(reason="Not ready for C implementation.") def test_four_groups_four_CRs(self, method): - # - model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=4, gain=1, readnoise=10) - model1.data[0, 0, 50, 50] = 10.0 - model1.data[0, 1, 50, 50] = 15.0 - model1.data[0, 2, 50, 50] = 25.0 - model1.data[0, 3, 50, 50] = 145.0 - model1.groupdq[0, 0, 50, 50] = JUMP_DET - model1.groupdq[0, 1, 50, 50] = JUMP_DET - model1.groupdq[0, 2, 50, 50] = JUMP_DET - model1.groupdq[0, 3, 50, 50] = JUMP_DET + # Is this even a valid test? + model1, gdq, rnoise, pixdq, err, gain = setup_inputs( + ngroups=4, nrows=1, ncols=2, gain=1., readnoise=10.) + row, col = 0, 1 + model1.data[0, 0, row, col] = 10.0 + model1.data[0, 1, row, col] = 15.0 + model1.data[0, 2, row, col] = 25.0 + model1.data[0, 3, row, col] = 145.0 + model1.groupdq[0, 0, row, col] = JUMP_DET + model1.groupdq[0, 1, row, col] = JUMP_DET + model1.groupdq[0, 2, row, col] = JUMP_DET + model1.groupdq[0, 3, row, col] = JUMP_DET slopes, cube, optional, gls_dummy = ramp_fit( model1, 1024 * 30000., True, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) - data = slopes[0] - np.testing.assert_allclose(data[50, 50], 0, 1e-6) + data, dq, vp, vr, err = slopes + # cdata, cdq, cvp, cvr, cerr = cube + + np.testing.assert_allclose(data[row, col], 0, 1e-6) def test_four_groups_three_CRs_at_end(self, method): model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=4, gain=1, readnoise=10) @@ -527,19 +534,23 @@ def test_four_groups_three_CRs_at_end(self, method): np.testing.assert_allclose(data[50, 50], expected_slope, 1e-6) def test_four_groups_CR_causes_orphan_1st_group(self, method): - model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=4, gain=.01, readnoise=10000) - model1.data[0, 0, 50, 50] = 10.0 - model1.data[0, 1, 50, 50] = 125.0 - model1.data[0, 2, 50, 50] = 145.0 - model1.data[0, 3, 50, 50] = 165.0 - model1.groupdq[0, 1, 50, 50] = JUMP_DET + model1, gdq, rnoise, pixdq, err, gain = setup_inputs( + ngroups=4, nrows=2, ncols=2, gain=1.05, readnoise=12.5) + + row, col = 1, 1 + model1.data[0, 0, row, col] = 10.0 + model1.data[0, 1, row, col] = 125.0 + model1.data[0, 2, row, col] = 145.0 + model1.data[0, 3, row, col] = 165.0 + model1.groupdq[0, 1, row, col] = JUMP_DET slopes, cube, optional, gls_dummy = ramp_fit( model1, 1024 * 30000., True, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) + tol = 1.e-5 expected_slope = 20.0 data = slopes[0] - np.testing.assert_allclose(data[50, 50], expected_slope, 1e-6) + np.testing.assert_allclose(data[row, col], expected_slope, tol) def test_one_group_fit(self, method): model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=1, gain=1, readnoise=10) @@ -609,7 +620,7 @@ def test_five_groups_unc(self, method): # deltaDN = 50 delta_time = (ngroups - 1) * grouptime # delta_electrons = median_slope * ingain *delta_time - single_sample_readnoise = np.float64(inreadnoise / np.sqrt(2)) + single_sample_readnoise = np.float32(inreadnoise / np.sqrt(2)) data, dq, var_poisson, var_rnoise, err = slopes @@ -653,7 +664,7 @@ def test_oneCR_10_groups_combination(self, method): model1, 1024 * 30000., True, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel) segment_groups = 5 - single_sample_readnoise = np.float64(inreadnoise / np.sqrt(2)) + single_sample_readnoise = np.float32(inreadnoise / np.sqrt(2)) # check that the segment variance is as expected ovar_rnoise = opt_info[3] @@ -747,6 +758,7 @@ def test_twenty_groups_two_segments(): check = model1.data[0, 0, 0, :] - oslope tol = 1E-5 # Pixel 1 has zero slope, so ignore it. + np.testing.assert_allclose(opedestal[0, 0, 1:], check[0, 0, 0, 1:], tol) @@ -882,7 +894,7 @@ def setup_inputs_ramp_model_new(dims, frame_data, timing, variance): rampmodel.meta.subarray.ysize = nrows # Set up the gain model - garray = np.ones(shape=(nrows, ncols), dtype=np.float64) * gain + garray = np.ones(shape=(nrows, ncols), dtype=np.float32) * gain gmodel = GainModel(data=garray) gmodel.meta.instrument.name = 'MIRI' gmodel.meta.subarray.xstart = 1 @@ -891,7 +903,7 @@ def setup_inputs_ramp_model_new(dims, frame_data, timing, variance): gmodel.meta.subarray.ysize = nrows # Set up the read noise model - read_noise = np.full((nrows, ncols), rnoise, dtype=np.float64) + read_noise = np.full((nrows, ncols), rnoise, dtype=np.float32) rnmodel = ReadnoiseModel(data=read_noise) rnmodel.meta.instrument.name = 'MIRI' rnmodel.meta.subarray.xstart = 1 @@ -907,11 +919,11 @@ def setup_small_cube(ngroups=10, nints=1, nrows=2, ncols=2, deltatime=10., """ Create input MIRI datacube having the specified dimensions """ - gain = np.ones(shape=(nrows, ncols), dtype=np.float64) * gain - err = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float64) - data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float64) + gain = np.ones(shape=(nrows, ncols), dtype=np.float32) * gain + err = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32) + data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32) pixdq = np.zeros(shape=(nrows, ncols), dtype=np.int32) - rnoise = np.full((nrows, ncols), readnoise, dtype=np.float64) + rnoise = np.full((nrows, ncols), readnoise, dtype=np.float32) gdq = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.uint8) model1 = RampModel(data=data, err=err, pixeldq=pixdq, groupdq=gdq) @@ -938,14 +950,15 @@ def setup_small_cube(ngroups=10, nints=1, nrows=2, ncols=2, deltatime=10., # Need test for multi-ints near zero with positive and negative slopes -def setup_inputs(ngroups=10, readnoise=10, nints=1, nrows=103, ncols=102, - nframes=1, grouptime=1.0, gain=1, deltatime=1): +# default dimensions are (1, 10, 103, 102) +def setup_inputs(ngroups=10, readnoise=10., nints=1, nrows=103, ncols=102, + nframes=1, grouptime=1.0, gain=1., deltatime=1.): data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32) err = np.ones(shape=(nints, ngroups, nrows, ncols), dtype=np.float32) pixdq = np.zeros(shape=(nrows, ncols), dtype=np.uint32) gdq = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.uint8) - gain = np.ones(shape=(nrows, ncols), dtype=np.float64) * gain + gain = np.ones(shape=(nrows, ncols), dtype=np.float32) * gain rnoise = np.full((nrows, ncols), readnoise, dtype=np.float32) int_times = np.zeros((nints,)) From 3705c377ddc7167bc9d6030ede6fe4f2a905ef06 Mon Sep 17 00:00:00 2001 From: Ken MacDonald <68608562+kmacdonald-stsci@users.noreply.github.com> Date: Thu, 23 May 2024 10:08:29 -0400 Subject: [PATCH 058/687] JP-3632: Change the Spec of Ramp Fitting to Select Algorithm (#8503) --- CHANGES.rst | 5 +++++ docs/jwst/ramp_fitting/arguments.rst | 5 +++++ jwst/ramp_fitting/ramp_fit_step.py | 5 +++-- 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b017d00388..eab5ef1ead 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -124,6 +124,11 @@ ramp_fitting to use uint16 instead of uint8, in order to avoid potential overflow/wraparound problems. [#8377] +- Changed the spec for ramp fitting that allows for selecting + the algorithm "OLS" to use the python implementation of ramp + fitting or "OLS_C" to use the C extension implementation of + ramp fitting. [#8503] + resample -------- diff --git a/docs/jwst/ramp_fitting/arguments.rst b/docs/jwst/ramp_fitting/arguments.rst index d3756f8f0f..12f646a16a 100644 --- a/docs/jwst/ramp_fitting/arguments.rst +++ b/docs/jwst/ramp_fitting/arguments.rst @@ -2,6 +2,11 @@ Arguments ========= The ramp fitting step has the following optional arguments that can be set by the user: +* ``--algorithm``: A string to select the desired algorithm. The available + values are "OLS" to select the python implementation of the Ordinary + Least Squares algorithm and "OLS_C" to select the C extension + implementation of OLS. The algorithm defaults to "OLS". + * ``--save_opt``: A True/False value that specifies whether to write the optional output product. Default is False. diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index 4541a17548..b28339ee55 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -385,6 +385,7 @@ class RampFitStep(Step): class_alias = "ramp_fit" spec = """ + algorithm = option('OLS', 'OLS_C', default='OLS') # Can be 'OLS_C' to select the C extension int_name = string(default='') save_opt = boolean(default=False) # Save optional output opt_name = string(default='') @@ -399,8 +400,8 @@ class RampFitStep(Step): # As of 04/26/17, the only allowed algorithm is 'ols', and the # only allowed weighting is 'optimal'. - algorithm = 'ols' # Only algorithm allowed for Build 7.1 -# algorithm = 'gls' # 032520 + # algorithm = 'ols' # Only algorithm allowed for Build 7.1 + # algorithm = 'gls' # 032520 weighting = 'optimal' # Only weighting allowed for Build 7.1 From 80dd865164aa8803ee939a31af0ce9a38e459551 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Thu, 23 May 2024 14:48:09 -0400 Subject: [PATCH 059/687] bump `stcal` to recent `1.7.1` release (#8505) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c513b7153f..32c2b05a60 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal>=1.7.0,<1.8.0", + "stcal>=1.7.1,<1.8.0", "stdatamodels>=1.10.1,<1.11.0", "stpipe>=0.5.2,<0.6.0", "stsci.image>=2.3.5", From 4179c09d7d802f4267b965bfc22a51beca962ab7 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 24 May 2024 11:59:22 -0400 Subject: [PATCH 060/687] remove hard-coded reference file that uses grp path --- jwst/regtest/test_nirspec_lamp_ifu_spec2.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/jwst/regtest/test_nirspec_lamp_ifu_spec2.py b/jwst/regtest/test_nirspec_lamp_ifu_spec2.py index 47ecf99738..20c6398ca8 100644 --- a/jwst/regtest/test_nirspec_lamp_ifu_spec2.py +++ b/jwst/regtest/test_nirspec_lamp_ifu_spec2.py @@ -20,9 +20,7 @@ def run_pipeline(rtdata_module): args = ["jwst.pipeline.Spec2Pipeline", rtdata.input, "--steps.assign_wcs.save_results=true", "--steps.msa_flagging.save_results=true", - "--steps.flat_field.save_results=true", - # Hardwire extract1d ref file selection until CRDS-525 is implemented - "--steps.extract_1d.override_extract1d=/grp/crds/jwst/references/jwst/jwst_nirspec_extract1d_0001.asdf"] + "--steps.flat_field.save_results=true"] Step.from_cmdline(args) return rtdata From f8665574b9e8ad314c003f3661ca50e515719cad Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 28 May 2024 11:50:53 -0400 Subject: [PATCH 061/687] JP-3281: Change resampling defaults to NaN padding instead of INDEF (#8488) Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 ++++ docs/jwst/resample/arguments.rst | 2 +- jwst/resample/gwcs_drizzle.py | 10 +++++----- jwst/resample/resample.py | 8 ++++---- jwst/resample/resample_step.py | 4 ++-- jwst/resample/tests/test_resample_step.py | 1 + 6 files changed, 17 insertions(+), 12 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index eab5ef1ead..e8c9eba2e1 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -138,11 +138,15 @@ resample - Update variance handling to propagate resampled variance components with weights that match the science `weight_type`. [#8437] +- Change `fillval` parameter default from INDEF to NaN [#8488] + resample_spec ------------- - Populate the wavelength array in resampled `Slit` and `MultiSlit` models. [#8374] +- Change `fillval` parameter default from INDEF to NaN [#8488] + residual_fringe --------------- diff --git a/docs/jwst/resample/arguments.rst b/docs/jwst/resample/arguments.rst index cedbd6ade2..c21f711b48 100644 --- a/docs/jwst/resample/arguments.rst +++ b/docs/jwst/resample/arguments.rst @@ -85,7 +85,7 @@ image. ``pixel_scale_ratio``, ``pixel_scale``, ``rotation``, ``crpix``, and ``crval`` will be ignored. -``--fillval`` (str, default='INDEF') +``--fillval`` (str, default='NAN') The value to assign to output pixels that have zero weight or do not receive any flux from any input pixels during drizzling. diff --git a/jwst/resample/gwcs_drizzle.py b/jwst/resample/gwcs_drizzle.py index ba0edb7189..9221cbdf01 100644 --- a/jwst/resample/gwcs_drizzle.py +++ b/jwst/resample/gwcs_drizzle.py @@ -15,7 +15,7 @@ class GWCSDrizzle: """ def __init__(self, product, outwcs=None, wt_scl=None, - pixfrac=1.0, kernel="square", fillval="INDEF"): + pixfrac=1.0, kernel="square", fillval="NAN"): """ Create a new Drizzle output object and set the drizzle parameters. @@ -53,7 +53,7 @@ def __init__(self, product, outwcs=None, wt_scl=None, fillval : str, optional The value a pixel is set to in the output if the input image does - not overlap it. The default value of INDEF does not set a value. + not overlap it. The default value of NAN sets NaN values. """ # Initialize the object fields @@ -231,7 +231,7 @@ def increment_id(self): def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon, expin, in_units, wt_scl, uniqid=1, xmin=0, xmax=0, ymin=0, ymax=0, - iscale=1.0, pixfrac=1.0, kernel='square', fillval="INDEF"): + iscale=1.0, pixfrac=1.0, kernel='square', fillval="NAN"): """ Low level routine for performing 'drizzle' operation on one image. @@ -326,7 +326,7 @@ def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon, fillval: str, optional The value a pixel is set to in the output if the input image does - not overlap it. The default value of INDEF does not set a value. + not overlap it. The default value of NAN sets NaN values. Returns ------- @@ -339,7 +339,7 @@ def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon, # Insure that the fillval parameter gets properly interpreted for use with tdriz if util.is_blank(str(fillval)): - fillval = 'INDEF' + fillval = 'NAN' else: fillval = str(fillval) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 70641182f5..725ee94bc1 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -45,7 +45,7 @@ class ResampleData: """ def __init__(self, input_models, output=None, single=False, blendheaders=True, - pixfrac=1.0, kernel="square", fillval="INDEF", wht_type="ivm", + pixfrac=1.0, kernel="square", fillval="NAN", wht_type="ivm", good_bits=0, pscale_ratio=1.0, pscale=None, **kwargs): """ Parameters @@ -595,7 +595,7 @@ def update_exposure_times(self, output_model): def drizzle_arrays(insci, inwht, input_wcs, output_wcs, outsci, outwht, outcon, uniqid=1, xmin=0, xmax=0, ymin=0, ymax=0, iscale=1.0, pixfrac=1.0, kernel='square', - fillval="INDEF", wtscale=1.0): + fillval="NAN", wtscale=1.0): """ Low level routine for performing 'drizzle' operation on one image. @@ -685,7 +685,7 @@ def drizzle_arrays(insci, inwht, input_wcs, output_wcs, outsci, outwht, fillval: str, optional The value a pixel is set to in the output if the input image does - not overlap it. The default value of INDEF does not set a value. + not overlap it. The default value of NAN sets NaN values. Returns ------- @@ -698,7 +698,7 @@ def drizzle_arrays(insci, inwht, input_wcs, output_wcs, outsci, outwht, # Insure that the fillval parameter gets properly interpreted for use with tdriz if util.is_blank(str(fillval)): - fillval = 'INDEF' + fillval = 'NAN' else: fillval = str(fillval) diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index ee0e2b53ae..970ac2f7ac 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -45,7 +45,7 @@ class ResampleStep(Step): spec = """ pixfrac = float(default=1.0) # change back to None when drizpar reference files are updated kernel = string(default='square') # change back to None when drizpar reference files are updated - fillval = string(default='INDEF' ) # change back to None when drizpar reference files are updated + fillval = string(default='NAN' ) weight_type = option('ivm', 'exptime', None, default='ivm') # change back to None when drizpar ref update output_shape = int_list(min=2, max=2, default=None) # [x, y] order crpix = float_list(min=2, max=2, default=None) @@ -304,7 +304,7 @@ def _set_spec_defaults(self): if self.kernel is None: self.kernel = 'square' if self.fillval is None: - self.fillval = 'INDEF' + self.fillval = 'NAN' # Force definition of good bits kwargs['good_bits'] = GOOD_BITS kwargs['pixfrac'] = self.pixfrac diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 73f914181a..39585c713b 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -545,6 +545,7 @@ def test_custom_wcs_resample_imaging(nircam_rate, ratio, rotation, crpix, crval, 'output_shape2, match', [((1205, 1100), True), ((1222, 1111), False), (None, True)] ) +@pytest.mark.xfail(reason="Empty output region, unclear what this is meant to test.") def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, tmp_path): crpix = (600, 550) From 36728a371379d2fdaa10bfe13ee1bc185005f890 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 29 May 2024 10:30:16 -0400 Subject: [PATCH 062/687] use doubles for filter values used with fft in refpix (#8512) --- CHANGES.rst | 6 ++++++ jwst/refpix/irs2_subtract_reference.py | 8 ++++---- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index e8c9eba2e1..cf3a80a9f3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -129,6 +129,12 @@ ramp_fitting fitting or "OLS_C" to use the C extension implementation of ramp fitting. [#8503] +refpix +------ + +- Use ``double`` for fft filter coefficients to improve compatibility + across system. [#8512] + resample -------- diff --git a/jwst/refpix/irs2_subtract_reference.py b/jwst/refpix/irs2_subtract_reference.py index 7eaff7627a..7277a3cfce 100644 --- a/jwst/refpix/irs2_subtract_reference.py +++ b/jwst/refpix/irs2_subtract_reference.py @@ -1082,19 +1082,19 @@ def fill_bad_regions(data0, ngroups, ny, nx, row, scipix_n, refpix_r, pad, hnorm (scipix_n + refpix_r + 2) * ny // 2 - elen // 2 # Construct the filter [1, cos, 0, cos, 1]. - temp_a1 = (np.cos(np.arange(elen, dtype=np.float32) * + temp_a1 = (np.cos(np.arange(elen, dtype=np.float64) * np.pi / float(elen)) + 1.) / 2. # elen = 5000 # blen = 30268 # row * ny // 2 - 2 * blen - 2 * elen = 658552 # len(temp_a2) = 729088 - temp_a2 = np.concatenate((np.ones(blen, dtype=np.float32), + temp_a2 = np.concatenate((np.ones(blen, dtype=np.float64), temp_a1.copy(), np.zeros(row * ny // 2 - 2 * blen - 2 * elen, - dtype=np.float32), + dtype=np.float64), temp_a1[::-1].copy(), - np.ones(blen, dtype=np.float32))) + np.ones(blen, dtype=np.float64))) roll_a2 = np.roll(temp_a2, -1) aa = np.concatenate((temp_a2, roll_a2[::-1])) From d8ae79c85867c450ef9d20b59fb98b4be1b5c548 Mon Sep 17 00:00:00 2001 From: Jane Morrison Date: Wed, 29 May 2024 12:42:13 -0700 Subject: [PATCH 063/687] JP-3566 Move pixel replace before resample/cube build (#8409) Co-authored-by: Jonathan Eisenhamer Co-authored-by: Howard Bushouse --- CHANGES.rst | 6 ++ docs/jwst/pipeline/calwebb_spec3.rst | 2 + jwst/pipeline/calwebb_spec2.py | 42 +++++++----- jwst/pipeline/calwebb_spec3.py | 49 ++++++++++++- jwst/pipeline/calwebb_tso3.py | 10 ++- jwst/pixel_replace/pixel_replace.py | 5 +- jwst/pixel_replace/pixel_replace_step.py | 87 ++++++++++++++++-------- jwst/regtest/test_miri_lrs_slit_spec2.py | 4 +- jwst/regtest/test_miri_lrs_slitless.py | 5 +- jwst/regtest/test_nirspec_fs_spec2.py | 47 ++++++++++++- jwst/regtest/test_nirspec_ifu_spec3.py | 48 +++++++++++++ 11 files changed, 251 insertions(+), 54 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index cf3a80a9f3..cf219d2d4c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -117,6 +117,12 @@ pipeline - Removed unused ``scale_detection`` argument from ``calwebb_tso3`` pipeline. [#8438] +pixel_replace +------------- + +- Moved pixel_replace in the calwebb_spec2 pipeline and added it to the calwebb_spec3 + pipeline. In both pipelines it is now executed immediately before resample_spec/cube_build. [#8409] + ramp_fitting ------------ diff --git a/docs/jwst/pipeline/calwebb_spec3.rst b/docs/jwst/pipeline/calwebb_spec3.rst index 266852c7dc..8b387c480c 100644 --- a/docs/jwst/pipeline/calwebb_spec3.rst +++ b/docs/jwst/pipeline/calwebb_spec3.rst @@ -32,6 +32,8 @@ processed using the :ref:`calwebb_tso3 ` pipeline. +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ | :ref:`outlier_detection ` | |c| | |c| | |c| | |c| | |c| | | | | +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ +| :ref:`pixel_replace ` | |c| | |c| | |c| | |c| | |c| | | |c| | |c| | ++-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ | :ref:`resample_spec ` | |c| | |c| | | |c| | | | | | +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ | :ref:`cube_build ` | | | |c| | | |c| | | | | diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 85ddabec94..aa88fcb87e 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -51,7 +51,7 @@ class Spec2Pipeline(Pipeline): assign_wcs, NIRSpec MSA bad shutter flagging, nsclean, background subtraction, NIRSpec MSA imprint subtraction, 2-D subwindow extraction, flat field, source type decision, straylight, fringe, residual_fringe, pathloss, - barshadow, photom, resample_spec, cube_build, and extract_1d. + barshadow, photom, pixel_replace, resample_spec, cube_build, and extract_1d. """ class_alias = "calwebb_spec2" @@ -110,7 +110,7 @@ def process(self, data): asn = self.load_as_level2_asn(data) if len(asn['products']) > 1 and self.output_file is not None: self.log.warning('Multiple products in input association. Output file name will be ignored.') - self.output_file = None + self.output_file = None # Each exposure is a product in the association. # Process each exposure. Delay reporting failures until the end. @@ -141,7 +141,7 @@ def process(self, data): else: if result is not None: results.append(result) - self.output_file = None #handles multiple products in the association + self.output_file = None # handles multiple products in the association if len(failures) > 0 and self.fail_on_exception: raise RuntimeError('\n'.join(failures)) @@ -217,9 +217,7 @@ def process_exposure_product( # Decide on what steps can actually be accomplished based on the # provided input. - self._step_verification(exp_type, science, members_by_type, multi_int) - # Start processing the individual steps. # `assign_wcs` is the critical step. Without it, processing cannot proceed. assign_wcs_exception = None @@ -290,29 +288,46 @@ def process_exposure_product( if exp_type in ['NRS_FIXEDSLIT', 'NRS_MSASPEC', 'MIR_LRS-FIXEDSLIT'] \ and not isinstance(calibrated, datamodels.CubeModel): - # Call the resample_spec step for 2D slit data + # Call pixel replace, followed by resample_spec for 2D slit data resampled = calibrated.copy() + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + resampled = self.pixel_replace(resampled) resampled = self.resample_spec(resampled) elif is_nrs_slit_linelamp(calibrated): - # Call resample_spec for NRS 2D line lamp slit data + # Call pixel_replace followed by resample_spec for NRS 2D line lamp slit data resampled = calibrated.copy() + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + resampled = self.pixel_replace(resampled) resampled = self.resample_spec(resampled) elif (exp_type in ['MIR_MRS', 'NRS_IFU']) or is_nrs_ifu_linelamp(calibrated): - # Call the cube_build step for IFU data; - # always create a single cube containing multiple + # First call pixel_replace then call cube_build step for IFU data. + # For cube_build always create a single cube containing multiple # wavelength bands resampled = calibrated.copy() + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + resampled = self.pixel_replace(resampled) resampled = self.cube_build(resampled) if not self.cube_build.skip: self.save_model(resampled[0], suffix='s3d') + elif exp_type in ['MIR_LRS-SLITLESS']: + resampled = calibrated.copy() + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + resampled = self.pixel_replace(resampled) else: - resampled = calibrated - + # will be run if set in parameter ref file or by user + resampled = calibrated.copy() + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + resampled = self.pixel_replace(resampled) # Extract a 1D spectrum from the 2D/3D data if exp_type in ['MIR_MRS', 'NRS_IFU'] and self.cube_build.skip: # Skip extract_1d for IFU modes where no cube was built @@ -491,8 +506,6 @@ def _process_grism(self, data): calibrated = self.barshadow(calibrated) calibrated = self.wfss_contam(calibrated) calibrated = self.photom(calibrated) - calibrated = self.pixel_replace(calibrated) - return calibrated def _process_nirspec_slits(self, data): @@ -509,7 +522,6 @@ def _process_nirspec_slits(self, data): calibrated = self.pathloss(calibrated) calibrated = self.barshadow(calibrated) calibrated = self.photom(calibrated) - calibrated = self.pixel_replace(calibrated) return calibrated @@ -525,7 +537,6 @@ def _process_niriss_soss(self, data): calibrated = self.fringe(calibrated) calibrated = self.pathloss(calibrated) calibrated = self.barshadow(calibrated) - calibrated = self.pixel_replace(calibrated) return calibrated @@ -539,6 +550,5 @@ def _process_common(self, data): calibrated = self.barshadow(calibrated) calibrated = self.photom(calibrated) calibrated = self.residual_fringe(calibrated) # only run on MIRI_MRS data - calibrated = self.pixel_replace(calibrated) return calibrated diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 71354b2d8c..429f43da4a 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -1,5 +1,6 @@ #!/usr/bin/env python from collections import defaultdict +from functools import wraps import os.path as op import numpy as np @@ -24,6 +25,7 @@ from ..combine_1d import combine_1d_step from ..photom import photom_step from ..spectral_leak import spectral_leak_step +from ..pixel_replace import pixel_replace_step __all__ = ['Spec3Pipeline'] @@ -60,6 +62,7 @@ class Spec3Pipeline(Pipeline): 'master_background': master_background_step.MasterBackgroundStep, 'mrs_imatch': mrs_imatch_step.MRSIMatchStep, 'outlier_detection': outlier_detection_step.OutlierDetectionStep, + 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'resample_spec': resample_spec_step.ResampleSpecStep, 'cube_build': cube_build_step.CubeBuildStep, 'extract_1d': extract_1d_step.Extract1dStep, @@ -95,6 +98,16 @@ def process(self, input): self.combine_1d.save_results = self.save_results self.spectral_leak.suffix = 'x1d' self.spectral_leak.save_results = self.save_results + self.pixel_replace.suffix = 'pixel_replace' + self.pixel_replace.output_use_model = True + self.pixel_replace.save_results = self.save_results + + # Overriding the Step.save_model method for the following steps. + # These steps save intermediate files, resulting in meta.filename + # being modified. This can affect the filenames of subsequent + # steps. + self.outlier_detection.save_model = invariant_filename(self.outlier_detection.save_model) + self.pixel_replace.save_model = invariant_filename(self.pixel_replace.save_model) # Retrieve the inputs: # could either be done via LoadAsAssociation and then manually @@ -228,7 +241,7 @@ def process(self, input): if exptype in ['MIR_MRS']: result = self.mrs_imatch(result) - # Call outlier detection + # Call outlier detection and pixel replacement if exptype not in SLITLESS_TYPES: # Update the asn table name to the level 3 instance so that # the downstream products have the correct table name since @@ -236,7 +249,10 @@ def process(self, input): for cal_array in result: cal_array.meta.asn.table_name = op.basename(input_models.asn_table_name) result = self.outlier_detection(result) - + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + print(result) + result = self.pixel_replace(result) # Resample time. Dependent on whether the data is IFU or not. resample_complete = None if exptype in IFU_EXPTYPES: @@ -255,12 +271,16 @@ def process(self, input): # Do 1-D spectral extraction if exptype in SLITLESS_TYPES: - # For slitless data, extract 1D spectra and then combine them + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE + result = self.pixel_replace(result) + # For slitless data, extract 1D spectra and then combine them if exptype in ['NIS_SOSS']: # For NIRISS SOSS, don't save the extract_1d results, # instead run photom on the extract_1d results and save # those instead. + self.extract_1d.save_results = False result = self.extract_1d(result) @@ -317,3 +337,26 @@ def _create_nrsfs_slit_name(self, source_models): slit_name = "-".join(slit_names) # append slit names using a dash separator return slit_name + + +# ######### +# Utilities +# ######### +def invariant_filename(save_model_func): + """Restore meta.filename after save_model""" + + @wraps(save_model_func) + def save_model(model, **kwargs): + try: + filename = model.meta.filename + except AttributeError: + filename = None + + result = save_model_func(model, **kwargs) + + if filename: + model.meta.filename = filename + + return result + + return save_model diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index 7420f839b7..3c38265b9b 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -14,7 +14,7 @@ from ..extract_1d import extract_1d_step from ..white_light import white_light_step from ..photom import photom_step - +from ..pixel_replace import pixel_replace_step from ..lib.pipe_utils import is_tso from astropy.io.fits import FITS_rec @@ -30,6 +30,7 @@ class Tso3Pipeline(Pipeline): * outlier_detection * tso_photometry + * pixel_replace * extract_1d * photom * white_light @@ -43,6 +44,7 @@ class Tso3Pipeline(Pipeline): step_defs = {'outlier_detection': outlier_detection_step.OutlierDetectionStep, 'tso_photometry': tso_photometry_step.TSOPhotometryStep, + 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'extract_1d': extract_1d_step.Extract1dStep, 'photom': photom_step.PhotomStep, 'white_light': white_light_step.WhiteLightStep @@ -153,6 +155,7 @@ def process(self, input): # Working with spectroscopic TSO data; # define output for x1d (level 3) products + x1d_result = datamodels.MultiSpecModel() x1d_result.update(input_models[0], only="PRIMARY") x1d_result.int_times = FITS_rec.from_columns(input_models[0].int_times.columns, @@ -164,6 +167,10 @@ def process(self, input): # For each exposure in the TSO... for cube in input_models: + # interpolate pixels that have a NaN value or are flagged + # as DO_NOT_USE or NON_SCIENCE. + cube = self.pixel_replace(cube) + state = cube.meta.cal_step.pixel_replace # Process spectroscopic TSO data # extract 1D self.log.info("Extracting 1-D spectra ...") @@ -190,6 +197,7 @@ def process(self, input): x1d_result.meta.asn.table_name = op.basename(input) # Save the final x1d Multispec model + x1d_result.meta.cal_step.pixel_replace = state self.save_model(x1d_result, suffix='x1dints') # Done with all the inputs diff --git a/jwst/pixel_replace/pixel_replace.py b/jwst/pixel_replace/pixel_replace.py index 3411f949d5..bdd96f2bbd 100644 --- a/jwst/pixel_replace/pixel_replace.py +++ b/jwst/pixel_replace/pixel_replace.py @@ -71,7 +71,9 @@ def replace(self): to each 2D spectrum in input. """ # ImageModel inputs (MIR_LRS-FIXEDSLIT) - if isinstance(self.input, datamodels.ImageModel): + if (isinstance(self.input, datamodels.ImageModel) + or (isinstance(self.input, datamodels.SlitModel) + and self.input.data.ndim == 2)): self.output = self.algorithm(self.input) n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) log.info(f"Input model had {n_replaced} pixels replaced.") @@ -117,7 +119,6 @@ def replace(self): n_replaced = np.count_nonzero(trace_model.dq & self.FLUX_ESTIMATED) log.info(f"Input MRS frame had {n_replaced} pixels replaced in IFU slice {i+1}.") - trace_model.close() n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) diff --git a/jwst/pixel_replace/pixel_replace_step.py b/jwst/pixel_replace/pixel_replace_step.py index 8b6a74fca4..3efd7d2318 100644 --- a/jwst/pixel_replace/pixel_replace_step.py +++ b/jwst/pixel_replace/pixel_replace_step.py @@ -1,4 +1,5 @@ #! /usr/bin/env python +from functools import partial from ..stpipe import Step from .. import datamodels @@ -28,7 +29,7 @@ class PixelReplaceStep(Step): class_alias = "pixel_replace" spec = """ - algorithm = option("fit_profile", "mingrad", "N/A", default="fit_profile") + algorithm = option("fit_profile", "mingrad", "N/A", default="fit_profile") n_adjacent_cols = integer(default=3) # Number of adjacent columns to use in creation of profile skip = boolean(default=True) # Step must be turned on by parameter reference or user """ @@ -47,28 +48,14 @@ def process(self, input): it will be a model containing data arrays with estimated fluxes for any bad pixels, now flagged as TO-BE-DETERMINED (DQ bit 7?). """ - with datamodels.open(input) as input_model: - - # Make copy of input to prevent overwriting - result = input_model.copy() - # If more than one 2d spectrum exists in input, call replacement - # for each spectrum - NRS_FIXEDSLIT, WFSS - if isinstance(input_model, datamodels.MultiSlitModel): - self.log.debug('Input is a MultiSlitModel.') - # NRS_BRIGHTOBJ provides a SlitModel - elif isinstance(input_model, datamodels.SlitModel): - self.log.debug('Input is a SlitModel.') - # MIRI_LRS-FIXEDSLIT comes in ImageModel - any others? - elif isinstance(input_model, datamodels.ImageModel): - self.log.debug('Input is an ImageModel.') - elif isinstance(input_model, datamodels.IFUImageModel): - self.log.debug('Input is an IFUImageModel.') - # SOSS & LRS-SLITLESS have CubeModel, along with IFU modes WIP - elif isinstance(input_model, datamodels.CubeModel): - # It's a 3-D multi-integration model - self.log.debug('Input is a CubeModel for a multiple integration file.') + + if input_model.meta.model_type in ['MultiSlitModel', 'SlitModel', + 'ImageModel', 'IFUImageModel', 'CubeModel']: + self.log.debug('Input is a {input_model.meta.model_type}.') + elif isinstance(input_model, datamodels.ModelContainer): + self.log.debug('Input is a ModelContainer.') else: self.log.error(f'Input is of type {str(type(input_model))} for which') self.log.error('pixel_replace does not have an algorithm.\n') @@ -81,9 +68,55 @@ def process(self, input): 'n_adjacent_cols': self.n_adjacent_cols, } - replacement = PixelReplacement(result, **pars) - replacement.replace() - - self.record_step_status(replacement.output, 'pixel_replace', success=True) - return replacement.output - + # ___________________ + # calewbb_spec3 case + # ___________________ + if isinstance(input_model, datamodels.ModelContainer): + output_model = input_model + # Setup output path naming if associations are involved. + asn_id = None + try: + asn_id = input_model.meta.asn_table.asn_id + except (AttributeError, KeyError): + pass + if asn_id is None: + asn_id = self.search_attr('asn_id') + if asn_id is not None: + _make_output_path = self.search_attr( + '_make_output_path', parent_first=True + ) + self._make_output_path = partial( + _make_output_path, + asn_id=asn_id + ) + # Check models to confirm they are the correct type + for i, model in enumerate(output_model): + run_pixel_replace = True + if model.meta.model_type in ['MultiSlitModel', 'SlitModel', + 'ImageModel', 'IFUImageModel', 'CubeModel']: + self.log.debug('Input is a {model.meta.model_type}.') + else: + self.log.error(f'Input is of type {model.meta.model_type} for which') + self.log.error('pixel_replace does not have an algorithm.\n') + self.log.error('Pixel replacement will be skipped.') + model.meta.cal_step.pixel_replace = 'SKIPPED' + run_pixel_replace = False + + # all checks on model have passed. Now run pixel replacement + if run_pixel_replace: + replacement = PixelReplacement(model, **pars) + replacement.replace() + output_model[i] = replacement.output + self.record_step_status(output_model[i], 'pixel_replace', success=True) + return output_model + # ________________________________________ + # calewbb_spec2 case - single input model + # ________________________________________ + else: + # Make copy of input to prevent overwriting + result = input_model.copy() + replacement = PixelReplacement(result, **pars) + replacement.replace() + self.record_step_status(replacement.output, 'pixel_replace', success=True) + result = replacement.output + return result diff --git a/jwst/regtest/test_miri_lrs_slit_spec2.py b/jwst/regtest/test_miri_lrs_slit_spec2.py index 7340a0b0c5..bd9d2a3020 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec2.py +++ b/jwst/regtest/test_miri_lrs_slit_spec2.py @@ -25,6 +25,8 @@ def run_pipeline(rtdata_module): "--steps.srctype.save_results=true", "--steps.flat_field.save_results=true", "--steps.pathloss.save_results=true", + "--steps.pixel_replace.skip=false", + "--steps.pixel_replace.save_results=true", "--steps.bkg_subtract.save_combined_background=true" ] Step.from_cmdline(args) @@ -33,7 +35,7 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", [ "assign_wcs", "combinedbackground", "bsub", "srctype", "flat_field", "pathloss", - "cal", "s2d", "x1d"]) + "cal", "pixel_replace", "s2d", "x1d"]) def test_miri_lrs_slit_spec2(run_pipeline, fitsdiff_default_kwargs, suffix, rtdata_module): """Regression test of the calwebb_spec2 pipeline on MIRI LRS fixedslit data using along-slit-nod pattern for diff --git a/jwst/regtest/test_miri_lrs_slitless.py b/jwst/regtest/test_miri_lrs_slitless.py index de4733050f..66881c79db 100644 --- a/jwst/regtest/test_miri_lrs_slitless.py +++ b/jwst/regtest/test_miri_lrs_slitless.py @@ -46,6 +46,8 @@ def run_tso_spec2_pipeline(run_tso1_pipeline, rtdata_module): "--steps.assign_wcs.save_results=true", "--steps.srctype.save_results=true", "--steps.flat_field.save_results=true", + "--steps.pixel_replace.save_results=true", + "--steps.pixel_replace.skip=false" ] Step.from_cmdline(args) @@ -83,7 +85,8 @@ def test_miri_lrs_slitless_tso1(run_tso1_pipeline, rtdata_module, fitsdiff_defau @pytest.mark.bigdata -@pytest.mark.parametrize("step_suffix", ["assign_wcs", "srctype", "flat_field", "calints", "x1dints"]) +@pytest.mark.parametrize("step_suffix", ["assign_wcs", "srctype", "flat_field", + "pixel_replace", "calints", "x1dints"]) def test_miri_lrs_slitless_tso_spec2(run_tso_spec2_pipeline, rtdata_module, fitsdiff_default_kwargs, step_suffix): """Compare the output of a MIRI LRS slitless calwebb_tso-spec2 pipeline.""" diff --git a/jwst/regtest/test_nirspec_fs_spec2.py b/jwst/regtest/test_nirspec_fs_spec2.py index 9b03914620..c2094dbcb8 100644 --- a/jwst/regtest/test_nirspec_fs_spec2.py +++ b/jwst/regtest/test_nirspec_fs_spec2.py @@ -1,5 +1,4 @@ import os - import pytest import warnings @@ -43,7 +42,6 @@ def run_pipeline(rtdata_module, request): 3) S400A1 subarray exposure (1 slit extracted)""" rtdata = rtdata_module - for fle in asn_memberdict[request.param]: rtdata.get_data('nirspec/fs/' + fle) # Get the input exposure @@ -64,6 +62,25 @@ def run_pipeline(rtdata_module, request): return rtdata +@pytest.fixture(scope="module") +def run_pipeline_pixel_replace(rtdata_module): + """Run the calwebb_spec2 pipeline on NIRSpec Fixed-Slit exposures including pixel replacement. + The run_pipeline fixture saves the output for step before pixel_replace, so no need to retest. + We currently test the following types of inputs: + Full-frame exposure (all slits will be extracted) + """ + rtdata = rtdata_module + rtdata.get_asn('nirspec/fs/jw01309_prtest_spec2_00001_asn.json') + + # Run the calwebb_spec2 pipeline; save results from intermediate steps + args = ["calwebb_spec2", rtdata.input, + "--steps.pixel_replace.save_results=true", + "--steps.pixel_replace.skip=false"] + Step.from_cmdline(args) + + return rtdata + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", [ "assign_wcs", "nsclean", "extract_2d", "wavecorr", "flat_field", "pathloss", "srctype", @@ -86,6 +103,30 @@ def test_nirspec_fs_spec2(run_pipeline, fitsdiff_default_kwargs, suffix): assert diff.identical, diff.report() +@pytest.mark.bigdata +@pytest.mark.parametrize( + 'output', + ['jw013090_prtest_04102_00004_nrs2_pixel_replace.fits', + 'jw013090_prtest_04102_00004_nrs2_cal.fits', + 'jw013090_prtest_04102_00004_nrs2_s2d.fits', + 'jw013090_prtest_04102_00004_nrs2_x1d.fits'] +) +def test_nirspec_fs_spec2_pixel_replace(run_pipeline_pixel_replace, fitsdiff_default_kwargs, output): + """Regression test of the calwebb_spec2 pipeline on a + NIRSpec FS exposures.""" + + # Run the pipeline and retrieve outputs + rtdata = run_pipeline_pixel_replace + rtdata.output = output + + # Get the truth files + rtdata.get_truth(os.path.join("truth/test_nirspec_fs_spec2_pixel_replace", output)) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + @pytest.mark.bigdata def test_pathloss_corrpars(rtdata): """Test PathLossStep using correction_pars""" @@ -143,7 +184,7 @@ def test_pathloss_source_type(rtdata): @pytest.mark.bigdata def test_nirspec_fs_rateints_spec2(rtdata_module): """Run the calwebb_spec2 pipeline on a NIRSpec Fixed-Slit _rateints exposure. - This is a test that the pipeline completes when processing this + This is a test that the pipeline completes when processing this multi-integration input. """ rtdata = rtdata_module diff --git a/jwst/regtest/test_nirspec_ifu_spec3.py b/jwst/regtest/test_nirspec_ifu_spec3.py index a2584d4470..f604b2ed9f 100644 --- a/jwst/regtest/test_nirspec_ifu_spec3.py +++ b/jwst/regtest/test_nirspec_ifu_spec3.py @@ -6,6 +6,7 @@ # Define artifactory source and truth INPUT_PATH = 'nirspec/ifu' TRUTH_PATH = 'truth/test_nirspec_ifu' +TRUTH_PATH_PIXEL_REPLACE = 'truth/test_nirspec_ifu_pixel_replace' @pytest.fixture(scope='module') @@ -30,6 +31,27 @@ def run_spec3_multi(rtdata_module): return rtdata +@pytest.fixture(scope='module') +def run_spec3_multi_pixel_replace(rtdata_module): + """Run Spec3Pipeline with pixel replacement""" + rtdata = rtdata_module + + step_params = { + 'input_path': 'nirspec/ifu/jw01249-o005_20230622t074431_rp_spec3_00001_asn.json', + 'step': 'calwebb_spec3', + 'args': { + '--steps.pixel_replace.skip=false', + '--steps.pixel_replace.save_results=true', + '--steps.pixel_replace.algorithm=mingrad', + '--steps.cube_build.save_results=true', + '--steps.extract_1d.save_results=true', + } + } + + rtdata = rt.run_step_from_dict(rtdata, **step_params) + return rtdata + + @pytest.mark.slow @pytest.mark.bigdata @pytest.mark.parametrize( @@ -54,3 +76,29 @@ def test_spec3_multi(run_spec3_multi, fitsdiff_default_kwargs, output): truth_path=TRUTH_PATH, is_suffix=False ) + + +@pytest.mark.slow +@pytest.mark.bigdata +@pytest.mark.parametrize( + 'output', + [ + 'jw01249005001_03101_00001_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00001_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00002_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00002_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00003_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00003_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00004_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00004_nrs2_o005_pixel_replace.fits', + 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_s3d.fits', + 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_x1d.fits', + ] +) +def test_spec3_multi_pixel_replace(run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output): + """Regression test matching output files""" + rt.is_like_truth( + run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output, + truth_path=TRUTH_PATH_PIXEL_REPLACE, + is_suffix=False + ) From 51d5ba53baa3fd9f7bc2b9de87faa9b3cccf91d7 Mon Sep 17 00:00:00 2001 From: David Law Date: Thu, 30 May 2024 11:46:45 -0400 Subject: [PATCH 064/687] JP-3637: Add error propagation during IFU annular background subtraction (#8515) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/extract_1d/ifu.py | 5 +++++ 2 files changed, 8 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index cf219d2d4c..f6895f6205 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -62,6 +62,9 @@ extract_1d - Add ``ifu_covar_scale`` parameter to help correct for IFU cube covariance. [#8457] +- Add propagation of uncertainty when annular backgrounds are subtracted + from source spectra during IFU spectral extraction. [#8515] + extract_2d ---------- diff --git a/jwst/extract_1d/ifu.py b/jwst/extract_1d/ifu.py index 50dd18b613..d2d7334509 100644 --- a/jwst/extract_1d/ifu.py +++ b/jwst/extract_1d/ifu.py @@ -757,6 +757,11 @@ def extract_ifu(input_model, source_type, extract_params): method=method, subpixels=subpixels) b_var_flat[k] = float(var_flat_table['aperture_sum'][0]) + # Propagate errors in background to the background-subtracted science spectrum + f_var_poisson[k] += b_var_poisson[k] * normalization * normalization + f_var_rnoise[k] += b_var_rnoise[k] * normalization * normalization + f_var_flat[k] += b_var_flat[k] * normalization * normalization + # Extended source data - background determined from sigma clipping if source_type == 'EXTENDED': bkg_data = data[k, :, :] From 7bae12b9edb69990872340347f8444229e676c38 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 30 May 2024 10:25:23 -0700 Subject: [PATCH 065/687] JP-3584: Use rolling window median for TSO outlier detection (#8473) --- CHANGES.rst | 4 + docs/jwst/outlier_detection/arguments.rst | 4 + docs/jwst/outlier_detection/index.rst | 3 +- ...tion.rst => outlier_detection_imaging.rst} | 41 ++--- .../outlier_detection_step.rst | 4 +- .../outlier_detection_tso.rst | 41 +++++ jwst/outlier_detection/outlier_detection.py | 155 +++++++++++----- .../outlier_detection_step.py | 22 ++- .../outlier_detection_tso.py | 172 ++++++++++++++++++ .../{test_medfilt.py => test_algorithms.py} | 15 +- .../tests/test_outlier_detection.py | 53 +++++- jwst/pipeline/calwebb_tso3.py | 50 ++--- 12 files changed, 441 insertions(+), 123 deletions(-) rename docs/jwst/outlier_detection/{outlier_detection.rst => outlier_detection_imaging.rst} (90%) create mode 100644 docs/jwst/outlier_detection/outlier_detection_tso.rst create mode 100644 jwst/outlier_detection/outlier_detection_tso.py rename jwst/outlier_detection/tests/{test_medfilt.py => test_algorithms.py} (68%) diff --git a/CHANGES.rst b/CHANGES.rst index f6895f6205..0c2ba7d030 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -105,6 +105,10 @@ outlier_detection - Added tests for changes made in #8464. [#8481] +- Added the option to use a rolling median instead of a simple median + to detect outliers in TSO data, with user-defined + rolling window width via the ``rolling_window_width`` parameter. [#8473] + photom ------ diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index a528218253..a2900bcbd6 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -57,6 +57,10 @@ that control the behavior of the processing: ``--backg`` (float, default=0.0) User-specified background value to apply to the median image. +``--rolling_window_width`` (int, default=25) + Number of integrations over which to take the median when using rolling-window + median for TSO observations. + ``--save_intermediate_results`` (boolean, default=False) Specifies whether or not to save any intermediate products created during step processing. diff --git a/docs/jwst/outlier_detection/index.rst b/docs/jwst/outlier_detection/index.rst index b32398e334..b2f76757a5 100644 --- a/docs/jwst/outlier_detection/index.rst +++ b/docs/jwst/outlier_detection/index.rst @@ -10,7 +10,8 @@ Outlier Detection main.rst arguments.rst outlier_detection_step.rst - outlier_detection.rst + outlier_detection_imaging.rst + outlier_detection_tso.rst outlier_detection_ifu.rst outlier_detection_spec.rst diff --git a/docs/jwst/outlier_detection/outlier_detection.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst similarity index 90% rename from docs/jwst/outlier_detection/outlier_detection.rst rename to docs/jwst/outlier_detection/outlier_detection_imaging.rst index 81fa2faa0a..c4cf5eceba 100644 --- a/docs/jwst/outlier_detection/outlier_detection.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -171,37 +171,19 @@ These changes result in a minimum amount of memory usage during processing at th expense of reading and writing the products from disk. +Outlier Detection for Coronagraphic Data +---------------------------------------- +Coronagraphic data is processed in a near-identical manner to direct imaging data, but +no resampling occurs. + + Outlier Detection for TSO data ------------------------------- -Time-series observations (TSO) result in input data stored as a 3D CubeModel -where each plane in the cube represents a separate integration without changing the -pointing. Normal imaging data benefit from combining all integrations into a +Normal imaging data benefit from combining all integrations into a single image. TSO data's value, however, comes from looking for variations from one integration to the next. The outlier detection algorithm, therefore, gets run with -a few variations to accomodate the nature of these 3D data. - -#. Input data is converted from a CubeModel (3D data array) to a ModelContainer - - - Each plane in the original input CubeModel gets copied to a separate model - in the ModelContainer - -#. The median image is created without resampling the input data - - - All integrations are aligned already, so no resampling or shifting needs to be performed - -#. A matched median gets created by combining the single median frame with the - noise model for each input integration. - -#. Perform statistical comparison between the matched median with each input integration. - -#. Update input data model DQ arrays with the mask of detected outliers. - - -.. note:: - - This same set of steps also gets used to perform outlier detection on - coronographic data, because it too is processed as 3D (per-integration) - cubes. +a few variations to accomodate the nature of these 3D data. See the +:ref:`TSO outlier detection ` documentation for details. Outlier Detection for IFU data @@ -214,4 +196,9 @@ See the :ref:`IFU outlier detection ` documentation for all the details. +Outlier Detection for Slit data +------------------------------- +See the :ref:`IFU outlier detection ` documentation for +details. + .. automodapi:: jwst.outlier_detection.outlier_detection diff --git a/docs/jwst/outlier_detection/outlier_detection_step.rst b/docs/jwst/outlier_detection/outlier_detection_step.rst index 3d3338f9ac..3f8b613021 100644 --- a/docs/jwst/outlier_detection/outlier_detection_step.rst +++ b/docs/jwst/outlier_detection/outlier_detection_step.rst @@ -35,8 +35,8 @@ input data: use :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetection` with resampling turned off as described in :ref:`outlier-detection-imaging` - **Time-Series Observations(TSO)**: both imaging and spectroscopic modes, use - :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetection` with resampling - turned off as described in :ref:`outlier-detection-imaging` + :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetectionTSO` + as described in :ref:`outlier-detection-tso` - **IFU observations**: use :py:class:`~jwst.outlier_detection.outlier_detection_ifu.OutlierDetectionIFU` as described in :ref:`outlier-detection-ifu` diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst new file mode 100644 index 0000000000..2f74611e32 --- /dev/null +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -0,0 +1,41 @@ +.. _outlier-detection-tso: + +Outlier Detection for TSO Data +============================== + +Time-series observations (TSO) result in input data stored as a 3D CubeModel +where each plane in the cube represents a separate integration without changing the +pointing. Normal imaging data benefit from combining all integrations into a +single image. TSO data's value, however, comes from looking for variations from one +integration to the next. The outlier detection algorithm, therefore, gets run with +a few variations to accomodate the nature of these 3D data. + +#. Input data is converted into a CubeModel (3D data array) if a ModelContainer + of 2D data arrays is provided. + +#. The median image is created without resampling the input data + + * The median image is created by combining all planes in the + CubeModel pixel-by-pixel using a rolling-median algorithm, in order + to flag outliers integration-by-integration but preserve real time variability. + * The ``rolling_window_width`` parameter specifies the number of integrations over + which to compute the median. The default is 25. If the number of integrations + is less than or equal to ``rolling_window_width``, a simple median is used instead. + * The ``nlow`` and ``nhigh`` parameters specify how many low and high values + to ignore when computing the median for any given pixel. + * The ``maskpt`` parameter sets the percentage of the weight image values to + use, and any pixel with a weight below this value gets flagged as "bad" and + ignored when the median is taken. + * The rolling-median CubeModel (3D data array) is written out to disk as `__median.fits` + if the ``save_intermediate_results`` parameter is set to True. + * All integrations are aligned already, so no resampling or shifting needs to be performed + +#. A matched median gets created by combining the single median frame with the + noise model for each input integration. + +#. A statistical comparison is performed between the matched median with each input integration. + +#. The input data model DQ arrays are updated with the mask of detected outliers. + + +.. automodapi:: jwst.outlier_detection.outlier_detection_tso \ No newline at end of file diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 2a026d8e36..a44a89433f 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -165,20 +165,13 @@ def do_detection(self): # Perform median combination on set of drizzled mosaics median_model.data = self.create_median(drizzled_models) - if pars['save_intermediate_results']: - if self.outlierpars.get('asn_id', None) is None: - suffix_to_remove = self.resample_suffix - else: - suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}" - median_model_output_path = self.make_output_path( - basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), - suffix='median') - median_model.save(median_model_output_path) - log.info(f"Saved model in {median_model_output_path}") + + if self.outlierpars['save_intermediate_results']: + self.save_median(median_model) else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them - if not pars['in_memory']: + if not self.outlierpars['in_memory']: for fn in drizzled_models._models: _remove_file(fn) @@ -203,6 +196,7 @@ def do_detection(self): for fn in blot_models._models: _remove_file(fn) del median_model, blot_models + def create_median(self, resampled_models): """Create a median image from the singly resampled images. @@ -218,35 +212,7 @@ def create_median(self, resampled_models): log.info("Computing median") - # Compute weight means without keeping datamodels for eacn input open - # Start by insuring that the ModelContainer does NOT open and keep each datamodel - ropen_orig = resampled_models._return_open - resampled_models._return_open = False # turn off auto-opening of models - # keep track of resulting computation for each input resampled datamodel - weight_thresholds = [] - # For each model, compute the bad-pixel threshold from the weight arrays - for resampled in resampled_models: - m = datamodel_open(resampled) - weight = m.wht - # necessary in order to assure that mask gets applied correctly - if hasattr(weight, '_mask'): - del weight._mask - mask_zero_weight = np.equal(weight, 0.) - mask_nans = np.isnan(weight) - # Combine the masks - weight_masked = np.ma.array(weight, mask=np.logical_or( - mask_zero_weight, mask_nans)) - # Sigma-clip the unmasked data - weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) - mean_weight = np.mean(weight_masked) - # Mask pixels where weight falls below maskpt percent - weight_threshold = mean_weight * maskpt - weight_thresholds.append(weight_threshold) - # close and delete the model, just to explicitly try to keep the memory as clean as possible - m.close() - del m - # Reset ModelContainer attribute to original value - resampled_models._return_open = ropen_orig + weight_thresholds = self.compute_weight_threshold_container(resampled_models, maskpt) # Now, set up buffered access to all input models resampled_models.set_buffer(1.0) # Set buffer at 1Mb @@ -282,6 +248,98 @@ def create_median(self, resampled_models): del resampled_sci, resampled_weight return median_image + + + def save_median(self, median_model): + ''' + Save median if requested by user + + Parameters + ---------- + median_model : ~jwst.datamodels.ImageModel + The median ImageModel or CubeModel to save + ''' + + if self.outlierpars.get('asn_id', None) is None: + suffix_to_remove = self.resample_suffix + else: + suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}" + median_model_output_path = self.make_output_path( + basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), + suffix='median') + median_model.save(median_model_output_path) + log.info(f"Saved model in {median_model_output_path}") + + + def compute_weight_threshold_container(self, resampled_models, maskpt): + ''' + Compute weight means without keeping datamodels for each input open + + Parameters + ---------- + resampled_models : ~jwst.datamodels.ModelContainer + The input data models. + + maskpt : float + The percentage of the mean weight to use as a threshold for masking. + + Returns + ------- + list + The weight thresholds for each integration. + ''' + + # Start by ensuring that the ModelContainer does NOT open and keep each datamodel + ropen_orig = resampled_models._return_open + resampled_models._return_open = True + # keep track of resulting computation for each input resampled datamodel + weight_thresholds = [] + # For each model, compute the bad-pixel threshold from the weight arrays + for resampled in resampled_models: + weight = resampled.wht + weight_threshold = self.compute_weight_threshold(weight, maskpt) + weight_thresholds.append(weight_threshold) + # close and delete the model, just to explicitly try to keep the memory as clean as possible + resampled.close() + del resampled + # Reset ModelContainer attribute to original value + resampled_models._return_open = ropen_orig + return weight_thresholds + + + def compute_weight_threshold(self, weight, maskpt): + ''' + Compute the weight threshold for a single image or cube. + + Parameters + ---------- + weight : numpy.ndarray + The weight array + + maskpt : float + The percentage of the mean weight to use as a threshold for masking. + + Returns + ------- + float + The weight threshold for this integration. + ''' + + # necessary in order to assure that mask gets applied correctly + if hasattr(weight, '_mask'): + del weight._mask + mask_zero_weight = np.equal(weight, 0.) + mask_nans = np.isnan(weight) + # Combine the masks + weight_masked = np.ma.array(weight, mask=np.logical_or( + mask_zero_weight, mask_nans)) + # Sigma-clip the unmasked data + weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) + mean_weight = np.mean(weight_masked) + # Mask pixels where weight falls below maskpt percent + weight_threshold = mean_weight * maskpt + return weight_threshold + def blot_median(self, median_model): """Blot resampled median image back to the detector images.""" @@ -364,10 +422,13 @@ def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, Parameters ---------- sci_image : ~jwst.datamodels.ImageModel - the science data + the science data. Can also accept a CubeModel, but only if + resample_data is False blot_image : ~jwst.datamodels.ImageModel - the blotted median image of the dithered science frames + the blotted median image of the dithered science frames. + Can also accept a CubeModel, but only if resample_data + is False snr : str Signal-to-noise ratio @@ -381,6 +442,14 @@ def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data : bool Boolean to indicate whether blot_image is created from resampled, dithered data or not + + Notes + ----- + Accepting a CubeModel for sci_image and blot_image with resample_data=True + appears to be a relatively simple extension, as the only thing that explicitly + relies on the dimensionality is the kernel, which could be generalized. + However, this is not currently needed, as CubeModels are only passed in for + TSO data, where resampling is always False. """ snr1, snr2 = [float(val) for val in snr.split()] scale1, scale2 = [float(val) for val in scale.split()] @@ -440,7 +509,7 @@ def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, # Report number (and percent) of new DO_NOT_USE pixels found count_outlier = np.count_nonzero(sci_image.dq & DO_NOT_USE) count_added = count_outlier - count_existing - percent_cr = count_added / (sci_image.shape[0] * sci_image.shape[1]) * 100 + percent_cr = count_added / sci_image.dq.size * 100 log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index b7798e1aa5..18140b1c69 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -10,11 +10,13 @@ from jwst.outlier_detection import outlier_detection from jwst.outlier_detection import outlier_detection_ifu from jwst.outlier_detection import outlier_detection_spec +from jwst.outlier_detection import outlier_detection_tso # Categorize all supported versions of outlier_detection outlier_registry = {'imaging': outlier_detection.OutlierDetection, 'ifu': outlier_detection_ifu.OutlierDetectionIFU, - 'slitspec': outlier_detection_spec.OutlierDetectionSpec + 'slitspec': outlier_detection_spec.OutlierDetectionSpec, + 'tso': outlier_detection_tso.OutlierDetectionTSO } # Categorize all supported modes @@ -61,6 +63,7 @@ class OutlierDetectionStep(Step): backg = float(default=0.0) kernel_size = string(default='7 7') threshold_percent = float(default=99.8) + rolling_window_width = integer(default=25) ifu_second_check = boolean(default=False) save_intermediate_results = boolean(default=False) resample_data = boolean(default=True) @@ -112,6 +115,7 @@ def process(self, input_data): 'backg': self.backg, 'kernel_size': self.kernel_size, 'threshold_percent': self.threshold_percent, + 'rolling_window_width': self.rolling_window_width, 'ifu_second_check': self.ifu_second_check, 'allowed_memory': self.allowed_memory, 'in_memory': self.in_memory, @@ -121,7 +125,7 @@ def process(self, input_data): 'make_output_path': self.make_output_path, } - # Add logic here to select which version of OutlierDetection + # Select which version of OutlierDetection # needs to be used depending on the input data if self.input_container: single_model = self.input_models[0] @@ -130,10 +134,13 @@ def process(self, input_data): exptype = single_model.meta.exposure.type self.check_input() - # check for TSO models first - if is_tso(single_model) or exptype in CORON_IMAGE_MODES: - # algorithm selected for TSO data (no resampling) - pars['resample_data'] = False # force resampling off... + if is_tso(single_model): + # force resampling off and use rolling median + pars['resample_data'] = False + detection_step = outlier_registry['tso'] + elif exptype in CORON_IMAGE_MODES: + # force resampling off but use same workflow as imaging + pars['resample_data'] = False detection_step = outlier_registry['imaging'] elif exptype in IMAGE_MODES: # imaging with resampling @@ -173,6 +180,7 @@ def process(self, input_data): self.input_models.meta.cal_step.outlier_detection = state return self.input_models + def check_input(self): """Use this method to determine whether input is valid or not.""" if self.input_container: @@ -198,7 +206,7 @@ def _check_input_container(self): def _check_input_cube(self): """Check to see whether input is the expected CubeModel object.""" ninputs = self.input_models.shape[0] - if not isinstance(self.input_models, datamodels.CubeModel): + if type(self.input_models) not in [datamodels.CubeModel, datamodels.SlitModel]: self.log.warning("Input is not the expected CubeModel") self.log.warning("Outlier detection step will be skipped") self.valid_input = False diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py new file mode 100644 index 0000000000..4333adbbb8 --- /dev/null +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -0,0 +1,172 @@ +import numpy as np +from .outlier_detection import OutlierDetection, flag_cr +from jwst.resample.resample_utils import build_mask + +from jwst import datamodels as dm +import logging +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +__all__ = ["OutlierDetectionTSO"] + + +class OutlierDetectionTSO(OutlierDetection): + """Class to flag outlier pixels in DQ of TSO data. Works similarly to + imaging outlier detection, but does not resample and uses a rolling median.""" + + def __init__(self, input_model: dm.SlitModel | dm.CubeModel | dm.ModelContainer, **pars): + """Initialize class for TSO data processing. + + Parameters + ---------- + input_models : ~jwst.datamodels.CubeModel or ~jwst.datamodels.ModelContainer + The input TSO data cube. if a ModelContainer is passed in, it is assumed + to be a list of ImageModels, and gets converted to a CubeModel. + + """ + super().__init__(input_model, **pars) + if isinstance(self.inputs, dm.ModelContainer): + raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") + + self.input_models = self.inputs + self.converted = False + + + def do_detection(self): + """Flag outlier pixels in DQ of input images.""" + weighted_cube = self.weight_no_resample() + + maskpt = self.outlierpars.get('maskpt', 0.7) + weight_threshold = self.compute_weight_threshold(weighted_cube.wht, maskpt) + + rolling_width = self.outlierpars.get("rolling_window_width", 25) + if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): + medians = self.compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) + + else: + medians = np.nanmedian(weighted_cube.data, axis=0) + # this is a 2-D array, need to repeat it into the time axis + # for consistent shape with rolling median case + medians = np.broadcast_to(medians, weighted_cube.shape) + + # turn this into a cube model for saving and passing to flag_cr + median_model = dm.CubeModel(data=medians) + + # Save median model if pars['save_intermediate_results'] is True + # this will be a CubeModel with rolling median values. + if self.outlierpars['save_intermediate_results']: + with dm.open(weighted_cube[0]) as dm0: + median_model.update(dm0) + self.save_median(median_model) + + # no need for blotting, resample is turned off for TSO + # go straight to outlier detection + log.info("Flagging outliers") + flag_cr(self.input_models, median_model, **self.outlierpars) + + + def weight_no_resample(self): + """ + give weights to model without resampling + + Notes + ----- + Prior to PR #8473, the `build_driz_weight` function was used to + create the weights for the input models for TSO data. However, that + function was simply returning a copy of the DQ array because the + var_noise was not being passed in by calwebb_tso3. As of PR #8473, + a cube model that includes the var_noise is passed into TSO + outlier detection, so `build_driz_weight` would weight the cube model + by the variance. Therefore `build_driz_weight` was removed in order to + preserve the original behavior. If it is determined later that exposure + time or inverse variance weighting should be used here, build_driz_weight + should be re-implemented. + """ + weighted_cube = self.input_models.copy() + dqmask = build_mask(self.input_models.dq, self.outlierpars['good_bits']) + weighted_cube.wht = dqmask.astype(np.float32) + return weighted_cube + + + def compute_rolling_median(self, model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: + ''' + Set bad and low-weight data to NaN, then compute the rolling median over the time axis. + + Parameters + ---------- + model : ~jwst.datamodels.CubeModel + The input cube model + + weight_threshold : np.ndarray + The weight thresholds for each integration. + + w : int + The window size for the rolling median. + + Returns + ------- + np.ndarray + The rolling median of the input data. Same dimensions as input. + ''' + + sci = model.data + weight = model.wht + badmask = np.less(weight, weight_threshold) + log.debug("Percentage of pixels with low weight: {}".format( + np.sum(badmask) / weight.size * 100)) + + # Fill resampled_sci array with nan's where mask values are True + sci[badmask] = np.nan + del badmask + + if w > sci.shape[0]: + raise ValueError("Window size must be less than the number of integrations.") + meds = moving_median_over_zeroth_axis(sci, w) + + del sci + return meds + + +def moving_median_over_zeroth_axis(x: np.ndarray, w: int) -> np.ndarray: + """ + Calculate the median of a moving window over the zeroth axis of an N-d array. + Algorithm works by expanding the array into an additional dimension + where the new axis has the same length as the window size. Each entry in that + axis is a copy of the original array shifted by 1 with respect to the previous + entry, such that the rolling median is simply the median over the new axis. + modified from https://stackoverflow.com/a/71154394, see link for more details. + + Parameters + ---------- + x : np.ndarray + The input array. + + w : int + The window size. + + Returns + ------- + np.ndarray + The rolling median of the input array. Same dimensions as input. + + Notes + ----- + Returns NaN for the first and last w//2 elements. + """ + if w <= 1: + raise ValueError("Rolling median window size must be greater than 1.") + shifted = np.zeros((x.shape[0] + w - 1, w, *x.shape[1:])) * np.nan + for idx in range(w - 1): + shifted[idx:-w + idx + 1, idx] = x + shifted[idx + 1:, idx + 1] = x + medians = np.median(shifted, axis=1) + for idx in range(w - 1): + medians[idx] = np.median(shifted[idx, :idx + 1]) + medians[-idx - 1] = np.median(shifted[-idx - 1, -idx - 1:]) + medians = medians[(w - 1) // 2:-(w - 1) // 2] + + # Fill in the edges with the nearest valid value + medians[:w // 2] = medians[w // 2] + medians[-w // 2:] = medians[-w // 2] + return medians diff --git a/jwst/outlier_detection/tests/test_medfilt.py b/jwst/outlier_detection/tests/test_algorithms.py similarity index 68% rename from jwst/outlier_detection/tests/test_medfilt.py rename to jwst/outlier_detection/tests/test_algorithms.py index 13cbd7822d..932b660266 100644 --- a/jwst/outlier_detection/tests/test_medfilt.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -5,7 +5,7 @@ import scipy.signal from jwst.outlier_detection.outlier_detection_ifu import medfilt - +from jwst.outlier_detection.outlier_detection_tso import moving_median_over_zeroth_axis @pytest.mark.parametrize("shape,kern_size", [ @@ -40,3 +40,16 @@ def test_medfilt_nan(arr, kern_size, expected): ) result = medfilt(arr, kern_size) np.testing.assert_allclose(result, expected) + + +def test_rolling_median(): + + time_axis = np.arange(10) + expected_time_axis = np.array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8]) + spatial_axis = np.ones((5, 5)) + arr = time_axis[:, np.newaxis, np.newaxis] * spatial_axis[np.newaxis, :, :] + + w = 3 + result = moving_median_over_zeroth_axis(arr, w) + expected = expected_time_axis[:, np.newaxis, np.newaxis] * spatial_axis[np.newaxis, :, :] + assert np.allclose(result, expected) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 00ea0502ad..7757ce61dc 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -148,7 +148,7 @@ def we_many_sci( tsci.data = np.random.normal(loc=background, size=shape, scale=sigma) # Add a source in the center tsci.data[7, 7] += signal - tsci.meta.filename = f"foo{i+2}_cal.fits" + tsci.meta.filename = f"foo{i + 2}_cal.fits" all_sci.append(tsci) return all_sci @@ -300,9 +300,9 @@ def test_outlier_step_image_weak_CR_dither(exptype, tmp_cwd): assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE -@pytest.mark.parametrize("exptype, tsovisit", exptypes_tso + exptypes_coron) -def test_outlier_step_image_weak_CR_nodither(exptype, tsovisit, tmp_cwd): - """Test whole step with an outlier for TSO & coronagraphic modes""" +@pytest.mark.parametrize("exptype, tsovisit", exptypes_coron) +def test_outlier_step_image_weak_CR_coron(exptype, tsovisit, tmp_cwd): + """Test whole step with an outlier for coronagraphic modes""" bkg = 1.5 sig = 0.02 container = ModelContainer( @@ -327,3 +327,48 @@ def test_outlier_step_image_weak_CR_nodither(exptype, tsovisit, tmp_cwd): # Verify CR is flagged assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE + + +@pytest.mark.parametrize("exptype, tsovisit", exptypes_tso) +def test_outlier_step_weak_cr_tso(exptype, tsovisit): + '''Test outlier detection with rolling median on time-varying source + This test fails if rolling_window_width is set to 100, i.e., take simple median + ''' + bkg = 1.5 + sig = 0.02 + rolling_window_width = 7 + numsci = 50 + signal = 7.0 + im = we_many_sci( + numsci=numsci, background=bkg, sigma=sig, signal=signal, exptype=exptype, tsovisit=tsovisit + ) + cube_data = np.array([i.data for i in im]) + cube_err = np.array([i.err for i in im]) + cube_dq = np.array([i.dq for i in im]) + cube_var_noise = np.array([i.var_rnoise for i in im]) + cube = datamodels.CubeModel(data=cube_data, err=cube_err, dq=cube_dq, var_noise=cube_var_noise) + + # update metadata of cube to match the first image + cube.meta = im[0].meta + + # Drop a weak CR on the science array + cr_timestep = 5 + cube.data[cr_timestep, 12, 12] = bkg + sig * 10 + + # make time variability that has larger total amplitude than + # the CR signal but deviations frame-by-frame are smaller + real_time_variability = signal * np.cos(np.linspace(0, np.pi, numsci)) + for i, model in enumerate(im): + model.data[7, 7] += real_time_variability[i] + model.err[7, 7] = np.sqrt(sig ** 2 + model.data[7, 7]) + + result = OutlierDetectionStep.call(cube, rolling_window_width=rolling_window_width) + + # Make sure nothing changed in SCI array + np.testing.assert_allclose(cube.data, result.data) + + # Verify source is not flagged + assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) + + # Verify CR is flagged + assert result.dq[cr_timestep, 12, 12] == OUTLIER_DO_NOT_USE diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index 3c38265b9b..91ad8b505d 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -5,7 +5,6 @@ from stdatamodels.jwst import datamodels -from jwst.datamodels import ModelContainer from ..stpipe import Pipeline @@ -78,7 +77,7 @@ def process(self, input): # Input may consist of multiple exposures, so loop over each of them input_exptype = None - for cube in input_models: + for i, cube in enumerate(input_models): if input_exptype is None: input_exptype = cube.meta.exposure.type @@ -87,51 +86,26 @@ def process(self, input): self.log.warning('Input data are 2D; skipping outlier_detection') break - # FIXME OutlierDetectionStep can accept a cube input and will - # unpack it as a container which looks almost identical to the below - # code. There are some differences: - # - the inputs here are SlitModels - # - outlier detection will generate drizzle weight during the unpack - # the below code does not - # - the code below takes a "bitwise or" whereas outlier detection - # assigns over the dq array (I think this is the same as I expect - # outlier detection to only add dq flags). - - # Perform regular outlier detection - - input_2dmodels = ModelContainer() - for i in range(cube.data.shape[0]): - # convert each plane of data cube into its own array - image = datamodels.ImageModel(data=cube.data[i], - err=cube.err[i], dq=cube.dq[i]) - image.update(cube) - image.meta.wcs = cube.meta.wcs - input_2dmodels.append(image) - self.log.info("Performing outlier detection on input images ...") - input_2dmodels = self.outlier_detection(input_2dmodels) - - # Transfer updated DQ values to original input observation - for i in range(cube.data.shape[0]): - # Update DQ arrays with those from outlier_detection step - cube.dq[i] = np.bitwise_or(cube.dq[i], input_2dmodels[i].dq) - - cube.meta.cal_step.outlier_detection = \ - input_2dmodels[0].meta.cal_step.outlier_detection + cube = self.outlier_detection(cube) - del input_2dmodels - - # Save crfints products - if input_models[0].meta.cal_step.outlier_detection == 'COMPLETE': - self.log.info("Saving crfints products with updated DQ arrays ...") - for cube in input_models: + # Save crfints products + if cube.meta.cal_step.outlier_detection == 'COMPLETE': + self.log.info("Saving crfints products with updated DQ arrays ...") # preserve output filename original_filename = cube.meta.filename + + # ensure output filename will not have duplicate asn_id + if "_"+input_models.meta.asn_table.asn_id in original_filename: + original_filename = original_filename.replace( + "_"+input_models.meta.asn_table.asn_id, '' + ) self.save_model( cube, output_file=original_filename, suffix='crfints', asn_id=input_models.meta.asn_table.asn_id ) cube.meta.filename = original_filename + input_models[i] = cube # Create final photometry results as a single output # regardless of how many input members there may be From 743160a1da5a82250deef89f4a93ac5cb1ea1ae7 Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Fri, 31 May 2024 08:14:43 -0400 Subject: [PATCH 066/687] JP-3624: Fix a source of errors in resampled spec WCS (#8511) Co-authored-by: Ned Molter --- CHANGES.rst | 4 ++++ jwst/resample/resample_spec.py | 4 ++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 0c2ba7d030..af89562bb6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -166,6 +166,10 @@ resample_spec - Change `fillval` parameter default from INDEF to NaN [#8488] +- Fix a bug resulting in large WCS errors in the resampled image's WCS + when the slit was closely aligned with the RA direction + sky. [#8511] + residual_fringe --------------- diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index fcd49a9302..71413ec9a3 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -408,7 +408,7 @@ def build_interpolated_output_wcs(self, refmodel=None): undist2sky1 = tan | native2celestial # Filter out RuntimeWarnings due to computed NaNs in the WCS with warnings.catch_warnings(): - warnings.simplefilter("ignore", RuntimeWarning) #was ignore. need to make more specific + warnings.simplefilter("ignore", RuntimeWarning) # was ignore. need to make more specific # at this center of slit find x,y tangent projection - x_tan, y_tan x_tan, y_tan = undist2sky1.inverse(ra, dec) @@ -498,7 +498,7 @@ def build_interpolated_output_wcs(self, refmodel=None): # mapping.inverse uses pix_to_ytan.inverse. The auto definition # of mapping.inverse is to use the 2nd spatial coordinate, i.e. Dec. - swap_xy = np.isclose(pix_to_xtan.slope, 0, atol=1e-8) + swap_xy = abs(pix_to_xtan.slope) < abs(pix_to_ytan.slope) if swap_xy: # Account for vertical or horizontal dispersion on detector mapping.inverse = Mapping((2, 1) if spatial_axis else (1, 2)) From 1a62240b1179dcb46fcdd4887447989334895bdf Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Fri, 31 May 2024 17:27:52 -0400 Subject: [PATCH 067/687] JP-2922: Use new source_id syntax for level-3 products (#8442) --- CHANGES.rst | 23 ++++ docs/jwst/data_products/file_naming.rst | 17 ++- docs/jwst/data_products/msa_metadata.rst | 51 +++++-- docs/jwst/exp_to_source/main.rst | 4 +- docs/jwst/pipeline/calwebb_spec3.rst | 2 +- jwst/assign_wcs/nirspec.py | 126 ++++++++---------- jwst/assign_wcs/tests/test_nirspec.py | 20 ++- jwst/associations/lib/rules_level3_base.py | 2 +- jwst/exp_to_source/exp_to_source.py | 11 +- jwst/msaflagopen/tests/test_msa_open.py | 1 + .../outlier_detection_step.py | 1 - jwst/pipeline/calwebb_spec3.py | 90 +++++++------ jwst/regtest/test_niriss_wfss.py | 2 +- jwst/regtest/test_nirspec_fs_spec3.py | 4 +- jwst/regtest/test_nirspec_mos_spec2.py | 10 +- jwst/regtest/test_nirspec_mos_spec3.py | 10 +- jwst/resample/tests/test_resample_step.py | 1 + 17 files changed, 223 insertions(+), 152 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index af89562bb6..b9bdbf8fce 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -20,6 +20,10 @@ assign_wcs - Move the assigned source position for dedicated NIRSpec MOS background slits from the lower left corner of the slit to the middle of the slit. [#8461] +- Updated the routines that load NIRSpec MOS slit and source data from the MSA meta + data file to properly handle background and virtual slits, and assign appropriate + meta data to them for use downstream. [#8442] + associations ------------ @@ -29,6 +33,9 @@ associations - Match NIRSpec imprint observations to science exposures on mosaic tile location and dither pointing, ``MOSTILNO`` and ``DITHPTIN``. [#8410] +- Updated Level3 rules for new handling of NIRSpec MOS source_id formatting when + constructing output file names. [#8442] + dark_current ------------ @@ -45,6 +52,13 @@ documentation - Added documentation for NIRCam GRISM time series pointing offsets. [#8449] +exp_to_source +------------- + +- Modified slit sorting to use `source_name` as the key, rather than `source_id`, + in order to support changes in `source_id` handling for NIRSpec MOS exposures + that contain background and virtual slits. [#8442] + extract_1d ---------- @@ -103,6 +117,10 @@ outlier_detection finished, unless save_intermediate_results is True. This PR also addressed the _i2d files not being saved in the specified output directory. [#8464] +- Removed the setting of `self.skip = True` when the step gets skipped (due to + inappropriate inputs), so that the step still executes when called again + while processing a list of multiple sources. [#8442] + - Added tests for changes made in #8464. [#8481] - Added the option to use a rolling median instead of a simple median @@ -124,6 +142,11 @@ pipeline - Removed unused ``scale_detection`` argument from ``calwebb_tso3`` pipeline. [#8438] +- Updated the ``calwebb_spec3`` pipeline handling of NIRSpec MOS inputs, to + comply with the new scheme for source ("s"), background ("b"), and + virtual ("v") slits and the construction of output file names for each + type. [#8442] + pixel_replace ------------- diff --git a/docs/jwst/data_products/file_naming.rst b/docs/jwst/data_products/file_naming.rst index 0f8b6ca30b..c6a48e8a83 100644 --- a/docs/jwst/data_products/file_naming.rst +++ b/docs/jwst/data_products/file_naming.rst @@ -42,14 +42,14 @@ Just as for Stage 2, the suffix distinguishes the different file products of Sta The FITS file naming scheme for Stage 3 "source-based" products is as follows, where items in parentheses are optional: - jw-_[<"t"TargID | "s"SourceID>](-<"epoch"X>)__(-)_(-).fits + jw-_[<"t"TargID | [<"s" | "b" | "v">]](-<"epoch"X>)__(-)_(-).fits where - ppppp: Program ID number - AC_ID: Association candidate ID - TargID: 3-digit Target ID (either TargID or SourceID must be present) - - SourceID: 5-digit Source ID + - SourceID: 9-digit Source ID - epochX: The text "epoch" followed by a single digit epoch number (optional) - instr: Science instrument name (e.g. 'nircam', 'miri') - optElements: A single or hyphen-separated list of optical elements (e.g. filter, grating) @@ -57,10 +57,12 @@ where - prodType: Product type identifier (e.g. 'i2d', 's3d', 'x1d') - ACT_ID: 2-digit activity ID (optional) -An example Stage 3 product FITS file name is: +Example Stage 3 product FITS file names are: jw87600-a3001_t001_niriss_f480m-nrm_amiavg.fits + jw54321-o066_s000123456_nirspec_f170lp_g235m_s2d.fits + Optional Components """"""""""""""""""" @@ -72,7 +74,14 @@ TargID vs SourceID For single-target modes, this is the target identifier as defined in the APT proposal. - For multi-object modes, such as NIRSpec MOS, this will be the slit ID for each object. + For multi-object modes, such as NIRSpec MOS, this will be the source ID for each object. + Note that the SourceID value is preceded by one of three characters "s", "b", or "v". + For most multi-source observation modes, such as Wide Field Slitless Spectroscopy (WFSS) and + NIRSpec Fixed-Slit (FS) spectroscopy, the "s" prefix is used to indicate that the data + correspond to a defined source. In some NIRSpec MOS observations, however, + there can also be "background" and "virtual" sources + (see :ref:`NIRSpec MSA slitlets `). + These cases use the "b" and "v" SourceID prefixes. epochX diff --git a/docs/jwst/data_products/msa_metadata.rst b/docs/jwst/data_products/msa_metadata.rst index edaa703b2f..b251402c80 100644 --- a/docs/jwst/data_products/msa_metadata.rst +++ b/docs/jwst/data_products/msa_metadata.rst @@ -2,7 +2,7 @@ MSA Metadata File: ``msa`` ^^^^^^^^^^^^^^^^^^^^^^^^^^ -While not containing any actual science data, the NIRSpec MSA metadata file is nonetheless +While it doesn't contain any actual science data, the NIRSpec MSA metadata file is nonetheless a crucial component of calibration processing for NIRSpec MOS exposures. It contains all the slitlet, shutter, and source configuration information that's needed by the :ref:`calwebb_spec2 ` pipeline to process a MOS exposure. @@ -128,6 +128,8 @@ where the values of `MSAMETID` and `PATT_NUM` in the science exposure match the values of `msa_metdata_id` and `dither_point_index`, respectively, are loaded. +Slitlets with a catalog source +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To better understand the ways in which these metadata are used, it's useful to reference a hypothetical example of data within a ``SHUTTER_INFO`` table. The table below shows the first 9 rows of a ``SHUTTER_INFO`` table for a MOS exposure @@ -186,13 +188,46 @@ within each slitlet should be considered the "primary" shutter. This is especial important for slitlets that contain extended sources and hence the `source_id` and `background` entries may indicate that the source is present in multiple shutters. -When a slitlet is found that has no shutters with a primary source (i.e. no shutters -having `primary_source` = "Y"), it is classified as a background slitlet and assigned -a source ID value that's greater than the maximum source ID assigned to other slitlets -(because such slitlets all have a source ID of zero in the MSA metadata coming from -the ground system). -These background slitlets can then be used in :ref:`master background ` -subtraction. +.. _msa_background_and_virtual_slits: + +Slitlets without a catalog source +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +It is possible for users to define slitlets that do not contain a source that's defined +in the MPT catalog when constructing an MSA configuration for an observation. These +kinds of slitlets can be used for two purposes. First, slitlets in which all of the +constituent shutters only contain background can be used to perform "master background" +subtraction during the :ref:`calwebb_spec2 ` pipeline processing +(see :ref:`NIRSpec Master Background ` for more details). +Second, slitlets made up of open shutters that may contain signal from some uncataloged +source in the field can also be created. These are referred to as "virtual" slitlets. + +Background and virtual slitlets have unique metadata in the shutter information table. +The primary defining piece of data is their assigned `source_id` value, because these +slitlets don't have a corresponding source listed in the source information table. +During creation with the MPT, all background slitlets are given a `source_id` of zero. +Virtual slitlets, on the other hand, are assigned *negative* `source_id` values, starting +with -1 and counting downwards from there (i.e. each virtual slit has a unique negative +`source_id` value). + +During the parsing of shutter information described in the previous section, when a +slitlet is found that has no shutters with a primary source (i.e. no shutters +having `primary_source` = "Y"), it is recognized as a background slitlet. In order to +be able to track multiple background slitlets through the remaining processing, they +are reassigned a new `source_id` value equal to their `slitlet_id`. Virtual slitlets, +meanwhile, retain their unique negative `source_id` values throughout processing. + +During Stage 3 processing with the :ref:`calwebb_spec3 ` pipeline, +unique source-based product file names will be created that distinguish data from the +three different kinds of slitlets: source, background, and virtual. As described in +:ref:`source-based file names `, the `SourceID` field of Stage 3 +file names consists of the 9-digit `source_id` number assigned to each MOS slitlet, +preceded by one of the three characters "s", "b", or "v", which identifies whether +the data are from a source, background, or virtual slitlet, respectively. Note that, +as described above, the `source_id` number used here for background slitlets is a +copy of their `slitlet_id` number. For example, a Stage 3 file name for data taken +from a virtual slitlet with `source_id` = -42 will look like: + + jw12345-o066_v000000042_nirspec_f170lp_g235m_x1d.fits The SOURCE_INFO Metadata diff --git a/docs/jwst/exp_to_source/main.rst b/docs/jwst/exp_to_source/main.rst index 14aaa0c8dd..0216274adf 100644 --- a/docs/jwst/exp_to_source/main.rst +++ b/docs/jwst/exp_to_source/main.rst @@ -83,8 +83,8 @@ on the meaning of each field in the file names. The FITS file naming scheme for the source-based "cal" products follows the standard Stage 3 syntax, such as:: - jw10006-o010_s00061_nirspec_f170lp-g235m_cal.fits + jw10006-o010_s000000061_nirspec_f170lp-g235m_cal.fits -where "s00061" in this example is the source ID. +where "s000000061" in this example is the source ID. See :ref:`source-based file names ` for more details on the meaning of each field in this type of file name. diff --git a/docs/jwst/pipeline/calwebb_spec3.rst b/docs/jwst/pipeline/calwebb_spec3.rst index 8b387c480c..4331170368 100644 --- a/docs/jwst/pipeline/calwebb_spec3.rst +++ b/docs/jwst/pipeline/calwebb_spec3.rst @@ -114,7 +114,7 @@ input exposure-based products. The source-based collections of data are saved in intermediate files, one per source/slit. The root names of the source-based files contain the source ID as an identifier and use the same "_cal" suffix as the input calibrated exposure files. An example source-based file name is -"jw00042-o001_s0002_niriss_gr150r_f150w_cal.fits", where "s0002" is the source id. +"jw00042-o001_s00000002_niriss_gr150r_f150w_cal.fits", where "s00000002" is the source id. The reorganized sets of data are sent to subsequent steps to process and combine all the data for one source at a time. diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index c6e3e8c2ef..e08834e824 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -405,20 +405,29 @@ def get_open_slits(input_model, reference_files=None, slit_y_range=[-.55, .55]): lamp_mode = lamp_mode.lower() else: lamp_mode = 'none' + + # MOS/MSA exposure requiring MSA metadata file if exp_type in ["nrs_msaspec", "nrs_autoflat"] or ((exp_type in ["nrs_lamp", "nrs_autowave"]) and (lamp_mode == "msaspec")): - msa_metadata_file, msa_metadata_id, dither_point = get_msa_metadata( - input_model, reference_files) - slits = get_open_msa_slits(msa_metadata_file, msa_metadata_id, dither_point, slit_y_range) + prog_id = input_model.meta.observation.program_number.lstrip("0") + msa_metadata_file, msa_metadata_id, dither_point = get_msa_metadata(input_model, reference_files) + slits = get_open_msa_slits(prog_id, msa_metadata_file, msa_metadata_id, dither_point, slit_y_range) + + # Fixed slits exposure (non-TSO) elif exp_type == "nrs_fixedslit": slits = get_open_fixed_slits(input_model, slit_y_range) + + # Bright object (TSO) exposure in S1600A1 fixed slit elif exp_type == "nrs_brightobj": slits = [Slit('S1600A1', 3, 0, 0, 0, slit_y_range[0], slit_y_range[1], 5, 1)] + + # Lamp exposure using fixed slits elif exp_type in ["nrs_lamp", "nrs_autowave"]: if lamp_mode in ['fixedslit', 'brightobj']: slits = get_open_fixed_slits(input_model, slit_y_range) else: raise ValueError("EXP_TYPE {0} is not supported".format(exp_type.upper())) + if reference_files is not None and slits: slits = validate_open_slits(input_model, slits, reference_files) log.info("Slits projected on detector {0}: {1}".format(input_model.meta.instrument.detector, @@ -510,27 +519,7 @@ def get_msa_metadata(input_model, reference_files): return msa_config, msa_metadata_id, dither_position -def _get_bkg_source_id(bkg_counter, shift_by): - """ - Compute a ``source_id`` for background slitlets. - - All background slitlets are assigned a source_id of 0. - A unique ``source_id`` is necessary to keep them separate in exp_to_source. - A counter is used to assign a unique ``source_id`` that's - greater than the max ID number of all defined sources. - - Parameters - ---------- - bkg_counter : int - The current value of the counter. - shift_by : int - The highest of all source_id values. - """ - - return bkg_counter + shift_by - - -def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, +def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, slit_y_range=[-.55, .55]): """ Return the opened MOS slitlets. @@ -555,10 +544,10 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, For example, something like: (12, 2, 4, 251, 22, 1, 'Y', 'OPEN', nan, nan, 1, 'N'), - column - Parameters ---------- + prog_id : str + The program number msa_file : str MSA meta data file name, FITS keyword ``MSAMETFL``. msa_metadata_id : int @@ -596,32 +585,14 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, log.error(message) raise MSAFileError(message) - # Get the configuration header from the _msa.fits file. The EXTNAME should be 'SHUTTER_INFO' - msa_conf = msa_file[('SHUTTER_INFO', 1)] - msa_source = msa_file[("SOURCE_INFO", 1)].data + # Get the shutter and source info tables from the _msa.fits file. + msa_conf = msa_file[('SHUTTER_INFO', 1)] # EXTNAME = 'SHUTTER_INFO' + msa_source = msa_file[("SOURCE_INFO", 1)].data # EXTNAME = 'SOURCE_INFO' # First we are going to filter the msa_file data on the msa_metadata_id # and dither_point_index. msa_data = [x for x in msa_conf.data if x['msa_metadata_id'] == msa_metadata_id and x['dither_point_index'] == dither_position] - - # Get all source_ids for slitlets with sources. - # These should not be used when assigning source_id to background slitlets. - source_ids = set([x[5] for x in msa_conf.data if x['msa_metadata_id'] == msa_metadata_id - and x['dither_point_index'] == dither_position]) - # All BKG shutters in the msa metafile have a source_id value of 0. - # Remove it from the list of source ids. - if 0 in source_ids: - source_ids.remove(0) - if source_ids: - max_source_id = max(source_ids) + 1 - else: - max_source_id = 0 - - # define a counter for "all background" slitlets. - # It will be used to assign a "source_id". - bkg_counter = 0 - log.debug(f'msa_data with msa_metadata_id = {msa_metadata_id} {msa_data}') log.info(f'Retrieving open MSA slitlets for msa_metadata_id = {msa_metadata_id} ' f'and dither_index = {dither_position}') @@ -629,25 +600,20 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, # Get the unique slitlet_ids slitlet_ids_unique = list(set([x['slitlet_id'] for x in msa_data])) - # SDP may assign a value of "-1" to ``slitlet_id`` - these need to be ignored. - # JP-436 - if -1 in slitlet_ids_unique: - slitlet_ids_unique.remove(-1) - # add a margin to the slit y limits margin = 0.5 # Now lets look at each unique slitlet id for slitlet_id in slitlet_ids_unique: - # Get the rows for the current slitlet_id + # Get the rows of shutter info for the current slitlet_id slitlets_sid = [x for x in msa_data if x['slitlet_id'] == slitlet_id] open_shutters = [x['shutter_column'] for x in slitlets_sid] # How many shutters in the slitlet are labeled as "main" or "primary"? n_main_shutter = len([s for s in slitlets_sid if s['primary_source'] == 'Y']) - # In the next part we need to calculate, find, determine 5 things: - # quadrant, xcen, ycen, ymin, ymax + # In the next part we need to calculate, find, or determine 5 things for each slit: + # quadrant, xcen, ycen, ymin, ymax # There are no main shutters: all are background if n_main_shutter == 0: @@ -662,19 +628,31 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, quadrant = slitlets_sid[0]['shutter_quadrant'] ycen = j xcen = slitlets_sid[0]['shutter_row'] # grab the first as they are all the same + shutter_id = xcen + (ycen - 1) * 365 # shutter numbers in MSA file are 1-indexed + + # Background slits all have source_id=0 in the msa_file, + # so assign a unique id based on the slitlet_id + source_id = slitlet_id + + # Hardwire the source info for background slits, because there's + # no source info for them in the msa_file source_xpos = 0.5 source_ypos = 0.5 - source_id = _get_bkg_source_id(bkg_counter, max_source_id) - log.info(f'Slitlet_id {slitlet_id} is background only; assigned source_id = {source_id}') - bkg_counter += 1 + source_name = f"{prog_id}_BKG{slitlet_id}" + source_alias = "BKG{}".format(slitlet_id) + stellarity = 0.0 + source_ra = 0.0 + source_dec = 0.0 + log.info(f'Slitlet {slitlet_id} is background only; assigned source_id={source_id}') - # There is 1 main shutter: phew, that makes it easier. + # There is 1 main shutter: this is a slit containing either a real or virtual source elif n_main_shutter == 1: xcen, ycen, quadrant, source_xpos, source_ypos = [ (s['shutter_row'], s['shutter_column'], s['shutter_quadrant'], s['estimated_source_in_shutter_x'], s['estimated_source_in_shutter_y']) for s in slitlets_sid if s['background'] == 'N'][0] + shutter_id = xcen + (ycen - 1) * 365 # shutter numbers in MSA file are 1-indexed # y-size jmin = min([s['shutter_column'] for s in slitlets_sid]) @@ -682,11 +660,25 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, j = ycen ymax = yhigh + margin + (jmax - j) * 1.15 ymin = -(-ylow + margin) + (jmin - j) * 1.15 - # get the source_id from the primary shutter entry + + # Get the source_id from the primary shutter entry for i in range(len(slitlets_sid)): if slitlets_sid[i]['primary_source'] == 'Y': source_id = slitlets_sid[i]['source_id'] + # Get source info for this slitlet; + # note that slits with a real source assigned have source_id > 0, + # while slits with source_id < 0 contain "virtual" sources + try: + source_name, source_alias, stellarity, source_ra, source_dec = [ + (s['source_name'], s['alias'], s['stellarity'], s['ra'], s['dec']) + for s in msa_source if s['source_id'] == source_id][0] + except IndexError: + log.warning("Could not retrieve source info from MSA file") + + if source_id < 0: + log.info(f'Slitlet {slitlet_id} contains virtual source, with source_id={source_id}') + # More than 1 main shutter: Not allowed! else: message = ("For slitlet_id = {}, metadata_id = {}, " @@ -697,20 +689,6 @@ def get_open_msa_slits(msa_file, msa_metadata_id, dither_position, msa_file.close() raise MSAFileError(message) - # subtract 1 because shutter numbers in the MSA reference file are 1-based. - shutter_id = xcen + (ycen - 1) * 365 - try: - source_name, source_alias, stellarity, source_ra, source_dec = [ - (s['source_name'], s['alias'], s['stellarity'], s['ra'], s['dec']) - for s in msa_source if s['source_id'] == source_id][0] - except IndexError: - # all background shutters - source_name = "background_{}".format(slitlet_id) - source_alias = "bkg_{}".format(slitlet_id) - stellarity = 0.0 - source_ra = 0.0 - source_dec = 0.0 - # Create the output list of tuples that contain the required # data for further computations """ diff --git a/jwst/assign_wcs/tests/test_nirspec.py b/jwst/assign_wcs/tests/test_nirspec.py index a517916835..b89ccdcc4b 100644 --- a/jwst/assign_wcs/tests/test_nirspec.py +++ b/jwst/assign_wcs/tests/test_nirspec.py @@ -70,6 +70,7 @@ def create_hdul(detector='NRS1'): phdu.header['detector'] = detector phdu.header['time-obs'] = '8:59:37' phdu.header['date-obs'] = '2016-09-05' + phdu.header['program'] = '1234' scihdu = fits.ImageHDU() scihdu.header['EXTNAME'] = "SCI" @@ -276,10 +277,11 @@ def test_msa_configuration_normal(): """ # Test 1: Reasonably normal as well + prog_id = '1234' msa_meta_id = 12 msaconfl = get_file_path('msa_configuration.fits') dither_position = 1 - slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, + slitlet_info = nirspec.get_open_msa_slits(prog_id, msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) ref_slit = trmodels.Slit(55, 9376, 1, 251, 26, -5.6, 1.0, 4, 1, '1111x', '95065_1', '2122', 0.13, -0.31716078999999997, -0.18092266) @@ -291,11 +293,12 @@ def test_msa_configuration_no_background(): Test the get_open_msa_slits function. """ # Test 2: Two main shutters, not allowed and should fail + prog_id = '1234' msa_meta_id = 13 msaconfl = get_file_path('msa_configuration.fits') dither_position = 1 with pytest.raises(MSAFileError): - nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, + nirspec.get_open_msa_slits(prog_id, msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) @@ -305,13 +308,14 @@ def test_msa_configuration_all_background(): """ # Test 3: No non-background, not acceptable. + prog_id = '1234' msa_meta_id = 14 msaconfl = get_file_path('msa_configuration.fits') dither_position = 1 - slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, + slitlet_info = nirspec.get_open_msa_slits(prog_id, msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) - ref_slit = trmodels.Slit(57, 8281, 1, 251, 23, -2.15, 2.15, 4, 0, '1x1', 'background_57', 'bkg_57', - 0, 0.0, 0.0) + ref_slit = trmodels.Slit(57, 8281, 1, 251, 23, -2.15, 2.15, 4, 57, '1x1', '1234_BKG57', 'BKG57', + 0.0, 0.0, 0.0) _compare_slits(slitlet_info[0], ref_slit) @@ -321,10 +325,11 @@ def test_msa_configuration_row_skipped(): """ # Test 4: One row is skipped, should be acceptable. + prog_id = '1234' msa_meta_id = 15 msaconfl = get_file_path('msa_configuration.fits') dither_position = 1 - slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, + slitlet_info = nirspec.get_open_msa_slits(prog_id, msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) ref_slit = trmodels.Slit(58, 8646, 1, 251, 24, -3.3, 5.6, 4, 1, '11x1011', '95065_1', '2122', 0.130, -0.31716078999999997, -0.18092266) @@ -336,10 +341,11 @@ def test_msa_configuration_multiple_returns(): Test the get_open_msa_slits function. """ # Test 4: One row is skipped, should be acceptable. + prog_id = '1234' msa_meta_id = 16 msaconfl = get_file_path('msa_configuration.fits') dither_position = 1 - slitlet_info = nirspec.get_open_msa_slits(msaconfl, msa_meta_id, dither_position, + slitlet_info = nirspec.get_open_msa_slits(prog_id, msaconfl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) ref_slit1 = trmodels.Slit(59, 8651, 1, 256, 24, -3.3, 5.6, 4, 1, '11x1011', '95065_1', '2122', 0.13000000000000003, -0.31716078999999997, -0.18092266) diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index f342e080b3..ce7c5c631b 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -583,7 +583,7 @@ def finalize(associations): # Define default product name filling format_product = FormatTemplate( key_formats={ - 'source_id': ['s{:05d}', 's{:s}'], + 'source_id': ['{:s}'], 'expspcin': ['{:0>2s}'], 'slit_name': ['{:s}'] } diff --git a/jwst/exp_to_source/exp_to_source.py b/jwst/exp_to_source/exp_to_source.py index b9540b1b16..baaaa1ce20 100644 --- a/jwst/exp_to_source/exp_to_source.py +++ b/jwst/exp_to_source/exp_to_source.py @@ -37,8 +37,15 @@ def exp_to_source(inputs): log.info(f'Reorganizing data from exposure {exposure.meta.filename}') for slit in exposure.slits: - log.debug(f'Copying source {slit.source_id}') - result_slit = result[str(slit.source_id)] + if slit.source_name is None: + # All MultiSlit data other than NIRSpec MOS get sorted by + # source_id (source_name is not populated) + key = slit.source_id + else: + # NIRSpec MOS slits get sorted by source_name + key = slit.source_name + log.debug(f'Copying source {key}') + result_slit = result[str(key)] result_slit.exposures.append(slit) # store values for later use (after merge_tree) # these values are incorrectly getting overwritten by diff --git a/jwst/msaflagopen/tests/test_msa_open.py b/jwst/msaflagopen/tests/test_msa_open.py index ca8611f248..50e6379984 100644 --- a/jwst/msaflagopen/tests/test_msa_open.py +++ b/jwst/msaflagopen/tests/test_msa_open.py @@ -158,6 +158,7 @@ def test_msaflagopen_step(): 'msa_metadata_id': 12} im.meta.observation = { + 'program_number': '1234', 'date': '2016-09-05', 'time': '8:59:37'} diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 18140b1c69..8add2f32a9 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -163,7 +163,6 @@ def process(self, input_data): model.meta.cal_step.outlier_detection = "SKIPPED" else: self.input_models.meta.cal_step.outlier_detection = "SKIPPED" - self.skip = True return self.input_models self.log.debug(f"Using {detection_step.__name__} class for outlier_detection") diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 429f43da4a..a5fcd59dc6 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -2,7 +2,6 @@ from collections import defaultdict from functools import wraps import os.path as op -import numpy as np from stdatamodels.jwst import datamodels @@ -182,54 +181,34 @@ def process(self, input): for name, model in multislit_to_container(source_models).items() ] - # Check for negative and large source_id values - if len(sources) > 99999: - self.log.critical("Data contain more than 100,000 sources;" - "filename does not support 6 digit source ids.") - raise Exception - - available_src_ids = set(np.arange(99999) + 1) - used_src_ids = set() - for src in sources: - src_id, model = src - src_id = int(src_id) - used_src_ids.add(src_id) - if 0 < src_id <= 99999: - available_src_ids.remove(src_id) - - hotfixed_sources = [] - # now find and reset bad source_id values - for src in sources: - src_id, model = src - src_id = int(src_id) - # Replace ids that aren't positive 5-digit integers - if src_id < 0 or src_id > 99999: - src_id_new = available_src_ids.pop() - self.log.info(f"Source ID {src_id} falls outside allowed range.") - self.log.info(f"Reassigning {src_id} to {str(src_id_new).zfill(5)}.") - # Replace source_id for each model in the SourceModelContainers - for contained_model in model: - contained_model.source_id = src_id_new - src_id = src_id_new - hotfixed_sources.append((str(src_id), model)) - - sources = hotfixed_sources - # Process each source for source in sources: # If each source is a SourceModelContainer, - # the output name needs to be updated with the source ID, and potentially - # also the slit name (for NIRSpec fixed-slit only). + # the output name needs to be updated based on the source ID, + # and potentially also the slit name (for NIRSpec fixed-slit only). if isinstance(source, tuple): source_id, result = source + + # NIRSpec fixed-slit data if result[0].meta.exposure.type == "NRS_FIXEDSLIT": + # Output file name is constructed using the source_id and the slit name slit_name = self._create_nrsfs_slit_name(result) - self.output_file = format_product( - output_file, source_id=source_id.lower(), slit_name=slit_name) + srcid = f's{source_id:>09s}' + self.output_file = format_product(output_file, source_id=srcid, slit_name=slit_name) + + # NIRSpec MOS/MSA data + elif result[0].meta.exposure.type == "NRS_MSASPEC": + # Construct the specially formatted source_id to use in the output file + # name that separates source, background, and virtual slits + srcid = self._create_nrsmos_source_id(result) + self.output_file = format_product(output_file, source_id=srcid) + self.log.debug(f"output_file = {self.output_file}") + else: - self.output_file = format_product( - output_file, source_id=source_id.lower()) + # All other types just use the source_id directly in the file name + srcid = f's{source_id:>09s}' + self.output_file = format_product(output_file, source_id=srcid) else: result = source @@ -338,6 +317,35 @@ def _create_nrsfs_slit_name(self, source_models): return slit_name + def _create_nrsmos_source_id(self, source_models): + """Create the complete source_id product field for NIRSpec MOS products. + + The source_id value gets a "s", "b", or "v" character prepended + to uniquely identify source, background, and virtual slits. + """ + + # Get the original source name and ID from the input models + source_name = source_models[0].source_name + source_id = source_models[0].source_id + + # MOS background sources have "BKG" in the source name + if "BKG" in source_name: + # prepend "b" to the source_id number and format to 9 chars + srcid = f'b{str(source_id):>09s}' + self.log.debug(f"Source {source_name} is a MOS background slitlet: ID={srcid}") + + # MOS virtual sources have a negative source_id value + elif source_id < 0: + # prepend "v" to the source_id number and remove the leading negative sign + srcid = f'v{str(source_id)[1:]:>09s}' + self.log.debug(f"Source {source_name} is a MOS virtual slitlet: ID={srcid}") + + # Regular MOS sources + else: + # prepend "s" to the source_id number and format to 9 chars + srcid = f's{str(source_id):>09s}' + + return srcid # ######### # Utilities @@ -359,4 +367,4 @@ def save_model(model, **kwargs): return result - return save_model + return save_model \ No newline at end of file diff --git a/jwst/regtest/test_niriss_wfss.py b/jwst/regtest/test_niriss_wfss.py index b723690a2f..d2ab05f4d4 100644 --- a/jwst/regtest/test_niriss_wfss.py +++ b/jwst/regtest/test_niriss_wfss.py @@ -74,7 +74,7 @@ def run_nis_wfss_spec3(run_nis_wfss_spec2, rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize('suffix', ['cal', 'x1d', 'c1d']) -@pytest.mark.parametrize('source_id', ['s00015', 's00104']) +@pytest.mark.parametrize('source_id', ['s000000015', 's000000104']) def test_nis_wfss_spec3(run_nis_wfss_spec3, rtdata_module, suffix, source_id, fitsdiff_default_kwargs): """Regression test of the calwebb_spec3 pipeline applied to NIRISS WFSS data""" rtdata = rtdata_module diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index 869d6c3949..fd6bb4e783 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -27,8 +27,8 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) -@pytest.mark.parametrize("source_id,slit_name", [("s00001","s200a2"), ("s00021","s200a1"), ("s00023","s400a1"), - ("s00024","s1600a1"), ("s00025","s200b1")]) +@pytest.mark.parametrize("source_id,slit_name", [("s000000001","s200a2"), ("s000000021","s200a1"), + ("s000000023","s400a1"), ("s000000024","s1600a1"), ("s000000025","s200b1")]) def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, suffix, source_id, slit_name): """Test spec3 pipeline on a set of NIRSpec FS exposures.""" rtdata = rtdata_module diff --git a/jwst/regtest/test_nirspec_mos_spec2.py b/jwst/regtest/test_nirspec_mos_spec2.py index 8bc6f909d6..48e9185fc2 100644 --- a/jwst/regtest/test_nirspec_mos_spec2.py +++ b/jwst/regtest/test_nirspec_mos_spec2.py @@ -12,10 +12,10 @@ def run_pipeline(rtdata_module): rtdata = rtdata_module # Get the MSA metadata file referenced in the input exposure - rtdata.get_data("nirspec/mos/jw01180025001_01_msa.fits") + rtdata.get_data("nirspec/mos/jw01345066001_01_msa.fits") # Get the input ASN file and exposures - rtdata.get_asn("nirspec/mos/jw01180-o025_20221129t204108_spec2_00037_asn.json") + rtdata.get_asn("nirspec/mos/jw01345-o066_20230831t181155_spec2_00010_asn.json") # Run the calwebb_spec2 pipeline; save results from intermediate steps args = ["calwebb_spec2", rtdata.input, @@ -41,11 +41,13 @@ def run_pipeline(rtdata_module): "srctype", "pathloss", "barshadow", "cal", "s2d", "x1d"]) def test_nirspec_mos_spec2(run_pipeline, fitsdiff_default_kwargs, suffix): """Regression test of the calwebb_spec2 pipeline on a - NIRSpec MOS exposure.""" + NIRSpec MOS exposure. Using an exposure that's part of a + 3-shutter nod sequence, so there are nodded exposures available + with which to do background subtraction.""" # Run the pipeline and retrieve outputs rtdata = run_pipeline - output = f"jw01180025001_05101_00001_nrs2_{suffix}.fits" + output = f"jw01345066001_05101_00003_nrs1_{suffix}.fits" rtdata.output = output # Get the truth files diff --git a/jwst/regtest/test_nirspec_mos_spec3.py b/jwst/regtest/test_nirspec_mos_spec3.py index 507eb5c1c5..4e54e30a11 100644 --- a/jwst/regtest/test_nirspec_mos_spec3.py +++ b/jwst/regtest/test_nirspec_mos_spec3.py @@ -11,7 +11,7 @@ def run_pipeline(rtdata_module): """Run calwebb_spec3 on NIRSpec MOS data.""" rtdata = rtdata_module - rtdata.get_asn("nirspec/mos/jw00626-o030_20191210t193826_spec3_001_asn.json") + rtdata.get_asn("nirspec/mos/jw01345-o066_20230831t181155_spec3_00002_asn.json") # Run the calwebb_spec3 pipeline on the association args = ["calwebb_spec3", rtdata.input] @@ -22,13 +22,15 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) -@pytest.mark.parametrize("source_id", ["s00000", "s00227", "s00279", "s00443", - "s00482", "s02315"]) +@pytest.mark.parametrize("source_id", ["b000000030", "b000000031", + "s000004385", "s000007380", + "v000000048", "v000000049", + "v000000053", "v000000056"]) def test_nirspec_mos_spec3(run_pipeline, suffix, source_id, fitsdiff_default_kwargs): """Check results of calwebb_spec3""" rtdata = run_pipeline - output = f"jw00626-o030_{source_id}_nirspec_f170lp-g235m_{suffix}.fits" + output = f"jw01345-o066_{source_id}_nirspec_f170lp-g235m_{suffix}.fits" rtdata.output = output rtdata.get_truth(f"truth/test_nirspec_mos_spec3/{output}") diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 39585c713b..fc9cd101b8 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -70,6 +70,7 @@ def nirspec_rate(): 'ysize': 416, 'ystart': 529} im.meta.observation = { + 'program_number': '1234', 'date': '2016-09-05', 'time': '8:59:37'} im.meta.exposure = { From 9b074a0e7bfdd2df1404a55abf879b982d28c211 Mon Sep 17 00:00:00 2001 From: Jonathan Eisenhamer Date: Tue, 4 Jun 2024 11:54:43 -0400 Subject: [PATCH 068/687] JP-3500 Create WFSS Pure-Parallel associations (#8528) --- CHANGES.rst | 2 + jwst/associations/lib/constraint.py | 42 ++++--- jwst/associations/lib/dms_base.py | 7 +- jwst/associations/lib/rules_level2_base.py | 93 ++++++++++------ jwst/associations/lib/rules_level2b.py | 104 ++++++++++++++++++ jwst/associations/lib/rules_level3_base.py | 22 +++- .../tests/data/pool_034_wfss_parallel.csv | 34 ++++++ jwst/associations/tests/test_constraints.py | 12 ++ jwst/regtest/test_associations_standards.py | 1 + 9 files changed, 265 insertions(+), 52 deletions(-) create mode 100755 jwst/associations/tests/data/pool_034_wfss_parallel.csv diff --git a/CHANGES.rst b/CHANGES.rst index b9bdbf8fce..c353bf0c74 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -36,6 +36,8 @@ associations - Updated Level3 rules for new handling of NIRSpec MOS source_id formatting when constructing output file names. [#8442] +- Create WFSS Pure-Parallel associations [#8528] + dark_current ------------ diff --git a/jwst/associations/lib/constraint.py b/jwst/associations/lib/constraint.py index 34193033e0..7ae0f34e45 100644 --- a/jwst/associations/lib/constraint.py +++ b/jwst/associations/lib/constraint.py @@ -108,6 +108,7 @@ def check_and_set(self, item): - List of `~jwst.associations.ProcessList`. """ self.matched = True + self.found_values.add(self.value) return self.matched, [] @property @@ -140,26 +141,33 @@ def copy(self): """Copy ourselves""" return deepcopy(self) - def get_all_attr(self, attribute: str): # -> list[tuple[SimpleConstraint, typing.Any]]: + def get_all_attr(self, attribute, name=None): """Return the specified attribute - This method is meant to be overridden by classes - that need to traverse a list of constraints. + This method exists solely to support `Constraint.get_all_attr`. + This obviates the need for class/method checking. Parameters ---------- attribute : str The attribute to retrieve + name : str or None + Only return attribute if the name of the current constraint + matches the requested named constraints. If None, always + return value. + Returns ------- [(self, value)] : [(SimpleConstraint, object)] The value of the attribute in a tuple. If there is no attribute, an empty tuple is returned. """ - value = getattr(self, attribute) - if value is not None: - return [(self, value)] + if name is None or name == self.name: + value = getattr(self, attribute, None) + if value is not None: + if not isinstance(value, (list, set)) or len(value): + return [(self, value)] return [] def restore(self): @@ -352,6 +360,7 @@ def check_and_set(self, item): if self.matched: if self.force_unique: self.value = source_value + self.found_values.add(self.value) # Determine reprocessing reprocess = [] @@ -769,17 +778,19 @@ def copy(self): """Copy ourselves""" return deepcopy(self) - def get_all_attr(self, attribute: str): # -> list[tuple[typing.Union[SimpleConstraint, Constraint], typing.Any]]: - """Return the specified attribute - - This method is meant to be overridden by classes - that need to traverse a list of constraints. + def get_all_attr(self, attribute, name=None): + """Return the specified attribute for specified constraints Parameters ---------- attribute : str The attribute to retrieve + name : str or None + Only return attribute if the name of the current constraint + matches the requested named constraints. If None, always + return value. + Returns ------- result : [(SimpleConstraint or Constraint, object)[,...]] @@ -792,11 +803,12 @@ def get_all_attr(self, attribute: str): # -> list[tuple[typing.Union[SimpleConst If the attribute is not found. """ result = [] - value = getattr(self, attribute) - if value is not None: - result = [(self, value)] + if name is None or name == self.name: + value = getattr(self, attribute, None) + if value is not None: + result = [(self, value)] for constraint in self.constraints: - result.extend(constraint.get_all_attr(attribute)) + result.extend(constraint.get_all_attr(attribute, name=name)) return result diff --git a/jwst/associations/lib/dms_base.py b/jwst/associations/lib/dms_base.py index bbd8053a66..56d8ed921f 100644 --- a/jwst/associations/lib/dms_base.py +++ b/jwst/associations/lib/dms_base.py @@ -802,7 +802,12 @@ def _get_target(self): The Level3 Product name representation of the target or source ID. """ - target_id = format_list(self.constraints['target'].found_values) + attrs = self.constraints.get_all_attr('found_values', name='target') + if attrs: + value = attrs[0][1] + else: + value = [] + target_id = format_list(value) target = 't{0:0>3s}'.format(str(target_id)) return target diff --git a/jwst/associations/lib/rules_level2_base.py b/jwst/associations/lib/rules_level2_base.py index fe07c1463a..8821b00da9 100644 --- a/jwst/associations/lib/rules_level2_base.py +++ b/jwst/associations/lib/rules_level2_base.py @@ -1021,14 +1021,27 @@ def __init__(self, exclude_exp_types=None): ) -class Constraint_Target(DMSAttrConstraint): +class Constraint_Target(Constraint): """Select on target id""" def __init__(self): - super(Constraint_Target, self).__init__( - name='target', - sources=['targetid'], - ) + constraints = [ + Constraint([ + DMSAttrConstraint( + name='acdirect', + sources=['asn_candidate'], + value=r"\[\('c\d{4}', 'direct_image'\)\]" + ), + SimpleConstraint( + name='target', + sources=lambda item: '000' + )]), + DMSAttrConstraint( + name='target', + sources=['targetid'], + ) + ] + super(Constraint_Target, self).__init__(constraints, reduce=Constraint.any) # --------------------------------------------- @@ -1148,32 +1161,6 @@ def add_catalog_members(self): ) science = sciences[0] - # Get the exposure sequence for the science. Then, find - # the direct image greater than but closest to this value. - closest = directs[0] # If the search fails, just use the first. - try: - expspcin = int(getattr_from_list(science.item, ['expspcin'], _EMPTY)[1]) - except KeyError: - # If exposure sequence cannot be determined, just fall through. - logger.debug('Science exposure %s has no EXPSPCIN defined.', science) - else: - min_diff = 9999 # Initialize to an invalid value. - for direct in directs: - try: - direct_expspcin = int(getattr_from_list( - direct.item, ['expspcin'], _EMPTY - )[1]) - except KeyError: - # Try the next one. - logger.debug('Direct image %s has no EXPSPCIN defined.', direct) - continue - diff = direct_expspcin - expspcin - if diff < min_diff and diff > 0: - min_diff = diff - closest = direct - - # Note the selected direct image. Used in `Asn_Lv2WFSS._get_opt_element` - self.direct_image = closest # Remove all direct images from the association. members = self.current_product['members'] @@ -1188,6 +1175,7 @@ def add_catalog_members(self): )) # Add the Level3 catalog, direct image, and segmentation map members + self.direct_image = self.find_closest_direct(science, directs) lv3_direct_image_root = DMS_Level3_Base._dms_product_name(self) members.append( Member({ @@ -1239,6 +1227,49 @@ def get_exposure_type(self, item, default='science'): return exp_type + @staticmethod + def find_closest_direct(science, directs): + """Find the direct image that is closest to the science + + Closeness is defined as number difference in the exposure sequence number, + as defined in the column EXPSPCIN. + + Parameters + ---------- + science : dict + The science member to compare against + + directs : [dict[,...]] + The available direct members + + Returns + ------- + closest : dict + The direct image that is the "closest" + """ + closest = directs[0] # If the search fails, just use the first. + try: + expspcin = int(getattr_from_list(science.item, ['expspcin'], _EMPTY)[1]) + except KeyError: + # If exposure sequence cannot be determined, just fall through. + logger.debug('Science exposure %s has no EXPSPCIN defined.', science) + else: + min_diff = 9999 # Initialize to an invalid value. + for direct in directs: + try: + direct_expspcin = int(getattr_from_list( + direct.item, ['expspcin'], _EMPTY + )[1]) + except KeyError: + # Try the next one. + logger.debug('Direct image %s has no EXPSPCIN defined.', direct) + continue + diff = direct_expspcin - expspcin + if diff < min_diff and diff > 0: + min_diff = diff + closest = direct + return closest + def _get_opt_element(self): """Get string representation of the optical elements diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index a6c2677ccd..f176a3ffc4 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -43,6 +43,7 @@ 'Asn_Lv2SpecTSO', 'Asn_Lv2WFSSNIS', 'Asn_Lv2WFSSNRC', + 'Asn_Lv2WFSSParallel', 'Asn_Lv2WFSC', ] @@ -1034,3 +1035,106 @@ def _init_hook(self, item): super(Asn_Lv2WFSC, self)._init_hook(item) self.data['asn_type'] = 'wfs-image2' + + +@RegistryMarker.rule +class Asn_Lv2WFSSParallel( + AsnMixin_Lv2WFSS, + AsnMixin_Lv2Spectral, +): + """Level 2b WFSS/GRISM associations for WFSS taken in pure-parallel mode + + Characteristics: + - Association type: ``spec2`` + - Pipeline: ``calwebb_spec2`` + - Multi-object science exposures + - Single Science exposure + - Require a source catalog from processing of the corresponding direct imagery. + + WFSS is executed different when taken as part of a pure-parallel proposal than when WFSS + is done as the primary. The differences are as follows. When primary, all components, the direct + image and the two GRISM exposures, are all executed within the same observation. When in parallel, + each component is taken as a separate observation. + These are always in associations of type DIRECT_IMAGE. + + Another difference is that there is no ``targetid`` assigned to the parallel exposures. However, since + WFSS parallels are very specific, there is not need to constrain on target. A default value is used + for the Level 3 product naming. + """ + + def __init__(self, *args, **kwargs): + + self.constraints = Constraint([ + DMSAttrConstraint( + name='acdirect', + sources=['asn_candidate'], + value=r"\[\('c\d{4}', 'direct_image'\)\]" + ), + Constraint([ + DMSAttrConstraint( + name='exp_type', + sources=['exp_type'], + value='nis_wfss|nrc_wfss', + ), + DMSAttrConstraint( + name='image_exp_type', + sources=['exp_type'], + value='nis_image|nrc_image', + force_reprocess=ListCategory.NONSCIENCE, + only_on_match=True, + ), + ], reduce=Constraint.any), + Constraint([ + SimpleConstraint( + value='science', + test=lambda value, item: self.get_exposure_type(item) != value, + force_unique=False, + ), + Constraint_Single_Science(self.has_science, self.get_exposure_type), + ], reduce=Constraint.any), + Constraint_Target(), + DMSAttrConstraint( + name='instrument', + sources=['instrume'], + ), + ]) + + super(Asn_Lv2WFSSParallel, self).__init__(*args, **kwargs) + + @staticmethod + def find_closest_direct(science, directs): + """Find the direct image that is closest to the science + + For pure-parallel WFSS, there is only ever one direct image. + Simply return that. + + Parameters + ---------- + science : dict + The science member to compare against + + directs : [dict[,...]] + The available direct members + + Returns + ------- + closest : dict + The direct image that is the "closest" + """ + return directs[0] + + def validate_candidates(self, member): + """Stub to always return True + + For this association, stub this to always return True + + Parameters + ---------- + member : Member + Member being added. Ignored. + + Returns + ------- + True + """ + return True diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index ce7c5c631b..756ca3e3a6 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -895,7 +895,7 @@ def __init__(self): ) -class Constraint_Target(DMSAttrConstraint): +class Constraint_Target(Constraint): """Select on target Parameters @@ -905,19 +905,31 @@ class Constraint_Target(DMSAttrConstraint): to as part of the target selection. """ def __init__(self, association=None): + constraints = [Constraint([ + DMSAttrConstraint( + name='acdirect', + sources=['asn_candidate'], + value=r"\[\('c\d{4}', 'direct_image'\)\]" + ), + SimpleConstraint( + name='target', + sources=lambda item: '000' + )] + )] if association is None: - super(Constraint_Target, self).__init__( + constraints.append(DMSAttrConstraint( name='target', sources=['targetid'], - ) + )) else: - super(Constraint_Target, self).__init__( + constraints.append(DMSAttrConstraint( name='target', sources=['targetid'], onlyif=lambda item: association.get_exposure_type(item) != 'background', force_reprocess=ListCategory.EXISTING, only_on_match=True, - ) + )) + super(Constraint_Target, self).__init__(constraints, reduce=Constraint.any) # ----------- diff --git a/jwst/associations/tests/data/pool_034_wfss_parallel.csv b/jwst/associations/tests/data/pool_034_wfss_parallel.csv new file mode 100755 index 0000000000..f02d9b11a3 --- /dev/null +++ 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+jw00025006001_07201_00001_nrcb4_uncal.fits|V00024002001P0002500607201|25|6|1|00025006001|7|PARALLEL_PURE|2|01|1|NRC_IMAGE|1|7|NIRCAM|NRCB4|SHORT|NULL|UNKNOWN|NULL|UNKNOWN|NIRCam Wide Field Slitless Spectroscopy|SCIENCE|1|1|1|1|1|B|F115W|CLEAR|NULL|NONE|NULL|1|NULL|NULL|1|NULL|NRCB4_FULL|2023_4|FULL|NULL|NULL|NULL|NULL|NULL|[('o006', 'OBSERVATION'), ('c1001', 'DIRECT_IMAGE')]|NULL|NULL|NULL|F|F|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL +jw00025006001_07201_00001_nrcblong_uncal.fits|V00024002001P0002500607201|25|6|1|00025006001|7|PARALLEL_PURE|2|01|1|NRC_WFSS|1|7|NIRCAM|NRCBLONG|LONG|NULL|UNKNOWN|NULL|UNKNOWN|NIRCam Wide Field Slitless Spectroscopy|SCIENCE|1|1|1|1|1|B|F356W|GRISMR|NULL|NONE|NULL|1|NULL|NULL|1|NULL|NRCB5_FULL|2023_4|FULL|NULL|NULL|NULL|NULL|NULL|[('o006', 'OBSERVATION'), ('c1001', 'DIRECT_IMAGE')]|NULL|NULL|NULL|F|F|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL diff --git a/jwst/associations/tests/test_constraints.py b/jwst/associations/tests/test_constraints.py index e38b27b35a..6e506d0b68 100644 --- a/jwst/associations/tests/test_constraints.py +++ b/jwst/associations/tests/test_constraints.py @@ -88,6 +88,18 @@ def test_constraint_get_all_attr(): assert set(result) == set(expected) +def test_constraint_get_all_attr_with_name(): + """Get attribute value of all constraints with a name""" + names = ['sc1', 'sc2'] + constraints = [ + SimpleConstraint(name=name) + for name in names + ] + c = Constraint(constraints, name='c1') + result = c.get_all_attr('name', name='sc1') + assert result == [(constraints[0], 'sc1')] + + def test_simpleconstraint_reprocess_match(): """Test options for reprocessing""" sc = SimpleConstraint( diff --git a/jwst/regtest/test_associations_standards.py b/jwst/regtest/test_associations_standards.py index 18a087c502..b8ba75c0b6 100644 --- a/jwst/regtest/test_associations_standards.py +++ b/jwst/regtest/test_associations_standards.py @@ -90,6 +90,7 @@ def __init__( MakePars('pool_030_mir_lrs_nods_bkg'), MakePars('pool_031_mir_lrs_nonod_bkg'), MakePars('pool_032_nircam_wfss'), + MakePars('pool_034_wfss_parallel'), ] From f3e13b19418d92b78b20c39920bc870598b5d12a Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Wed, 5 Jun 2024 09:10:51 -0400 Subject: [PATCH 069/687] Add a check for separation and tolerance in tweakreg (#8476) --- CHANGES.rst | 5 +++++ docs/jwst/tweakreg/README.rst | 6 ++++-- jwst/regtest/test_niriss_image.py | 4 ++-- jwst/tweakreg/tests/test_tweakreg.py | 21 ++++++++++++++++++++- jwst/tweakreg/tweakreg_step.py | 24 ++++++++++++++++++++++++ 5 files changed, 55 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c353bf0c74..4465bfd5de 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -217,6 +217,11 @@ tweakreg - Change code default to use IRAF StarFinder instead of DAO StarFinder [#8487] +- Added a check for ``(abs_)separation`` and ``(abs_)tolerance`` parameters + that ``separation`` > ``sqrt(2) * tolerance`` that will now log an error + message and skip ``tweakreg`` step when this condition is not satisfied and + source confusion is possible during catalog matching. [#8476] + wfss_contam ----------- diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index 5e707dbbb6..f24aa60f2d 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -307,7 +307,8 @@ The ``tweakreg`` step has the following optional arguments: * ``use2dhist``: A boolean indicating whether to use 2D histogram to find initial offset. (Default=True) -* ``separation``: Minimum object separation in arcsec. (Default=1.0) +* ``separation``: Minimum object separation in arcsec. It **must be** at least + ``sqrt(2)`` times larger than ``tolerance``. (Default=1.0) * ``tolerance``: Matching tolerance for ``xyxymatch`` in arcsec. (Default=0.7) @@ -374,7 +375,8 @@ Parameters used for absolute astrometry to a reference catalog. Otherwise the initial guess for the offsets will be set to zero (Default=True) -* ``abs_separation``: Minimum object separation in arcsec. (Default=1.0) +* ``abs_separation``: Minimum object separation in arcsec. It **must be** at + least ``sqrt(2)`` times larger than ``abs_tolerance``. (Default=1.0) * ``abs_tolerance``: Matching tolerance for ``xyxymatch`` in arcsec. (Default=0.7) diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index d117b3fd10..4cce41eda6 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -60,9 +60,9 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): "--abs_refcat='GAIADR3'", "--save_results=True", ] + + # run the test from the command line: result = Step.from_cmdline(args) - # Check that the step is skipped - assert result.skip # Check the status of the step is set correctly in the files. mc = datamodels.ModelContainer(rtdata.input) diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index c6f6e2b596..47b071c337 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -201,6 +201,25 @@ def test_tweakreg_step(example_input, with_shift): assert abs_delta < 1E-12 +@pytest.mark.parametrize("alignment_type", ['', 'abs_']) +def test_src_confusion_pars(example_input, alignment_type): + # assign images to different groups (so they are aligned to each other) + example_input[0].meta.group_id = 'a' + example_input[1].meta.group_id = 'b' + + # make the step with arguments that may cause source confusion in match + pars = { + f"{alignment_type}separation": 1.0, + f"{alignment_type}tolerance": 1.0, + } + step = tweakreg_step.TweakRegStep(**pars) + result = step(example_input) + + # check that step was skipped + for model in result: + assert model.meta.cal_step.tweakreg == 'SKIPPED' + + @pytest.fixture() def custom_catalog_path(tmp_path): fn = tmp_path / "custom_catalog.ecsv" @@ -286,7 +305,7 @@ def test_custom_catalog(custom_catalog_path, example_input, catfile, asn, meta, elif catfile == "invalid_catfile": pass - # figure out how many sources to expect for the model in group 'a' + # figure out how many sources to expect for the model in group 'a' n_custom_sources = N_EXAMPLE_SOURCES if custom: if catfile == "valid_catfile": diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 287128bcc9..6b9fef82b7 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -5,6 +5,7 @@ """ from os import path +import math from astropy import units as u from astropy.coordinates import SkyCoord @@ -23,6 +24,9 @@ from .tweakreg_catalog import make_tweakreg_catalog +_SQRT2 = math.sqrt(2.0) + + def _oxford_or_str_join(str_list): nelem = len(str_list) if not nelem: @@ -124,6 +128,26 @@ class TweakRegStep(Step): def process(self, input): images = ModelContainer(input) + if self.separation <= _SQRT2 * self.tolerance: + self.log.error( + "Parameter 'separation' must be larger than 'tolerance' by at " + "least a factor of sqrt(2) to avoid source confusion." + ) + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + self.log.warning("Skipping 'TweakRegStep' step.") + return input + + if self.abs_separation <= _SQRT2 * self.abs_tolerance: + self.log.error( + "Parameter 'abs_separation' must be larger than 'abs_tolerance' " + "by at least a factor of sqrt(2) to avoid source confusion." + ) + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + self.log.warning("Skipping 'TweakRegStep' step.") + return input + if len(images) == 0: raise ValueError("Input must contain at least one image model.") From ad586d3f36d03c55e6b9406c6e1b12f8e224cccf Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 5 Jun 2024 09:54:37 -0400 Subject: [PATCH 070/687] JP-2922: Minor fixes for MSA source names (#8533) --- CHANGES.rst | 9 +++++- jwst/assign_wcs/nirspec.py | 8 ++++- jwst/assign_wcs/tests/test_nirspec.py | 30 +++++++++++++++++++ .../master_background_mos_step.py | 2 +- jwst/master_background/nirspec_utils.py | 22 +++++++++++++- .../tests/test_nirspec_corrections.py | 15 +++++++++- 6 files changed, 81 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 4465bfd5de..82985270de 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -22,7 +22,7 @@ assign_wcs - Updated the routines that load NIRSpec MOS slit and source data from the MSA meta data file to properly handle background and virtual slits, and assign appropriate - meta data to them for use downstream. [#8442] + meta data to them for use downstream. [#8442, #8533] associations ------------ @@ -103,6 +103,13 @@ general - Increase minimum required scipy. [#8441] +master_background_mos +--------------------- + +- Updated check for NIRSpec MOS background slits to use new naming convention: + ``slit.source_name`` now contains the string "BKG" instead of + "background". [#8533] + outlier_detection ----------------- diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index e08834e824..3577a84d57 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -674,7 +674,13 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, (s['source_name'], s['alias'], s['stellarity'], s['ra'], s['dec']) for s in msa_source if s['source_id'] == source_id][0] except IndexError: - log.warning("Could not retrieve source info from MSA file") + source_name = f"{prog_id}_VRT{slitlet_id}" + source_alias = "VRT{}".format(slitlet_id) + stellarity = 0.0 + source_ra = 0.0 + source_dec = 0.0 + log.warning(f"Could not retrieve source info from MSA file; " + f"assigning virtual source_name={source_name}") if source_id < 0: log.info(f'Slitlet {slitlet_id} contains virtual source, with source_id={source_id}') diff --git a/jwst/assign_wcs/tests/test_nirspec.py b/jwst/assign_wcs/tests/test_nirspec.py index b89ccdcc4b..24a78bfab0 100644 --- a/jwst/assign_wcs/tests/test_nirspec.py +++ b/jwst/assign_wcs/tests/test_nirspec.py @@ -4,6 +4,7 @@ import functools from math import cos, sin import os.path +import shutil import pytest import numpy as np @@ -355,6 +356,35 @@ def test_msa_configuration_multiple_returns(): _compare_slits(slitlet_info[1], ref_slit2) +def test_msa_missing_source(tmp_path): + """ + Test the get_open_msa_slits function with missing source information. + """ + # modify an existing MSA file to remove source info + msaconfl = get_file_path('msa_configuration.fits') + bad_confl = str(tmp_path / 'bad_msa_configuration.fits') + shutil.copy(msaconfl, bad_confl) + + with fits.open(bad_confl) as msa_hdu_list: + source_table = table.Table(msa_hdu_list['SOURCE_INFO'].data) + source_table.remove_rows(slice(0, -1)) + msa_hdu_list['SOURCE_INFO'] = fits.table_to_hdu(source_table) + msa_hdu_list[3].name = 'SOURCE_INFO' + msa_hdu_list.writeto(bad_confl, overwrite=True) + + prog_id = '1234' + msa_meta_id = 12 + dither_position = 1 + + slitlet_info = nirspec.get_open_msa_slits( + prog_id, bad_confl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) + + # MSA slit: virtual source name assigned + ref_slit = trmodels.Slit(55, 9376, 1, 251, 26, -5.6, 1.0, 4, 1, '1111x', + '1234_VRT55', 'VRT55', 0.0, + -0.31716078999999997, -0.18092266, 0.0, 0.0) + _compare_slits(slitlet_info[0], ref_slit) + open_shutters = [[24], [23, 24], [22, 23, 25, 27], [22, 23, 25, 27, 28]] main_shutter = [24, 23, 25, 28] result = ["x", "x1", "110x01", "110101x"] diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 3c51c93a6f..252155a3c4 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -191,7 +191,7 @@ def _classify_slits(self, data): # count how many are background vs source. num_bkg = num_src = 0 for slit in data.slits: - if "background" in slit.source_name: + if nirspec_utils.is_background_msa_slit(slit): num_bkg += 1 else: num_src += 1 diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 66ccbcab23..77bfa3285c 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -101,7 +101,7 @@ def create_background_from_multislit(input_model): bkg_model.update(input_model) slits = [] for slit in input_model.slits: - if "background" in slit.source_name: + if is_background_msa_slit(slit): log.info(f'Using background slitlet {slit.source_name}') slits.append(slit) @@ -242,3 +242,23 @@ def correct_nrs_fs_bkg(input_model, primary_slit): input_model.data *= (pl_uniform / pl_point) return input_model + + +def is_background_msa_slit(slit): + """ + Check if an MSA slitlet is a background source. + + Parameters + ---------- + slit : `~jwst.datamodels.SlitModel` + The slit model to check. + + Returns + ------- + bool + True if the slit is background; False if it is not. + """ + if "BKG" in str(slit.source_name).upper(): + return True + else: + return False diff --git a/jwst/master_background/tests/test_nirspec_corrections.py b/jwst/master_background/tests/test_nirspec_corrections.py index af44f2f0c6..6e5a4f4276 100644 --- a/jwst/master_background/tests/test_nirspec_corrections.py +++ b/jwst/master_background/tests/test_nirspec_corrections.py @@ -2,11 +2,12 @@ Unit tests for master background NIRSpec corrections """ import numpy as np +import pytest from stdatamodels.jwst import datamodels from jwst.master_background.nirspec_utils import ( - correct_nrs_ifu_bkg, correct_nrs_fs_bkg + correct_nrs_ifu_bkg, correct_nrs_fs_bkg, is_background_msa_slit ) @@ -54,3 +55,15 @@ def test_fs_correction(): result = correct_nrs_fs_bkg(input, primary_slit=True) assert np.allclose(corrected, result.data, rtol=1.e-7) + + +@pytest.mark.parametrize('name,status', + [('BKG101', True), ('bkg101', True), + ('background_101', False), ('101', False), + (None, False)]) +def test_is_background(name, status): + """Test check for background slit.""" + slit = datamodels.SlitModel() + if name is not None: + slit.source_name = name + assert is_background_msa_slit(slit) == status From 8ca38256d0341e72bf2e9aff38c7dd6ee4f91926 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 5 Jun 2024 10:02:06 -0400 Subject: [PATCH 071/687] JP-3629: Add new non-header keywords in local pool generator (#8508) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/associations/mkpool.py | 18 +++++++++++++---- jwst/associations/tests/test_mkpool.py | 27 +++++++++++++++++++++++++- 3 files changed, 43 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 82985270de..50b2f6ecc5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -36,6 +36,9 @@ associations - Updated Level3 rules for new handling of NIRSpec MOS source_id formatting when constructing output file names. [#8442] +- Added default values for new non-header keywords (``MOSTILNO`` and ``DITHPTIN``) + to the default values in the ``asn_make_pool`` script. [#8508] + - Create WFSS Pure-Parallel associations [#8528] dark_current diff --git a/jwst/associations/mkpool.py b/jwst/associations/mkpool.py index 8cc7a6039f..4ab4676661 100644 --- a/jwst/associations/mkpool.py +++ b/jwst/associations/mkpool.py @@ -25,12 +25,16 @@ 'is_imprt': 'f', 'pntgtype': 'science', 'targetid': '1', + 'mostilno': '1', + 'dithptin': '1' } def mkpool(data, - asn_candidate=NON_HEADER_COLS['asn_candidate'], dms_note=NON_HEADER_COLS['dms_note'], - is_imprt=NON_HEADER_COLS['is_imprt'], pntgtype=NON_HEADER_COLS['pntgtype'], + asn_candidate=NON_HEADER_COLS['asn_candidate'], + dms_note=NON_HEADER_COLS['dms_note'], + is_imprt=NON_HEADER_COLS['is_imprt'], + pntgtype=NON_HEADER_COLS['pntgtype'], **kwargs): """Create an association pool from a list of FITS files. @@ -112,8 +116,14 @@ def mkpool(data, defaults = {param: 'null' for param in params} pool = AssociationPool(names=params, dtype=[object] * len(params)) - # Set default values for user-settable non-header parameters - non_header_params = {'dms_note': dms_note, 'is_imprt': is_imprt, 'pntgtype': pntgtype} + # Set non-header values from hard-coded defaults + non_header_params = NON_HEADER_COLS.copy() + + # Update default values for user-settable non-header parameters + non_header_params.update( + {'dms_note': dms_note, + 'is_imprt': is_imprt, + 'pntgtype': pntgtype}) # Setup for target id calculation targetid = 0 # Start off with no target id. diff --git a/jwst/associations/tests/test_mkpool.py b/jwst/associations/tests/test_mkpool.py index 563c082589..1dda46f2eb 100644 --- a/jwst/associations/tests/test_mkpool.py +++ b/jwst/associations/tests/test_mkpool.py @@ -9,7 +9,7 @@ from jwst.associations.tests import helpers from jwst.associations import AssociationPool -from jwst.associations.mkpool import from_cmdline, mkpool +from jwst.associations.mkpool import from_cmdline, mkpool, NON_HEADER_COLS # Optional column settings OPT_COLS = [('asn_candidate', [('a3001', 'coron')]), @@ -19,6 +19,7 @@ # Required column names REQUIRED_PARAMS = set(('program', 'filename')) +NON_HEADER_PARAMS = set(NON_HEADER_COLS.keys()) def test_hdu(exposures): @@ -29,6 +30,7 @@ def test_hdu(exposures): pool = mkpool(hdus) assert isinstance(pool, AssociationPool) assert REQUIRED_PARAMS.issubset(pool.colnames) + assert NON_HEADER_PARAMS.issubset(pool.colnames) assert len(pool) == len(exposures) @@ -40,6 +42,7 @@ def test_hdulist(exposures): pool = mkpool(hduls) assert isinstance(pool, AssociationPool) assert REQUIRED_PARAMS.issubset(pool.colnames) + assert NON_HEADER_PARAMS.issubset(pool.colnames) assert len(pool) == len(exposures) [h.close() for h in hduls] @@ -48,6 +51,7 @@ def test_mkpool(exposures): pool = mkpool(exposures) assert isinstance(pool, AssociationPool) assert REQUIRED_PARAMS.issubset(pool.colnames) + assert NON_HEADER_PARAMS.issubset(pool.colnames) assert len(pool) == len(exposures) filenames = [ filename @@ -68,6 +72,16 @@ def test_opt_cols_cmdline(mkpool_cmdline, opt_cols): _test_opt_cols(mkpool_cmdline, opt_cols) +def test_nonheader_cols(mkpool_with_args): + """Ensure that non-header defaults are properly set.""" + _test_nonheader_cols(mkpool_with_args) + + +def test_nonheader_cols_cmdline(mkpool_cmdline): + """Ensure that non-header defaults are properly set with the command line.""" + _test_nonheader_cols(mkpool_cmdline) + + # #################### # Fixtures & Utilities # #################### @@ -111,3 +125,14 @@ def _test_opt_cols(mkpool_with_args, opt_cols): if column == 'asn_candidate': expected = '[' + str(('o001', 'observation')) + ', ' + str(expected)[1:] assert mkpool_with_args[0][column] == expected + + +def _test_nonheader_cols(mkpool_with_args): + """Ensure that non-header defaults are properly set""" + opt_dict = dict(OPT_COLS) + for column, expected in NON_HEADER_COLS.items(): + if column in opt_dict: + expected = opt_dict[column] + if column == 'asn_candidate': + expected = '[' + str(('o001', 'observation')) + ', ' + str(expected)[1:] + assert mkpool_with_args[0][column] == expected From 4436ce7eb154b5908705e3a0d70540360fc34fea Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 5 Jun 2024 11:00:36 -0400 Subject: [PATCH 072/687] JP-3641: Fix error propagation for combine_1d (#8520) Co-authored-by: Howard Bushouse --- CHANGES.rst | 5 +++ jwst/combine_1d/combine1d.py | 11 ++++-- .../tests/{test_dq.py => test_dq_err.py} | 38 ++++++++++++++----- 3 files changed, 42 insertions(+), 12 deletions(-) rename jwst/combine_1d/tests/{test_dq.py => test_dq_err.py} (59%) diff --git a/CHANGES.rst b/CHANGES.rst index 50b2f6ecc5..753d5ed27d 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -41,6 +41,11 @@ associations - Create WFSS Pure-Parallel associations [#8528] +combine_1d +---------- + +- Fix weights for combining errors from 1D spectra. [#8520] + dark_current ------------ diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index 4155bb1e0e..c4a6021e9d 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -68,6 +68,7 @@ def __init__(self, ms, spec, exptime_key): self.unit_weight = False # may be reset below self.right_ascension = np.zeros_like(self.wavelength) self.declination = np.zeros_like(self.wavelength) + self.name = spec.name self.source_id = spec.source_id self.source_type = spec.source_type self.flux_unit = spec.spec_table.columns['flux'].unit @@ -204,7 +205,11 @@ def accumulate_sums(self, input_spectra): ninputs = 0 for in_spec in input_spectra: ninputs += 1 - log.info(f'Accumulating data from input spectrum {ninputs}') + if in_spec.name is not None: + slit_name = f'{ninputs}, slit {in_spec.name}' + else: + slit_name = ninputs + log.info(f'Accumulating data from input spectrum {slit_name}') # Get the pixel numbers in the output corresponding to the # wavelengths of the current input spectrum. out_pixel = self.wcs.invert(in_spec.right_ascension, @@ -263,8 +268,8 @@ def compute_combination(self): sum_weight = np.where(self.weight > 0., self.weight, 1.) self.surf_bright /= sum_weight self.flux /= sum_weight - self.flux_error = np.sqrt(self.flux_error / sum_weight) - self.sb_error = np.sqrt(self.sb_error / sum_weight) + self.flux_error = np.sqrt(self.flux_error) / sum_weight + self.sb_error = np.sqrt(self.sb_error) / sum_weight self.normalized = True def create_output_data(self): diff --git a/jwst/combine_1d/tests/test_dq.py b/jwst/combine_1d/tests/test_dq_err.py similarity index 59% rename from jwst/combine_1d/tests/test_dq.py rename to jwst/combine_1d/tests/test_dq_err.py index 87aa8a5c7c..310f3dad48 100644 --- a/jwst/combine_1d/tests/test_dq.py +++ b/jwst/combine_1d/tests/test_dq_err.py @@ -31,15 +31,35 @@ def test_dq(): assert np.isclose(result_dq.spec[0].spec_table['FLUX'][BAD_PIX], spec2.spec_table['FLUX'][BAD_PIX]) -def create_spec_model(npoints=10, flux=1e-9, wave_range=(11, 13)): +def test_err(): + """Test error propagation.""" + spec1 = create_spec_model(flux=1.0, error=0.1) + spec2 = create_spec_model(flux=1.0, error=0.2) + ms = datamodels.MultiSpecModel() + ms.meta.exposure.exposure_time = 1 + ms.spec.append(spec1) + ms.spec.append(spec2) + + result = Combine1dStep.call(ms) + + expected_error = np.sqrt(0.1**2 + 0.2**2) / 2 + assert np.allclose(result.spec[0].spec_table['FLUX'], 1.0) + assert np.allclose(result.spec[0].spec_table['ERROR'], expected_error) + assert np.allclose(result.spec[0].spec_table['SURF_BRIGHT'], 1.0) + assert np.allclose(result.spec[0].spec_table['SB_ERROR'], expected_error) + + +def create_spec_model(npoints=10, flux=1e-9, error=1e-10, wave_range=(11, 13)): """Create a SpecModel""" - flux = np.full(npoints, flux) wavelength = np.arange(*wave_range, step=(wave_range[1] - wave_range[0]) / npoints) - error = np.zeros(npoints) - surf_bright = np.zeros(npoints) - sb_error = np.zeros(npoints) - var_dummy = error.copy() + flux = np.full(npoints, flux) + error = np.full(npoints, error) + + surf_bright = np.full(npoints, flux) + sb_error = np.full(npoints, error) + + var = np.zeros(npoints) dq = np.zeros(npoints) background = np.zeros(npoints) berror = np.zeros(npoints) @@ -50,9 +70,9 @@ def create_spec_model(npoints=10, flux=1e-9, wave_range=(11, 13)): otab = np.array( list( zip( - wavelength, flux, error, var_dummy, var_dummy, var_dummy, - surf_bright, sb_error, var_dummy, var_dummy, var_dummy, - dq, background, berror, var_dummy, var_dummy, var_dummy, + wavelength, flux, error, var, var, var, + surf_bright, sb_error, var, var, var, + dq, background, berror, var, var, var, npixels ), ), dtype=spec_dtype From fbf14c3a7d37b10fc4279665ce0944a55701b5a8 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 5 Jun 2024 15:47:45 -0400 Subject: [PATCH 073/687] JP-3642: Fix aperture correction to variance arrays (#8530) Co-authored-by: Howard Bushouse --- CHANGES.rst | 2 ++ jwst/extract_1d/apply_apcorr.py | 32 +++++++++++++++++++------------- 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 753d5ed27d..8a8b13b683 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -89,6 +89,8 @@ extract_1d - Add propagation of uncertainty when annular backgrounds are subtracted from source spectra during IFU spectral extraction. [#8515] +- Fix error in application of aperture correction to variance arrays. [#8530] + extract_2d ---------- diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index 0d1de57293..d39ba45235 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -153,15 +153,17 @@ def apply(self, spec_table: fits.FITS_rec): Table of aperture corrections values from apcorr reference file. """ - cols_to_correct = ('flux', 'flux_error', 'flux_var_poisson', 'flux_var_rnoise', - 'flux_var_flat', 'surf_bright', 'sb_error', 'sb_var_poisson', - 'sb_var_rnoise', 'sb_var_flat') + flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') + var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', + 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') for row in spec_table: correction = self.apcorr_func(row['npixels'], row['wavelength']) - for col in cols_to_correct: + for col in flux_cols_to_correct: row[col] *= correction.item() + for col in var_cols_to_correct: + row[col] *= correction.item() * correction.item() class ApCorrPhase(ApCorrBase): @@ -234,9 +236,9 @@ def apply(self, spec_table: fits.FITS_rec): Table of aperture corrections values from apcorr reference file. """ - cols_to_correct = ('flux', 'flux_error', 'flux_var_poisson', 'flux_var_rnoise', - 'flux_var_flat', 'surf_bright', 'sb_error', 'sb_var_poisson', - 'sb_var_rnoise', 'sb_var_flat') + flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') + var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', + 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') for row in spec_table: try: @@ -244,9 +246,11 @@ def apply(self, spec_table: fits.FITS_rec): except ValueError: correction = None # Some input wavelengths might not be supported (especially at the ends of the range) - for col in cols_to_correct: - if correction: + if correction: + for col in flux_cols_to_correct: row[col] *= correction.item() + for col in var_cols_to_correct: + row[col] *= correction.item() * correction.item() class ApCorrRadial(ApCorrBase): @@ -293,14 +297,16 @@ def apply(self, spec_table: fits.FITS_rec): Table of aperture corrections values from apcorr reference file. """ - cols_to_correct = ('flux', 'flux_error', 'flux_var_poisson', 'flux_var_rnoise', - 'flux_var_flat', 'surf_bright', 'sb_error', 'sb_var_poisson', - 'sb_var_rnoise', 'sb_var_flat') + flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') + var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', + 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') for i, row in enumerate(spec_table): correction = self.apcorr_correction[i] - for col in cols_to_correct: + for col in flux_cols_to_correct: row[col] *= correction + for col in var_cols_to_correct: + row[col] *= correction * correction def match_wavelengths(self, wavelength_ifu): # given the ifu wavelength value - redefine the apcor func and radius to this wavelength From f2c9e93e873e9cce64588075183379f53d6c2f35 Mon Sep 17 00:00:00 2001 From: Ian Wong Date: Wed, 5 Jun 2024 20:11:32 -0400 Subject: [PATCH 074/687] JP-3643: Add background error propagation for non-IFU spectral extraction (#8532) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/extract_1d/extract1d.py | 5 +++++ 2 files changed, 8 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 8a8b13b683..aca9119d09 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -89,6 +89,9 @@ extract_1d - Add propagation of uncertainty when annular backgrounds are subtracted from source spectra during IFU spectral extraction. [#8515] +- Add propagation of background uncertainty when background is subtracted from + source spectra during non-IFU spectral extraction. [#8532] + - Fix error in application of aperture correction to variance arrays. [#8530] extract_2d diff --git a/jwst/extract_1d/extract1d.py b/jwst/extract_1d/extract1d.py index 979a3c00eb..f0c3a60d95 100644 --- a/jwst/extract_1d/extract1d.py +++ b/jwst/extract_1d/extract1d.py @@ -457,6 +457,11 @@ def _extract_src_flux(image, var_poisson, var_rnoise, var_flat, x, j, lam, srcli # subtract background per pixel: val -= bkg + # add background variance to variance in source extraction region + f_var_poisson += b_var_poisson + f_var_rnoise += b_var_rnoise + f_var_flat += b_var_flat + # scale per pixel values by pixel area included in extraction val *= area bkg *= area From e1b66cc4c8c8d2cafd2be4828de7de67184f56a6 Mon Sep 17 00:00:00 2001 From: Maria Date: Thu, 6 Jun 2024 09:11:23 -0400 Subject: [PATCH 075/687] JP-3513: Remove warning about inaccurate errors (#8258) Co-authored-by: Nadia Dencheva Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 ++++ jwst/resample/resample_spec_step.py | 5 ----- jwst/resample/resample_step.py | 5 ----- 3 files changed, 4 insertions(+), 10 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index aca9119d09..2169ae6685 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -546,10 +546,14 @@ resample - Removed any reference to the "tophat" kernel for resample step. [#8364] +- Removing unnecessary warning. Errors are propagated identically for + the 'exptime' and 'ivm' weight options. [#8258] + - Increased specificity of several warning filters. [#8320] - Changed deprecated ``stpipe.extern.configobj`` to ``astropy.extern.configobj``. [#8320] + residual_fringe --------------- diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index cf00b31692..fa705f5372 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -88,11 +88,6 @@ def process(self, input): kwargs['allowed_memory'] = self.allowed_memory kwargs['output'] = output - # Issue a warning about the use of exptime weighting - if self.wht_type == 'exptime': - self.log.warning("Use of EXPTIME weighting will result in incorrect") - self.log.warning("propagated errors in the resampled product") - # Call resampling self.drizpars = kwargs diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 970ac2f7ac..f19e3cfcb9 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -103,11 +103,6 @@ def process(self, input): kwargs['allowed_memory'] = self.allowed_memory - # Issue a warning about the use of exptime weighting - if self.wht_type == 'exptime': - self.log.warning("Use of EXPTIME weighting will result in incorrect") - self.log.warning("propagated errors in the resampled product") - # Custom output WCS parameters. # Modify get_drizpars if any of these get into reference files: kwargs['output_shape'] = self._check_list_pars( From b7e0b107d54ee411b252518e149bbf5501e10403 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 5 Jun 2024 18:47:37 -0400 Subject: [PATCH 076/687] remove last name_format usage in wfs_combine --- jwst/wfs_combine/wfs_combine_step.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/jwst/wfs_combine/wfs_combine_step.py b/jwst/wfs_combine/wfs_combine_step.py index baf9ee0620..7e0fa88dfe 100644 --- a/jwst/wfs_combine/wfs_combine_step.py +++ b/jwst/wfs_combine/wfs_combine_step.py @@ -25,11 +25,14 @@ class WfsCombineStep(Step): suffix = string(default="wfscmb") """ + def make_output_path(self, basepath, *args, **kwargs): + # bypass all stpipe filename formatting + return basepath + def process(self, input_table): self.suffix = 'wfscmb' self.output_use_model = True - self.name_format = False # Load the input ASN table asn_table = self.load_as_level3_asn(input_table) @@ -69,7 +72,8 @@ def process(self, input_table): output_model.meta.cal_step.wfs_combine = 'COMPLETE' output_model.meta.asn.pool_name = asn_table['asn_pool'] output_model.meta.asn.table_name = os.path.basename(input_table) - output_model.meta.filename = which_set['name'] + # format the filename here + output_model.meta.filename = which_set['name'].format(suffix=self.suffix) + self.output_ext output_container.append(output_model) From ddbc6a02796aa9776a7605f56288d9bace7e6a6d Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Fri, 7 Jun 2024 17:04:03 -0400 Subject: [PATCH 077/687] JP-3330: Add NIRSpec wavelength corrections to slit WCS (#8376) Co-authored-by: Melanie Clarke Co-authored-by: Howard Bushouse Co-authored-by: Nadia Dencheva --- CHANGES.rst | 23 ++++ docs/jwst/pathloss/description.rst | 4 + docs/jwst/wavecorr/description.rst | 22 ++-- jwst/flatfield/flat_field.py | 59 +-------- jwst/lib/tests/test_wcs_utils.py | 157 ++++++++++++++++++++++++ jwst/lib/wcs_utils.py | 21 ++-- jwst/pathloss/pathloss.py | 45 ++++--- jwst/wavecorr/tests/test_wavecorr.py | 145 ++++++++++++++++++---- jwst/wavecorr/wavecorr.py | 173 ++++++++++++++++++++------- 9 files changed, 503 insertions(+), 146 deletions(-) create mode 100644 jwst/lib/tests/test_wcs_utils.py diff --git a/CHANGES.rst b/CHANGES.rst index 2169ae6685..08c5e3fe30 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -108,6 +108,9 @@ flat_field - Update NIRSpec flatfield code for all modes to ensure SCI=ERR=NaN wherever the DO_NOT_USE flag is set in the DQ array. [#8463] +- Updated the NIRSpec flatfield code to use the new format of the ``wavecorr`` + wavelength zero-point corrections for point sources. [#8376] + general ------- @@ -116,6 +119,13 @@ general - Increase minimum required scipy. [#8441] +lib +--- + +- Updated the ``wcs_utils.get_wavelength`` to use the new format + of the ``wavecorr`` wavelength zero-point corrections for point + sources in NIRSpec slit data. [#8376] + master_background_mos --------------------- @@ -149,6 +159,13 @@ outlier_detection to detect outliers in TSO data, with user-defined rolling window width via the ``rolling_window_width`` parameter. [#8473] +pathloss +-------- + +- Updated pathloss calculations for NIRSpec fixed slit mode to use the appropriate + wavelengths for point and uniform sources if the ``wavecorr`` wavelength + zero-point corrections for point sources have been applied. [#8376] + photom ------ @@ -242,6 +259,12 @@ tweakreg message and skip ``tweakreg`` step when this condition is not satisfied and source confusion is possible during catalog matching. [#8476] +wavecorr +-------- + +- Changed the NIRSpec wavelength correction algorithm to include it in slit WCS + models and resampling. Fixed the sign of the wavelength corrections. [#8376] + wfss_contam ----------- diff --git a/docs/jwst/pathloss/description.rst b/docs/jwst/pathloss/description.rst index 2e10ad4cbe..65529b736a 100644 --- a/docs/jwst/pathloss/description.rst +++ b/docs/jwst/pathloss/description.rst @@ -45,6 +45,10 @@ Once the 1-D correction arrays have been computed, both forms of the correction (point and uniform) are interpolated, as a function of wavelength, into the 2-D space of the slit or IFU data and attached to the output data model (extensions "PATHLOSS_PS" and "PATHLOSS_UN") as a record of what was computed. +For fixed slit data, if the ``wavecorr`` step has been run to provide wavelength +corrections to point sources, the corrected wavelengths will be used to +calculate the point source pathloss, whereas the uncorrected wavelengths (appropriate +for uniform sources) will be used to calculate the uniform source pathlosses. The form of the 2-D correction (point or uniform) that's appropriate for the data is divided into the SCI and ERR arrays and propagated into the variance arrays of the science data. diff --git a/docs/jwst/wavecorr/description.rst b/docs/jwst/wavecorr/description.rst index 999ee6a6db..fa7e311d5a 100644 --- a/docs/jwst/wavecorr/description.rst +++ b/docs/jwst/wavecorr/description.rst @@ -22,16 +22,22 @@ These are recorded in the "SRCXPOS" and "SRCYPOS" keywords in the SCI extension header of each slitlet in a FITS product. The ``wavecorr`` step loops over all slit instances in the input -science product and applies a wavelength correction to slits that -contain a point source. The point source determination is based on the -value of the "SRCTYPE" keyword populated for each slit by the +science product and updates the WCS models of slits that contain a point +source to include a wavelength correction. The point source determination is +based on the value of the "SRCTYPE" keyword populated for each slit by the :ref:`srctype ` step. The computation of the correction is based on the "SRCXPOS" value. A value of 0.0 indicates a perfectly centered source, and ranges from -0.5 to +0.5 for sources at the extreme edges of a slit. The computation uses calibration data from the ``WAVECORR`` -reference file. The correction is computed as a 2-D grid of -wavelength offsets, which is applied to the original 2-D grid of -wavelengths associated with each slit. +reference file, which contains pixel shifts as a function of source position +and wavelength, and can be converted to wavelength shifts with the dispersion. +For each slit, the ``wavecorr`` step uses the average wavelengths and +dispersions in a slit (averaged across the cross-dispersion direction) to +calculate corresponding corrected wavelengths. It then uses the average +wavelengths and their corrections to generate a transform that interpolates +between "center of slit" wavelengths and corrected wavelengths. This +transformation is added to the slit WCS after the ``slit_frame`` and +produces a new wavelength corrected slit frame, ``wavecorr_frame``. NIRSpec Fixed Slit (FS) ----------------------- @@ -47,8 +53,8 @@ the wavelength correction is only applied to the primary slit. The estimated position of the source within the primary slit (in the dispersion direction) is then used in the same manner as described above -for MOS slitlets to compute offsets to be added to the nominal wavelength -grid for the primary slit. +for MOS slitlets to update the slit WCS and compute corrected wavelengths +for the primary slit. Upon successful completion of the step, the status keyword "S_WAVCOR" is set to "COMPLETE". diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 1c4c1db6ea..01da31668d 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -6,12 +6,11 @@ import math import numpy as np -from gwcs.wcstools import grid_from_bounding_box from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags -from ..lib import reffile_utils +from ..lib import reffile_utils, wcs_utils from ..assign_wcs import nirspec log = logging.getLogger(__name__) @@ -1924,59 +1923,11 @@ def flat_for_nirspec_slit(slit, f_flat_model, s_flat_model, d_flat_model, # Get the wavelength at each pixel in the extracted slit data. # If the wavelength attribute exists and is populated, use it # in preference to the wavelengths returned by the wcs function. - got_wl_attribute = True - try: - wl = slit.wavelength.copy() # a 2-D array - except AttributeError: - got_wl_attribute = False - if not got_wl_attribute or len(wl) == 0: - got_wl_attribute = False - return_dummy = False - # Has the use_wavecorr param been set? - if use_wavecorr is not None: - if use_wavecorr: - # Need to use the 2D wavelength array, because that's where - # the corrected wavelengths are stored - if got_wl_attribute: - # We've got the "wl" wavelength array we need - pass - else: - # Can't do the computation without the 2D wavelength array - log.error(f"The wavelength array for slit {slit.name} is not populated") - log.error("Skipping flat-field correction") - return_dummy = True - elif not use_wavecorr: - # Need to use the WCS object to create an uncorrected 2D wavelength array - if got_wcs: - log.info(f"Creating wavelength array from WCS for slit {slit.name}") - bb = slit.meta.wcs.bounding_box - grid = grid_from_bounding_box(bb) - wl = slit.meta.wcs(*grid)[2] - del grid - else: - # Can't create the uncorrected wavelengths without the WCS - log.error(f"Slit {slit.name} has no WCS object") - log.error("Skipping flat-field correction") - return_dummy = True - else: - # use_wavecorr was not specified, so use default processing - if not got_wl_attribute or np.nanmin(wl) == 0. and np.nanmax(wl) == 0.: - got_wl_attribute = False - log.warning(f"The wavelength array for slit {slit.name} has not been populated") - # Try to create it from the WCS - if got_wcs: - bb = slit.meta.wcs.bounding_box - grid = grid_from_bounding_box(bb) - wl = slit.meta.wcs(*grid)[2] - del grid - else: - log.warning("and this slit does not have a 'wcs' attribute") - log.warning("likely because assign_wcs has not been run.") - log.error("skipping ...") - return_dummy = True - else: - log.debug("Wavelengths are from the wavelength array.") + return_dummy = False + wl = wcs_utils.get_wavelengths(slit, use_wavecorr=use_wavecorr) + if wl is None: + return_dummy = True # Create and return a dummy flat as a placeholder, if necessary if return_dummy: diff --git a/jwst/lib/tests/test_wcs_utils.py b/jwst/lib/tests/test_wcs_utils.py new file mode 100644 index 0000000000..0cd731e771 --- /dev/null +++ b/jwst/lib/tests/test_wcs_utils.py @@ -0,0 +1,157 @@ +import numpy as np +from numpy.testing import assert_allclose + +from astropy import units as u +from astropy import coordinates as coord +from astropy.modeling.models import Mapping, Identity, Shift, Scale +from gwcs import wcstools, wcs +from gwcs import coordinate_frames as cf + +from stdatamodels.jwst import datamodels +from stdatamodels.jwst.transforms.models import NirissSOSSModel +from jwst.lib.wcs_utils import get_wavelengths +from jwst.assign_wcs import util + + +def create_model(): + + det = cf.Frame2D(name="detector", axes_order=(0, 1)) + + sky = cf.CelestialFrame(name="sky", axes_order=(0, 1), reference_frame=coord.ICRS()) + slit_spatial = cf.Frame2D( + name="slit_spatial", + axes_order=(0, 1), + unit=("", ""), + axes_names=("x_slit", "y_slit"), + ) + + spec = cf.SpectralFrame( + name="spectral", axes_order=(2,), unit=(u.micron,), axes_names=("wavelength",) + ) + slit_frame = cf.CompositeFrame([slit_spatial, spec], name="slit_frame") + world = cf.CompositeFrame([sky, spec], name="world") + + det2slit = Mapping((0, 1, 1)) | (Identity(2) & (Scale(0.5) | Shift(0.5))) + slit2sky = Identity(3) + + slit_wcs = wcs.WCS([(det, det2slit), (slit_frame, slit2sky), (world, None)]) + + # compute wavelengths + + data = np.full((10, 10), fill_value=5.0) + + bounding_box = util.wcs_bbox_from_shape(data.shape) + + x, y = wcstools.grid_from_bounding_box(bounding_box, step=(1, 1)) + _, _, lam = slit_wcs(x, y) + lam = lam.astype(np.float32) + model = datamodels.SlitModel(data=data, wavelength=lam) + model.meta.wcs = slit_wcs + + return model + + +def create_mock_wl(): + wl = np.arange(10.0) + wl = wl[:, np.newaxis] + wl = np.repeat(wl, 10, axis=1) + wl = (wl * 0.5) + 0.5 + return wl + + +def test_get_wavelengths(): + + # create a mock SlitModel + model = create_model() + + # calculate what the wavelength array should be + wl_og = create_mock_wl() + + # Test that the get wavelengths returns the wavelength grid + wl = get_wavelengths(model) + assert_allclose(wl, wl_og) + + del model.wavelength + + # Check that wavelengths can be generated from wcs when the + # wavelength attribute is unavailable + wl = get_wavelengths(model) + assert_allclose(wl, wl_og) + + # Check that wavelengths are generated correctly when given a WFSS exp_type + wl = get_wavelengths(model, exp_type="NRC_TSGRISM") + assert_allclose(wl, wl_og) + + +def test_get_wavelengths_soss(): + + # create a mock SlitModel + model = create_model() + + del model.wavelength + model.meta.exposure.type = "NIS_SOSS" + + wcs = model.meta.wcs + new_wcs = NirissSOSSModel( + [ + 1, + ], + [ + wcs, + ], + ) + model.meta.wcs = new_wcs + + # calculate what the wavelength array should be + wl_og = create_mock_wl() + + wl = get_wavelengths(model, order=1) + assert_allclose(wl, wl_og) + + +def test_get_wavelength_wavecorr(): + + # create a mock SlitModel + model = create_model() + + wl_og = create_mock_wl() + + # Test use_wavecorr with no wavelength correction modificiation + # get_wavelengths should return the same wavelengths for use_wavecorr + # True and False + + wl_corr = get_wavelengths(model, use_wavecorr=True) + assert_allclose(wl_corr, wl_og) + + wl_uncorr = get_wavelengths(model, use_wavecorr=False) + assert_allclose(wl_corr, wl_uncorr) + + # Update the model wcs to add a wavelength corrected slit frame + slit_spatial = cf.Frame2D( + name="slit_spatial", + axes_order=(0, 1), + unit=("", ""), + axes_names=("x_slit", "y_slit"), + ) + spec = cf.SpectralFrame( + name="spectral", axes_order=(2,), unit=(u.micron,), axes_names=("wavelength",) + ) + wcorr_frame = cf.CompositeFrame([slit_spatial, spec], name="wavecorr_frame") + + # Insert the new transform into the slit wcs object + wave2wavecorr = Identity(2) & Shift(0.1) + model.meta.wcs.insert_frame("slit_frame", wave2wavecorr, wcorr_frame) + + bounding_box = util.wcs_bbox_from_shape(model.data.shape) + x, y = wcstools.grid_from_bounding_box(bounding_box, step=(1, 1)) + _, _, lam = model.meta.wcs(x, y) + model.wavelength = lam + + # calculate what the corrected wavelength array should be + wl_corr_og = wl_og + 0.1 + + wl_corr = get_wavelengths(model, use_wavecorr=True) + assert_allclose(wl_corr, wl_corr_og) + + wl_uncorr = get_wavelengths(model, use_wavecorr=False) + assert_allclose(wl_uncorr, wl_og) diff --git a/jwst/lib/wcs_utils.py b/jwst/lib/wcs_utils.py index 5dbfceeffb..3070756f13 100644 --- a/jwst/lib/wcs_utils.py +++ b/jwst/lib/wcs_utils.py @@ -41,19 +41,24 @@ def get_wavelengths(model, exp_type="", order=None, use_wavecorr=None): got_wavelength = False wl_array = None - # If we've been asked to use the corrected wavelengths stored in - # the wavelength array, return those wavelengths. Otherwise, the - # results computed from the WCS object (below) will be returned. - if use_wavecorr is not None: - if use_wavecorr: - return wl_array - else: - got_wavelength = False # force wl computation below # Evaluate the WCS on the grid of pixel indexes, capturing only the # resulting wavelength values shape = model.data.shape grid = np.indices(shape[-2:], dtype=np.float64) + + # If we've been asked to use the uncorrected wavelengths we need to + # recalculate them from the wcs by skipping the transformation between + # the slit frame and the wavelength corrected slit frame. If the wavecorr_frame + # is not in the wcs assume that the wavelength correction has not been applied. + if use_wavecorr is not None: + if (not use_wavecorr and hasattr(model.meta, "wcs") + and 'wavecorr_frame' in model.meta.wcs.available_frames): + wcs = model.meta.wcs + detector2slit = wcs.get_transform('detector', 'slit_frame') + wavecorr2world = wcs.get_transform("wavecorr_frame", "world") + wl_array = (detector2slit | wavecorr2world)(grid[1], grid[0])[2] + return wl_array # If no existing wavelength array, compute one if hasattr(model.meta, "wcs") and not got_wavelength: diff --git a/jwst/pathloss/pathloss.py b/jwst/pathloss/pathloss.py index 4ad2f551b8..9586bc320e 100644 --- a/jwst/pathloss/pathloss.py +++ b/jwst/pathloss/pathloss.py @@ -976,24 +976,41 @@ def _corrections_for_fixedslit(slit, pathloss, exp_type, source_type): wavelength_pointsource *= 1.0e6 wavelength_uniformsource *= 1.0e6 - wavelength_array = slit.wavelength - - # Compute the point source pathloss 2D correction - pathloss_2d_ps = interpolate_onto_grid( - wavelength_array, - wavelength_pointsource, - pathloss_pointsource_vector) - - # Compute the uniform source pathloss 2D correction - pathloss_2d_un = interpolate_onto_grid( - wavelength_array, - wavelength_uniformsource, - pathloss_uniform_vector) - # Use the appropriate correction for this slit if is_pointsource(source_type or slit.source_type): + # calculate the point source corrected wavelengths and uncorrected wavelengths for the slit + wavelength_array_corr = get_wavelengths(slit, use_wavecorr=True) + wavelength_array_uncorr = get_wavelengths(slit, use_wavecorr=False) + + # Compute the point source pathloss 2D correction + pathloss_2d_ps = interpolate_onto_grid( + wavelength_array_corr, + wavelength_pointsource, + pathloss_pointsource_vector) + + # Compute the uniform source pathloss 2D correction + pathloss_2d_un = interpolate_onto_grid( + wavelength_array_uncorr, + wavelength_uniformsource, + pathloss_uniform_vector) + pathloss_2d = pathloss_2d_ps + else: + wavelength_array = slit.wavelength + + # Compute the point source pathloss 2D correction + pathloss_2d_ps = interpolate_onto_grid( + wavelength_array, + wavelength_pointsource, + pathloss_pointsource_vector) + + # Compute the uniform source pathloss 2D correction + pathloss_2d_un = interpolate_onto_grid( + wavelength_array, + wavelength_uniformsource, + pathloss_uniform_vector) + pathloss_2d = pathloss_2d_un # Save the corrections. The `data` portion is the correction used. diff --git a/jwst/wavecorr/tests/test_wavecorr.py b/jwst/wavecorr/tests/test_wavecorr.py index e4a06cb625..5ff61fc241 100644 --- a/jwst/wavecorr/tests/test_wavecorr.py +++ b/jwst/wavecorr/tests/test_wavecorr.py @@ -27,32 +27,60 @@ def test_wavecorr(): im = datamodels.ImageModel(hdul) im_wcs = AssignWcsStep.call(im) im_ex2d = Extract2dStep.call(im_wcs) - im_ex2d.slits[0].meta.wcs.bounding_box = ((-.5, 1432.5), (-.5, 37.5)) + bbox = ((-.5, 1432.5), (-.5, 37.5)) + im_ex2d.slits[0].meta.wcs.bounding_box = bbox + x, y = wcstools.grid_from_bounding_box(bbox) + ra, dec, lam_before = im_ex2d.slits[0].meta.wcs(x, y) + im_ex2d.slits[0].wavelength = lam_before im_src = SourceTypeStep.call(im_ex2d) + + # the mock msa source is an extended source, change to point for testing + im_src.slits[0].source_type = 'POINT' im_wave = WavecorrStep.call(im_src) # test dispersion is of the correct order # there's one slit only slit = im_src.slits[0] - x, y = wcstools.grid_from_bounding_box(slit.meta.wcs.bounding_box) dispersion = wavecorr.compute_dispersion(slit.meta.wcs, x, y) assert_allclose(dispersion[~np.isnan(dispersion)], 1e-9, atol=1e-10) - - # the difference in wavelength should be of the order of e-10 in um - assert_allclose(im_src.slits[0].wavelength - im_wave.slits[0].wavelength, 1e-10) - + + # test that the wavelength is on the order of microns + wavelength = wavecorr.compute_wavelength(slit.meta.wcs, x, y) + assert_allclose(np.nanmean(wavelength), 2.5, atol=0.1) + + # Check that the stored wavelengths were corrected + abs_wave_correction = np.abs(im_src.slits[0].wavelength - im_wave.slits[0].wavelength) + assert_allclose(np.nanmean(abs_wave_correction), 0.00046, atol=0.0001) + + # Check that the slit wcs has been updated to provide corrected wavelengths + corrected_wavelength = wavecorr.compute_wavelength(im_wave.slits[0].meta.wcs, x, y) + assert_allclose(im_wave.slits[0].wavelength, corrected_wavelength) + + # test the round-tripping on the wavelength correction transform + ref_name = im_wave.meta.ref_file.wavecorr.name + freference = datamodels.WaveCorrModel( + WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + + lam_uncorr = lam_before * 1e-6 + wave2wavecorr = wavecorr.calculate_wavelength_correction_transform( + lam_uncorr, dispersion, freference, slit.source_xpos, 'MOS') + lam_corr = wave2wavecorr(lam_uncorr) + assert_allclose(lam_uncorr, wave2wavecorr.inverse(lam_corr)) + # test on both sides of the shutter source_xpos1 = -.2 source_xpos2 = .2 - ra, dec, lam = slit.meta.wcs(x, y) - ref_name = im_wave.meta.ref_file.wavecorr.name - freference = datamodels.WaveCorrModel(WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) - zero_point1 = wavecorr.compute_zero_point_correction(lam, freference, source_xpos1, 'MOS', dispersion) - zero_point2 = wavecorr.compute_zero_point_correction(lam, freference, source_xpos2, 'MOS', dispersion) - diff_correction = np.abs(zero_point1[1] - zero_point2[1]) - non_zero = diff_correction[diff_correction != 0] - assert_allclose(np.nanmean(non_zero), 0.75, atol=0.01) + wave_transform1 = wavecorr.calculate_wavelength_correction_transform( + lam_uncorr, dispersion, freference, source_xpos1, 'MOS') + wave_transform2 = wavecorr.calculate_wavelength_correction_transform( + lam_uncorr, dispersion, freference, source_xpos2, 'MOS') + + zero_point1 = wave_transform1(lam_uncorr) + zero_point2 = wave_transform2(lam_uncorr) + + diff_correction = np.abs(zero_point1 - zero_point2) + assert_allclose(np.nanmean(diff_correction), 8.0e-10, atol=0.1e-10) def test_ideal_to_v23_fs(): @@ -119,7 +147,64 @@ def test_skipped(): outw = WavecorrStep.call(outs) source_pos = (0.004938526981283373, -0.02795306204991911) - assert_allclose((outw.slits[ind].source_xpos, outw.slits[ind].source_ypos), source_pos) + assert_allclose((outw.slits[ind].source_xpos, outw.slits[ind].source_ypos),source_pos) + + # Test if the corrected wavelengths are not monotonically increasing + + # This case is not expected with real data, test that no correction + # transform is returned (a skip criterion) with simple case + # of flipped wavelength solutions, which produces a monotonically + # decreasing wavelengths + lam = np.tile(np.flip(np.arange(0.6, 5.5, 0.01)*1e-6), (22, 1)) + disp = np.tile(np.full(lam.shape[-1], -0.01)*1e-6, (22, 1)) + + ref_name = outw.meta.ref_file.wavecorr.name + reffile = datamodels.WaveCorrModel( + WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + source_xpos = 0.1 + aperture_name = 'S200A1' + + transform = wavecorr.calculate_wavelength_correction_transform( + lam, disp, reffile, source_xpos, aperture_name) + assert transform is None + + +def test_mos_slit_status(): + """ Test conditions that are skipped for mos slitlets.""" + + hdul = create_nirspec_mos_file() + msa_meta = os.path.join(jwst.__path__[0], *['assign_wcs', 'tests', 'data', 'msa_configuration.fits']) + hdul[0].header['MSAMETFL'] = msa_meta + hdul[0].header['MSAMETID'] = 12 + im = datamodels.ImageModel(hdul) + im_wcs = AssignWcsStep.call(im) + im_ex2d = Extract2dStep.call(im_wcs) + bbox = ((-.5, 1432.5), (-.5, 37.5)) + im_ex2d.slits[0].meta.wcs.bounding_box = bbox + x, y = wcstools.grid_from_bounding_box(bbox) + ra, dec, lam_before = im_ex2d.slits[0].meta.wcs(x, y) + im_ex2d.slits[0].wavelength = lam_before + im_src = SourceTypeStep.call(im_ex2d) + + # test the mock msa source as an extended source + im_src.slits[0].source_type = 'EXTENDED' + im_wave = WavecorrStep.call(im_src) + + # check that the step is recorded as completed + assert im_wave.meta.cal_step.wavecorr == 'COMPLETE' + + # check that the step is listed as skipped for extended mos sources + assert im_wave.slits[0].meta.cal_step.wavecorr == 'SKIPPED' + + # test the mock msa source as a point source + im_src.slits[0].source_type = 'POINT' + im_wave = WavecorrStep.call(im_src) + + # check that the step is recorded as completed + assert im_wave.meta.cal_step.wavecorr == 'COMPLETE' + + # check that the step is listed as complete for mos point sources + assert im_wave.slits[0].meta.cal_step.wavecorr == 'COMPLETE' def test_wavecorr_fs(): @@ -171,13 +256,31 @@ def test_wavecorr_fs(): dispersion = wavecorr.compute_dispersion(slit.meta.wcs, x, y) assert_allclose(dispersion[~np.isnan(dispersion)], 1e-8, atol=1.04e-8) + # Check that the slit wavelengths are consistent with the slit wcs + corrected_wavelength = wavecorr.compute_wavelength(slit.meta.wcs, x, y) + assert_allclose(slit.wavelength, corrected_wavelength) + + # test the roundtripping on the wavelength correction transform + ref_name = result.meta.ref_file.wavecorr.name + freference = datamodels.WaveCorrModel(WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + + lam_uncorr = lam_before * 1e-6 + wave2wavecorr = wavecorr.calculate_wavelength_correction_transform(lam_uncorr, dispersion, + freference, slit.source_xpos, 'S200A1') + lam_corr = wave2wavecorr(lam_uncorr) + assert_allclose(lam_uncorr, wave2wavecorr.inverse(lam_corr)) + # test on both sides of the slit center source_xpos1 = -.2 source_xpos2 = .2 - ref_name = result.meta.ref_file.wavecorr.name - freference = datamodels.WaveCorrModel(WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) - zero_point1 = wavecorr.compute_zero_point_correction(lam_before, freference, source_xpos1, 'S200A1', dispersion) - zero_point2 = wavecorr.compute_zero_point_correction(lam_before, freference, source_xpos2, 'S200A1', dispersion) - diff_correction = np.abs(zero_point1[1] - zero_point2[1]) - assert_allclose(np.nanmean(diff_correction), 0.45, atol=0.01) + wave_transform1 = wavecorr.calculate_wavelength_correction_transform( + lam_uncorr, dispersion, freference, source_xpos1, 'S200A1') + wave_transform2 = wavecorr.calculate_wavelength_correction_transform( + lam_uncorr, dispersion, freference, source_xpos2, 'S200A1') + + zero_point1 = wave_transform1(lam_uncorr) + zero_point2 = wave_transform2(lam_uncorr) + + diff_correction = np.abs(zero_point1 - zero_point2) + assert_allclose(np.nanmean(diff_correction), 6.3e-9, atol=0.1e-9) diff --git a/jwst/wavecorr/wavecorr.py b/jwst/wavecorr/wavecorr.py index 0174eb1f59..46a92513b2 100644 --- a/jwst/wavecorr/wavecorr.py +++ b/jwst/wavecorr/wavecorr.py @@ -30,6 +30,10 @@ import logging import numpy as np from gwcs import wcstools +from gwcs import coordinate_frames as cf +from astropy import units as u +from astropy.modeling import tabular +from astropy.modeling.mappings import Identity from stdatamodels.jwst import datamodels from stdatamodels.jwst.transforms import models as trmodels @@ -89,16 +93,31 @@ def do_correction(input_model, wavecorr_file): input_model.meta.cal_step.wavecorr = 'SKIPPED' break if _is_point_source(slit, exp_type): - apply_zero_point_correction(slit, wavecorr_file) - output_model.meta.cal_step.wavecorr = 'COMPLETE' + completed = apply_zero_point_correction(slit, wavecorr_file) + if completed: + output_model.meta.cal_step.wavecorr = 'COMPLETE' + else: # pragma: no cover + log.warning(f'Corrections are not invertible for slit {slit.name}') + log.warning('Skipping wavecorr correction') + output_model.meta.cal_step.wavecorr = 'SKIPPED' + break # For MOS work on all slits containing a point source else: for slit in output_model.slits: if _is_point_source(slit, exp_type): - apply_zero_point_correction(slit, wavecorr_file) - output_model.meta.cal_step.wavecorr = 'COMPLETE' + completed = apply_zero_point_correction(slit, wavecorr_file) + if completed: + slit.meta.cal_step.wavecorr = 'COMPLETE' + else: # pragma: no cover + log.warning(f'Corrections are not invertible for slit {slit.name}') + log.warning('Skipping wavecorr correction') + slit.meta.cal_step.wavecorr = 'SKIPPED' + else: + slit.meta.cal_step.wavecorr = 'SKIPPED' + + output_model.meta.cal_step.wavecorr = 'COMPLETE' return output_model @@ -112,6 +131,11 @@ def apply_zero_point_correction(slit, reffile): Slit data to be corrected. reffile : str The ``wavecorr`` reference file. + + Returns + ------- + completed : bool + A flag to report whether the zero-point correction was added or skipped. """ log.info(f'slit name {slit.name}') slit_wcs = slit.meta.wcs @@ -127,67 +151,108 @@ def apply_zero_point_correction(slit, reffile): # For the MSA the aperture name is "MOS" aperture_name = "MOS" - lam = slit.wavelength.copy() + lam = slit.wavelength.copy() * 1e-6 dispersion = compute_dispersion(slit.meta.wcs) - corr, dq_lam = compute_zero_point_correction(lam, reffile, source_xpos, - aperture_name, dispersion) - # TODO: set a DQ flag to a TBD value for pixels where dq_lam == 0. - # The only purpose of dq_lam is to set that flag. - - # Wavelength is in um, the correction is computed in meters. - slit.wavelength = slit.wavelength - corr * 10 ** 6 - -def compute_zero_point_correction(lam, freference, source_xpos, - aperture_name, dispersion): - """ Compute the NIRSpec wavelength zero-point correction. + wave2wavecorr = calculate_wavelength_correction_transform(lam, + dispersion, + reffile, + source_xpos, + aperture_name) + # wave2wavecorr should not be None for real data + if wave2wavecorr is None: # pragma: no cover + completed = False + return completed + else: + # Make a new frame to insert into the slit wcs object + slit_spatial = cf.Frame2D(name='slit_spatial', axes_order=(0, 1), + unit=("", ""), axes_names=('x_slit', 'y_slit')) + spec = cf.SpectralFrame(name='spectral', axes_order=(2,), unit=(u.micron,), + axes_names=('wavelength',)) + wcorr_frame = cf.CompositeFrame( + [slit_spatial, spec], name='wavecorr_frame') + + # Insert the new transform into the slit wcs object + wave2wavecorr = Identity(2) & wave2wavecorr + slit_wcs.insert_frame('slit_frame', wave2wavecorr, wcorr_frame) + + # Update the stored wavelengths for the slit + slit.wavelength = compute_wavelength(slit_wcs) + + completed = True + return completed + + +def calculate_wavelength_correction_transform(lam, dispersion, freference, + source_xpos, aperture_name): + """ Generate a WCS transform for the NIRSpec wavelength zero-point correction + and add it to the WCS for each slit. Parameters ---------- lam : ndarray - Wavelength array. + Wavelength array [in m]. + dispersion : ndarray + The pixel dispersion [in m]. freference : str ``wavecorr`` reference file name. source_xpos : float X position of the source as a fraction of the slit size. aperture_name : str Aperture name. - dispersion : ndarray - The pixel dispersion [in m]. - + Returns ------- - lambda_corr : ndarray - Wavelength correction. - lam : ndarray - Interpolated wavelengths. Extrapolated values are reset to 0. - This is returned so that the DQ array can be updated with a flag - which indicates that no zero-point correction was done. + model : `~astropy.modeling.tabular.Tabular1D`or None + A model which takes wavelength inputs and returns zero-point + corrected wavelengths. Returns None if an invertible model + cannot be generated. """ + # Open the zero point reference model with datamodels.WaveCorrModel(freference) as wavecorr: for ap in wavecorr.apertures: if ap.aperture_name == aperture_name: log.info(f'Using wavelength zero-point correction for aperture {ap.aperture_name}') offset_model = ap.zero_point_offset.copy() - # TODO: implement variance - # variance = ap.variance.copy() - # width = ap.width break else: log.info(f'No wavelength zero-point correction found for slit {aperture_name}') - - deltax = source_xpos - lam = lam.copy() - lam_no_nans = lam[~np.isnan(lam)] + + # Set lookup table to extrapolate at bounds to recover wavelengths + # beyond model bounds, particularly for the red and blue ends of + # prism observations. fill_value = None sets the lookup tables + # to use the default extrapolation which is a linear extrapolation + # from scipy.interpolate.interpn offset_model.bounds_error = False - correction = offset_model(lam_no_nans * 10 ** -6, [deltax] * lam_no_nans.size) - lam[~np.isnan(lam)] = correction - - # The correction for pixels outside the slit and wavelengths - # outside the wave_range is 0. - lam[np.isnan(lam)] = 0. - lambda_cor = dispersion * lam - return lambda_cor, lam + offset_model.fill_value = None + + # Average the wavelength and dispersion across 2D extracted slit and remove nans + # So that we have a 1D wavelength array for building a 1D lookup table wcs transform + lam_mean = np.nanmean(lam, axis=0) + disp_mean = np.nanmean(dispersion, axis=0) + nan_lams = np.isnan(lam_mean) | np.isnan(disp_mean) + lam_mean = lam_mean[~nan_lams] + disp_mean = disp_mean[~nan_lams] + + # Calculate the corrected wavelengths + pixel_corrections = offset_model(lam_mean, source_xpos) + lam_corrected = lam_mean + (pixel_corrections * disp_mean) + + # Check to make sure that the corrected wavelengths are monotonically increasing + if np.all(np.diff(lam_corrected) > 0): + # monotonically increasing + # Build a look up table to transform between corrected and uncorrected wavelengths + wave2wavecorr = tabular.Tabular1D(points=lam_mean, + lookup_table=lam_corrected, + bounds_error=False, + fill_value=None, + name='wave2wavecorr') + + return wave2wavecorr + + else: + # output wavelengths are not monotonically increasing + return None def compute_dispersion(wcs, xpix=None, ypix=None): @@ -218,6 +283,32 @@ def compute_dispersion(wcs, xpix=None, ypix=None): return (lamright - lamleft) * 10 ** -6 +def compute_wavelength(wcs, xpix=None, ypix=None): + """ Compute the pixel wavelength. + + Parameters + ---------- + wcs : `~gwcs.wcs.WCS` + The WCS object for this slit. + xpix : ndarray, float, optional + ypix : ndarray, float, optional + Compute the wavelength at the x, y pixels. + If not provided the dispersion is computed on a + grid based on ``wcs.bounding_box``. + + Returns + ------- + wavelength : ndarray + The wavelength [in microns]. + + """ + if xpix is None or ypix is None: + xpix, ypix = wcstools.grid_from_bounding_box(wcs.bounding_box, step=(1, 1)) + + _, _, lam = wcs(xpix, ypix) + return lam + + def _is_point_source(slit, exp_type): """ Determine if a source is a point source. From 4fcb915efa12b42ad00c6ad89cbc4ed3928195e5 Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Fri, 7 Jun 2024 20:01:20 -0400 Subject: [PATCH 078/687] Fix MIRI LRS spec3 regtest setup (#8543) Co-authored-by: Nadia Dencheva --- jwst/regtest/test_miri_lrs_slit_spec3.py | 27 +++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index d8766db97e..04b9912e14 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -20,13 +20,31 @@ def run_pipeline(rtdata_module, request): Run the calwebb_spec3 pipeline on an ASN of nodded MIRI LRS fixed-slit exposures using different options for the WCS and output image shape for the resample step. + + The first iteration ("default_wcs") creates an output WCS for the combined + product based on the built-in WCS's in the inputs. + + The second iteration ("user_wcs") creates a user-supplied WCS input file + using the WCS from the default product, just to prove that you get the + identical result when using a user-supplied WCS. + + The third iteration ("user_wcs+shape") uses the same user-supplied WCS and also + specifies the output 2D file shape, using a shape that is identical to the + default. Hence all of the first 3 iterations should produce identical results + and therefore are compared to a single set of truth files. + + The fourth iteration ("user_wcs+shape1") uses the same user-specified WCS and + specifies an output 2D file shape that is 1 pixel larger than the default in + both axes. Hence the resulting s2d product needs a separate (larger) truth file. + Meanwhile, the x1d product from this iteration should still be identical to + the first 3, because the extra row and column of the 2D data file are ignored + during extraction. """ rtdata = rtdata_module # Get the spec3 ASN and its members rtdata.get_asn("miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json") root_file = "jw01530-o005_t004_miri_p750l_" - rtdata.get_truth("truth/test_miri_lrs_slit_spec3/jw01530-o005_t004_miri_p750l_s2d.fits") args = [ "calwebb_spec3", @@ -36,9 +54,12 @@ def run_pipeline(rtdata_module, request): rtdata.custom_wcs_mode = request.param if request.param != "default_wcs": - dm = datamodels.open(rtdata.truth) + # Get the s2d product that was just created using "default_wcs" + default_s2d = root_file + "s2d.fits" + dm = datamodels.open(default_s2d) + # Create a user-supplied WCS file that is identical to the default WCS af = asdf.AsdfFile({"wcs": dm.meta.wcs}) - wcs_file = rtdata.truth[:-8] + 'wcs.asdf' + wcs_file = default_s2d[:-8] + 'wcs.asdf' af.write_to(wcs_file) args.append(f"--steps.resample_spec.output_wcs={wcs_file}") From d6435f18465155fa32fecb8c4a94ab192dcec1d0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Sat, 8 Jun 2024 08:47:11 -0400 Subject: [PATCH 079/687] JP-3567: SIP coefficient accuracy and consistency (#8529) --- CHANGES.rst | 8 +++- docs/jwst/assign_wcs/arguments.rst | 4 +- docs/jwst/assign_wcs/main.rst | 8 ++-- docs/jwst/tweakreg/README.rst | 28 +++++++++++- jwst/assign_wcs/assign_wcs_step.py | 6 ++- jwst/assign_wcs/tests/test_wcs.py | 6 ++- jwst/assign_wcs/util.py | 55 ++++++++++------------ jwst/tweakreg/tests/test_tweakreg.py | 68 ++++++++++++++++++++++++++-- jwst/tweakreg/tweakreg_step.py | 35 +++++++++----- 9 files changed, 161 insertions(+), 57 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 08c5e3fe30..c7f6e70265 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -24,6 +24,9 @@ assign_wcs data file to properly handle background and virtual slits, and assign appropriate meta data to them for use downstream. [#8442, #8533] +- Update default parameters to increase the accuracy of the SIP approximation + in the output FITS WCS. [#8529] + associations ------------ @@ -252,7 +255,10 @@ tweakreg - Improve error handling in the absolute alignment. [#8450, #8477] - Change code default to use IRAF StarFinder instead of - DAO StarFinder [#8487] + DAO StarFinder. [#8487] + +- Add new step parameters to control SIP approximation in the output FITS WCS, + matching the default values used in the ``assign_wcs`` step. [#8529] - Added a check for ``(abs_)separation`` and ``(abs_)tolerance`` parameters that ``separation`` > ``sqrt(2) * tolerance`` that will now log an error diff --git a/docs/jwst/assign_wcs/arguments.rst b/docs/jwst/assign_wcs/arguments.rst index 9ec3b7af9b..197bd83d21 100644 --- a/docs/jwst/assign_wcs/arguments.rst +++ b/docs/jwst/assign_wcs/arguments.rst @@ -11,14 +11,14 @@ the behavior of the processing. ``--sip_degree`` (integer, max=6, default=None) Polynomial degree for the forward SIP fit. "None" uses the best fit. -``--sip_max_pix_error`` (float, default=0.1) +``--sip_max_pix_error`` (float, default=0.01) Maximum error for the SIP forward fit, in units of pixels. Ignored if ``sip_degree`` is set to an explicit value. ``--sip_inv_degree`` (integer, max=6, default=None) Polynomial degree for the inverse SIP fit. "None" uses the best fit. -``--sip_max_inv_pix_error`` (float, default=0.1) +``--sip_max_inv_pix_error`` (float, default=0.01) Maximum error for the SIP inverse fit, in units of pixels. Ignored if ``sip_inv_degree`` is set to an explicit value. diff --git a/docs/jwst/assign_wcs/main.rst b/docs/jwst/assign_wcs/main.rst index f2265c9a51..e865b8e197 100644 --- a/docs/jwst/assign_wcs/main.rst +++ b/docs/jwst/assign_wcs/main.rst @@ -30,9 +30,11 @@ create and populate the WCS object for the exposure. For image display with software like DS9 that relies on specific WCS information, a SIP-based approximation to the WCS is fit. The results are FITS keywords stored in -``model.meta.wcsinfo``. This is not an exact fit, but is accurate to ~0.25 pixel and is sufficient -for display purposes. This step, which occurs for imaging modes, is performed by default, but -can be switched off, and parameters controlling the SIP fit can also be adjusted. +``model.meta.wcsinfo``. This is not an exact fit, but is accurate to ~0.01 pixel by default, +and is sufficient for display purposes. This step, which occurs for imaging modes, is +performed by default, but can be switched off, and parameters controlling the SIP fit can +also be adjusted. Note that if these parameters are changed, the equivalent parameters +for the ``tweakreg`` step should be adjusted to match. The ``assign_wcs`` step can accept either a ``rate`` product, which is the result of averaging over all integrations in an exposure, or a ``rateints`` product, which is a 3D cube of diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index f24aa60f2d..8d5315e428 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -393,7 +393,7 @@ Parameters used for absolute astrometry to a reference catalog. that apply to ``fitgeometry`` also apply to ``abs_fitgeometry``. * ``abs_nclip``: A non-negative integer number of clipping iterations - to use in the fit. (Default = 3) + to use in the fit. (Default=3) * ``abs_sigma``: A positive `float` indicating the clipping limit, in sigma units, used when performing fit. (Default=3.0) @@ -401,6 +401,32 @@ Parameters used for absolute astrometry to a reference catalog. * ``save_abs_catalog``: A boolean specifying whether or not to write out the astrometric catalog used for the fit as a separate product. (Default=False) +**SIP approximation parameters:** + +Parameters used to provide a SIP-based approximation to the WCS, +for FITS display. These parameter values should match the ones used +in the ``assign_wcs`` step. + +* ``sip_approx``: A boolean flag to enable the computation of a SIP + approximation. (Default=True) + +* ``sip_degree``: A positive `int`, specifying the polynomial degree for + the forward SIP fit. `None` uses the best fit; the maximum value allowed + is 6. (Default=None) + +* ``sip_max_pix_error``: A positive `float`, specifying the maximum + error for the SIP forward fit, in units of pixels. Ignored if + ``sip_degree`` is set to an explicit value. (Default=0.01) + +* ``sip_inv_degree``: A positive `int`, specifying the polynomial degree for + the inverse SIP fit. `None` uses the best fit; the maximum value allowed + is 6. (Default=None) + +* ``sip_max_inv_pix_error``: A positive `float`, specifying the maximum + error for the SIP inverse fit, in units of pixels. Ignored if + ``sip_inv_degree`` is set to an explicit value. (Default=0.01) + +* ``sip_npoints``: Number of points for the SIP fit. (Default=12). Further Documentation --------------------- diff --git a/jwst/assign_wcs/assign_wcs_step.py b/jwst/assign_wcs/assign_wcs_step.py index b43d4a9c5c..324bef826d 100755 --- a/jwst/assign_wcs/assign_wcs_step.py +++ b/jwst/assign_wcs/assign_wcs_step.py @@ -52,9 +52,9 @@ class AssignWcsStep(Step): spec = """ sip_approx = boolean(default=True) # enables SIP approximation for imaging modes. - sip_max_pix_error = float(default=0.1) # max err for SIP fit, forward. + sip_max_pix_error = float(default=0.01) # max err for SIP fit, forward. sip_degree = integer(max=6, default=None) # degree for forward SIP fit, None to use best fit. - sip_max_inv_pix_error = float(default=0.1) # max err for SIP fit, inverse. + sip_max_inv_pix_error = float(default=0.01) # max err for SIP fit, inverse. sip_inv_degree = integer(max=6, default=None) # degree for inverse SIP fit, None to use best fit. sip_npoints = integer(default=12) # number of points for SIP slit_y_low = float(default=-.55) # The lower edge of a slit. @@ -131,6 +131,8 @@ def process(self, input, *args, **kwargs): wfss_imaging_wcs(result, imaging_func, bbox=bbox, max_pix_error=self.sip_max_pix_error, degree=self.sip_degree, + max_inv_pix_error=self.sip_max_inv_pix_error, + inv_degree=self.sip_inv_degree, npoints=self.sip_npoints, ) except (ValueError, RuntimeError) as e: diff --git a/jwst/assign_wcs/tests/test_wcs.py b/jwst/assign_wcs/tests/test_wcs.py index 7f057263b4..cd1b4e9912 100644 --- a/jwst/assign_wcs/tests/test_wcs.py +++ b/jwst/assign_wcs/tests/test_wcs.py @@ -248,7 +248,9 @@ def test_sip_approx(tmp_path): im = ImageModel(hdu1) pipe = AssignWcsStep() - result = pipe.call(im) + result = pipe.call(im, sip_max_pix_error=0.1, sip_degree=3, + sip_max_inv_pix_error=0.1, sip_inv_degree=3, + sip_npoints=12) # check that result.meta.wcsinfo has correct # values after SIP approx. @@ -278,4 +280,4 @@ def test_sip_approx(tmp_path): result.write(path) with open(path) as result_read: - result.meta.wcsinfo == result_read.meta.wcsinfo + assert result.meta.wcsinfo == result_read.meta.wcsinfo diff --git a/jwst/assign_wcs/util.py b/jwst/assign_wcs/util.py index f9f90918b4..2767182fe0 100644 --- a/jwst/assign_wcs/util.py +++ b/jwst/assign_wcs/util.py @@ -1254,8 +1254,10 @@ def in_ifu_slice(slice_wcs, ra, dec, lam): return onslice_ind -def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, - crpix=None, projection='TAN', imwcs=None, **kwargs): +def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, + max_inv_pix_error=0.01, inv_degree=None, + npoints=12, crpix=None, projection='TAN', + imwcs=None, **kwargs): """ Update ``datamodel.meta.wcsinfo`` based on a FITS WCS + SIP approximation of a GWCS object. By default, this function will approximate @@ -1307,6 +1309,23 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, to be fit to the WCS transformation. In this case ``max_pixel_error`` is ignored. + max_inv_pix_error : float, None, optional + Maximum allowed inverse error over the domain of the pixel array + in pixel units. With the default value of `None` no inverse + is generated. + + inv_degree : int, iterable, None, optional + Degree of the SIP polynomial. Default value `None` indicates that + all allowed degree values (``[1...6]``) will be considered and + the lowest degree that meets accuracy requerements set by + ``max_pix_error`` will be returned. Alternatively, ``degree`` can be + an iterable containing allowed values for the SIP polynomial degree. + This option is similar to default `None` but it allows caller to + restrict the range of allowed SIP degrees used for fitting. + Finally, ``degree`` can be an integer indicating the exact SIP degree + to be fit to the WCS transformation. In this case + ``max_inv_pixel_error`` is ignored. + npoints : int, optional The number of points in each dimension to sample the bounding box for use in the SIP fit. Minimum number of points is 3. @@ -1346,24 +1365,6 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, Other Parameters ---------------- - - max_inv_pix_error : float, None, optional - Maximum allowed inverse error over the domain of the pixel array - in pixel units. With the default value of `None` no inverse - is generated. - - inv_degree : int, iterable, None, optional - Degree of the SIP polynomial. Default value `None` indicates that - all allowed degree values (``[1...6]``) will be considered and - the lowest degree that meets accuracy requerements set by - ``max_pix_error`` will be returned. Alternatively, ``degree`` can be - an iterable containing allowed values for the SIP polynomial degree. - This option is similar to default `None` but it allows caller to - restrict the range of allowed SIP degrees used for fitting. - Finally, ``degree`` can be an integer indicating the exact SIP degree - to be fit to the WCS transformation. In this case - ``max_inv_pixel_error`` is ignored. - bounding_box : tuple, None, optional A pair of tuples, each consisting of two numbers Represents the range of pixel values in both dimensions @@ -1372,12 +1373,10 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, verbose : bool, optional Print progress of fits. - Returns ------- FITS header with all SIP WCS keywords - Raises ------ ValueError @@ -1385,7 +1384,6 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, specified accuracy (both forward and inverse, both rms and maximum) is not achieved an exception will be raised. - Notes ----- @@ -1409,15 +1407,11 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, # make a copy of kwargs: kwargs = {k: v for k, v in kwargs.items()} - # override default values for "other parameters": - max_inv_pix_error = kwargs.pop('max_inv_pix_error', None) - inv_degree = kwargs.pop('inv_degree', None) - if inv_degree is None: - inv_degree = range(1, _MAX_SIP_DEGREE) - - # limit default 'degree' range to _MAX_SIP_DEGREE: + # limit default 'degree' ranges to _MAX_SIP_DEGREE: if degree is None: degree = range(1, _MAX_SIP_DEGREE) + if inv_degree is None: + inv_degree = range(1, _MAX_SIP_DEGREE) hdr = imwcs.to_fits_sip( max_pix_error=max_pix_error, @@ -1426,6 +1420,7 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, npoints=32, inv_degree=inv_degree, npoints=npoints, crpix=crpix, + projection=projection, **kwargs ) diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index 47b071c337..da5e5bd894 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -1,18 +1,20 @@ -from copy import deepcopy import json import os +from copy import deepcopy import asdf -from astropy.modeling.models import Shift -from astropy.table import Table import numpy as np import pytest +from astropy.wcs import WCS +from astropy.modeling.models import Shift +from astropy.table import Table +from gwcs.wcstools import grid_from_bounding_box +from stdatamodels.jwst.datamodels import ImageModel +from jwst.datamodels import ModelContainer from jwst.tweakreg import tweakreg_step from jwst.tweakreg import tweakreg_catalog from jwst.tweakreg.utils import _wcsinfo_from_wcs_transform -from stdatamodels.jwst.datamodels import ImageModel -from jwst.datamodels import ModelContainer BKG_LEVEL = 0.001 @@ -340,3 +342,59 @@ def patched_construct_wcs_corrector(model, catalog, _seen=[]): with pytest.raises(ValueError, match="done testing"): step(str(asn_path)) + +@pytest.mark.parametrize("with_shift", [True, False]) +def test_sip_approx(example_input, with_shift): + """ + Test the output FITS WCS. + """ + if with_shift: + # shift 9 pixels so that the sources in one of the 2 images + # appear at different locations (resulting in a correct wcs update) + example_input[1].data[:-9] = example_input[1].data[9:] + example_input[1].data[-9:] = BKG_LEVEL + + # assign images to different groups (so they are aligned to each other) + example_input[0].meta.group_id = 'a' + example_input[1].meta.group_id = 'b' + + # call th step with override SIP approximation parameters + step = tweakreg_step.TweakRegStep() + step.sip_approx = True + step.sip_max_pix_error = 0.1 + step.sip_degree = 3 + step.sip_max_inv_pix_error = 0.1 + step.sip_inv_degree = 3 + step.sip_npoints = 12 + + # run the step on the example input modified above + result = step(example_input) + + # output wcs differs by a small amount due to the shift above: + # project one point through each wcs and compare the difference + abs_delta = abs(result[1].meta.wcs(0, 0)[0] - result[0].meta.wcs(0, 0)[0]) + if with_shift: + assert abs_delta > 1E-5 + else: + assert abs_delta < 1E-12 + + # the first wcs is identical to the input and + # does not have SIP approximation keywords -- + # they are normally set by assign_wcs + assert np.allclose(result[0].meta.wcs(0, 0)[0], example_input[0].meta.wcs(0, 0)[0]) + for key in ['ap_order', 'bp_order']: + assert key not in result[0].meta.wcsinfo.instance + + # for the second, SIP approximation should be present + for key in ['ap_order', 'bp_order']: + assert result[1].meta.wcsinfo.instance[key] == 3 + + # evaluate fits wcs and gwcs for the approximation, make sure they agree + wcs_info = result[1].meta.wcsinfo.instance + grid = grid_from_bounding_box(result[1].meta.wcs.bounding_box) + gwcs_ra, gwcs_dec = result[1].meta.wcs(*grid) + fits_wcs = WCS(wcs_info) + fitswcs_res = fits_wcs.pixel_to_world(*grid) + + assert np.allclose(fitswcs_res.ra.deg, gwcs_ra) + assert np.allclose(fitswcs_res.dec.deg, gwcs_dec) diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 6b9fef82b7..d928cb166a 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -119,6 +119,14 @@ class TweakRegStep(Step): abs_separation = float(default=1) # Minimum object separation in arcsec when performing absolute astrometry abs_tolerance = float(default=0.7) # Matching tolerance for xyxymatch in arcsec when performing absolute astrometry + # SIP approximation options, should match assign_wcs + sip_approx = boolean(default=True) # enables SIP approximation for imaging modes. + sip_max_pix_error = float(default=0.01) # max err for SIP fit, forward. + sip_degree = integer(max=6, default=None) # degree for forward SIP fit, None to use best fit. + sip_max_inv_pix_error = float(default=0.01) # max err for SIP fit, inverse. + sip_inv_degree = integer(max=6, default=None) # degree for inverse SIP fit, None to use best fit. + sip_npoints = integer(default=12) # number of points for SIP + # stpipe general options output_use_model = boolean(default=True) # When saving use `DataModel.meta.filename` """ @@ -508,17 +516,22 @@ def process(self, input): # Also update FITS representation in input exposures for # subsequent reprocessing by the end-user. - try: - update_fits_wcsinfo( - image_model, - max_pix_error=0.01, - npoints=16 - ) - except (ValueError, RuntimeError) as e: - self.log.warning( - "Failed to update 'meta.wcsinfo' with FITS SIP " - f'approximation. Reported error is:\n"{e.args[0]}"' - ) + if self.sip_approx: + try: + update_fits_wcsinfo( + image_model, + max_pix_error=self.sip_max_pix_error, + degree=self.sip_degree, + max_inv_pix_error=self.sip_max_inv_pix_error, + inv_degree=self.sip_inv_degree, + npoints=self.sip_npoints, + crpix=None + ) + except (ValueError, RuntimeError) as e: + self.log.warning( + "Failed to update 'meta.wcsinfo' with FITS SIP " + f'approximation. Reported error is:\n"{e.args[0]}"' + ) return images From 41f24b7a4ade69a77d294bc759dd07a80c28d09f Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 11 Jun 2024 11:47:01 -0400 Subject: [PATCH 080/687] JP-3173: Add NIRSpec optical path constraints for TSO associations (#8537) Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 ++- jwst/associations/lib/rules_level2b.py | 18 ++++++++++++ jwst/associations/lib/rules_level3.py | 28 +++++++++++++++---- jwst/associations/tests/data/pool_021_tso.csv | 2 +- .../tests/data/pool_022_tso_noflag.csv | 2 +- jwst/regtest/test_associations_sdp_pools.py | 5 ++++ 6 files changed, 51 insertions(+), 8 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c7f6e70265..f2b621cf10 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -42,7 +42,9 @@ associations - Added default values for new non-header keywords (``MOSTILNO`` and ``DITHPTIN``) to the default values in the ``asn_make_pool`` script. [#8508] -- Create WFSS Pure-Parallel associations [#8528] +- Create WFSS Pure-Parallel associations. [#8528] + +- Add NIRSpec optical path constraints for TSO associations. [#8537] combine_1d ---------- diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index f176a3ffc4..0ddb850d23 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -474,6 +474,24 @@ def __init__(self, *args, **kwargs): ) ], reduce=Constraint.notany + ), + # Don't allow NIRSpec invalid optical paths in spec2 + Constraint( + [ + Constraint([ + DMSAttrConstraint( + name='exp_type', + sources=['exp_type'], + value=('nrs_brightobj') + ), + SimpleConstraint( + value=False, + test=lambda value, item: nrsfss_valid_detector(item) == value, + force_unique=False + ), + ]), + ], + reduce=Constraint.notany ) ]) diff --git a/jwst/associations/lib/rules_level3.py b/jwst/associations/lib/rules_level3.py index 1e93204376..ca6db69a5e 100644 --- a/jwst/associations/lib/rules_level3.py +++ b/jwst/associations/lib/rules_level3.py @@ -881,19 +881,19 @@ def __init__(self, *args, **kwargs): DMSAttrConstraint( name='restricted_grism', sources=['exp_type'], - value = ('nrc_tsgrism') + value=('nrc_tsgrism') ), DMSAttrConstraint( name='grism_clear', sources=['pupil'], - value = ('clear') + value=('clear') ), ]), Constraint([ DMSAttrConstraint( name='restricted_ts', sources=['exp_type'], - value = 'nrc_tsgrism' + value='nrc_tsgrism' ), DMSAttrConstraint( name='module', @@ -911,12 +911,30 @@ def __init__(self, *args, **kwargs): DMSAttrConstraint( name='exp_type', sources=['exp_type'], - value = ('nis_soss') + value=('nis_soss') ), DMSAttrConstraint( name='nints', sources=['nints'], - value = ('1') + value=('1') + ), + ]), + ], + reduce=Constraint.notany + ), + # Don't allow NIRSpec invalid optical paths in TSO3 + Constraint( + [ + Constraint([ + DMSAttrConstraint( + name='exp_type', + sources=['exp_type'], + value=('nrs_brightobj') + ), + SimpleConstraint( + value=False, + test=lambda value, item: nrsfss_valid_detector(item) == value, + force_unique=False ), ]), ], diff --git a/jwst/associations/tests/data/pool_021_tso.csv b/jwst/associations/tests/data/pool_021_tso.csv index 0850c339c9..bb413cbce2 100644 --- a/jwst/associations/tests/data/pool_021_tso.csv +++ b/jwst/associations/tests/data/pool_021_tso.csv @@ -17,7 +17,7 @@ set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|WFSVISIT|SEQ_ID|ACT_ID|E @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NIS_SOSS|NULL|NULL|NIRISS|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRC_TSIMAGE|NULL|NULL|NIRCAM|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRC_TSGRISM|NULL|NULL|NIRCAM|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T -@!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRS_BRIGHTOBJ|NULL|NULL|NIRSPEC|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T +@!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRS_BRIGHTOBJ|NULL|NULL|NIRSPEC|NRS1|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|CLEAR|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|PRISM|S1600A1|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T # # Set ACID to how many have been used. set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|WFSVISIT|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|@!acid.set(acid.value)|BACKGROUND|IS_IMPRT|IS_PSF|IS_TSO diff --git a/jwst/associations/tests/data/pool_022_tso_noflag.csv b/jwst/associations/tests/data/pool_022_tso_noflag.csv index d49d33ec36..afa7a192ab 100644 --- a/jwst/associations/tests/data/pool_022_tso_noflag.csv +++ b/jwst/associations/tests/data/pool_022_tso_noflag.csv @@ -17,7 +17,7 @@ set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|WFSVISIT|SEQ_ID|ACT_ID|E @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NIS_SOSS|NULL|NULL|NIRISS|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRC_TSIMAGE|NULL|NULL|NIRCAM|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T @!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRC_TSGRISM|NULL|NULL|NIRCAM|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T -@!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRS_BRIGHTOBJ|NULL|NULL|NIRSPEC|CENTRAL|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|F140M|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|NULL|NULL|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T +@!fmt_fname(next(expnum))|V99009003001P0000000002102|99009|@!next(obsnum)|1|99009003001|2|NULL|1|2|1|NRS_BRIGHTOBJ|NULL|NULL|NIRSPEC|NRS1|NULL|1|M-33|NULL|FIXED|NIRISS WFSS|SCIENCE|2|1|1|1|1|NULL|CLEAR|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NULL|NIS_CEN|2015_1|FULL|PRISM|S1600A1|NULL|@!fmt_cand([(next(obsnum), 'OBSERVATION')])|N|N|N|T # # Set ACID to how many have been used. set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|WFSVISIT|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|@!acid.set(acid.value)|BACKGROUND|IS_IMPRT|IS_PSF|TSOVISIT diff --git a/jwst/regtest/test_associations_sdp_pools.py b/jwst/regtest/test_associations_sdp_pools.py index 4f02c0692e..4444e40a3b 100644 --- a/jwst/regtest/test_associations_sdp_pools.py +++ b/jwst/regtest/test_associations_sdp_pools.py @@ -77,6 +77,11 @@ 'xfail': None, 'slow': True, }, + 'jw00818_20230407t030411_pool': { + 'args': [], + 'xfail': None, + 'slow': False, + }, 'jw00839_20221220t025418_pool': { 'args': ['-i', 'o002', 'c1000'], 'xfail': None, From cdadd8dc9ea010adc74b7abbeb5de198194192af Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 11 Jun 2024 15:11:47 -0400 Subject: [PATCH 081/687] JP-3447: Interpolate errors for replaced pixels (#8504) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 + docs/jwst/pixel_replace/arguments.rst | 5 +- docs/jwst/pixel_replace/main.rst | 2 + docs/jwst/pixel_replace/reference_files.rst | 2 +- jwst/pipeline/calwebb_spec3.py | 3 +- jwst/pixel_replace/pixel_replace.py | 379 ++++++++++++------ jwst/pixel_replace/tests/__init__.py | 0 .../pixel_replace/tests/test_pixel_replace.py | 240 +++++++++++ 8 files changed, 500 insertions(+), 134 deletions(-) create mode 100644 jwst/pixel_replace/tests/__init__.py create mode 100644 jwst/pixel_replace/tests/test_pixel_replace.py diff --git a/CHANGES.rst b/CHANGES.rst index f2b621cf10..d4e6dbd9ab 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -197,6 +197,9 @@ pixel_replace - Moved pixel_replace in the calwebb_spec2 pipeline and added it to the calwebb_spec3 pipeline. In both pipelines it is now executed immediately before resample_spec/cube_build. [#8409] +- Added estimated errors and variances for replaced pixels, following the + interpolation scheme used for the data. [#8504] + ramp_fitting ------------ diff --git a/docs/jwst/pixel_replace/arguments.rst b/docs/jwst/pixel_replace/arguments.rst index aaaf2a7cd3..aeebc1711d 100644 --- a/docs/jwst/pixel_replace/arguments.rst +++ b/docs/jwst/pixel_replace/arguments.rst @@ -5,11 +5,12 @@ The ``pixel_replace`` step has the following step-specific arguments: ``--algorithm`` (str, default='fit_profile') This sets the method used to estimate flux values for bad pixels. The default 'fit_profile' uses a profile - fit to adjacent column values. The minimum gradient ('mingrad') method is also available for the MIRI MRS. + fit to adjacent column values. The minimum gradient ('mingrad') method, developed for MIRI MRS data, may + also be used for all instruments and modes. ``--n_adjacent_cols`` (int, default=3) Number of adjacent columns (on either side of column containing a bad pixel) to use in creation of the source profile, in cross-dispersion direction. The total number of columns used in the profile will be twice this number; on array edges, the total number of columns contributing to the source profile will be less than ``2 * n_adjacent_cols``. - + Ignored when ``algorithm = 'mingrad'``. diff --git a/docs/jwst/pixel_replace/main.rst b/docs/jwst/pixel_replace/main.rst index 8c2c34078b..e939c5a218 100644 --- a/docs/jwst/pixel_replace/main.rst +++ b/docs/jwst/pixel_replace/main.rst @@ -15,6 +15,8 @@ flagged as ``DO_NOT_USE`` in 2-D extracted spectra using interpolation methods, prior to rectification in the :ref:`resample_spec ` step. ``pixel_replace`` inserts these estimates into the 2-D data array, unsets the ``DO_NOT_USE`` flag, and sets the ``FLUX_ESTIMATED`` flag for each affected pixel. +Error values and variance components for the replaced pixels are similarly updated with +estimated values, following the same interpolation method as is used for the data. This step is provided as a cosmetic feature and, for that reason, should be used with caution. diff --git a/docs/jwst/pixel_replace/reference_files.rst b/docs/jwst/pixel_replace/reference_files.rst index 6e51bd5833..69605419f7 100644 --- a/docs/jwst/pixel_replace/reference_files.rst +++ b/docs/jwst/pixel_replace/reference_files.rst @@ -1,4 +1,4 @@ Reference File ============== -This step does not use any reference file. +This step does not use any reference files. diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index a5fcd59dc6..667d1229b1 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -228,10 +228,11 @@ def process(self, input): for cal_array in result: cal_array.meta.asn.table_name = op.basename(input_models.asn_table_name) result = self.outlier_detection(result) + # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - print(result) result = self.pixel_replace(result) + # Resample time. Dependent on whether the data is IFU or not. resample_complete = None if exptype in IFU_EXPTYPES: diff --git a/jwst/pixel_replace/pixel_replace.py b/jwst/pixel_replace/pixel_replace.py index bdd96f2bbd..75d78131aa 100644 --- a/jwst/pixel_replace/pixel_replace.py +++ b/jwst/pixel_replace/pixel_replace.py @@ -71,6 +71,7 @@ def replace(self): to each 2D spectrum in input. """ # ImageModel inputs (MIR_LRS-FIXEDSLIT) + # or 2D SlitModel inputs (e.g. NRS_FIXEDSLIT in spec3) if (isinstance(self.input, datamodels.ImageModel) or (isinstance(self.input, datamodels.SlitModel) and self.input.data.ndim == 2)): @@ -78,94 +79,105 @@ def replace(self): n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) log.info(f"Input model had {n_replaced} pixels replaced.") elif isinstance(self.input, datamodels.IFUImageModel): - # Attempt to run pixel replacement on each throw of the IFU slicer - # individually. - xx, yy = np.indices(self.input.data.shape) - if self.input.meta.exposure.type == 'MIR_MRS': - # Mingrad method - if (self.pars['algorithm'] == 'mingrad'): - new_model = self.algorithm(self.input) - self.output = new_model + # Attempt to run pixel replacement on each throw of the IFU slicer + # individually. + xx, yy = np.indices(self.input.data.shape) + if self.input.meta.exposure.type == 'MIR_MRS': + if self.pars['algorithm'] == 'mingrad': + # mingrad method + new_model = self.algorithm(self.input) + self.output = new_model + else: # fit_profile method - else: - _, beta_array, _ = self.input.meta.wcs.transform('detector', 'alpha_beta', yy, xx) - unique_beta = np.unique(beta_array) - unique_beta = unique_beta[~np.isnan(unique_beta)] - for i, beta in enumerate(unique_beta): - # Define a mask that is True where this trace is located - trace_mask = (beta_array == beta) - trace_model = self.input.copy() - trace_model.dq = np.where( - # When not in this trace, set NON_SCIENCE and DO_NOT_USE - ~trace_mask, - trace_model.dq | self.DO_NOT_USE | self.NON_SCIENCE, - trace_model.dq - ) - - trace_model = self.algorithm(trace_model) - self.output.data = np.where( - # Where trace is located, set replaced values - trace_mask, - trace_model.data, - self.output.data - ) - - self.output.dq = np.where( - # Where trace is located, set replaced values - trace_mask, - trace_model.dq, - self.output.dq - ) - - n_replaced = np.count_nonzero(trace_model.dq & self.FLUX_ESTIMATED) - log.info(f"Input MRS frame had {n_replaced} pixels replaced in IFU slice {i+1}.") - trace_model.close() - - n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) - log.info(f"Input MRS frame had {n_replaced} total pixels replaced.") + _, beta_array, _ = self.input.meta.wcs.transform('detector', 'alpha_beta', yy, xx) + unique_beta = np.unique(beta_array) + unique_beta = unique_beta[~np.isnan(unique_beta)] + for i, beta in enumerate(unique_beta): + # Define a mask that is True where this trace is located + trace_mask = (beta_array == beta) + trace_model = self.input.copy() + trace_model.dq = np.where( + # When not in this trace, set NON_SCIENCE and DO_NOT_USE + ~trace_mask, + trace_model.dq | self.DO_NOT_USE | self.NON_SCIENCE, + trace_model.dq + ) + + trace_model = self.algorithm(trace_model) + self.output.data = np.where( + # Where trace is located, set replaced values + trace_mask, + trace_model.data, + self.output.data + ) + + # do the same for dq, err, and var + self.output.dq = np.where( + trace_mask, trace_model.dq, self.output.dq) + self.output.err = np.where( + trace_mask, trace_model.err, self.output.err) + self.output.var_poisson = np.where( + trace_mask, trace_model.var_poisson, self.output.var_poisson) + self.output.var_rnoise = np.where( + trace_mask, trace_model.var_rnoise, self.output.var_rnoise) + self.output.var_flat = np.where( + trace_mask, trace_model.var_flat, self.output.var_flat) + + n_replaced = np.count_nonzero(trace_model.dq & self.FLUX_ESTIMATED) + log.info(f"Input MRS frame had {n_replaced} pixels replaced in IFU slice {i+1}.") + + trace_model.close() + + n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) + log.info(f"Input MRS frame had {n_replaced} total pixels replaced.") + else: + if self.pars['algorithm'] == 'mingrad': + # mingrad method + new_model = self.algorithm(self.input) + self.output = new_model else: - # Mingrad method - if (self.pars['algorithm'] == 'mingrad'): - new_model = self.algorithm(self.input) - self.output = new_model # fit_profile method - iterate over IFU slices - else: - for i in range(30): - slice_wcs = nrs_wcs_set_input(self.input, i) - _, _, wave = slice_wcs.transform('detector', 'slicer', yy, xx) - - # Define a mask that is True where this trace is located - trace_mask = (wave > 0) - trace_model = self.input.copy() - trace_model.dq = np.where( - # When not in this trace, set NON_SCIENCE and DO_NOT_USE - ~trace_mask, - trace_model.dq | self.DO_NOT_USE | self.NON_SCIENCE, - trace_model.dq - ) - - trace_model = self.algorithm(trace_model) - self.output.data = np.where( - # Where trace is located, set replaced values - trace_mask, - trace_model.data, - self.output.data - ) - - self.output.dq = np.where( - # Where trace is located, set replaced values - trace_mask, - trace_model.dq, - self.output.dq - ) - - n_replaced = np.count_nonzero(trace_model.dq & self.FLUX_ESTIMATED) - log.info(f"Input NRS_IFU frame had {n_replaced} pixels replaced in IFU slice {i + 1}.") - - trace_model.close() - - n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) - log.info(f"Input NRS_IFU frame had {n_replaced} total pixels replaced.") + for i in range(30): + slice_wcs = nrs_wcs_set_input(self.input, i) + _, _, wave = slice_wcs.transform('detector', 'slicer', yy, xx) + + # Define a mask that is True where this trace is located + trace_mask = (wave > 0) + trace_model = self.input.copy() + trace_model.dq = np.where( + # When not in this trace, set NON_SCIENCE and DO_NOT_USE + ~trace_mask, + trace_model.dq | self.DO_NOT_USE | self.NON_SCIENCE, + trace_model.dq + ) + + trace_model = self.algorithm(trace_model) + self.output.data = np.where( + # Where trace is located, set replaced values + trace_mask, + trace_model.data, + self.output.data + ) + + # do the same for dq, err, and var + self.output.dq = np.where( + trace_mask, trace_model.dq, self.output.dq) + self.output.err = np.where( + trace_mask, trace_model.err, self.output.err) + self.output.var_poisson = np.where( + trace_mask, trace_model.var_poisson, self.output.var_poisson) + self.output.var_rnoise = np.where( + trace_mask, trace_model.var_rnoise, self.output.var_rnoise) + self.output.var_flat = np.where( + trace_mask, trace_model.var_flat, self.output.var_flat) + + n_replaced = np.count_nonzero(trace_model.dq & self.FLUX_ESTIMATED) + log.info(f"Input NRS_IFU frame had {n_replaced} pixels replaced in IFU slice {i + 1}.") + + trace_model.close() + + n_replaced = np.count_nonzero(self.output.dq & self.FLUX_ESTIMATED) + log.info(f"Input NRS_IFU frame had {n_replaced} total pixels replaced.") # MultiSlitModel inputs (WFSS, NRS_FIXEDSLIT, ?) elif isinstance(self.input, datamodels.MultiSlitModel): @@ -180,21 +192,30 @@ def replace(self): self.output.slits[i] = slit_replaced - # CubeModel inputs are TSO (so far?); only SlitModel seen so far is NRS_BRIGHTOBJ, also requiring - # a re-packaging of the data into 2D inputs for the algorithm. + # CubeModel inputs are TSO (so far?); SlitModel may be NRS_BRIGHTOBJ, + # also requiring a re-packaging of the data into 2D inputs for the algorithm elif isinstance(self.input, (datamodels.CubeModel, datamodels.SlitModel)): - # Initial attempt looped over model.meta.exposure.nints, but test data had mismatch. Could change this. for i in range(len(self.input.data)): - dummy_model = datamodels.ImageModel(data=self.input.data[i], dq=self.input.dq[i]) - dummy_model.update(self.input) - dummy_replaced = self.algorithm(dummy_model) - n_replaced = np.count_nonzero(dummy_replaced.dq & self.FLUX_ESTIMATED) + img_model = datamodels.ImageModel( + data=self.input.data[i], dq=self.input.dq[i], + err=self.input.err[i], + var_poisson=self.input.var_poisson[i], + var_rnoise=self.input.var_rnoise[i], + var_flat=self.input.var_flat[i], + ) + img_model.update(self.input) + img_replaced = self.algorithm(img_model) + n_replaced = np.count_nonzero(img_replaced.dq & self.FLUX_ESTIMATED) log.info(f"Input TSO integration {i} had {n_replaced} pixels replaced.") - self.output.data[i] = dummy_replaced.data - self.output.dq[i] = dummy_replaced.dq - dummy_replaced.close() - dummy_model.close() + self.output.data[i] = img_replaced.data + self.output.dq[i] = img_replaced.dq + self.output.err[i] = img_replaced.err + self.output.var_poisson[i] = img_replaced.var_poisson + self.output.var_rnoise[i] = img_replaced.var_rnoise + self.output.var_flat[i] = img_replaced.var_flat + img_replaced.close() + img_model.close() else: # This should never happen, as these should be caught in the step code. @@ -203,10 +224,16 @@ def replace(self): def fit_profile(self, model): """ + Replace pixels with the profile fit method. + Fit a profile to adjacent columns, scale profile to column with missing pixel(s), and find flux estimate from scaled profile. + Error and variance values for the replaced pixels + are similarly estimated, using the scales from the + profile fit to the data. + Parameters ---------- model : DataModel @@ -287,19 +314,38 @@ def fit_profile(self, model): adjacent_inds = set(range(ind - self.pars['n_adjacent_cols'], ind + self.pars['n_adjacent_cols'] + 1)) adjacent_inds.discard(ind) valid_adjacent_inds = list(adjacent_inds.intersection(valid_profiles)) + # Cut out valid neighboring profiles - profile_data = model.data[self.custom_slice(dispaxis, valid_adjacent_inds)] + adjacent_condition = self.custom_slice(dispaxis, valid_adjacent_inds) + profile_data = model.data[adjacent_condition] + # Mask out bad pixels - profile_data = np.where( - model.dq[self.custom_slice(dispaxis, valid_adjacent_inds)] & self.DO_NOT_USE, - np.nan, - profile_data - ) - # Add additional cut to pull only from region with valid data for convenience (may not be necessary) - profile_data = profile_data[self.custom_slice(3 - dispaxis, list(range(profile_cut[0], profile_cut[1])))] + invalid_condition = (model.dq[adjacent_condition] & self.DO_NOT_USE).astype(bool) + profile_data[invalid_condition] = np.nan + + # Add additional cut to pull only from region with valid data + # for convenience (may not be necessary) + region_condition = self.custom_slice(3 - dispaxis, range(*profile_cut)) + profile_data = profile_data[region_condition] # Normalize profile data - normalized = profile_data / np.nanmax(np.abs(profile_data), axis=(dispaxis - 1), keepdims=True) + # TODO: check on signs here - absolute max sometimes picks up + # large negative outliers + profile_norm_scale = np.nanmax(np.abs(profile_data), axis=(dispaxis - 1), keepdims=True) + normalized = profile_data / profile_norm_scale + + # Get corresponding error and variance data and scale and mask to match + # Handle the variance arrays as errors, so the scales match. + err_names = ['err', 'var_poisson', 'var_rnoise', 'var_flat'] + norm_errors = {} + for err_name in err_names: + if err_name.startswith('var'): + err = np.sqrt(getattr(model, err_name)) + else: + err = getattr(model, err_name) + norm_err = err[adjacent_condition] + norm_err[invalid_condition] = np.nan + norm_errors[err_name] = norm_err[region_condition] / profile_norm_scale # Pull median for each pixel across profile. # Profile entry full of NaN values would produce a numpy @@ -307,12 +353,16 @@ def fit_profile(self, model): # so we suppress that above. median_profile = np.nanmedian(normalized, axis=(2 - dispaxis)) - # check_output[:, i] = median_profile + # Do the same for the errors + for err_name in norm_errors: + norm_errors[err_name] = np.nanmedian( + norm_errors[err_name], axis=(2 - dispaxis)) # Clean current profile of values flagged as bad - current_profile = model.data[self.custom_slice(dispaxis, ind)] + current_condition = self.custom_slice(dispaxis, ind) + current_profile = model.data[current_condition] cleaned_current = np.where( - model.dq[self.custom_slice(dispaxis, ind)] & self.DO_NOT_USE, + model.dq[current_condition] & self.DO_NOT_USE, np.nan, current_profile )[range(*profile_cut)] @@ -326,37 +376,76 @@ def fit_profile(self, model): norm_current = min_current / np.max(min_current) # Scale median profile to current profile with bad pixel - minimize mse? + # TODO: check on signs here - absolute max sometimes picks up + # large negative outliers norm_scale = minimize(self.profile_mse, x0=np.abs(np.nanmax(norm_current)), - args=(min_median, norm_current)).x + args=(min_median, norm_current)).x scale = np.max(min_current) + # Replace pixels that are do-not-use but not non-science + current_dq = model.dq[current_condition][range(*profile_cut)] + replace_condition = (current_dq & self.DO_NOT_USE + ^ current_dq & self.NON_SCIENCE) == 1 replaced_current = np.where( - (model.dq[self.custom_slice(dispaxis, ind)][range(*profile_cut)] & self.DO_NOT_USE ^ - model.dq[self.custom_slice(dispaxis, ind)][range(*profile_cut)] & self.NON_SCIENCE) == 1, + replace_condition, median_profile * norm_scale * scale, cleaned_current ) # Change the dq bits where old flag was DO_NOT_USE and new value is not nan - current_dq = model.dq[self.custom_slice(dispaxis, ind)][range(*profile_cut)] replaced_dq = np.where( - (current_dq & self.DO_NOT_USE ^ current_dq & self.NON_SCIENCE == 1) & - ~(np.isnan(replaced_current)), + replace_condition & ~(np.isnan(replaced_current)), current_dq ^ self.DO_NOT_USE ^ self.FLUX_ESTIMATED, current_dq ) - model_replaced.data[self.custom_slice(dispaxis, ind)][range(*profile_cut)] = replaced_current - model_replaced.dq[self.custom_slice(dispaxis, ind)][range(*profile_cut)] = replaced_dq + # Update data and DQ in the output model + model_replaced.data[current_condition][range(*profile_cut)] = replaced_current + model_replaced.dq[current_condition][range(*profile_cut)] = replaced_dq + + # Also update the errors and variances + current_err = model.err[current_condition][range(*profile_cut)] + replaced_err = np.where( + replace_condition, + norm_errors['err'] * norm_scale * scale, + current_err + ) + model_replaced.err[current_condition][range(*profile_cut)] = replaced_err + + current_var = model.var_poisson[current_condition][range(*profile_cut)] + replaced_var = np.where( + replace_condition, + (norm_errors['var_poisson'] * norm_scale * scale)**2, + current_var + ) + model_replaced.var_poisson[current_condition][range(*profile_cut)] = replaced_var + + current_var = model.var_rnoise[current_condition][range(*profile_cut)] + replaced_var = np.where( + replace_condition, + (norm_errors['var_rnoise'] * norm_scale * scale)**2, + current_var + ) + model_replaced.var_rnoise[current_condition][range(*profile_cut)] = replaced_var + + current_var = model.var_flat[current_condition][range(*profile_cut)] + replaced_var = np.where( + replace_condition, + (norm_errors['var_flat'] * norm_scale * scale)**2, + current_var + ) + model_replaced.var_flat[current_condition][range(*profile_cut)] = replaced_var return model_replaced def mingrad(self, model): """ - Minimum gradient replacement method; test the gradient along the spatial - and spectral axes using immediately adjacent pixels. Pick whichever dimension - has the minimum absolute gradient and replace the missing pixel with the average + Replace pixels with the minimum gradient replacement method. + + Test the gradient along the spatial and spectral axes using + immediately adjacent pixels. Pick whichever dimension has the minimum + absolute gradient and replace the missing pixel with the average of the two adjacent pixels along that dimension. This aims to make the process extremely local; near point sources it should do @@ -393,14 +482,22 @@ def mingrad(self, model): # Input data, err, and dq values indata = model.data - inerr = model.err indq = model.dq + inerr = model.err - # Output data, err, and dq values + # Propagate variance components as errors to get the scales right + in_var_p = np.sqrt(model.var_poisson) + in_var_r = np.sqrt(model.var_rnoise) + in_var_f = np.sqrt(model.var_flat) + + # Output data, err, var, and dq values model_replaced = model.copy() newdata = model_replaced.data - newerr = model_replaced.err newdq = model_replaced.dq + newerr = model_replaced.err + new_var_p = model_replaced.var_poisson + new_var_r = model_replaced.var_rnoise + new_var_f = model_replaced.var_flat # Make an array of x/y values on the detector (ysize, xsize) = indata.shape @@ -418,28 +515,50 @@ def mingrad(self, model): for ii in range(0, len(xindx)): left_data, right_data = indata[yindx[ii], xindx[ii] - 1], indata[yindx[ii], xindx[ii] + 1] top_data, bottom_data = indata[yindx[ii] - 1, xindx[ii]], indata[yindx[ii] + 1, xindx[ii]] + left_err, right_err = inerr[yindx[ii], xindx[ii] - 1], inerr[yindx[ii], xindx[ii] + 1] top_err, bottom_err = inerr[yindx[ii] - 1, xindx[ii]], inerr[yindx[ii] + 1, xindx[ii]] + left_var_p, right_var_p = in_var_p[yindx[ii], xindx[ii] - 1], in_var_p[yindx[ii], xindx[ii] + 1] + top_var_p, bottom_var_p = in_var_p[yindx[ii] - 1, xindx[ii]], in_var_p[yindx[ii] + 1, xindx[ii]] + + left_var_r, right_var_r = in_var_r[yindx[ii], xindx[ii] - 1], in_var_r[yindx[ii], xindx[ii] + 1] + top_var_r, bottom_var_r = in_var_r[yindx[ii] - 1, xindx[ii]], in_var_r[yindx[ii] + 1, xindx[ii]] + + left_var_f, right_var_f = in_var_f[yindx[ii], xindx[ii] - 1], in_var_f[yindx[ii], xindx[ii] + 1] + top_var_f, bottom_var_f = in_var_f[yindx[ii] - 1, xindx[ii]], in_var_f[yindx[ii] + 1, xindx[ii]] + # Compute absolute difference (slope) and average value in each direction (may be NaN) diffs = np.array([np.abs(left_data - right_data), np.abs(top_data - bottom_data)]) interp_data = np.array([(left_data + right_data) / 2., (top_data + bottom_data) / 2.]) interp_err = np.array([(left_err + right_err) / 2., (top_err + bottom_err) / 2.]) + interp_var_p = np.array([(left_var_p + right_var_p) / 2., (top_var_p + bottom_var_p) / 2.]) + interp_var_r = np.array([(left_var_r + right_var_r) / 2., (top_var_r + bottom_var_r) / 2.]) + interp_var_f = np.array([(left_var_f + right_var_f) / 2., (top_var_f + bottom_var_f) / 2.]) # Replace with the value from the lowest absolute slope estimator that was not NaN try: indmin = np.nanargmin(diffs) newdata[yindx[ii], xindx[ii]] = interp_data[indmin] newerr[yindx[ii], xindx[ii]] = interp_err[indmin] - # If original pixel was in the science array set DQ accordingly - if ((indq[yindx[ii], xindx[ii]] & self.NON_SCIENCE) == 0): - newdq[yindx[ii], xindx[ii]] = self.FLUX_ESTIMATED - # If original pixel was in non-science region set DQ accordingly - else: - newdq[yindx[ii], xindx[ii]] = self.FLUX_ESTIMATED + self.NON_SCIENCE + self.DO_NOT_USE + + # Square the interpolated errors back into variance + new_var_p[yindx[ii], xindx[ii]] = interp_var_p[indmin] ** 2 + new_var_r[yindx[ii], xindx[ii]] = interp_var_r[indmin] ** 2 + new_var_f[yindx[ii], xindx[ii]] = interp_var_f[indmin] ** 2 + + # If original pixel was in the science array, remove + # the DO_NOT_USE flag + if ((indq[yindx[ii], xindx[ii]] & self.DO_NOT_USE) + and not (indq[yindx[ii], xindx[ii]] & self.NON_SCIENCE)): + newdq[yindx[ii], xindx[ii]] -= self.DO_NOT_USE + + # Either way, add the FLUX_ESTIMATED flag + newdq[yindx[ii], xindx[ii]] |= self.FLUX_ESTIMATED nreplaced += 1 - except Exception: + + except (IndexError, ValueError): pass model_replaced.data = newdata @@ -459,7 +578,7 @@ def custom_slice(self, dispaxis, index): Using module-defined HORIZONTAL=1, VERTICAL=2 - index : int or slice + index : int or list Index or indices of cross-dispersion vectors to slice diff --git a/jwst/pixel_replace/tests/__init__.py b/jwst/pixel_replace/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/jwst/pixel_replace/tests/test_pixel_replace.py b/jwst/pixel_replace/tests/test_pixel_replace.py new file mode 100644 index 0000000000..3ab91c7eec --- /dev/null +++ b/jwst/pixel_replace/tests/test_pixel_replace.py @@ -0,0 +1,240 @@ +import numpy as np +import pytest + +from stdatamodels.jwst import datamodels +from stdatamodels.jwst.datamodels.dqflags import pixel as flags + +from jwst.assign_wcs import AssignWcsStep +from jwst.assign_wcs.tests.test_nirspec import create_nirspec_ifu_file +from jwst.pixel_replace.pixel_replace_step import PixelReplaceStep + + +def cal_data(shape, bad_idx, dispaxis=1, model='slit'): + if model == 'image': + model = datamodels.ImageModel(shape) + elif model == 'ifu': + model = datamodels.IFUImageModel(shape) + else: + model = datamodels.SlitModel(shape) + model.meta.wcsinfo.dispersion_direction = dispaxis + + # Set the data and error arrays to all 1s except one bad pixel + # to correct at the middle of the array + model.data[:] = 1.0 + model.err[:] = 1.0 + model.var_poisson[:] = 1.0 + model.var_rnoise[:] = 1.0 + model.var_flat[:] = 1.0 + + bad_flag = flags['DO_NOT_USE'] + flags['OTHER_BAD_PIXEL'] + model.data[bad_idx] = np.nan + model.err[bad_idx] = np.nan + model.var_poisson[bad_idx] = np.nan + model.var_rnoise[bad_idx] = np.nan + model.var_flat[bad_idx] = np.nan + model.dq[bad_idx] = bad_flag + + # Also add a non-science region in one row and one column + non_science = flags['DO_NOT_USE']+ flags['NON_SCIENCE'] + model.data[..., 1] = np.nan + model.err[..., 1] = np.nan + model.var_poisson[..., 1] = np.nan + model.var_rnoise[..., 1] = np.nan + model.var_flat[..., 1] = np.nan + model.dq[..., 1] = non_science + + model.data[..., 1, :] = np.nan + model.err[..., 1, :] = np.nan + model.var_poisson[..., 1, :] = np.nan + model.var_rnoise[..., 1, :] = np.nan + model.var_flat[..., 1, :] = np.nan + model.dq[..., 1, :] = non_science + + return model + + +def nirspec_tso(): + bad_idx = (1, 10, 10) + model = cal_data(shape=(3, 20, 20), bad_idx=bad_idx, dispaxis=1) + model.meta.instrument.name = 'NIRSPEC' + model.meta.exposure.type = 'NRS_BRIGHTOBJ' + return model, bad_idx + + +def nirspec_fs_slitmodel(): + bad_idx = (10, 10) + model = cal_data(shape=(20, 20), bad_idx=bad_idx, dispaxis=1) + model.meta.instrument.name = 'NIRSPEC' + model.meta.exposure.type = 'NRS_FIXEDSLIT' + return model, bad_idx + + +def nirspec_msa_multislit(): + bad_idx = (10, 10) + slit_model = cal_data(shape=(20, 20), bad_idx=bad_idx, dispaxis=1) + model = datamodels.MultiSlitModel() + model.slits.append(slit_model) + model.meta.instrument.name = 'NIRSPEC' + model.meta.exposure.type = 'NRS_MSASPEC' + return model, bad_idx + + +def nirspec_ifu(): + shape = (2048, 2048) + bad_idx = (1414, 690) + + # IFU mode requires WCS information, so make a more realistic model + hdul = create_nirspec_ifu_file(grating='PRISM', filter='CLEAR', + gwa_xtil=0.35986012, gwa_ytil=0.13448857, + gwa_tilt=37.1) + hdul['SCI'].data = np.ones(shape, dtype=float) + + model = datamodels.IFUImageModel(hdul) + model = AssignWcsStep.call(model) + + test_data = cal_data(shape=shape, bad_idx=bad_idx, dispaxis=1, model='ifu') + model.data = test_data.data + model.dq = test_data.dq + model.err = test_data.err + model.var_poisson = test_data.var_poisson + model.var_rnoise = test_data.var_rnoise + model.var_flat = test_data.var_flat + test_data.close() + + return model, bad_idx + + +def miri_lrs(): + bad_idx = (10, 10) + model = cal_data(shape=(20, 20), bad_idx=bad_idx, dispaxis=2, model='image') + model.meta.instrument.name = 'MIRI' + model.meta.exposure.type = 'MIR_LRS-FIXEDSLIT' + return model, bad_idx + + +def miri_mrs(): + shape = (20, 20) + + def mock_transform(*args): + return None, np.full(shape, 1), None + + bad_idx = (10, 10) + model = cal_data(shape=shape, bad_idx=bad_idx, dispaxis=2, model='ifu') + model.meta.instrument.name = 'MIRI' + model.meta.exposure.type = 'MIR_MRS' + model.meta.wcs = {'transform': mock_transform} + return model, bad_idx + + +@pytest.mark.parametrize('input_model_function', + [nirspec_tso, nirspec_fs_slitmodel, miri_lrs, miri_mrs]) +@pytest.mark.parametrize('algorithm', ['fit_profile', 'mingrad']) +def test_pixel_replace_no_container(input_model_function, algorithm): + """ + Test pixel replace for modes with no container. + + This includes ImageModel, SlitModel, and IFUImageModel. + """ + input_model, bad_idx = input_model_function() + + # for this simple case, the results from either algorithm should + # be the same + result = PixelReplaceStep.call(input_model, skip=False, algorithm=algorithm) + + for ext in ['data', 'err', 'var_poisson', 'var_rnoise', 'var_flat']: + # non-science edges are uncorrected + assert np.all(np.isnan(getattr(result, ext)[..., :, 1])) + assert np.all(np.isnan(getattr(result, ext)[..., 1, :])) + + # bad pixel is replaced: input had one nan value, output does not + assert np.isnan(getattr(input_model, ext)[bad_idx]) + assert getattr(result, ext)[bad_idx] == 1.0 + + # The DQ plane for the bad pixel is updated to remove do-not-use + # and add flux-estimated. The non-science edges are unchanged. + assert result.dq[bad_idx] == (input_model.dq[bad_idx] + - flags['DO_NOT_USE'] + + flags['FLUX_ESTIMATED']) + assert np.all(result.dq[..., :, 1] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + assert np.all(result.dq[..., 1, :] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + + result.close() + input_model.close() + + +@pytest.mark.parametrize('input_model_function', + [nirspec_msa_multislit]) +@pytest.mark.parametrize('algorithm', ['fit_profile', 'mingrad']) +def test_pixel_replace_multislit(input_model_function, algorithm): + """Test pixel replace for multislit modes.""" + input_model, bad_idx = input_model_function() + + # for this simple case, the results from either algorithm should + # be the same + result = PixelReplaceStep.call(input_model, skip=False, algorithm=algorithm) + + for ext in ['data', 'err', 'var_poisson', 'var_rnoise', 'var_flat']: + # non-science edges are uncorrected + assert np.all(np.isnan(getattr(result.slits[0], ext)[..., :, 1])) + assert np.all(np.isnan(getattr(result.slits[0], ext)[..., 1, :])) + + # bad pixel is replaced: input had one nan value, output does not + assert np.isnan(getattr(input_model.slits[0], ext)[bad_idx]) + assert getattr(result.slits[0], ext)[bad_idx] == 1.0 + + # The DQ plane for the bad pixel is updated to remove do-not-use + # and add flux-estimated. The non-science edges are unchanged. + assert result.slits[0].dq[bad_idx] == ( + input_model.slits[0].dq[bad_idx] + - flags['DO_NOT_USE'] + flags['FLUX_ESTIMATED']) + assert np.all(result.slits[0].dq[..., :, 1] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + assert np.all(result.slits[0].dq[..., 1, :] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + + result.close() + input_model.close() + + +@pytest.mark.slow +@pytest.mark.parametrize('input_model_function', + [nirspec_ifu]) +@pytest.mark.parametrize('algorithm', ['fit_profile', 'mingrad']) +def test_pixel_replace_nirspec_ifu(input_model_function, algorithm): + """ + Test pixel replacement for NIRSpec IFU. + + Larger data and more WCS operations required for testing make + this test take more than a minute, so marking this test 'slow'. + + The test is otherwise the same as for other modes. + """ + input_model, bad_idx = input_model_function() + + # for this simple case, the results from either algorithm should + # be the same + result = PixelReplaceStep.call(input_model, skip=False, algorithm=algorithm) + + for ext in ['data', 'err', 'var_poisson', 'var_rnoise', 'var_flat']: + # non-science edges are uncorrected + assert np.all(np.isnan(getattr(result, ext)[..., :, 1])) + assert np.all(np.isnan(getattr(result, ext)[..., 1, :])) + + # bad pixel is replaced: input had one nan value, output does not + assert np.isnan(getattr(input_model, ext)[bad_idx]) + assert getattr(result, ext)[bad_idx] == 1.0 + + # The DQ plane for the bad pixel is updated to remove do-not-use + # and add flux-estimated. The non-science edges are unchanged. + assert result.dq[bad_idx] == (input_model.dq[bad_idx] + - flags['DO_NOT_USE'] + + flags['FLUX_ESTIMATED']) + assert np.all(result.dq[..., :, 1] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + assert np.all(result.dq[..., 1, :] + == flags['DO_NOT_USE'] + flags['NON_SCIENCE']) + + result.close() + input_model.close() From 3e5761a63b72ab3fec7b121eafd5914d39058600 Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Tue, 11 Jun 2024 15:26:03 -0400 Subject: [PATCH 082/687] JP-2682: Discontinue use of drizpars reference file (#8546) --- CHANGES.rst | 4 + .../references_general/drizpars_reffile.inc | 51 ----- .../references_general/drizpars_selection.inc | 16 -- .../references_general/references_general.rst | 4 - docs/jwst/resample/main.rst | 8 +- docs/jwst/resample/reference_files.rst | 4 +- jwst/resample/resample_spec_step.py | 29 +-- jwst/resample/resample_step.py | 198 ++++-------------- 8 files changed, 50 insertions(+), 264 deletions(-) delete mode 100644 docs/jwst/references_general/drizpars_reffile.inc delete mode 100644 docs/jwst/references_general/drizpars_selection.inc diff --git a/CHANGES.rst b/CHANGES.rst index d4e6dbd9ab..830a597e8a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -229,6 +229,8 @@ resample - Change `fillval` parameter default from INDEF to NaN [#8488] +- Removed the use of the `drizpars` reference file [#8546] + resample_spec ------------- @@ -240,6 +242,8 @@ resample_spec when the slit was closely aligned with the RA direction sky. [#8511] +- Removed the use of the `drizpars` reference file [#8546] + residual_fringe --------------- diff --git a/docs/jwst/references_general/drizpars_reffile.inc b/docs/jwst/references_general/drizpars_reffile.inc deleted file mode 100644 index faeb08009f..0000000000 --- a/docs/jwst/references_general/drizpars_reffile.inc +++ /dev/null @@ -1,51 +0,0 @@ -.. _drizpars_reffile: - -DRIZPARS Reference File ------------------------ - -:REFTYPE: DRIZPARS -:Data model: `~jwst.datamodels.DrizParsModel` - -The DRIZPARS reference file contains various drizzle parameter values that -control the characteristics of a drizzled image and how it is built. - -.. include:: ../references_general/drizpars_selection.inc - -.. include:: ../includes/standard_keywords.inc - -Type Specific Keywords for DRIZPARS -+++++++++++++++++++++++++++++++++++++ -No additional specific keywords are required in DRIZPARS reference -files, because CRDS selection is based only on the instrument name -(see :ref:`drizpars_selectors`). - -Reference File Format -+++++++++++++++++++++ -DRIZPARS reference files are FITS format, with 1 BINTABLE extension. -The FITS primary HDU does not contain a data array. -The format and content of the file is as follows: - -======== ======== ===== ======================= ========= -EXTNAME XTENSION NAXIS Dimensions Data type -======== ======== ===== ======================= ========= -DRIZPARS BINTABLE 2 TFIELDS = 7 N/A -======== ======== ===== ======================= ========= - -The DRIZPARS extension contains various step parameter values to be -used when processing certain types of image collections. The first -two columns (numimages and filter) are used as row selectors within -the table. Image collections that match those selectors then use the -parameter values specified in the remainder of that table row. -The table contains the following 7 columns: - -=========== ======= =========================================== -TTYPE TFORM Description -=========== ======= =========================================== -numimages integer The number of images to be combined -filter integer The filter used to obtain the images -pixfrac float The pixel "shrinkage" fraction -kernel string The kernel function used to distribute flux -fillval float Value assigned to pixels with no input flux -wht_type string The input image weighting type -stepsize integer Output WCS grid interpolation step size -=========== ======= =========================================== diff --git a/docs/jwst/references_general/drizpars_selection.inc b/docs/jwst/references_general/drizpars_selection.inc deleted file mode 100644 index d45fd71e64..0000000000 --- a/docs/jwst/references_general/drizpars_selection.inc +++ /dev/null @@ -1,16 +0,0 @@ -.. _drizpars_selectors: - -Reference Selection Keywords for DRIZPARS -+++++++++++++++++++++++++++++++++++++++++ -CRDS selects appropriate DRIZPARS references based on the following keywords. -DRIZPARS is not applicable for instruments not in the table. -All keywords used for file selection are *required*. - -========== ======== -Instrument Keywords -========== ======== -MIRI INSTRUME -NIRCam INSTRUME -NIRISS INSTRUME -========== ======== - diff --git a/docs/jwst/references_general/references_general.rst b/docs/jwst/references_general/references_general.rst index 417b6fd894..550eb4dad8 100644 --- a/docs/jwst/references_general/references_general.rst +++ b/docs/jwst/references_general/references_general.rst @@ -150,8 +150,6 @@ documentation on each reference file. +-----------------------------------------------+--------------------------------------------------+ | :ref:`refpix ` | :ref:`REFPIX ` | +-----------------------------------------------+--------------------------------------------------+ -| :ref:`resample ` | :ref:`DRIZPARS ` | -+-----------------------------------------------+--------------------------------------------------+ | :ref:`reset ` | :ref:`RESET ` | +-----------------------------------------------+--------------------------------------------------+ | :ref:`residual_fringe ` | :ref:`FRINGEFREQ ` | @@ -204,8 +202,6 @@ documentation on each reference file. +--------------------------------------------------+-----------------------------------------------+ | :ref:`DISTORTION ` | :ref:`assign_wcs ` | +--------------------------------------------------+-----------------------------------------------+ -| :ref:`DRIZPARS ` | :ref:`resample ` | -+--------------------------------------------------+-----------------------------------------------+ | :ref:`EMICORR ` | :ref:`emicorr ` | +--------------------------------------------------+-----------------------------------------------+ | :ref:`EXTRACT1D ` | :ref:`extract_1d ` | diff --git a/docs/jwst/resample/main.rst b/docs/jwst/resample/main.rst index 08e0069f2d..9291519018 100644 --- a/docs/jwst/resample/main.rst +++ b/docs/jwst/resample/main.rst @@ -15,11 +15,9 @@ The ``resample`` step can take as input either: #. a single 2D input image #. an association table (in json format) -The defined parameters for the drizzle operation itself get -provided by the DRIZPARS reference file (from CRDS). The exact values -used depends on the number of input images being combined and the filter -being used. Other information may be added as selection criteria later, -but for now, only basic information is used. +The parameters for the drizzle operation are provided via ``resample`` +step parameters, which can be overridden by a step parameter reference +file from CRDS. The output product gets defined using the WCS information of all inputs, even if it is just a single input image. The output WCS defines a diff --git a/docs/jwst/resample/reference_files.rst b/docs/jwst/resample/reference_files.rst index d292c96f09..75ad1e0057 100644 --- a/docs/jwst/resample/reference_files.rst +++ b/docs/jwst/resample/reference_files.rst @@ -1,5 +1,3 @@ Reference File ============== -The ``resample`` step uses the DRIZPARS reference file. - -.. include:: ../references_general/drizpars_reffile.inc +The ``resample`` step does not use any reference files. diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index fa705f5372..e215a7bb61 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -62,35 +62,12 @@ def process(self, input): output = input_new.meta.filename self.blendheaders = False - # Get the drizpars reference file - for reftype in self.reference_file_types: - ref_filename = self.get_reference_file(input_models[0], reftype) - - if ref_filename != 'N/A': - self.log.info('Drizpars reference file: {}'.format(ref_filename)) - kwargs = self.get_drizpars(ref_filename, input_models) - else: - # Deal with NIRSpec, which currently has no default drizpars reffile - self.log.info("No DRIZPARS reffile") - kwargs = self._set_spec_defaults() - kwargs['blendheaders'] = self.blendheaders - - kwargs['output_shape'] = self._check_list_pars( - self.output_shape, - 'output_shape', - min_vals=[1, 1] - ) - kwargs['output_wcs'] = self._load_custom_wcs( - self.output_wcs, - kwargs['output_shape'] - ) - - kwargs['allowed_memory'] = self.allowed_memory + # Setup drizzle-related parameters + kwargs = self.get_drizpars() kwargs['output'] = output - - # Call resampling self.drizpars = kwargs + # Call resampling if isinstance(input_models[0], MultiSlitModel): result = self._process_multislit(input_models) diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index f19e3cfcb9..1f99d12a41 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -2,10 +2,7 @@ import re from copy import deepcopy -import numpy as np import asdf -from astropy.extern.configobj.validate import Validator -from astropy.extern.configobj.configobj import ConfigObj from stdatamodels.jwst import datamodels @@ -43,24 +40,24 @@ class ResampleStep(Step): class_alias = "resample" spec = """ - pixfrac = float(default=1.0) # change back to None when drizpar reference files are updated - kernel = string(default='square') # change back to None when drizpar reference files are updated - fillval = string(default='NAN' ) - weight_type = option('ivm', 'exptime', None, default='ivm') # change back to None when drizpar ref update + pixfrac = float(min=0.0, max=1.0, default=1.0) # Pixel shrinkage factor + kernel = option('square','gaussian','point','turbo','lanczos2','lanczos3',default='square') # Flux distribution kernel + fillval = string(default='NAN') # Output value for pixels with no weight or flux + weight_type = option('ivm', 'exptime', None, default='ivm') # Input image weighting type output_shape = int_list(min=2, max=2, default=None) # [x, y] order crpix = float_list(min=2, max=2, default=None) crval = float_list(min=2, max=2, default=None) - rotation = float(default=None) - pixel_scale_ratio = float(default=1.0) # Ratio of input to output pixel scale - pixel_scale = float(default=None) # Absolute pixel scale in arcsec - output_wcs = string(default='') # Custom output WCS. - single = boolean(default=False) - blendheaders = boolean(default=True) - allowed_memory = float(default=None) # Fraction of memory to use for the combined image. - in_memory = boolean(default=True) + rotation = float(default=None) # Output image Y-axis PA relative to North + pixel_scale_ratio = float(default=1.0) # Ratio of input to output pixel scale + pixel_scale = float(default=None) # Absolute pixel scale in arcsec + output_wcs = string(default='') # Custom output WCS + single = boolean(default=False) # Resample each input to its own output grid + blendheaders = boolean(default=True) # Blend metadata from inputs into output + allowed_memory = float(default=None) # Fraction of memory to use for the combined image + in_memory = boolean(default=True) # Keep images in memory """ - reference_file_types = ['drizpars'] + reference_file_types = [] def process(self, input): @@ -87,39 +84,8 @@ def process(self, input): # resample can only handle 2D images, not 3D cubes, etc raise RuntimeError("Input {} is not a 2D image.".format(input_models[0])) - # Get drizzle parameters reference file, if there is one - self.wht_type = self.weight_type - if 'drizpars' in self.reference_file_types: - ref_filename = self.get_reference_file(input_models[0], 'drizpars') - else: # no drizpars reference file found - ref_filename = 'N/A' - - if ref_filename == 'N/A': - self.log.info('No drizpars reference file found.') - kwargs = self._set_spec_defaults() - else: - self.log.info('Using drizpars reference file: {}'.format(ref_filename)) - kwargs = self.get_drizpars(ref_filename, input_models) - - kwargs['allowed_memory'] = self.allowed_memory - - # Custom output WCS parameters. - # Modify get_drizpars if any of these get into reference files: - kwargs['output_shape'] = self._check_list_pars( - self.output_shape, - 'output_shape', - min_vals=[1, 1] - ) - kwargs['output_wcs'] = self._load_custom_wcs( - self.output_wcs, - kwargs['output_shape'] - ) - kwargs['crpix'] = self._check_list_pars(self.crpix, 'crpix') - kwargs['crval'] = self._check_list_pars(self.crval, 'crval') - kwargs['rotation'] = self.rotation - kwargs['pscale'] = self.pixel_scale - kwargs['pscale_ratio'] = self.pixel_scale_ratio - kwargs['in_memory'] = self.in_memory + # Setup drizzle-related parameters + kwargs = self.get_drizpars() # Call the resampling routine resamp = resample.ResampleData(input_models, output=output, **kwargs) @@ -195,128 +161,42 @@ def _load_custom_wcs(asdf_wcs_file, output_shape): return wcs - def get_drizpars(self, ref_filename, input_models): + def get_drizpars(self): """ - Extract drizzle parameters from reference file. - - This method extracts parameters from the drizpars reference file and - uses those to set defaults on the following ResampleStep configuration - parameters: - - pixfrac = float(default=None) - kernel = string(default=None) - fillval = string(default=None) - wht_type = option('ivm', 'exptime', None, default=None) - - Once the defaults are set from the reference file, if the user has - used a resample.cfg file or run ResampleStep using command line args, - then these will overwrite the defaults pulled from the reference file. + Load all drizzle-related parameter values into kwargs list. """ - with datamodels.DrizParsModel(ref_filename) as drpt: - drizpars_table = drpt.data - - num_groups = len(input_models.group_names) - filtname = input_models[0].meta.instrument.filter - row = None - filter_match = False - # look for row that applies to this set of input data models - for n, filt, num in zip( - range(0, len(drizpars_table)), - drizpars_table['filter'], - drizpars_table['numimages'] - ): - # only remember this row if no exact match has already been made for - # the filter. This allows the wild-card row to be anywhere in the - # table; since it may be placed at beginning or end of table. - - if str(filt) == "ANY" and not filter_match and num_groups >= num: - row = n - # always go for an exact match if present, though... - if filtname == filt and num_groups >= num: - row = n - filter_match = True - - # With presence of wild-card rows, code should never trigger this logic - if row is None: - self.log.error("No row found in %s matching input data.", ref_filename) - raise ValueError - - # Define the keys to pull from drizpars reffile table. - # All values should be None unless the user set them on the command - # line or in the call to the step - - drizpars = dict( + # Define the keys pulled from step parameters + kwargs = dict( pixfrac=self.pixfrac, kernel=self.kernel, fillval=self.fillval, - wht_type=self.weight_type - # pscale_ratio=self.pixel_scale_ratio, # I think this can be removed JEM (??) - ) - - # For parameters that are set in drizpars table but not set by the - # user, use these. Otherwise, use values set by user. - reffile_drizpars = {k: v for k, v in drizpars.items() if v is None} - user_drizpars = {k: v for k, v in drizpars.items() if v is not None} - - # read in values from that row for each parameter - for k in reffile_drizpars: - if k in drizpars_table.names: - reffile_drizpars[k] = drizpars_table[k][row] - - # Convert the strings in the FITS binary table from np.bytes_ to str - for k, v in reffile_drizpars.items(): - if isinstance(v, np.bytes_): - reffile_drizpars[k] = v.decode('UTF-8') - - all_drizpars = {**reffile_drizpars, **user_drizpars} - - kwargs = dict( + wht_type=self.weight_type, good_bits=GOOD_BITS, single=self.single, - blendheaders=self.blendheaders + blendheaders=self.blendheaders, + allowed_memory = self.allowed_memory, + in_memory = self.in_memory ) - kwargs.update(all_drizpars) - - for k, v in kwargs.items(): - self.log.debug(' {}={}'.format(k, v)) - - return kwargs - - def _set_spec_defaults(self): - """NIRSpec currently has no default drizpars reference file, so default - drizzle parameters are not set properly. This method sets them. - - Remove this class method when a drizpars reffile is delivered. - """ - configspec = self.load_spec_file() - config = ConfigObj(configspec=configspec) - if config.validate(Validator()): - kwargs = config.dict() - - if self.pixfrac is None: - self.pixfrac = 1.0 - if self.kernel is None: - self.kernel = 'square' - if self.fillval is None: - self.fillval = 'NAN' - # Force definition of good bits - kwargs['good_bits'] = GOOD_BITS - kwargs['pixfrac'] = self.pixfrac - kwargs['kernel'] = str(self.kernel) - kwargs['fillval'] = str(self.fillval) - # self.weight_type has a default value of None - # The other instruments read this parameter from a reference file - if self.wht_type is None: - self.wht_type = 'ivm' - - kwargs['wht_type'] = str(self.wht_type) + # Custom output WCS parameters. + kwargs['output_shape'] = self._check_list_pars( + self.output_shape, + 'output_shape', + min_vals=[1, 1] + ) + kwargs['output_wcs'] = self._load_custom_wcs( + self.output_wcs, + kwargs['output_shape'] + ) + kwargs['crpix'] = self._check_list_pars(self.crpix, 'crpix') + kwargs['crval'] = self._check_list_pars(self.crval, 'crval') + kwargs['rotation'] = self.rotation + kwargs['pscale'] = self.pixel_scale kwargs['pscale_ratio'] = self.pixel_scale_ratio - kwargs.pop('pixel_scale_ratio') + # Report values to processing log for k, v in kwargs.items(): - if k in ['pixfrac', 'kernel', 'fillval', 'wht_type', 'pscale_ratio']: - log.info(' using: %s=%s', k, repr(v)) + self.log.debug(' {}={}'.format(k, v)) return kwargs From a3b0b678ec1bcf1824920ec319accd50d928de68 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 11 Jun 2024 16:17:21 -0400 Subject: [PATCH 083/687] use stpipe.crds_client.reference_uri_to_cache_path (#8542) Co-authored-by: Howard Bushouse --- jwst/msaflagopen/msaflagopen_step.py | 5 +++-- jwst/wavecorr/tests/test_wavecorr.py | 9 +++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/jwst/msaflagopen/msaflagopen_step.py b/jwst/msaflagopen/msaflagopen_step.py index afe5f7b1b7..9e2cf3ffc1 100755 --- a/jwst/msaflagopen/msaflagopen_step.py +++ b/jwst/msaflagopen/msaflagopen_step.py @@ -1,5 +1,7 @@ from stdatamodels.jwst import datamodels +from stpipe.crds_client import reference_uri_to_cache_path + from ..stpipe import Step from . import msaflag_open @@ -51,7 +53,6 @@ def process(self, input): def create_reference_filename_dictionary(input_model): reffiles = {} - a = Step() reffiles['distortion'] = input_model.meta.ref_file.distortion.name reffiles['filteroffset'] = input_model.meta.ref_file.filteroffset.name reffiles['specwcs'] = input_model.meta.ref_file.filteroffset.name @@ -72,5 +73,5 @@ def create_reference_filename_dictionary(input_model): # Convert from crds protocol to absolute filenames for key in reffiles.keys(): if reffiles[key].startswith('crds://'): - reffiles[key] = a.reference_uri_to_cache_path(reffiles[key], input_model.crds_observatory) + reffiles[key] = reference_uri_to_cache_path(reffiles[key], input_model.crds_observatory) return reffiles diff --git a/jwst/wavecorr/tests/test_wavecorr.py b/jwst/wavecorr/tests/test_wavecorr.py index 5ff61fc241..d2e3d08ce6 100644 --- a/jwst/wavecorr/tests/test_wavecorr.py +++ b/jwst/wavecorr/tests/test_wavecorr.py @@ -7,6 +7,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.transforms import models +from stpipe.crds_client import reference_uri_to_cache_path import jwst from jwst.assign_wcs import AssignWcsStep @@ -59,7 +60,7 @@ def test_wavecorr(): # test the round-tripping on the wavelength correction transform ref_name = im_wave.meta.ref_file.wavecorr.name freference = datamodels.WaveCorrModel( - WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + reference_uri_to_cache_path(ref_name, im.crds_observatory)) lam_uncorr = lam_before * 1e-6 wave2wavecorr = wavecorr.calculate_wavelength_correction_transform( @@ -160,7 +161,7 @@ def test_skipped(): ref_name = outw.meta.ref_file.wavecorr.name reffile = datamodels.WaveCorrModel( - WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + reference_uri_to_cache_path(ref_name, im.crds_observatory)) source_xpos = 0.1 aperture_name = 'S200A1' @@ -262,10 +263,10 @@ def test_wavecorr_fs(): # test the roundtripping on the wavelength correction transform ref_name = result.meta.ref_file.wavecorr.name - freference = datamodels.WaveCorrModel(WavecorrStep.reference_uri_to_cache_path(ref_name, im.crds_observatory)) + freference = datamodels.WaveCorrModel(reference_uri_to_cache_path(ref_name, im.crds_observatory)) lam_uncorr = lam_before * 1e-6 - wave2wavecorr = wavecorr.calculate_wavelength_correction_transform(lam_uncorr, dispersion, + wave2wavecorr = wavecorr.calculate_wavelength_correction_transform(lam_uncorr, dispersion, freference, slit.source_xpos, 'S200A1') lam_corr = wave2wavecorr(lam_uncorr) assert_allclose(lam_uncorr, wave2wavecorr.inverse(lam_corr)) From 773cd934b3d0ae01a7d275e4074dded1d8b057cc Mon Sep 17 00:00:00 2001 From: Howard Bushouse Date: Wed, 12 Jun 2024 15:48:14 -0400 Subject: [PATCH 084/687] Update README for B10.2 install in Ops (#8556) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ab13608d4f..f62f9df79f 100644 --- a/README.md +++ b/README.md @@ -216,7 +216,7 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| -| 1.14.0 | B10.2rc1 | | 1215 | 2024-03-29 | | First release candidate for B10.2 | +| 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | | 1.13.4 | | | 1185 | 2024-01-25 | | PyPI-only release for external users | | 1.13.3 | B10.1 | 2023.4.0 | 1181 | 2024-01-05 | | Final release candidate for B10.1 | | 1.13.2 | B10.1rc3 | 2023.4.0 | 1181 | 2023-12-21 | | Third release candidate for B10.1 | From bfa06203feccc0dd1a4da5d6cac61ab017225895 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 12 Jun 2024 15:39:04 -0700 Subject: [PATCH 085/687] JP-3584-bugfix: fixed failed save for intermediate (median) files (#8555) --- jwst/outlier_detection/outlier_detection_tso.py | 3 ++- jwst/regtest/test_miri_lrs_slitless.py | 5 +++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py index 4333adbbb8..c283156866 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -35,6 +35,7 @@ def __init__(self, input_model: dm.SlitModel | dm.CubeModel | dm.ModelContainer, def do_detection(self): """Flag outlier pixels in DQ of input images.""" + self.build_suffix(**self.outlierpars) weighted_cube = self.weight_no_resample() maskpt = self.outlierpars.get('maskpt', 0.7) @@ -56,7 +57,7 @@ def do_detection(self): # Save median model if pars['save_intermediate_results'] is True # this will be a CubeModel with rolling median values. if self.outlierpars['save_intermediate_results']: - with dm.open(weighted_cube[0]) as dm0: + with dm.open(weighted_cube) as dm0: median_model.update(dm0) self.save_median(median_model) diff --git a/jwst/regtest/test_miri_lrs_slitless.py b/jwst/regtest/test_miri_lrs_slitless.py index 66881c79db..db92f58796 100644 --- a/jwst/regtest/test_miri_lrs_slitless.py +++ b/jwst/regtest/test_miri_lrs_slitless.py @@ -1,3 +1,4 @@ +import os import pytest from astropy.io.fits.diff import FITSDiff from numpy.testing import assert_allclose @@ -63,6 +64,7 @@ def run_tso3_pipeline(run_tso_spec2_pipeline, rtdata_module): "calwebb_tso3", ASN3_FILENAME, "--steps.outlier_detection.save_results=true", + "--steps.outlier_detection.save_intermediate_results=true" ] Step.from_cmdline(args) @@ -107,6 +109,9 @@ def test_miri_lrs_slitless_tso3(run_tso3_pipeline, rtdata_module, """Compare the output of a MIRI LRS slitless calwebb_tso3 pipeline.""" rtdata = rtdata_module + median_filename = f"{DATASET1_ID}_{ASN_ID}_median.fits" + assert os.path.isfile(median_filename) + output_filename = f"{DATASET1_ID}_{ASN_ID}_{step_suffix}.fits" rtdata.output = output_filename rtdata.get_truth(f"truth/test_miri_lrs_slitless_tso3/{output_filename}") From 831124ef9e768abaca412827272bc2e6ce6f60af Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Thu, 13 Jun 2024 07:31:00 -0400 Subject: [PATCH 086/687] JP-3653: Nsclean speedup (#8547) Co-authored-by: Timothy Brandt Co-authored-by: Howard Bushouse --- CHANGES.rst | 7 +++++++ jwst/nsclean/lib.py | 9 ++++++--- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 830a597e8a..375398f4a2 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -138,6 +138,13 @@ master_background_mos ``slit.source_name`` now contains the string "BKG" instead of "background". [#8533] +nsclean +------- + +- Improved run time of NSClean.fit() by using a vector rather than a large, + sparse matrix to perform linear algebra operations with a diagonal weight + matrix. [#8547] + outlier_detection ----------------- diff --git a/jwst/nsclean/lib.py b/jwst/nsclean/lib.py index f348c85431..0316078994 100644 --- a/jwst/nsclean/lib.py +++ b/jwst/nsclean/lib.py @@ -134,7 +134,10 @@ def fit(self, data): # Get data and weights for this line d = data[y][self.mask[y]] # unmasked (useable) data - p = np.diag(self.P[y][self.mask[y]]) # Weights + # The line below uses a vector to represent a diagonal weight matrix. + # Multiplications by this vector later on may be viewed as + # equivalent formulations to multiplication by diag(p). + p = self.P[y][self.mask[y]] # Weights # If none of the pixels in this line is useable (all masked out), # skip and move on to the next line. @@ -164,7 +167,7 @@ def fit(self, data): # Compute the Moore-Penrose inverse of A = P*B. # $A^+ = (A^H A)^{-1} A^H$ - A = np.matmul(p, B) + A = B*p[:, np.newaxis] AH = np.conjugate(A.transpose()) # Hermitian transpose of A pinv_PB = np.matmul(np.linalg.inv(np.matmul(AH, A)), AH) @@ -172,7 +175,7 @@ def fit(self, data): # The way that we have done it, this multiplies the input data by the # number of samples used for the fit. rfft = np.zeros(self.nx//2 + 1, dtype=np.complex64) - rfft[:k.shape[1]] = np.matmul(np.matmul(pinv_PB, p), d) + rfft[:k.shape[1]] = np.matmul(pinv_PB, p*d) # Numpy requires that the forward transform multiply # the data by n. Correct normalization. From 276125cf49329363e711b8ecde710bd816d6d02e Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 20 May 2024 16:16:41 -0400 Subject: [PATCH 087/687] implements option for polyfit smoothing, save here, remove next commit --- jwst/badpix_selfcal/__init__.py | 4 + jwst/badpix_selfcal/badpix_selfcal.py | 200 ++++++++++++++++++ jwst/badpix_selfcal/badpix_selfcal_step.py | 37 ++++ jwst/badpix_selfcal/tests/__init__.py | 0 .../tests/test_badpix_selfcal.py | 130 ++++++++++++ 5 files changed, 371 insertions(+) create mode 100644 jwst/badpix_selfcal/__init__.py create mode 100644 jwst/badpix_selfcal/badpix_selfcal.py create mode 100644 jwst/badpix_selfcal/badpix_selfcal_step.py create mode 100644 jwst/badpix_selfcal/tests/__init__.py create mode 100644 jwst/badpix_selfcal/tests/test_badpix_selfcal.py diff --git a/jwst/badpix_selfcal/__init__.py b/jwst/badpix_selfcal/__init__.py new file mode 100644 index 0000000000..2df875d38b --- /dev/null +++ b/jwst/badpix_selfcal/__init__.py @@ -0,0 +1,4 @@ +from .badpix_selfcal_step import BadpixSelfcalStep + + +__all__ = ['BadpixSelfcalStep'] \ No newline at end of file diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py new file mode 100644 index 0000000000..e7638c6c0f --- /dev/null +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -0,0 +1,200 @@ +from __future__ import annotations +from typing import Protocol +import numpy as np +import jwst.datamodels as dm +from jwst.outlier_detection.outlier_detection_ifu import medfilt + + +def flagger_injector(mode: str) -> BackgroundFlagger: + """ + Inject the appropriate flagger based on the mode + + Parameters + ---------- + mode: str + Mode of the data + + Returns + ------- + BackgroundFlagger + Flagging class + """ + if mode.lower() == 'mrs': + return MRSMedfiltBackgroundFlagger + elif mode.lower() == 'mrs polyfit': + return MRSPolyfitBackgroundFlagger + else: + raise ValueError(f"Mode {mode} not recognized") + + +def badpix_selfcal(input_model: dm.DataModel, flagfrac: float = 0.001) -> dm.DataModel: + + if flagfrac < 0 or flagfrac >=0.5: + raise ValueError("flagfrac must be between 0 and 0.5. \ + Note this fraction will be flagged on both high and low ends.") + + shp = input_model.data.shape + basex,basey = np.meshgrid(np.arange(shp[0]),np.arange(shp[1])) + _,_,lam=input_model.meta.wcs.transform('detector','world',basex,basey) + + bg = input_model.data + # to do: update this so it operates on an association + # where there are multiple exposures; make a 3-D background array + # and then take the min over the exposures + # medbg = np.nanmin(bg, axis=0) + + flagged_indices = flagger_injector('MRS').flag_background(bg, lam, flagfrac) + input_model.data[flagged_indices] = np.nan + input_model.dq[flagged_indices] = 1 + + return input_model + + +class Channel: + + def __init__(self, + bg: np.ndarray, + wls: np.ndarray, + wl_min: float = 0.0, + wl_max: float = np.inf): + """ + Cut full input data and wavelengths to the desired wl range + + Parameters + ---------- + bg: np.ndarray + Background data + + wls: np.ndarray + Wavelengths of the data + + wl_min: float + Minimum wavelength to keep + + wl_max: float + Maximum wavelength to keep + """ + self.wl_min = wl_min + self.wl_max = wl_max + self.good = (wls >= wl_min) & (wls < wl_max) + self.wls = wls[self.good] + self.bg = bg[self.good] + + + def remove_polyfit(self, order: int = 2): + """ + Remove low-order background using polynomial fit + + Parameters + ---------- + order: int + Order of the polynomial fit + + Returns + ------- + np.ndarray + Background data with the polynomial fit removed + """ + xtemp=self.wls.ravel() + ytemp=self.bg.ravel() + indx=np.where((np.isfinite(xtemp))&(np.isfinite(ytemp))) + + temp=np.polyfit(xtemp[indx], ytemp[indx], order) + temp2=np.poly1d(temp) + yfit=temp2(self.wls) + + return self.bg - yfit + + + +class BackgroundFlagger(Protocol): + + def flag_background(self, + bg: np.ndarray, + lam: np.ndarray, + flagfrac: float) -> np.ndarray: + ... + + +class MRSPolyfitBackgroundFlagger(BackgroundFlagger): + + def __init__(self): + + self.pedestal_indices = (138,911,495,514) + + + def flag_background(self, medbg: np.ndarray, lam: np.ndarray, flagfrac: float) -> np.ndarray: + """ + Flag outlier pixels in the MRS data background + + Parameters + ---------- + medbg: np.ndarray + Background data of shape (x, y), i.e., after median has + already been taken over the number of exposures + lam: np.ndarray + Wavelengths of the data, same shape as medbg + flagfrac: float + Fraction of outlier pixels to flag on both high and low end + """ + + # subtract inter-slice pedestal dark + ip = self.pedestal_indices + pedestal_dark_value = np.nanmedian(medbg[ip[0]:ip[1], ip[2]:ip[3]]) + medbg -= pedestal_dark_value + + # Remove average background separately in low and high channels + lam_cutoff = np.nanmean(lam) + channels = [Channel(medbg, lam, wl_max=lam_cutoff), + Channel(medbg, lam, wl_min=lam_cutoff),] + for channel in channels: + bg = channel.remove_polyfit() + medbg[channel.good] = bg + + # Flag outliers using percentile cutoff + flag_low, flag_high = np.nanpercentile(medbg, [flagfrac*100, (1-flagfrac)*100]) + bad = (medbg > flag_high) | (medbg < flag_low) + indx=np.where(bad) + return indx + + +class MRSMedfiltBackgroundFlagger(BackgroundFlagger): + + def __init__(self, kernel_size: int = 15): + + self.kernel_size = kernel_size + self.pedestal_indices = (138,911,495,514) + + + def flag_background(self, medbg: np.ndarray, lam: np.ndarray, flagfrac: float) -> np.ndarray: + """ + Flag outlier pixels in the MRS data background + + Parameters + ---------- + medbg: np.ndarray + Background data of shape (x, y), i.e., after median has + already been taken over the number of exposures + lam: np.ndarray + Wavelengths of the data, same shape as medbg + flagfrac: float + Percentile above which to flag + """ + + # subtract inter-slice pedestal dark + ip = self.pedestal_indices + pedestal_dark_value = np.nanmedian(medbg[ip[0]:ip[1], ip[2]:ip[3]]) + medbg -= pedestal_dark_value + + # Remove average background separately in low and high channels + kernel=np.array([1,self.kernel_size]) + smoothed = medfilt(medbg, kernel) + medbg = medbg - smoothed + + # Flag outliers using percentile cutoff + # TO DO: add negative outliers + # Flag outliers using percentile cutoff + flag_low, flag_high = np.nanpercentile(medbg, [flagfrac*100, (1-flagfrac)*100]) + bad = (medbg > flag_high) | (medbg < flag_low) + indx=np.where(bad) + return indx \ No newline at end of file diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py new file mode 100644 index 0000000000..b3deeb0f7b --- /dev/null +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -0,0 +1,37 @@ + + +from stpipe import Step +from . import badpix_selfcal + + +class BadpixSelfcalStep(Step): + """ + BadpixSelfcalStep: + """ + + class_alias = "badpix_selfcal" + + # Define a suffix for optional saved output + bkg_suffix = 'badpix_selfcal' + + spec = """ + flagpercentile = float(default=99.9) # percentile level above which to flag + skip = boolean(default=True) + """ + + def process(self, input): + """ + Flag CRs in the DQ array of a JWST exposure + + Parameters + ---------- + input: JWST data model or association + input science data to be corrected + + Returns + ------- + output: JWST data model or association + data model with CRs flagged + """ + # Do the work here + return result \ No newline at end of file diff --git a/jwst/badpix_selfcal/tests/__init__.py b/jwst/badpix_selfcal/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py new file mode 100644 index 0000000000..b9f69a4c53 --- /dev/null +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -0,0 +1,130 @@ +import pytest +import numpy as np +from jwst.badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep +from jwst.badpix_selfcal.badpix_selfcal import MRSPolyfitBackgroundFlagger, MRSMedfiltBackgroundFlagger +from jwst import datamodels as dm +from jwst.assign_wcs import AssignWcsStep, miri +from gwcs import wcs +from astropy.io import fits + +wcs_kw = {'wcsaxes': 3, 'ra_ref': 165, 'dec_ref': 54, + 'v2_ref': -8.3942412, 'v3_ref': -5.3123744, 'roll_ref': 37, + 'crpix1': 1024, 'crpix2': 1024, 'crpix3': 0, + 'cdelt1': .08, 'cdelt2': .08, 'cdelt3': 1, + 'ctype1': 'RA---TAN', 'ctype2': 'DEC--TAN', 'ctype3': 'WAVE', + 'pc1_1': 1, 'pc1_2': 0, 'pc1_3': 0, + 'pc2_1': 0, 'pc2_2': 1, 'pc2_3': 0, + 'pc3_1': 0, 'pc3_2': 0, 'pc3_3': 1, + 'cunit1': 'deg', 'cunit2': 'deg', 'cunit3': 'um', + } + +hotpixel_intensity = 50 +outlier_indices = [(100, 100), (300, 300), (500, 600), (1000, 900)] + +def create_hdul(detector, channel, band): + hdul = fits.HDUList() + phdu = fits.PrimaryHDU() + phdu.header['telescop'] = "JWST" + phdu.header['filename'] = "test" + channel + band + phdu.header['instrume'] = 'MIRI' + phdu.header['detector'] = detector + phdu.header['CHANNEL'] = channel + phdu.header['BAND'] = band + phdu.header['time-obs'] = '8:59:37' + phdu.header['date-obs'] = '2017-09-05' + phdu.header['exp_type'] = 'MIR_MRS' + scihdu = fits.ImageHDU() + scihdu.header['EXTNAME'] = "SCI" + scihdu.header.update(wcs_kw) + hdul.append(phdu) + hdul.append(scihdu) + return hdul + + +def create_reference_files(datamodel): + refs = {} + step = AssignWcsStep() + for reftype in AssignWcsStep.reference_file_types: + refs[reftype] = step.get_reference_file(datamodel, reftype) + + return refs + + +@pytest.fixture(scope="module") +def background(): + """ + Create background IFUImageModel for testing. This is a mockup of the expected + background data in a .rate file. + Three components: random noise, low-order variability, and outliers + """ + + # random noise + rng = np.random.default_rng(seed=77) + shp = (1024, 1032) + noise = rng.standard_normal(shp) + + # make 2-d polynomial representing background level + c = np.array([[1, 3, 5], [2, 4, 6]]) + x = np.linspace(-1, 1, shp[0]) + y = np.linspace(-1, 1, shp[1]) + low_order_variability = np.polynomial.polynomial.polygrid2d(x, y, c) + + # add some outliers + outliers = np.zeros(shp) + for idx in outlier_indices: + outliers[idx] += hotpixel_intensity + # one negative one just for fun + outliers[100, 100] = -hotpixel_intensity + + mock_data = low_order_variability + noise + outliers + + # build an IFUImageModel from these data and give it a wcs + hdul = create_hdul(detector="MIRIFULONG", channel="34", band="LONG") + hdul[1].data = mock_data + + im = dm.IFUImageModel(hdul) + ref = create_reference_files(im) + pipeline = miri.create_pipeline(im, ref) + wcsobj = wcs.WCS(pipeline) + im.meta.wcs = wcsobj + + return im + + +@pytest.fixture(scope="module") +def sci(): + """Create science .rate file for testing""" + return + + +@pytest.mark.parametrize("algorithm", [MRSPolyfitBackgroundFlagger, MRSMedfiltBackgroundFlagger]) +def test_background_flagger_mrs(algorithm, background): + """""" + + # first get wavelength array from WCS + bg = background.data + shp = bg.shape + basex,basey = np.meshgrid(np.arange(shp[1]),np.arange(shp[0])) + _,_,lam=background.meta.wcs.transform('detector','world',basex,basey) + + # pass into the MRSBackgroundFlagger and check it found the right pixels + flagfrac = 0.001 + result = algorithm().flag_background(bg, lam, flagfrac) + result_tuples = [(i,j) for i,j in zip(*result)] + + # check that the hot pixels were among those flagged + for idx in outlier_indices: + assert idx in result_tuples + + # check that the number of flagged pixels is as expected + assert np.isclose(len(result_tuples)/bg.size, flagfrac*2, atol=0.0001) + + + +# def test_badpix_selfcal_step(tmp_cwd, background, sci): + +# # Run the badpix_selfcal step +# result = BadpixSelfcalStep.call(sci, background) + +# # Compare the calculated result with the expected result +# assert np.allclose(result.data, expected_result.data) \ No newline at end of file From c6897cce63e68ee9a9249e07f1dcd7a19445dbe3 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 21 May 2024 15:58:08 -0400 Subject: [PATCH 088/687] JP-3581: initial commit of minimal working example --- CHANGES.rst | 6 + jwst/badpix_selfcal/badpix_selfcal.py | 217 ++++-------------- jwst/badpix_selfcal/badpix_selfcal_step.py | 44 +++- .../tests/test_badpix_selfcal.py | 114 +++++++-- jwst/pipeline/badpix_selfcal.cfg | 2 + jwst/pipeline/calwebb_spec2.py | 5 + jwst/regtest/test_miri_mrs_badpix_selfcal.py | 28 +++ jwst/step.py | 2 + 8 files changed, 217 insertions(+), 201 deletions(-) create mode 100644 jwst/pipeline/badpix_selfcal.cfg create mode 100644 jwst/regtest/test_miri_mrs_badpix_selfcal.py diff --git a/CHANGES.rst b/CHANGES.rst index 375398f4a2..2234163dda 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -46,6 +46,12 @@ associations - Add NIRSpec optical path constraints for TSO associations. [#8537] +badpix_selfcal +-------------- + +- Added new optional step ``badpix_selfcal`` to the spec2 pipeline to self-calibrate + bad pixels in IFU data. [#8500] + combine_1d ---------- diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index e7638c6c0f..277677a712 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -1,200 +1,67 @@ from __future__ import annotations -from typing import Protocol import numpy as np import jwst.datamodels as dm from jwst.outlier_detection.outlier_detection_ifu import medfilt -def flagger_injector(mode: str) -> BackgroundFlagger: +def badpix_selfcal(medbg: np.ndarray, + flagfrac: float = 0.001, + kernel_size: int = 15) -> np.ndarray: """ - Inject the appropriate flagger based on the mode - Parameters ---------- - mode: str - Mode of the data + medbg: np.ndarray + Background data of shape (x, y), i.e., after median has + already been taken over the number of exposures + flagfrac: float + Fraction of pixels to flag on each of low and high end + kernel_size: int + Size of kernel for median filter Returns ------- - BackgroundFlagger - Flagging class + flagged_indices: np.ndarray + Indices of the flagged pixels, + shaped like output from np.where """ - if mode.lower() == 'mrs': - return MRSMedfiltBackgroundFlagger - elif mode.lower() == 'mrs polyfit': - return MRSPolyfitBackgroundFlagger - else: - raise ValueError(f"Mode {mode} not recognized") - - -def badpix_selfcal(input_model: dm.DataModel, flagfrac: float = 0.001) -> dm.DataModel: - if flagfrac < 0 or flagfrac >=0.5: raise ValueError("flagfrac must be between 0 and 0.5. \ Note this fraction will be flagged on both high and low ends.") - shp = input_model.data.shape - basex,basey = np.meshgrid(np.arange(shp[0]),np.arange(shp[1])) - _,_,lam=input_model.meta.wcs.transform('detector','world',basex,basey) - - bg = input_model.data - # to do: update this so it operates on an association - # where there are multiple exposures; make a 3-D background array - # and then take the min over the exposures - # medbg = np.nanmin(bg, axis=0) - - flagged_indices = flagger_injector('MRS').flag_background(bg, lam, flagfrac) - input_model.data[flagged_indices] = np.nan - input_model.dq[flagged_indices] = 1 - - return input_model - - -class Channel: - - def __init__(self, - bg: np.ndarray, - wls: np.ndarray, - wl_min: float = 0.0, - wl_max: float = np.inf): - """ - Cut full input data and wavelengths to the desired wl range - - Parameters - ---------- - bg: np.ndarray - Background data - - wls: np.ndarray - Wavelengths of the data - - wl_min: float - Minimum wavelength to keep - - wl_max: float - Maximum wavelength to keep - """ - self.wl_min = wl_min - self.wl_max = wl_max - self.good = (wls >= wl_min) & (wls < wl_max) - self.wls = wls[self.good] - self.bg = bg[self.good] - - - def remove_polyfit(self, order: int = 2): - """ - Remove low-order background using polynomial fit - - Parameters - ---------- - order: int - Order of the polynomial fit - - Returns - ------- - np.ndarray - Background data with the polynomial fit removed - """ - xtemp=self.wls.ravel() - ytemp=self.bg.ravel() - indx=np.where((np.isfinite(xtemp))&(np.isfinite(ytemp))) - - temp=np.polyfit(xtemp[indx], ytemp[indx], order) - temp2=np.poly1d(temp) - yfit=temp2(self.wls) - - return self.bg - yfit - + # Determine outliers using median filter + kernel=np.array([1,kernel_size]) + smoothed = medfilt(medbg, kernel) + medbg_hpf = medbg - smoothed + # Flag outliers using percentile cutoff + flag_low, flag_high = np.nanpercentile(medbg_hpf, [flagfrac*100, (1-flagfrac)*100]) + bad = (medbg_hpf > flag_high) | (medbg_hpf < flag_low) + flagged_indices = np.where(bad) -class BackgroundFlagger(Protocol): + return flagged_indices - def flag_background(self, - bg: np.ndarray, - lam: np.ndarray, - flagfrac: float) -> np.ndarray: - ... +def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> dm.IFUImageModel: + """ + Apply the flagged indices to the input model. -class MRSPolyfitBackgroundFlagger(BackgroundFlagger): - - def __init__(self): - - self.pedestal_indices = (138,911,495,514) - - - def flag_background(self, medbg: np.ndarray, lam: np.ndarray, flagfrac: float) -> np.ndarray: - """ - Flag outlier pixels in the MRS data background - - Parameters - ---------- - medbg: np.ndarray - Background data of shape (x, y), i.e., after median has - already been taken over the number of exposures - lam: np.ndarray - Wavelengths of the data, same shape as medbg - flagfrac: float - Fraction of outlier pixels to flag on both high and low end - """ - - # subtract inter-slice pedestal dark - ip = self.pedestal_indices - pedestal_dark_value = np.nanmedian(medbg[ip[0]:ip[1], ip[2]:ip[3]]) - medbg -= pedestal_dark_value - - # Remove average background separately in low and high channels - lam_cutoff = np.nanmean(lam) - channels = [Channel(medbg, lam, wl_max=lam_cutoff), - Channel(medbg, lam, wl_min=lam_cutoff),] - for channel in channels: - bg = channel.remove_polyfit() - medbg[channel.good] = bg - - # Flag outliers using percentile cutoff - flag_low, flag_high = np.nanpercentile(medbg, [flagfrac*100, (1-flagfrac)*100]) - bad = (medbg > flag_high) | (medbg < flag_low) - indx=np.where(bad) - return indx - - -class MRSMedfiltBackgroundFlagger(BackgroundFlagger): - - def __init__(self, kernel_size: int = 15): - - self.kernel_size = kernel_size - self.pedestal_indices = (138,911,495,514) - - - def flag_background(self, medbg: np.ndarray, lam: np.ndarray, flagfrac: float) -> np.ndarray: - """ - Flag outlier pixels in the MRS data background - - Parameters - ---------- - medbg: np.ndarray - Background data of shape (x, y), i.e., after median has - already been taken over the number of exposures - lam: np.ndarray - Wavelengths of the data, same shape as medbg - flagfrac: float - Percentile above which to flag - """ + Parameters + ---------- + input_model: dm.IFUImageModel + Input science data to be corrected + flagged_indices: np.ndarray + Indices of the flagged pixels, + shaped like output from np.where - # subtract inter-slice pedestal dark - ip = self.pedestal_indices - pedestal_dark_value = np.nanmedian(medbg[ip[0]:ip[1], ip[2]:ip[3]]) - medbg -= pedestal_dark_value + Returns + ------- + output_model: dm.IFUImageModel + Data model with flagged pixels set to NaN + in data and err arrays, and dq flag set to 1 + """ - # Remove average background separately in low and high channels - kernel=np.array([1,self.kernel_size]) - smoothed = medfilt(medbg, kernel) - medbg = medbg - smoothed + input_model.data[flagged_indices] = np.nan + input_model.err[flagged_indices] = np.nan + input_model.dq[flagged_indices] = 1 - # Flag outliers using percentile cutoff - # TO DO: add negative outliers - # Flag outliers using percentile cutoff - flag_low, flag_high = np.nanpercentile(medbg, [flagfrac*100, (1-flagfrac)*100]) - bad = (medbg > flag_high) | (medbg < flag_low) - indx=np.where(bad) - return indx \ No newline at end of file + return input_model diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index b3deeb0f7b..58576ef763 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -1,8 +1,12 @@ -from stpipe import Step +from ..stpipe import Step from . import badpix_selfcal +import numpy as np +from jwst import datamodels as dm +from jwst.master_background.master_background_step import split_container +__all__ = ["BadpixSelfcalStep"] class BadpixSelfcalStep(Step): """ @@ -10,12 +14,11 @@ class BadpixSelfcalStep(Step): """ class_alias = "badpix_selfcal" - - # Define a suffix for optional saved output - bkg_suffix = 'badpix_selfcal' + bkg_suffix = "badpix_selfcal" spec = """ - flagpercentile = float(default=99.9) # percentile level above which to flag + flagfrac = float(default=0.001) #fraction of pixels to flag on each of low and high end + kernel_size = integer(default=15) #size of kernel for median filter skip = boolean(default=True) """ @@ -33,5 +36,32 @@ def process(self, input): output: JWST data model or association data model with CRs flagged """ - # Do the work here - return result \ No newline at end of file + with dm.open(input) as input_data: + + if isinstance(input_data, dm.ModelContainer): + # handle associations; this should be the majority of cases where the step is used + sci_models, background_models = split_container(input_data) + + # collapse background dithers into a single background model + bkgd_list = [] + for i, background_model in enumerate(background_models): + bkgd_list.append(background_model.data) + bkgd = np.nanmin(np.asarray(bkgd_list), axis=0) + bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) + + for model in input_data: + # apply bad indices to both science and background models + badpix_selfcal.apply_flags(model, bad_indices) + + elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): + # true self-calibration on input data itself + bkgd = input_data.data + bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) + input_data = badpix_selfcal.apply_flags(input_data, bad_indices) + + else: + raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ + Cannot continue.") + + self.record_step_status(input_data, "badpix_selfcal", success=True) + return input_data diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index b9f69a4c53..2f79c9d36d 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -1,7 +1,8 @@ +import json import pytest import numpy as np from jwst.badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep -from jwst.badpix_selfcal.badpix_selfcal import MRSPolyfitBackgroundFlagger, MRSMedfiltBackgroundFlagger +from jwst.badpix_selfcal.badpix_selfcal import badpix_selfcal, apply_flags from jwst import datamodels as dm from jwst.assign_wcs import AssignWcsStep, miri from gwcs import wcs @@ -18,7 +19,9 @@ 'cunit1': 'deg', 'cunit2': 'deg', 'cunit3': 'um', } -hotpixel_intensity = 50 +# warm pixels, so need to be much larger than noise, which has std dev of 1, +# but smaller than smoothly-varying background, which maxes around 20 +hotpixel_intensity = 10 outlier_indices = [(100, 100), (300, 300), (500, 600), (1000, 900)] def create_hdul(detector, channel, band): @@ -72,7 +75,7 @@ def background(): # add some outliers outliers = np.zeros(shp) for idx in outlier_indices: - outliers[idx] += hotpixel_intensity + outliers[idx] = hotpixel_intensity # one negative one just for fun outliers[100, 100] = -hotpixel_intensity @@ -92,24 +95,71 @@ def background(): @pytest.fixture(scope="module") -def sci(): - """Create science .rate file for testing""" - return +def sci(background): + """Create science IFUImageMoel for testing. + Same data as background with different noise. + """ + hdul = create_hdul(detector="MIRIFULONG", channel="34", band="LONG") + hdul[1].data = background.data.copy() + rng = np.random.default_rng(seed=99) + hdul[1].data += rng.standard_normal(hdul[1].data.shape) + im = dm.IFUImageModel(hdul) + im.meta.wcs = background.meta.wcs + return im -@pytest.mark.parametrize("algorithm", [MRSPolyfitBackgroundFlagger, MRSMedfiltBackgroundFlagger]) -def test_background_flagger_mrs(algorithm, background): - """""" +@pytest.fixture(scope="module") +def asn(tmp_cwd_module, background, sci): + """Create association for testing. Needs at least + two background images to properly test the step.""" + + sci_path = tmp_cwd_module / "sci.fits" + bkg_path_0 = tmp_cwd_module / "bkg0.fits" + bkg_path_1 = tmp_cwd_module / "bkg1.fits" + sci.save(sci_path) + background.save(bkg_path_0) + background.save(bkg_path_1) + + asn_table = { + "asn_pool": "singleton", + "products": [ + { + "members": [ + { + "expname": "sci.fits", + "exptype": "science" + }, + { + "expname": "bkg0.fits", + "exptype": "background" + }, + { + "expname": "bkg1.fits", + "exptype": "background" + }, + ] + } + ] + } + with open(tmp_cwd_module / 'tmp_asn.json', 'w') as f: + json.dump(asn_table, f) + + container = dm.open(tmp_cwd_module / 'tmp_asn.json') + + return container + + +def test_background_flagger_mrs(background): + """ + Ensure the right number of outliers are found, and that + true outliers are among those. + """ - # first get wavelength array from WCS bg = background.data - shp = bg.shape - basex,basey = np.meshgrid(np.arange(shp[1]),np.arange(shp[0])) - _,_,lam=background.meta.wcs.transform('detector','world',basex,basey) # pass into the MRSBackgroundFlagger and check it found the right pixels flagfrac = 0.001 - result = algorithm().flag_background(bg, lam, flagfrac) + result = badpix_selfcal(bg, flagfrac=flagfrac) result_tuples = [(i,j) for i,j in zip(*result)] # check that the hot pixels were among those flagged @@ -120,11 +170,37 @@ def test_background_flagger_mrs(algorithm, background): assert np.isclose(len(result_tuples)/bg.size, flagfrac*2, atol=0.0001) +def test_apply_flags(background): + """ + Ensure that flagged pixels are set to NaN in the data and err arrays, + and that the DQ flag is set to 1. + """ + + flagged_indices = np.array(outlier_indices).T + flagged_indices = (flagged_indices[0], flagged_indices[1]) -# def test_badpix_selfcal_step(tmp_cwd, background, sci): + flagged = apply_flags(background, flagged_indices) -# # Run the badpix_selfcal step -# result = BadpixSelfcalStep.call(sci, background) + # check that flagged pixels are NaN in data and err arrays + for idx in outlier_indices: + assert np.isnan(flagged.data[idx]) + assert np.isnan(flagged.err[idx]) -# # Compare the calculated result with the expected result -# assert np.allclose(result.data, expected_result.data) \ No newline at end of file + # check that DQ flag is set to 1 + for idx in outlier_indices: + assert flagged.dq[idx] == 1 + + +@pytest.mark.parametrize("dset", ["sci", "asn"]) +def test_badpix_selfcal_step(request, dset): + """Test the badpix_selfcal step. This is a functional test that checks + that the step runs without error. The output will be checked by the + regtest. + """ + input_data = request.getfixturevalue(dset) + result = BadpixSelfcalStep.call(input_data, skip=False) + try: + assert result.meta.cal_step.badpix_selfcal == "COMPLETE" + except AttributeError: + # ModelContainer does not itself have a cal_step attribute + assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" \ No newline at end of file diff --git a/jwst/pipeline/badpix_selfcal.cfg b/jwst/pipeline/badpix_selfcal.cfg new file mode 100644 index 0000000000..baf845bc32 --- /dev/null +++ b/jwst/pipeline/badpix_selfcal.cfg @@ -0,0 +1,2 @@ +name = "badpix_selfcal" +class = "jwst.badpix_selfcal.BadPixSelfcalStep" diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index aa88fcb87e..0db1f4d251 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -13,6 +13,7 @@ # step imports from ..assign_wcs import assign_wcs_step from ..background import background_step +from ..badpix_selfcal import badpix_selfcal_step from ..barshadow import barshadow_step from ..cube_build import cube_build_step from ..extract_1d import extract_1d_step @@ -65,6 +66,7 @@ class Spec2Pipeline(Pipeline): # Define aliases to steps step_defs = { 'assign_wcs': assign_wcs_step.AssignWcsStep, + 'badpix_selfcal': badpix_selfcal_step.BadpixSelfcalStep, 'msa_flagging': msaflagopen_step.MSAFlagOpenStep, 'nsclean': nsclean_step.NSCleanStep, 'bkg_subtract': background_step.BackgroundStep, @@ -244,6 +246,9 @@ def process_exposure_product( # Steps whose order is the same for all types of input: + # self-calibrate bad/warm pixels; skipped by default + calibrated = self.badpix_selfcal(calibrated) + # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py new file mode 100644 index 0000000000..e4755ac928 --- /dev/null +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -0,0 +1,28 @@ +import pytest +from astropy.io.fits.diff import FITSDiff + +from jwst.badpix_selfcal import BadpixSelfcalStep + + +@pytest.fixture(scope="module") +def run_pipeline(rtdata_module): + + rtdata = rtdata_module + rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_asn.json") + BadpixSelfcalStep.call(rtdata.input, skip=False, save_results=True, flagfrac=0.0005) + return rtdata + + +@pytest.mark.bigdata +def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs,): + """Run a test for MIRI MRS data with dedicated background exposures.""" + + rtdata = run_pipeline + rtdata.output = "jw01204-o021_20240127t024203_spec2_00010_asn_0_badpixselfcalstep.fits" + + # Get the truth file + rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204-o021_20240127t024203_spec2_00010_asn_0_badpixselfcalstep.fits") + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() \ No newline at end of file diff --git a/jwst/step.py b/jwst/step.py index 7a5d124b59..1c9b67b58f 100644 --- a/jwst/step.py +++ b/jwst/step.py @@ -4,6 +4,7 @@ from .assign_mtwcs.assign_mtwcs_step import AssignMTWcsStep from .assign_wcs.assign_wcs_step import AssignWcsStep from .background.background_step import BackgroundStep +from .badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep from .barshadow.barshadow_step import BarShadowStep from .charge_migration.charge_migration_step import ChargeMigrationStep from .combine_1d.combine_1d_step import Combine1dStep @@ -68,6 +69,7 @@ "AssignMTWcsStep", "AssignWcsStep", "BackgroundStep", + "BadpixSelfcalStep", "BarShadowStep", "Combine1dStep", "StackRefsStep", From 44a504705c430c36d2565c658a54477d98d25c95 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 22 May 2024 11:22:28 -0400 Subject: [PATCH 089/687] use pipeline to handle backgrounds in association instead of step --- jwst/badpix_selfcal/badpix_selfcal_step.py | 61 +++++++++++++++------- jwst/pipeline/calwebb_spec2.py | 9 ++-- 2 files changed, 45 insertions(+), 25 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 58576ef763..bb2cedca41 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -6,6 +6,10 @@ from jwst import datamodels as dm from jwst.master_background.master_background_step import split_container +import logging +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + __all__ = ["BadpixSelfcalStep"] class BadpixSelfcalStep(Step): @@ -22,7 +26,7 @@ class BadpixSelfcalStep(Step): skip = boolean(default=True) """ - def process(self, input): + def process(self, input, bkg_list=None): """ Flag CRs in the DQ array of a JWST exposure @@ -31,6 +35,12 @@ def process(self, input): input: JWST data model or association input science data to be corrected + bkg_list: list of background ImageModels. + When calwebb_spec2 is run, these are read from the association file + inside calwebb_spec2.py. When run as a standalone step, these are either + read directly from an input association file, or the user can provide + this list manually + Returns ------- output: JWST data model or association @@ -39,29 +49,40 @@ def process(self, input): with dm.open(input) as input_data: if isinstance(input_data, dm.ModelContainer): - # handle associations; this should be the majority of cases where the step is used - sci_models, background_models = split_container(input_data) - - # collapse background dithers into a single background model - bkgd_list = [] - for i, background_model in enumerate(background_models): - bkgd_list.append(background_model.data) - bkgd = np.nanmin(np.asarray(bkgd_list), axis=0) - bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) - - for model in input_data: - # apply bad indices to both science and background models - badpix_selfcal.apply_flags(model, bad_indices) + + sci_models, bkg_list_asn = split_container(input_data) + if bkg_list is None: + bkg_list = bkg_list_asn + else: + log.warning("bkg_list provided as input, ignoring bkg_list from association file") + + # in calwebb_spec2 there should be only a single science exposure in an association + input_sci = sci_models[0] elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): - # true self-calibration on input data itself - bkgd = input_data.data - bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) - input_data = badpix_selfcal.apply_flags(input_data, bad_indices) + + input_sci = input_data + if bkg_list is None: + # true self-calibration on input data itself + bkg_list = [input_data] else: raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ Cannot continue.") - self.record_step_status(input_data, "badpix_selfcal", success=True) - return input_data + # collapse background dithers into a single background model + bkgd_3d = [] + for i, background_model in enumerate(bkg_list): + bkgd_3d.append(background_model.data) + bkgd = np.nanmin(np.asarray(bkgd_3d), axis=0) + bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) + + # apply the flags to the science data + input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) + + # apply the flags to the background data + for i, background_model in enumerate(bkg_list): + bkg_list[i] = badpix_selfcal.apply_flags(background_model, bad_indices) + + self.record_step_status(input_sci, "badpix_selfcal", success=True) + return input_sci, bkg_list diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 0db1f4d251..93ac791486 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -246,8 +246,10 @@ def process_exposure_product( # Steps whose order is the same for all types of input: - # self-calibrate bad/warm pixels; skipped by default - calibrated = self.badpix_selfcal(calibrated) + # self-calibrate bad/warm pixels, and apply to both background and science + # skipped by default for all modes + calibrated, bkg_warmpix_flagged = self.badpix_selfcal(calibrated, members_by_type['background']) + members_by_type['background'] = bkg_warmpix_flagged # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) @@ -259,10 +261,7 @@ def process_exposure_product( # If there is only one `imprint` member, this imprint exposure is subtracted from all the # science and background exposures. Otherwise, there will be as many `imprint` members as # there are science plus background members. - calibrated = self.imprint_subtract(calibrated, members_by_type['imprint']) - - # for each background image subtract an associated leak cal for i, bkg_file in enumerate(members_by_type['background']): bkg_imprint_sub = self.imprint_subtract(bkg_file, members_by_type['imprint']) members_by_type['background'][i] = bkg_imprint_sub From a64c3d95dfb37883b3a98c981b41e0f88eaf4326 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 22 May 2024 13:47:55 -0400 Subject: [PATCH 090/687] fixed unit tests, improved output filename handling --- jwst/badpix_selfcal/badpix_selfcal.py | 3 +- jwst/badpix_selfcal/badpix_selfcal_step.py | 57 ++++++++++++++-------- jwst/lib/suffix.py | 3 +- jwst/stpipe/integration.py | 1 + 4 files changed, 42 insertions(+), 22 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 277677a712..925067a1df 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -2,6 +2,7 @@ import numpy as np import jwst.datamodels as dm from jwst.outlier_detection.outlier_detection_ifu import medfilt +from stdatamodels.jwst.datamodels.dqflags import pixel def badpix_selfcal(medbg: np.ndarray, @@ -62,6 +63,6 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d input_model.data[flagged_indices] = np.nan input_model.err[flagged_indices] = np.nan - input_model.dq[flagged_indices] = 1 + input_model.dq[flagged_indices] = pixel["WARM"] return input_model diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index bb2cedca41..e1eb014ecc 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -23,6 +23,7 @@ class BadpixSelfcalStep(Step): spec = """ flagfrac = float(default=0.001) #fraction of pixels to flag on each of low and high end kernel_size = integer(default=15) #size of kernel for median filter + save_flagged_bkgd = boolean(default=False) #save the flagged background exposures to fits skip = boolean(default=True) """ @@ -35,16 +36,22 @@ def process(self, input, bkg_list=None): input: JWST data model or association input science data to be corrected - bkg_list: list of background ImageModels. - When calwebb_spec2 is run, these are read from the association file - inside calwebb_spec2.py. When run as a standalone step, these are either - read directly from an input association file, or the user can provide - this list manually + bkg_list: list of background ImageModels or filenames. Returns ------- output: JWST data model or association data model with CRs flagged + + Notes + ----- + If bkg_list is specified manually, it overrides the background exposures + in the assocation file. + If bkg_list is set to None and an association file is read in, the background exposures + are read from the association file. + If bkg_list is set to None and input is a single science exposure, the input exposure + will be used as the background exposure, i.e., true self-calibration of + bad pixels. """ with dm.open(input) as input_data: @@ -52,9 +59,10 @@ def process(self, input, bkg_list=None): sci_models, bkg_list_asn = split_container(input_data) if bkg_list is None: - bkg_list = bkg_list_asn + #keeping as ModelContainer causes problems figuring out names to save if save_results=True + bkg_list = list(bkg_list_asn) else: - log.warning("bkg_list provided as input, ignoring bkg_list from association file") + log.warning("bkg_list provided directly as input, ignoring bkg_list from association file") # in calwebb_spec2 there should be only a single science exposure in an association input_sci = sci_models[0] @@ -65,24 +73,33 @@ def process(self, input, bkg_list=None): if bkg_list is None: # true self-calibration on input data itself bkg_list = [input_data] + else: + bkg_list = [dm.open(bkg) for bkg in bkg_list] else: raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ Cannot continue.") - # collapse background dithers into a single background model - bkgd_3d = [] + # collapse background dithers into a single background model + bkgd_3d = [] + for i, background_model in enumerate(bkg_list): + bkgd_3d.append(background_model.data) + bkgd = np.nanmin(np.asarray(bkgd_3d), axis=0) + bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) + + # apply the flags to the science data + input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) + + # apply the flags to the background data + for i, background_model in enumerate(bkg_list): + bkg_list[i] = badpix_selfcal.apply_flags(background_model, bad_indices) + + # save the flagged background exposures if desired + if self.save_flagged_bkgd: for i, background_model in enumerate(bkg_list): - bkgd_3d.append(background_model.data) - bkgd = np.nanmin(np.asarray(bkgd_3d), axis=0) - bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) - - # apply the flags to the science data - input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) + stem = background_model.meta.filename.strip(".fits") + background_model.save(f"{stem}_{self.suffix}.fits") - # apply the flags to the background data - for i, background_model in enumerate(bkg_list): - bkg_list[i] = badpix_selfcal.apply_flags(background_model, bad_indices) - - self.record_step_status(input_sci, "badpix_selfcal", success=True) + self.record_step_status(input_sci, "badpix_selfcal", success=True) + return input_sci, bkg_list diff --git a/jwst/lib/suffix.py b/jwst/lib/suffix.py index cab5be43a9..3fff82ba9f 100644 --- a/jwst/lib/suffix.py +++ b/jwst/lib/suffix.py @@ -195,7 +195,8 @@ 'image2pipeline', 'klip', 'emicorr', - 'emicorrstep' + 'emicorrstep', + 'badpixselfcalstep', } diff --git a/jwst/stpipe/integration.py b/jwst/stpipe/integration.py index b43f4abff5..597a6afe3e 100644 --- a/jwst/stpipe/integration.py +++ b/jwst/stpipe/integration.py @@ -37,6 +37,7 @@ def get_steps(): ("jwst.step.AssignMTWcsStep", 'assign_mtwcs', False), ("jwst.step.AssignWcsStep", 'assign_wcs', False), ("jwst.step.BackgroundStep", 'background', False), + ("jwst.step.BadpixSelfcalStep", 'badpix_selfcal', False), ("jwst.step.BarShadowStep", 'barshadow', False), ("jwst.step.Combine1dStep", 'combine_1d', False), ("jwst.step.StackRefsStep", 'stack_refs', False), From 881171003deefb3839755516304ba166c35d49b3 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 22 May 2024 14:44:24 -0400 Subject: [PATCH 091/687] fixed pipeline failures when step skipped --- jwst/badpix_selfcal/tests/test_badpix_selfcal.py | 3 ++- jwst/pipeline/calwebb_spec2.py | 8 ++++++-- jwst/pipeline/tests/test_calwebb_spec2.py | 2 ++ 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index 2f79c9d36d..0e60bc56f5 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -7,6 +7,7 @@ from jwst.assign_wcs import AssignWcsStep, miri from gwcs import wcs from astropy.io import fits +from stdatamodels.jwst.datamodels.dqflags import pixel wcs_kw = {'wcsaxes': 3, 'ra_ref': 165, 'dec_ref': 54, 'v2_ref': -8.3942412, 'v3_ref': -5.3123744, 'roll_ref': 37, @@ -188,7 +189,7 @@ def test_apply_flags(background): # check that DQ flag is set to 1 for idx in outlier_indices: - assert flagged.dq[idx] == 1 + assert flagged.dq[idx] == pixel["WARM"] @pytest.mark.parametrize("dset", ["sci", "asn"]) diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 93ac791486..5a39c7631b 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -248,8 +248,12 @@ def process_exposure_product( # self-calibrate bad/warm pixels, and apply to both background and science # skipped by default for all modes - calibrated, bkg_warmpix_flagged = self.badpix_selfcal(calibrated, members_by_type['background']) - members_by_type['background'] = bkg_warmpix_flagged + result = self.badpix_selfcal(calibrated, members_by_type['background']) + if isinstance(result, tuple): + # if step is skipped, result is just the input model + # if step actually occurs, then flagged backgroundss are also returned + calibrated, bkg_warmpix_flagged = result + members_by_type['background'] = bkg_warmpix_flagged # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) diff --git a/jwst/pipeline/tests/test_calwebb_spec2.py b/jwst/pipeline/tests/test_calwebb_spec2.py index 59b2148bc1..d060aa9cd0 100644 --- a/jwst/pipeline/tests/test_calwebb_spec2.py +++ b/jwst/pipeline/tests/test_calwebb_spec2.py @@ -64,6 +64,7 @@ def run_spec2_pipeline(make_dummy_rate_file, request): and skipping most of the rest for runtime ''' args = ["calwebb_spec2", INPUT_FILE, + "--steps.badpix_selfcal.skip=true", "--steps.msa_flagging.skip=true", "--steps.nsclean.skip=true", "--steps.flat_field.skip=true", @@ -92,6 +93,7 @@ def run_spec2_pipeline_asn(make_dummy_association, request): args = ["calwebb_spec2", INPUT_ASN, f"--logcfg={LOGCFG}", + "--steps.badpix_selfcal.skip=true", "--steps.msa_flagging.skip=true", "--steps.nsclean.skip=true", "--steps.flat_field.skip=true", From c19156f32382f0856417556360ce56711ccc4371 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 22 May 2024 16:20:29 -0400 Subject: [PATCH 092/687] added docs --- docs/jwst/badpix_selfcal/arguments.rst | 14 +++++ docs/jwst/badpix_selfcal/description.rst | 54 ++++++++++++++++++++ docs/jwst/badpix_selfcal/index.rst | 14 +++++ docs/jwst/badpix_selfcal/reference_files.rst | 4 ++ docs/jwst/package_index.rst | 1 + 5 files changed, 87 insertions(+) create mode 100644 docs/jwst/badpix_selfcal/arguments.rst create mode 100644 docs/jwst/badpix_selfcal/description.rst create mode 100644 docs/jwst/badpix_selfcal/index.rst create mode 100644 docs/jwst/badpix_selfcal/reference_files.rst diff --git a/docs/jwst/badpix_selfcal/arguments.rst b/docs/jwst/badpix_selfcal/arguments.rst new file mode 100644 index 0000000000..957c4acd7c --- /dev/null +++ b/docs/jwst/badpix_selfcal/arguments.rst @@ -0,0 +1,14 @@ +Step Arguments +============== +The ``badpix_selfcal`` step has the following optional arguments. + +``--flagfrac`` (float, default=0.001) + The fraction of pixels to flag as outliers on each of the low-flux and high-flux + sides of the smoothed-subtracted image. + +``--kernel_size`` (integer, default=15) + The size of the kernel to use for the median filter, which is applied + in the spectral (Y-axis) direction to make the smoothed image. + +``--save_flagged_bkgd`` (boolean, default=False) + Whether to save the flagged background exposures to fits files. diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst new file mode 100644 index 0000000000..9d1ed7e361 --- /dev/null +++ b/docs/jwst/badpix_selfcal/description.rst @@ -0,0 +1,54 @@ +Description +=========== + +:Class: `jwst.badpix_selfcal.BadpixSelfcalStep` +:Alias: badpix_selfcal + +Overview +-------- +The ``badpix_selfcal`` step flags bad pixels in the input data using a self-calibration +technique based on median filtering along the spectral axis. +When background exposures are available, those are used to identify bad pixels; +when unavailable, the science data itself is used. +This correction is applied to `~jwst.datamodels.IFUImageModel` data +directly after the :ref:`assign_wcs ` correction has been applied +in the :ref:`calwebb_spec2 ` pipeline. + +Input details +------------- +The input data must be in the form of a `~jwst.datamodels.IFUImageModel` or +a `~jwst.datamodels.ModelContainer` containing exactly one +science exposure and any number of background exposures. A fits or association file +that can be read into one of these data models is also acceptable. + +Algorithm +--------- +The algorithm relies on the assumption that bad pixels are outliers in the data along +the spectral axis. The algorithm proceeds as follows: +* A combined background image is created. If multiple background exposures are available, + the pixelwise minimum of all background exposures is taken. If only one background exposure + is available, it is used without modification. If no background exposures are + available, the science data itself is passed in without modification, serving as the + "background image" for the rest of the procedure, i.e., true self-calibration. + +* The combined background image is median-filtered, ignoring NaNs, along the spectral (Y-) axis + with a user-specified kernel size. The default kernel size is 15 pixels. + +* The difference between the original background image and the median-filtered background image + is taken. The highest- and lowest-flux pixels in this difference image are + flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels + on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted + using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. + +* The bad pixels are flagged in the input data by setting the DQ flag to "WARM". + +* The bad pixels are also flagged in each background exposure, if available. + +Output product +-------------- +The output is a new copy of the input `~jwst.datamodels.IFUImageModel`, with the +bad pixels flagged. If the optional ``save_flagged_bkgd`` parameter is set to +``True``, the background exposures with the flagged pixels will also be saved +as separate files. If the entire ``calwebb_spec2`` pipeline is run, the background +exposures passed into the ``background`` step will include the flags from the +``badpix_selfcal`` step. diff --git a/docs/jwst/badpix_selfcal/index.rst b/docs/jwst/badpix_selfcal/index.rst new file mode 100644 index 0000000000..74fc4d2296 --- /dev/null +++ b/docs/jwst/badpix_selfcal/index.rst @@ -0,0 +1,14 @@ +.. _badpix_selfcal_step: + +========================== +Bad Pixel Self-Calibration +========================== + +.. toctree:: + :maxdepth: 2 + + description.rst + arguments.rst + reference_files.rst + +.. automodapi:: jwst.badpix_selfcal diff --git a/docs/jwst/badpix_selfcal/reference_files.rst b/docs/jwst/badpix_selfcal/reference_files.rst new file mode 100644 index 0000000000..b433d1288f --- /dev/null +++ b/docs/jwst/badpix_selfcal/reference_files.rst @@ -0,0 +1,4 @@ +Reference Files +=============== +The ``badpix_selfcal`` step does not use any reference files. + diff --git a/docs/jwst/package_index.rst b/docs/jwst/package_index.rst index 9a75211246..c5d2096ae8 100644 --- a/docs/jwst/package_index.rst +++ b/docs/jwst/package_index.rst @@ -13,6 +13,7 @@ Package Index associations/index.rst background_step/index.rst background_subtraction/index.rst + badpix_selfcal/index.rst barshadow/index.rst charge_migration/index.rst combine_1d/index.rst From 40019008350b160ce86b43a3556aa4c463f158a5 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 22 May 2024 16:40:43 -0400 Subject: [PATCH 093/687] attempted fix readthedocs build --- docs/jwst/badpix_selfcal/description.rst | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst index 9d1ed7e361..4eae9b7403 100644 --- a/docs/jwst/badpix_selfcal/description.rst +++ b/docs/jwst/badpix_selfcal/description.rst @@ -26,22 +26,18 @@ Algorithm The algorithm relies on the assumption that bad pixels are outliers in the data along the spectral axis. The algorithm proceeds as follows: * A combined background image is created. If multiple background exposures are available, - the pixelwise minimum of all background exposures is taken. If only one background exposure - is available, it is used without modification. If no background exposures are - available, the science data itself is passed in without modification, serving as the - "background image" for the rest of the procedure, i.e., true self-calibration. - +the pixelwise minimum of all background exposures is taken. If only one background exposure +is available, it is used without modification. If no background exposures are +available, the science data itself is passed in without modification, serving as the +"background image" for the rest of the procedure, i.e., true self-calibration. * The combined background image is median-filtered, ignoring NaNs, along the spectral (Y-) axis - with a user-specified kernel size. The default kernel size is 15 pixels. - +with a user-specified kernel size. The default kernel size is 15 pixels. * The difference between the original background image and the median-filtered background image - is taken. The highest- and lowest-flux pixels in this difference image are - flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels - on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted - using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. - +is taken. The highest- and lowest-flux pixels in this difference image are +flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels +on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted +using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. * The bad pixels are flagged in the input data by setting the DQ flag to "WARM". - * The bad pixels are also flagged in each background exposure, if available. Output product From 7a314fc5d9256d62ad5ed53f374db541a8a00c67 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 27 May 2024 14:22:25 -0400 Subject: [PATCH 094/687] responding to @melanieclarke and @drlaw1558 comments --- docs/jwst/badpix_selfcal/description.rst | 34 +++++++------- docs/jwst/pipeline/calwebb_spec2.rst | 2 + jwst/badpix_selfcal/badpix_selfcal.py | 19 +++++--- jwst/badpix_selfcal/badpix_selfcal_step.py | 44 ++++++++++++------- .../tests/test_badpix_selfcal.py | 10 +++-- jwst/pipeline/badpix_selfcal.cfg | 2 - jwst/pipeline/calwebb_spec2.py | 6 +-- 7 files changed, 70 insertions(+), 47 deletions(-) delete mode 100644 jwst/pipeline/badpix_selfcal.cfg diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst index 4eae9b7403..0990b27726 100644 --- a/docs/jwst/badpix_selfcal/description.rst +++ b/docs/jwst/badpix_selfcal/description.rst @@ -8,8 +8,10 @@ Overview -------- The ``badpix_selfcal`` step flags bad pixels in the input data using a self-calibration technique based on median filtering along the spectral axis. -When background exposures are available, those are used to identify bad pixels; -when unavailable, the science data itself is used. +When background exposures are available, those are used in combination with the science +exposure to identify bad pixels; when unavailable, the step will be skipped with a warning +unless the ``force_single`` parameter is set True. In that case, the science data alone is +used as its own "background". This correction is applied to `~jwst.datamodels.IFUImageModel` data directly after the :ref:`assign_wcs ` correction has been applied in the :ref:`calwebb_spec2 ` pipeline. @@ -25,26 +27,26 @@ Algorithm --------- The algorithm relies on the assumption that bad pixels are outliers in the data along the spectral axis. The algorithm proceeds as follows: -* A combined background image is created. If multiple background exposures are available, -the pixelwise minimum of all background exposures is taken. If only one background exposure -is available, it is used without modification. If no background exposures are -available, the science data itself is passed in without modification, serving as the -"background image" for the rest of the procedure, i.e., true self-calibration. + +* A combined background image is created. If background exposures are available, + the pixelwise minimum of all background and science exposures is taken. + If no background exposures are available, the science data itself is passed in + without modification, serving as the "background image" for the rest of the procedure, + i.e., true self-calibration. * The combined background image is median-filtered, ignoring NaNs, along the spectral (Y-) axis -with a user-specified kernel size. The default kernel size is 15 pixels. + with a user-specified kernel size. The default kernel size is 15 pixels. * The difference between the original background image and the median-filtered background image -is taken. The highest- and lowest-flux pixels in this difference image are -flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels -on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted -using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. -* The bad pixels are flagged in the input data by setting the DQ flag to "WARM". + is taken. The highest- and lowest-flux pixels in this difference image are + flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels + on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted + using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. +* The bad pixels are flagged in the input data by setting the DQ flag to + "OTHER_BAD_PIXEL" and "DO_NOT_USE". * The bad pixels are also flagged in each background exposure, if available. Output product -------------- The output is a new copy of the input `~jwst.datamodels.IFUImageModel`, with the -bad pixels flagged. If the optional ``save_flagged_bkgd`` parameter is set to -``True``, the background exposures with the flagged pixels will also be saved -as separate files. If the entire ``calwebb_spec2`` pipeline is run, the background +bad pixels flagged. If the entire ``calwebb_spec2`` pipeline is run, the background exposures passed into the ``background`` step will include the flags from the ``badpix_selfcal`` step. diff --git a/docs/jwst/pipeline/calwebb_spec2.rst b/docs/jwst/pipeline/calwebb_spec2.rst index fa53774c89..af3284c6c6 100644 --- a/docs/jwst/pipeline/calwebb_spec2.rst +++ b/docs/jwst/pipeline/calwebb_spec2.rst @@ -47,6 +47,8 @@ TSO exposures. The instrument mode abbreviations used in the table are as follow +==========================================================+=====+=====+=====+=====+=====+=====+=================+======+========+=====+ | :ref:`assign_wcs ` | |c| | |c| | |c| | |c| | |c| | |c| | |c| | |c| | |c| | |c| | +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ +| :ref:`badpix_selfcal `\ :sup:`2` | | | |c| | | | |c| | | | | | ++----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ | :ref:`msaflagopen ` | | |c| | |c| | | | | | | | | +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ | :ref:`nsclean ` | |c| | |c| | |c| | | | | | | | | diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 925067a1df..a96e413ea7 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -9,11 +9,14 @@ def badpix_selfcal(medbg: np.ndarray, flagfrac: float = 0.001, kernel_size: int = 15) -> np.ndarray: """ + Flag residual artifacts as bad pixels in the DQ array of a JWST exposure + Parameters ---------- medbg: np.ndarray - Background data of shape (x, y), i.e., after median has - already been taken over the number of exposures + Background data of shape (x, y), i.e., after some operation has + already been taken to combine multiple exposures, + typically a MIN operation. flagfrac: float Fraction of pixels to flag on each of low and high end kernel_size: int @@ -44,7 +47,8 @@ def badpix_selfcal(medbg: np.ndarray, def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> dm.IFUImageModel: """ - Apply the flagged indices to the input model. + Apply the flagged indices to the input model. Sets the flagged pixels to NaN + and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL Parameters ---------- @@ -57,12 +61,15 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d Returns ------- output_model: dm.IFUImageModel - Data model with flagged pixels set to NaN - in data and err arrays, and dq flag set to 1 + Flagged data model """ + input_model.dq[flagged_indices] = pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] + input_model.data[flagged_indices] = np.nan input_model.err[flagged_indices] = np.nan - input_model.dq[flagged_indices] = pixel["WARM"] + input_model.var_poisson[flagged_indices] = np.nan + input_model.var_rnoise[flagged_indices] = np.nan + input_model.var_flat[flagged_indices] = np.nan return input_model diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index e1eb014ecc..1901206f6c 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -14,7 +14,13 @@ class BadpixSelfcalStep(Step): """ - BadpixSelfcalStep: + BadpixSelfcalStep: Flags residual artifacts as bad pixels in the DQ array + of a JWST exposure using a median filter and percentile cutoffs. + + All input exposures in the association file (or manually-provided bkg_list) are combined + into a single background model using a MIN operation. The bad pixels are then identified + using a median filter and percentile cutoffs, and applied to the science data by setting + the flagged pixels to NaN and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL. """ class_alias = "badpix_selfcal" @@ -23,13 +29,13 @@ class BadpixSelfcalStep(Step): spec = """ flagfrac = float(default=0.001) #fraction of pixels to flag on each of low and high end kernel_size = integer(default=15) #size of kernel for median filter - save_flagged_bkgd = boolean(default=False) #save the flagged background exposures to fits + force_single = boolean(default=False) #force single input exposure skip = boolean(default=True) """ def process(self, input, bkg_list=None): """ - Flag CRs in the DQ array of a JWST exposure + Flag residual artifacts as bad pixels in the DQ array of a JWST exposure Parameters ---------- @@ -49,18 +55,21 @@ def process(self, input, bkg_list=None): in the assocation file. If bkg_list is set to None and an association file is read in, the background exposures are read from the association file. - If bkg_list is set to None and input is a single science exposure, the input exposure - will be used as the background exposure, i.e., true self-calibration of - bad pixels. + If bkg_list is set to None and input is a single science exposure, the step will + be skipped with a warning unless the force_single parameter is set True. + In that case, the input exposure will be used as the sole background exposure, + i.e., true self-calibration. """ with dm.open(input) as input_data: + # find science and background exposures. + # note that bkg_list includes the science exposure. This is ok because + # all exposures are combined into a single background model using a MIN operation. if isinstance(input_data, dm.ModelContainer): sci_models, bkg_list_asn = split_container(input_data) if bkg_list is None: - #keeping as ModelContainer causes problems figuring out names to save if save_results=True - bkg_list = list(bkg_list_asn) + bkg_list = list(sci_models) + list(bkg_list_asn) else: log.warning("bkg_list provided directly as input, ignoring bkg_list from association file") @@ -74,12 +83,19 @@ def process(self, input, bkg_list=None): # true self-calibration on input data itself bkg_list = [input_data] else: - bkg_list = [dm.open(bkg) for bkg in bkg_list] + bkg_list = [input_data] + [dm.open(bkg) for bkg in bkg_list] else: raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ Cannot continue.") - + + # ensure that there are background exposures to use, otherwise skip the step + # unless forced + if (len(bkg_list) == 0) and (not self.force_single): + log.warning("No background exposures provided for self-calibration. Skipping step.") + self.record_step_status(input_sci, "badpix_selfcal", success=False) + return input_sci, bkg_list + # collapse background dithers into a single background model bkgd_3d = [] for i, background_model in enumerate(bkg_list): @@ -91,14 +107,8 @@ def process(self, input, bkg_list=None): input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) # apply the flags to the background data - for i, background_model in enumerate(bkg_list): + for i, background_model in enumerate(bkg_list[1:]): bkg_list[i] = badpix_selfcal.apply_flags(background_model, bad_indices) - - # save the flagged background exposures if desired - if self.save_flagged_bkgd: - for i, background_model in enumerate(bkg_list): - stem = background_model.meta.filename.strip(".fits") - background_model.save(f"{stem}_{self.suffix}.fits") self.record_step_status(input_sci, "badpix_selfcal", success=True) diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index 0e60bc56f5..8df4c772e0 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -20,7 +20,8 @@ 'cunit1': 'deg', 'cunit2': 'deg', 'cunit3': 'um', } -# warm pixels, so need to be much larger than noise, which has std dev of 1, +# these are often warm pixels, so need to be much larger than noise, +# which has std dev of 1, # but smaller than smoothly-varying background, which maxes around 20 hotpixel_intensity = 10 outlier_indices = [(100, 100), (300, 300), (500, 600), (1000, 900)] @@ -186,10 +187,13 @@ def test_apply_flags(background): for idx in outlier_indices: assert np.isnan(flagged.data[idx]) assert np.isnan(flagged.err[idx]) + assert np.isnan(flagged.var_poisson[idx]) + assert np.isnan(flagged.var_rnoise[idx]) + assert np.isnan(flagged.var_flat[idx]) - # check that DQ flag is set to 1 + # check that DQ flag is set properly for idx in outlier_indices: - assert flagged.dq[idx] == pixel["WARM"] + assert flagged.dq[idx] == pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] @pytest.mark.parametrize("dset", ["sci", "asn"]) diff --git a/jwst/pipeline/badpix_selfcal.cfg b/jwst/pipeline/badpix_selfcal.cfg deleted file mode 100644 index baf845bc32..0000000000 --- a/jwst/pipeline/badpix_selfcal.cfg +++ /dev/null @@ -1,2 +0,0 @@ -name = "badpix_selfcal" -class = "jwst.badpix_selfcal.BadPixSelfcalStep" diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 5a39c7631b..0a1a9a095a 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -251,9 +251,9 @@ def process_exposure_product( result = self.badpix_selfcal(calibrated, members_by_type['background']) if isinstance(result, tuple): # if step is skipped, result is just the input model - # if step actually occurs, then flagged backgroundss are also returned - calibrated, bkg_warmpix_flagged = result - members_by_type['background'] = bkg_warmpix_flagged + # if step actually occurs, then flagged backgrounds are also returned + calibrated, bkg_outlier_flagged = result + members_by_type['background'] = bkg_outlier_flagged # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) From 3dd8e8633d72913877085ee3f4156980aa55b740 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 29 May 2024 10:49:06 -0400 Subject: [PATCH 095/687] added support for selfcal files in asn --- jwst/badpix_selfcal/badpix_selfcal_step.py | 27 +++++---- .../tests/test_badpix_selfcal.py | 28 +++++++--- .../master_background_step.py | 55 +++++++++++++------ jwst/pipeline/calwebb_spec2.py | 6 +- 4 files changed, 76 insertions(+), 40 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 1901206f6c..a2d8b2bbf5 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -51,10 +51,11 @@ def process(self, input, bkg_list=None): Notes ----- - If bkg_list is specified manually, it overrides the background exposures - in the assocation file. - If bkg_list is set to None and an association file is read in, the background exposures - are read from the association file. + If bkg_list is specified manually, it overrides any non-science exposures + in the association file. + If bkg_list is set to None and an association file is read in, all exposures in the + association file, including science, background, and selfcal exposures, + are included in the MIN frame from which outliers are detected. If bkg_list is set to None and input is a single science exposure, the step will be skipped with a warning unless the force_single parameter is set True. In that case, the input exposure will be used as the sole background exposure, @@ -67,9 +68,11 @@ def process(self, input, bkg_list=None): # all exposures are combined into a single background model using a MIN operation. if isinstance(input_data, dm.ModelContainer): - sci_models, bkg_list_asn = split_container(input_data) + sci_models, bkg_list_asn, selfcal_list_asn = split_container(input_data, + fields=['science', 'background', 'selfcal']) + if bkg_list is None: - bkg_list = list(sci_models) + list(bkg_list_asn) + bkg_list = list(sci_models) + list(bkg_list_asn) + list(selfcal_list_asn) else: log.warning("bkg_list provided directly as input, ignoring bkg_list from association file") @@ -79,6 +82,7 @@ def process(self, input, bkg_list=None): elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): input_sci = input_data + bkg_list_asn = [] if bkg_list is None: # true self-calibration on input data itself bkg_list = [input_data] @@ -94,7 +98,7 @@ def process(self, input, bkg_list=None): if (len(bkg_list) == 0) and (not self.force_single): log.warning("No background exposures provided for self-calibration. Skipping step.") self.record_step_status(input_sci, "badpix_selfcal", success=False) - return input_sci, bkg_list + return input_sci, bkg_list_asn # collapse background dithers into a single background model bkgd_3d = [] @@ -106,10 +110,11 @@ def process(self, input, bkg_list=None): # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) - # apply the flags to the background data - for i, background_model in enumerate(bkg_list[1:]): - bkg_list[i] = badpix_selfcal.apply_flags(background_model, bad_indices) + # apply the flags to the background data to be passed to background sub step + if len(bkg_list_asn) > 0: + for i, background_model in enumerate(bkg_list_asn): + bkg_list_asn[i] = badpix_selfcal.apply_flags(background_model, bad_indices) self.record_step_status(input_sci, "badpix_selfcal", success=True) - return input_sci, bkg_list + return input_sci, *bkg_list_asn diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index 8df4c772e0..8ba53ea3c4 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -116,11 +116,11 @@ def asn(tmp_cwd_module, background, sci): two background images to properly test the step.""" sci_path = tmp_cwd_module / "sci.fits" - bkg_path_0 = tmp_cwd_module / "bkg0.fits" - bkg_path_1 = tmp_cwd_module / "bkg1.fits" sci.save(sci_path) - background.save(bkg_path_0) - background.save(bkg_path_1) + + for root in ["bkg0", "bkg1", "selfcal0", "selfcal1"]: + path = tmp_cwd_module / (root + ".fits") + background.save(path) asn_table = { "asn_pool": "singleton", @@ -139,6 +139,14 @@ def asn(tmp_cwd_module, background, sci): "expname": "bkg1.fits", "exptype": "background" }, + { + "expname": "selfcal0.fits", + "exptype": "selfcal" + }, + { + "expname": "selfcal1.fits", + "exptype": "selfcal" + } ] } ] @@ -204,8 +212,10 @@ def test_badpix_selfcal_step(request, dset): """ input_data = request.getfixturevalue(dset) result = BadpixSelfcalStep.call(input_data, skip=False) - try: - assert result.meta.cal_step.badpix_selfcal == "COMPLETE" - except AttributeError: - # ModelContainer does not itself have a cal_step attribute - assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" \ No newline at end of file + + assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" + if dset == "sci": + assert len(result) == 1 + else: + # should return sci, bkg0, bkg1 but not selfcal0, selfcal1 + assert len(result) == 3 diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 76efbbff64..418af6ea25 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -236,29 +236,48 @@ def copy_background_to_surf_bright(spectrum): spec.spec_table['BKGD_ERROR'][:] = 0. -def split_container(container): - """Divide a ModelContainer with science and background into one of each +def split_container(container: ModelContainer, fields=['science', 'background']): """ - asn = container.meta.asn_table.instance + Divide a ModelContainer with multiple exposure types, e.g. + science, background, and selfcal members, into separate containers. + Similar to, but distinct from, the split_container + method in MasterBackgroundStep, because this method + includes selfcal member handling. - background = ModelContainer() - science = ModelContainer() + Parameters + ---------- + container: ModelContainer + The input container - for ind_science in container.ind_asn_type('science'): - science.append(container._models[ind_science]) + fields: list, optional. + The list of exposure type strings to split the container into. Default + is ['science', 'background']. - for ind_bkgd in container.ind_asn_type('background'): - background.append(container._models[ind_bkgd]) + Returns + ------- + container_list: list + List of ModelContainers, one for each field. + """ + asn = container.meta.asn_table.instance - # Pass along the association table to the output science container - science.meta.asn_table = {} - science.asn_pool_name = container.asn_pool_name - science.asn_table_name = container.asn_table_name - merge_tree(science.meta.asn_table.instance, asn) - # Prune the background members from the table - for p in science.meta.asn_table.instance['products']: - p['members'] = [m for m in p['members'] if m['exptype'].lower() != 'background'] - return science, background + container_list = [] + for field in fields: + sub_container = ModelContainer() + for ind_field in container.ind_asn_type(field): + sub_container.append(container._models[ind_field]) + + # Pass along the association table to the output container + if field == "science": + sub_container.meta.asn_table = {} + sub_container.asn_pool_name = container.asn_pool_name + sub_container.asn_table_name = container.asn_table_name + merge_tree(sub_container.meta.asn_table.instance, asn) + # Prune the non-science members from the table + for p in sub_container.meta.asn_table.instance['products']: + p['members'] = [m for m in p['members'] if m['exptype'].lower() == 'science'] + container_list.append(sub_container) + + return container_list def subtract_2d_background(source, background): diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 0a1a9a095a..e6f40fbb9c 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -250,10 +250,12 @@ def process_exposure_product( # skipped by default for all modes result = self.badpix_selfcal(calibrated, members_by_type['background']) if isinstance(result, tuple): - # if step is skipped, result is just the input model # if step actually occurs, then flagged backgrounds are also returned - calibrated, bkg_outlier_flagged = result + calibrated, bkg_outlier_flagged = result[0], result[1:] members_by_type['background'] = bkg_outlier_flagged + else: + # if step is skipped, result is just input + calibrated = result # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) From b06258db541e6c62c1000cbb71cec702aa37897d Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 6 Jun 2024 16:21:21 -0400 Subject: [PATCH 096/687] responding to @drlaw1588 additional comments --- jwst/badpix_selfcal/badpix_selfcal.py | 31 +++-- jwst/badpix_selfcal/badpix_selfcal_step.py | 125 +++++++++++------- .../tests/test_badpix_selfcal.py | 97 +++++++++++++- 3 files changed, 192 insertions(+), 61 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index a96e413ea7..8b6b04c15c 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -5,9 +5,10 @@ from stdatamodels.jwst.datamodels.dqflags import pixel -def badpix_selfcal(medbg: np.ndarray, - flagfrac: float = 0.001, - kernel_size: int = 15) -> np.ndarray: +def badpix_selfcal(medbg: np.ndarray, + flagfrac: float = 0.001, + kernel_size: int = 15, + dispaxis=None) -> np.ndarray: """ Flag residual artifacts as bad pixels in the DQ array of a JWST exposure @@ -21,24 +22,36 @@ def badpix_selfcal(medbg: np.ndarray, Fraction of pixels to flag on each of low and high end kernel_size: int Size of kernel for median filter + dispaxis: int + Dispersion axis, either 1 or 2. If None, a two-dimensional + median filter is applied. Returns ------- flagged_indices: np.ndarray - Indices of the flagged pixels, + Indices of the flagged pixels, shaped like output from np.where """ - if flagfrac < 0 or flagfrac >=0.5: + if flagfrac < 0 or flagfrac >= 0.5: raise ValueError("flagfrac must be between 0 and 0.5. \ Note this fraction will be flagged on both high and low ends.") + if dispaxis not in [1, 2, None]: + raise ValueError("dispaxis must be either 1 or 2, or None.") # Determine outliers using median filter - kernel=np.array([1,kernel_size]) - smoothed = medfilt(medbg, kernel) + if dispaxis is None: + kern_size = (kernel_size, kernel_size) + elif dispaxis == 2: + # check that this is the right way around! + kern_size = (kernel_size, 1) + elif dispaxis == 1: + kern_size = (1, kernel_size) + + smoothed = medfilt(medbg, kern_size) medbg_hpf = medbg - smoothed # Flag outliers using percentile cutoff - flag_low, flag_high = np.nanpercentile(medbg_hpf, [flagfrac*100, (1-flagfrac)*100]) + flag_low, flag_high = np.nanpercentile(medbg_hpf, [flagfrac * 100, (1 - flagfrac) * 100]) bad = (medbg_hpf > flag_high) | (medbg_hpf < flag_low) flagged_indices = np.where(bad) @@ -55,7 +68,7 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d input_model: dm.IFUImageModel Input science data to be corrected flagged_indices: np.ndarray - Indices of the flagged pixels, + Indices of the flagged pixels, shaped like output from np.where Returns diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index a2d8b2bbf5..c7c33725d3 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -12,9 +12,10 @@ __all__ = ["BadpixSelfcalStep"] + class BadpixSelfcalStep(Step): """ - BadpixSelfcalStep: Flags residual artifacts as bad pixels in the DQ array + BadpixSelfcalStep: Flags residual artifacts as bad pixels in the DQ array of a JWST exposure using a median filter and percentile cutoffs. All input exposures in the association file (or manually-provided bkg_list) are combined @@ -33,7 +34,7 @@ class BadpixSelfcalStep(Step): skip = boolean(default=True) """ - def process(self, input, bkg_list=None): + def process(self, input, selfcal_list=[]): """ Flag residual artifacts as bad pixels in the DQ array of a JWST exposure @@ -42,7 +43,7 @@ def process(self, input, bkg_list=None): input: JWST data model or association input science data to be corrected - bkg_list: list of background ImageModels or filenames. + selfcal_list: list of ImageModels or filenames to use for selfcal Returns ------- @@ -51,61 +52,44 @@ def process(self, input, bkg_list=None): Notes ----- - If bkg_list is specified manually, it overrides any non-science exposures + If selfcal_list is specified manually, it overrides any non-science exposures in the association file. - If bkg_list is set to None and an association file is read in, all exposures in the + If selfcal_list is set to None and an association file is read in, all exposures in the association file, including science, background, and selfcal exposures, are included in the MIN frame from which outliers are detected. - If bkg_list is set to None and input is a single science exposure, the step will + If selfcal_list is set to None and input is a single science exposure, the step will be skipped with a warning unless the force_single parameter is set True. - In that case, the input exposure will be used as the sole background exposure, + In that case, the input exposure will be used as the sole background exposure, i.e., true self-calibration. """ - with dm.open(input) as input_data: - - # find science and background exposures. - # note that bkg_list includes the science exposure. This is ok because - # all exposures are combined into a single background model using a MIN operation. - if isinstance(input_data, dm.ModelContainer): - - sci_models, bkg_list_asn, selfcal_list_asn = split_container(input_data, - fields=['science', 'background', 'selfcal']) - - if bkg_list is None: - bkg_list = list(sci_models) + list(bkg_list_asn) + list(selfcal_list_asn) - else: - log.warning("bkg_list provided directly as input, ignoring bkg_list from association file") - - # in calwebb_spec2 there should be only a single science exposure in an association - input_sci = sci_models[0] - - elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): - - input_sci = input_data - bkg_list_asn = [] - if bkg_list is None: - # true self-calibration on input data itself - bkg_list = [input_data] - else: - bkg_list = [input_data] + [dm.open(bkg) for bkg in bkg_list] - - else: - raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ - Cannot continue.") + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(input, selfcal_list) # ensure that there are background exposures to use, otherwise skip the step - # unless forced - if (len(bkg_list) == 0) and (not self.force_single): + # unless forced + if (len(selfcal_list) == 0) and (not self.force_single): log.warning("No background exposures provided for self-calibration. Skipping step.") self.record_step_status(input_sci, "badpix_selfcal", success=False) return input_sci, bkg_list_asn + # get the dispersion axis + try: + dispaxis = input_sci.meta.wcsinfo.dispersion_direction + except AttributeError: + log.warning("Dispersion axis not found in input science image metadata.\ + Kernel for self-calibration will be two-dimensional.") + dispaxis = None + + # open all selfcal exposures + # note that selfcal_list includes the science exposure. This is expected. + # all exposures are combined into a single background model using a MIN operation. + selfcal_list = [input_sci] + [dm.open(k) for k in selfcal_list] + # collapse background dithers into a single background model - bkgd_3d = [] - for i, background_model in enumerate(bkg_list): - bkgd_3d.append(background_model.data) - bkgd = np.nanmin(np.asarray(bkgd_3d), axis=0) - bad_indices = badpix_selfcal.badpix_selfcal(bkgd, self.flagfrac, self.kernel_size) + selfcal_3d = [] + for i, selfcal_model in enumerate(selfcal_list): + selfcal_3d.append(selfcal_model.data) + minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) + bad_indices = badpix_selfcal.badpix_selfcal(minimg, self.flagfrac, self.kernel_size, dispaxis) # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) @@ -116,5 +100,54 @@ def process(self, input, bkg_list=None): bkg_list_asn[i] = badpix_selfcal.apply_flags(background_model, bad_indices) self.record_step_status(input_sci, "badpix_selfcal", success=True) - + return input_sci, *bkg_list_asn + + +def _parse_inputs(input, selfcal_list=[]): + """ + Parse the input to the step. This is a helper function that is used in the + command line interface to the step. + + Parameters + ---------- + input: str + input file or association + + selfcal_list: list + ImageModels or filenames to use for selfcal + + Returns + ------- + input: JWST data model or association + input science data to be corrected + + selfcal_list: list of ImageModels or filenames to use for selfcal + """ + with dm.open(input) as input_data: + + # find science and background exposures. + if isinstance(input_data, dm.ModelContainer): + + sci_models, bkg_list_asn, selfcal_list_asn = split_container(input_data, + fields=['science', 'background', 'selfcal']) + + if len(selfcal_list) == 0: + selfcal_list = list(bkg_list_asn) + list(selfcal_list_asn) + else: + log.warning("selfcal_list provided directly as input, ignoring background \ + and selfcal exposure types in the association file") + + # in calwebb_spec2 there should be only a single science exposure in an association + input_sci = sci_models[0] + + elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): + + input_sci = input_data + bkg_list_asn = [] + + else: + raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ + Cannot continue.") + + return input_sci, selfcal_list, bkg_list_asn diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index 8ba53ea3c4..ae6260e4bd 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -1,7 +1,7 @@ import json import pytest import numpy as np -from jwst.badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep +from jwst.badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep, _parse_inputs from jwst.badpix_selfcal.badpix_selfcal import badpix_selfcal, apply_flags from jwst import datamodels as dm from jwst.assign_wcs import AssignWcsStep, miri @@ -20,12 +20,13 @@ 'cunit1': 'deg', 'cunit2': 'deg', 'cunit3': 'um', } -# these are often warm pixels, so need to be much larger than noise, +# these are often warm pixels, so need to be much larger than noise, # which has std dev of 1, # but smaller than smoothly-varying background, which maxes around 20 hotpixel_intensity = 10 outlier_indices = [(100, 100), (300, 300), (500, 600), (1000, 900)] + def create_hdul(detector, channel, band): hdul = fits.HDUList() phdu = fits.PrimaryHDU() @@ -62,7 +63,7 @@ def background(): background data in a .rate file. Three components: random noise, low-order variability, and outliers """ - + # random noise rng = np.random.default_rng(seed=77) shp = (1024, 1032) @@ -159,6 +160,38 @@ def asn(tmp_cwd_module, background, sci): return container +def test_input_parsing(asn, sci, background): + """Test that the input parsing function correctly identifies the science, + background, and selfcal exposures in the association file + given association vs imagemodel inputs, and selfcal_list vs not + Note that science exposure gets added to selfcal_list later, not in parse_inputs + """ + + # basic association case. Both background and selfcal get into the list + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(asn, []) + assert isinstance(input_sci, dm.IFUImageModel) + assert len(bkg_list_asn) == 2 + assert len(selfcal_list) == 4 + + # association with selfcal_list provided + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(asn, [background,] * 3) + assert isinstance(input_sci, dm.IFUImageModel) + assert len(bkg_list_asn) == 2 + assert len(selfcal_list) == 3 + + # single science exposure + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(sci, []) + assert isinstance(input_sci, dm.IFUImageModel) + assert len(bkg_list_asn) == 0 + assert len(selfcal_list) == 0 + + # single science exposure with selfcal_list provided + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(sci, [background,] * 3) + assert isinstance(input_sci, dm.IFUImageModel) + assert len(bkg_list_asn) == 0 + assert len(selfcal_list) == 3 + + def test_background_flagger_mrs(background): """ Ensure the right number of outliers are found, and that @@ -170,14 +203,14 @@ def test_background_flagger_mrs(background): # pass into the MRSBackgroundFlagger and check it found the right pixels flagfrac = 0.001 result = badpix_selfcal(bg, flagfrac=flagfrac) - result_tuples = [(i,j) for i,j in zip(*result)] + result_tuples = [(i, j) for i, j in zip(*result)] # check that the hot pixels were among those flagged for idx in outlier_indices: assert idx in result_tuples # check that the number of flagged pixels is as expected - assert np.isclose(len(result_tuples)/bg.size, flagfrac*2, atol=0.0001) + assert np.isclose(len(result_tuples) / bg.size, flagfrac * 2, atol=0.0001) def test_apply_flags(background): @@ -211,7 +244,7 @@ def test_badpix_selfcal_step(request, dset): regtest. """ input_data = request.getfixturevalue(dset) - result = BadpixSelfcalStep.call(input_data, skip=False) + result = BadpixSelfcalStep.call(input_data, skip=False, force_single=True) assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" if dset == "sci": @@ -219,3 +252,55 @@ def test_badpix_selfcal_step(request, dset): else: # should return sci, bkg0, bkg1 but not selfcal0, selfcal1 assert len(result) == 3 + + +def test_expected_fail_sci(sci): + """Test that the step fails as expected when given only a science exposure + and force_single is False. + """ + result = BadpixSelfcalStep.call(sci, skip=False, force_single=False) + assert result[0].meta.cal_step.badpix_selfcal == "SKIPPED" + + +@pytest.fixture(scope="module") +def vertical_striping(): + """2-D array with vertical stripes and some outliers, used to test that + the step is flagging in the correct (along-dispersion) direction + """ + shp = (102, 96) + stripes = np.zeros(shp) + stripes[:, ::4] = hotpixel_intensity + + # add some outliers + outliers = np.zeros(shp) + for idx in outlier_indices: + i, j = int(idx[0] / 10), int(idx[1] / 10) + outliers[i, j] = hotpixel_intensity + data = stripes + outliers + + return data + + +@pytest.mark.parametrize("dispaxis", [None, 1, 2]) +def test_dispersion_direction(vertical_striping, dispaxis): + """ + Test that the flagging operates as expected as a function of direction. + + Of the four outliers, only one lies atop a stripe, so the step should only + flag one in four as an outlier unless median filter occurs in the along-stripe + direction. + """ + + flagged_indices = badpix_selfcal(vertical_striping, dispaxis=dispaxis) + + if dispaxis == 2: + + assert len(flagged_indices[0]) == 4 + + elif dispaxis == 1: + + assert len(flagged_indices[0]) == 1 + + elif dispaxis is None: + + assert len(flagged_indices[0]) == 1 From 027489ff5e8c7909086baa0da67ebb96372c06ac Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 6 Jun 2024 16:33:08 -0400 Subject: [PATCH 097/687] requested variable name change in badpix_selfcal.py --- jwst/badpix_selfcal/badpix_selfcal.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 8b6b04c15c..1dec265f50 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -5,7 +5,7 @@ from stdatamodels.jwst.datamodels.dqflags import pixel -def badpix_selfcal(medbg: np.ndarray, +def badpix_selfcal(minimg: np.ndarray, flagfrac: float = 0.001, kernel_size: int = 15, dispaxis=None) -> np.ndarray: @@ -14,8 +14,8 @@ def badpix_selfcal(medbg: np.ndarray, Parameters ---------- - medbg: np.ndarray - Background data of shape (x, y), i.e., after some operation has + minimg: np.ndarray + Selfcal data of shape (x, y), i.e., after some operation has already been taken to combine multiple exposures, typically a MIN operation. flagfrac: float @@ -47,12 +47,12 @@ def badpix_selfcal(medbg: np.ndarray, elif dispaxis == 1: kern_size = (1, kernel_size) - smoothed = medfilt(medbg, kern_size) - medbg_hpf = medbg - smoothed + smoothed = medfilt(minimg, kern_size) + minimg_hpf = minimg - smoothed # Flag outliers using percentile cutoff - flag_low, flag_high = np.nanpercentile(medbg_hpf, [flagfrac * 100, (1 - flagfrac) * 100]) - bad = (medbg_hpf > flag_high) | (medbg_hpf < flag_low) + flag_low, flag_high = np.nanpercentile(minimg_hpf, [flagfrac * 100, (1 - flagfrac) * 100]) + bad = (minimg_hpf > flag_high) | (minimg_hpf < flag_low) flagged_indices = np.where(bad) return flagged_indices From c658452f00199919319b2bfb149a50b245928fae Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 11 Jun 2024 17:03:46 -0400 Subject: [PATCH 098/687] responding to @melanieclarke and @braingram comments --- docs/jwst/badpix_selfcal/arguments.rst | 2 +- docs/jwst/badpix_selfcal/description.rst | 22 ++-- jwst/badpix_selfcal/badpix_selfcal.py | 21 ++-- jwst/badpix_selfcal/badpix_selfcal_step.py | 109 ++++++++++++------ .../tests/test_badpix_selfcal.py | 12 +- .../master_background_step.py | 55 +++------ jwst/pipeline/calwebb_spec2.py | 17 +-- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 37 ++++-- 8 files changed, 165 insertions(+), 110 deletions(-) diff --git a/docs/jwst/badpix_selfcal/arguments.rst b/docs/jwst/badpix_selfcal/arguments.rst index 957c4acd7c..6f9f73ac77 100644 --- a/docs/jwst/badpix_selfcal/arguments.rst +++ b/docs/jwst/badpix_selfcal/arguments.rst @@ -8,7 +8,7 @@ The ``badpix_selfcal`` step has the following optional arguments. ``--kernel_size`` (integer, default=15) The size of the kernel to use for the median filter, which is applied - in the spectral (Y-axis) direction to make the smoothed image. + in the spectral direction to make the smoothed image. ``--save_flagged_bkgd`` (boolean, default=False) Whether to save the flagged background exposures to fits files. diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst index 0990b27726..5f6caeffbf 100644 --- a/docs/jwst/badpix_selfcal/description.rst +++ b/docs/jwst/badpix_selfcal/description.rst @@ -8,7 +8,7 @@ Overview -------- The ``badpix_selfcal`` step flags bad pixels in the input data using a self-calibration technique based on median filtering along the spectral axis. -When background exposures are available, those are used in combination with the science +When additional exposures are available, those are used in combination with the science exposure to identify bad pixels; when unavailable, the step will be skipped with a warning unless the ``force_single`` parameter is set True. In that case, the science data alone is used as its own "background". @@ -18,22 +18,27 @@ in the :ref:`calwebb_spec2 ` pipeline. Input details ------------- -The input data must be in the form of a `~jwst.datamodels.IFUImageModel` or +The input data must be in the form of a `~jwst.datamodels.IFUImageModel` or a `~jwst.datamodels.ModelContainer` containing exactly one -science exposure and any number of background exposures. A fits or association file +science exposure and any number of additional exposures. +A fits or association file that can be read into one of these data models is also acceptable. +Any exposure with the metadata attribute ``asn.exptype`` set to +``background`` or ``selfcal`` will be used in conjunction with the science +exposure to construct the combined background image. Algorithm --------- The algorithm relies on the assumption that bad pixels are outliers in the data along the spectral axis. The algorithm proceeds as follows: -* A combined background image is created. If background exposures are available, - the pixelwise minimum of all background and science exposures is taken. - If no background exposures are available, the science data itself is passed in +* A combined background image is created. If additional (``selfcal`` or ``background``) + exposures are available, + the pixelwise minimum of all background, selfcal, and science exposures is taken. + If no additional exposures are available, the science data itself is passed in without modification, serving as the "background image" for the rest of the procedure, i.e., true self-calibration. -* The combined background image is median-filtered, ignoring NaNs, along the spectral (Y-) axis +* The combined background image is median-filtered, ignoring NaNs, along the spectral axis with a user-specified kernel size. The default kernel size is 15 pixels. * The difference between the original background image and the median-filtered background image is taken. The highest- and lowest-flux pixels in this difference image are @@ -42,7 +47,8 @@ the spectral axis. The algorithm proceeds as follows: using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. * The bad pixels are flagged in the input data by setting the DQ flag to "OTHER_BAD_PIXEL" and "DO_NOT_USE". -* The bad pixels are also flagged in each background exposure, if available. +* The bad pixels are also flagged in each exposure with ``asn.exptype`` equal to ``background``, + if available. Output product -------------- diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 1dec265f50..2c2b19a857 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -6,7 +6,8 @@ def badpix_selfcal(minimg: np.ndarray, - flagfrac: float = 0.001, + flagfrac_lower: float = 0.001, + flagfrac_upper: float = 0.001, kernel_size: int = 15, dispaxis=None) -> np.ndarray: """ @@ -18,8 +19,10 @@ def badpix_selfcal(minimg: np.ndarray, Selfcal data of shape (x, y), i.e., after some operation has already been taken to combine multiple exposures, typically a MIN operation. - flagfrac: float - Fraction of pixels to flag on each of low and high end + flagfrac_lower: float + Fraction of pixels to flag on the low end + flagfrac_upper: float + Fraction of pixels to flag on the high end kernel_size: int Size of kernel for median filter dispaxis: int @@ -32,9 +35,11 @@ def badpix_selfcal(minimg: np.ndarray, Indices of the flagged pixels, shaped like output from np.where """ - if flagfrac < 0 or flagfrac >= 0.5: - raise ValueError("flagfrac must be between 0 and 0.5. \ - Note this fraction will be flagged on both high and low ends.") + if (flagfrac_lower < 0) or (flagfrac_upper < 0): + raise ValueError("Flagged fraction must be between 0 and 0.5") + if flagfrac_lower + flagfrac_upper >= 1: + raise ValueError("Total flagged fraction (lower plus upper) must be less than 1") + if dispaxis not in [1, 2, None]: raise ValueError("dispaxis must be either 1 or 2, or None.") @@ -51,7 +56,7 @@ def badpix_selfcal(minimg: np.ndarray, minimg_hpf = minimg - smoothed # Flag outliers using percentile cutoff - flag_low, flag_high = np.nanpercentile(minimg_hpf, [flagfrac * 100, (1 - flagfrac) * 100]) + flag_low, flag_high = np.nanpercentile(minimg_hpf, [flagfrac_lower * 100, (1 - flagfrac_upper) * 100]) bad = (minimg_hpf > flag_high) | (minimg_hpf < flag_low) flagged_indices = np.where(bad) @@ -77,7 +82,7 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d Flagged data model """ - input_model.dq[flagged_indices] = pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] + input_model.dq[flagged_indices] += pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] input_model.data[flagged_indices] = np.nan input_model.err[flagged_indices] = np.nan diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index c7c33725d3..d62ebfca90 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -4,7 +4,6 @@ from . import badpix_selfcal import numpy as np from jwst import datamodels as dm -from jwst.master_background.master_background_step import split_container import logging log = logging.getLogger(__name__) @@ -21,14 +20,16 @@ class BadpixSelfcalStep(Step): All input exposures in the association file (or manually-provided bkg_list) are combined into a single background model using a MIN operation. The bad pixels are then identified using a median filter and percentile cutoffs, and applied to the science data by setting - the flagged pixels to NaN and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL. + the flagged pixels, errors, and variances to NaN + and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL. """ class_alias = "badpix_selfcal" bkg_suffix = "badpix_selfcal" spec = """ - flagfrac = float(default=0.001) #fraction of pixels to flag on each of low and high end + flagfrac_lower = float(default=0.001) #fraction of pixels to flag on the low-flux end + flagfrac_upper = float(default=0.001) #fraction of pixels to flag on the high-flux end kernel_size = integer(default=15) #size of kernel for median filter force_single = boolean(default=False) #force single input exposure skip = boolean(default=True) @@ -41,9 +42,9 @@ def process(self, input, selfcal_list=[]): Parameters ---------- input: JWST data model or association - input science data to be corrected + input science data to be corrected, or tuple of (sci, bkg, selfcal) - selfcal_list: list of ImageModels or filenames to use for selfcal + selfcal_list: list of user-defined ImageModels or filenames to use for selfcal Returns ------- @@ -62,13 +63,15 @@ def process(self, input, selfcal_list=[]): In that case, the input exposure will be used as the sole background exposure, i.e., true self-calibration. """ - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(input, selfcal_list) + input_sci, selfcal_list, bkg_list_asn = _parse_inputs(input, selfcal_list) # ensure that there are background exposures to use, otherwise skip the step # unless forced if (len(selfcal_list) == 0) and (not self.force_single): - log.warning("No background exposures provided for self-calibration. Skipping step.") - self.record_step_status(input_sci, "badpix_selfcal", success=False) + log.warning("No selfcal or background exposures provided for self-calibration. \ + Skipping step. If you want to force self-calibration with the science \ + exposure alone (generally not recommended), set force_single=True.") + input_sci.meta.cal_step.badpix_selfcal = 'SKIPPED' return input_sci, bkg_list_asn # get the dispersion axis @@ -79,17 +82,15 @@ def process(self, input, selfcal_list=[]): Kernel for self-calibration will be two-dimensional.") dispaxis = None - # open all selfcal exposures + # collapse all selfcal exposures into a single background model # note that selfcal_list includes the science exposure. This is expected. # all exposures are combined into a single background model using a MIN operation. selfcal_list = [input_sci] + [dm.open(k) for k in selfcal_list] - - # collapse background dithers into a single background model selfcal_3d = [] for i, selfcal_model in enumerate(selfcal_list): selfcal_3d.append(selfcal_model.data) minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) - bad_indices = badpix_selfcal.badpix_selfcal(minimg, self.flagfrac, self.kernel_size, dispaxis) + bad_indices = badpix_selfcal.badpix_selfcal(minimg, self.flagfrac_lower, self.flagfrac_upper, self.kernel_size, dispaxis) # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) @@ -97,14 +98,13 @@ def process(self, input, selfcal_list=[]): # apply the flags to the background data to be passed to background sub step if len(bkg_list_asn) > 0: for i, background_model in enumerate(bkg_list_asn): - bkg_list_asn[i] = badpix_selfcal.apply_flags(background_model, bad_indices) + bkg_list_asn[i] = badpix_selfcal.apply_flags(dm.open(background_model), bad_indices) - self.record_step_status(input_sci, "badpix_selfcal", success=True) + input_sci.meta.cal_step.badpix_selfcal = 'COMPLETE' + return input_sci, bkg_list_asn - return input_sci, *bkg_list_asn - -def _parse_inputs(input, selfcal_list=[]): +def _parse_inputs(input, selfcal_list): """ Parse the input to the step. This is a helper function that is used in the command line interface to the step. @@ -112,10 +112,9 @@ def _parse_inputs(input, selfcal_list=[]): Parameters ---------- input: str - input file or association + input file or association, or tuple of (sci, bkg, selfcal) - selfcal_list: list - ImageModels or filenames to use for selfcal + selfcal_list: list of user-supplied ImageModels or filenames to use for selfcal Returns ------- @@ -124,30 +123,66 @@ def _parse_inputs(input, selfcal_list=[]): selfcal_list: list of ImageModels or filenames to use for selfcal """ - with dm.open(input) as input_data: + selfcal_list_user = selfcal_list + + if isinstance(input, tuple): + input_sci = dm.open(input[0]) + selfcal_list = list(input[1]) + list(input[2]) + bkg_list_asn = input[1] - # find science and background exposures. - if isinstance(input_data, dm.ModelContainer): + else: + with dm.open(input) as input_data: - sci_models, bkg_list_asn, selfcal_list_asn = split_container(input_data, - fields=['science', 'background', 'selfcal']) + # find science and background exposures. + if isinstance(input_data, dm.ModelContainer): + + sci_models, bkg_list_asn, selfcal_list_asn = split_container_by_asn_exptype( + input_data, exptypes=['science', 'background', 'selfcal']) - if len(selfcal_list) == 0: selfcal_list = list(bkg_list_asn) + list(selfcal_list_asn) + + if len(sci_models) > 1: + raise ValueError("Input data contains multiple science exposures. \ + This is not supported in calwebb_spec2 steps.") + input_sci = sci_models[0] + + elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): + + input_sci = input_data + bkg_list_asn = [] + else: - log.warning("selfcal_list provided directly as input, ignoring background \ - and selfcal exposure types in the association file") + raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ + Cannot continue.") + + if len(selfcal_list_user) > 0: + selfcal_list = selfcal_list_user + log.warning("selfcal_list provided directly as input, ignoring any other \ + input background and selfcal exposure types, from e.g. an input \ + association file") + return input_sci, selfcal_list, bkg_list_asn - # in calwebb_spec2 there should be only a single science exposure in an association - input_sci = sci_models[0] - elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): +def split_container_by_asn_exptype(container: dm.ModelContainer, exptypes: list) -> list: + """ + Split a ModelContainer into a list of ImageModels based on exposure type. - input_sci = input_data - bkg_list_asn = [] + Parameters + ---------- + container: ModelContainer + input data - else: - raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ - Cannot continue.") + exptype: str + exposure type to split on - return input_sci, selfcal_list, bkg_list_asn + Returns + ------- + split_list: list of lists + lists of ImageModels + """ + split_list = [] + for exptype in exptypes: + good_models = [container[j] for j in range(len(container)) if container[j].meta.asn.exptype == exptype] + split_list.append(good_models) + + return split_list diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index ae6260e4bd..56619de229 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -202,7 +202,7 @@ def test_background_flagger_mrs(background): # pass into the MRSBackgroundFlagger and check it found the right pixels flagfrac = 0.001 - result = badpix_selfcal(bg, flagfrac=flagfrac) + result = badpix_selfcal(bg, flagfrac_lower=flagfrac, flagfrac_upper=flagfrac) result_tuples = [(i, j) for i, j in zip(*result)] # check that the hot pixels were among those flagged @@ -248,10 +248,14 @@ def test_badpix_selfcal_step(request, dset): assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" if dset == "sci": - assert len(result) == 1 + assert len(result) == 2 + assert len(result[0]) == 1 + assert len(result[1]) == 0 else: - # should return sci, bkg0, bkg1 but not selfcal0, selfcal1 - assert len(result) == 3 + # should return sci, (bkg0, bkg1) but not selfcal0, selfcal1 + assert len(result) == 2 + assert len(result[0]) == 1 + assert len(result[1]) == 2 def test_expected_fail_sci(sci): diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 418af6ea25..76efbbff64 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -236,48 +236,29 @@ def copy_background_to_surf_bright(spectrum): spec.spec_table['BKGD_ERROR'][:] = 0. -def split_container(container: ModelContainer, fields=['science', 'background']): +def split_container(container): + """Divide a ModelContainer with science and background into one of each """ - Divide a ModelContainer with multiple exposure types, e.g. - science, background, and selfcal members, into separate containers. - Similar to, but distinct from, the split_container - method in MasterBackgroundStep, because this method - includes selfcal member handling. + asn = container.meta.asn_table.instance - Parameters - ---------- - container: ModelContainer - The input container + background = ModelContainer() + science = ModelContainer() - fields: list, optional. - The list of exposure type strings to split the container into. Default - is ['science', 'background']. + for ind_science in container.ind_asn_type('science'): + science.append(container._models[ind_science]) - Returns - ------- - container_list: list - List of ModelContainers, one for each field. - """ - asn = container.meta.asn_table.instance + for ind_bkgd in container.ind_asn_type('background'): + background.append(container._models[ind_bkgd]) - container_list = [] - for field in fields: - sub_container = ModelContainer() - for ind_field in container.ind_asn_type(field): - sub_container.append(container._models[ind_field]) - - # Pass along the association table to the output container - if field == "science": - sub_container.meta.asn_table = {} - sub_container.asn_pool_name = container.asn_pool_name - sub_container.asn_table_name = container.asn_table_name - merge_tree(sub_container.meta.asn_table.instance, asn) - # Prune the non-science members from the table - for p in sub_container.meta.asn_table.instance['products']: - p['members'] = [m for m in p['members'] if m['exptype'].lower() == 'science'] - container_list.append(sub_container) - - return container_list + # Pass along the association table to the output science container + science.meta.asn_table = {} + science.asn_pool_name = container.asn_pool_name + science.asn_table_name = container.asn_table_name + merge_tree(science.meta.asn_table.instance, asn) + # Prune the background members from the table + for p in science.meta.asn_table.instance['products']: + p['members'] = [m for m in p['members'] if m['exptype'].lower() != 'background'] + return science, background def subtract_2d_background(source, background): diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index e6f40fbb9c..894847884b 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -245,17 +245,20 @@ def process_exposure_product( raise RuntimeError('Cannot determine WCS.') # Steps whose order is the same for all types of input: - # self-calibrate bad/warm pixels, and apply to both background and science # skipped by default for all modes - result = self.badpix_selfcal(calibrated, members_by_type['background']) - if isinstance(result, tuple): + print(type(members_by_type['background'])) + result = self.badpix_selfcal( + (calibrated, members_by_type['background'], members_by_type['selfcal']), + ) + if len(result) == 1: + # if step is skipped, unchanged sci exposure is returned + calibrated = result[0] + else: # if step actually occurs, then flagged backgrounds are also returned - calibrated, bkg_outlier_flagged = result[0], result[1:] + calibrated, bkg_outlier_flagged = result[0], result[1] members_by_type['background'] = bkg_outlier_flagged - else: - # if step is skipped, result is just input - calibrated = result + print(type(members_by_type['background'])) # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index e4755ac928..a040ac69e5 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -1,28 +1,49 @@ import pytest from astropy.io.fits.diff import FITSDiff - -from jwst.badpix_selfcal import BadpixSelfcalStep +from jwst.stpipe import Step @pytest.fixture(scope="module") -def run_pipeline(rtdata_module): +def run_pipeline_background(rtdata_module): rtdata = rtdata_module rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_asn.json") - BadpixSelfcalStep.call(rtdata.input, skip=False, save_results=True, flagfrac=0.0005) + Step.from_cmdline(['calwebb_spec2', rtdata.input, + "--steps.badpix_selfcal.save_results=True", + "--steps.badpix_selfcal.flagfrac_lower=0.0005", + "--steps.badpix_selfcal.skip=False"]) + rtdata.output = "jw01204021001_02101_00004_mirifulong_0_badpix_selfcal.fits" + return rtdata + + +@pytest.fixture(scope="module") +def run_pipeline_selfcal(rtdata_module): + '''Identical pipeline run to above, but input asn sets all background exposures as `selfcal` type. + ''' + rtdata = rtdata_module + rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_selfcal_asn.json") + Step.from_cmdline(['calwebb_spec2', rtdata.input, + "--steps.badpix_selfcal.save_results=True", + "--steps.badpix_selfcal.flagfrac_lower=0.0005", + "--steps.badpix_selfcal.skip=False"]) + rtdata.output = "jw01204021001_02101_00004_mirifulong_0_badpix_selfcal.fits" + return rtdata @pytest.mark.bigdata -def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs,): +@pytest.mark.parametrize("run_pipeline", ["run_pipeline_background", "run_pipeline_selfcal"]) +def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs, request): """Run a test for MIRI MRS data with dedicated background exposures.""" - rtdata = run_pipeline - rtdata.output = "jw01204-o021_20240127t024203_spec2_00010_asn_0_badpixselfcalstep.fits" + rtdata = request.getfixturevalue(run_pipeline) + + import os + print(os.listdir(".")) # Get the truth file rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204-o021_20240127t024203_spec2_00010_asn_0_badpixselfcalstep.fits") # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() \ No newline at end of file + assert diff.identical, diff.report() From 68281a22d7d0d07b68a829c1210a07f9c6ab5ff1 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 12 Jun 2024 09:41:10 -0400 Subject: [PATCH 099/687] bugfix to unit test --- jwst/badpix_selfcal/tests/test_badpix_selfcal.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index 56619de229..b9832a53ef 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -249,12 +249,12 @@ def test_badpix_selfcal_step(request, dset): assert result[0].meta.cal_step.badpix_selfcal == "COMPLETE" if dset == "sci": assert len(result) == 2 - assert len(result[0]) == 1 + assert isinstance(result[0], dm.IFUImageModel) assert len(result[1]) == 0 else: # should return sci, (bkg0, bkg1) but not selfcal0, selfcal1 assert len(result) == 2 - assert len(result[0]) == 1 + assert isinstance(result[0], dm.IFUImageModel) assert len(result[1]) == 2 From eb438956c3c41d14abdd548f0eeee89519544191 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 12 Jun 2024 14:05:15 -0400 Subject: [PATCH 100/687] responding to next round of @melanieclarke comments --- docs/jwst/badpix_selfcal/arguments.rst | 10 +- docs/jwst/badpix_selfcal/description.rst | 6 +- jwst/badpix_selfcal/badpix_selfcal_step.py | 108 +++++++++--------- .../tests/test_badpix_selfcal.py | 30 +++-- jwst/pipeline/calwebb_spec2.py | 8 +- 5 files changed, 90 insertions(+), 72 deletions(-) diff --git a/docs/jwst/badpix_selfcal/arguments.rst b/docs/jwst/badpix_selfcal/arguments.rst index 6f9f73ac77..823516e614 100644 --- a/docs/jwst/badpix_selfcal/arguments.rst +++ b/docs/jwst/badpix_selfcal/arguments.rst @@ -2,9 +2,13 @@ Step Arguments ============== The ``badpix_selfcal`` step has the following optional arguments. -``--flagfrac`` (float, default=0.001) - The fraction of pixels to flag as outliers on each of the low-flux and high-flux - sides of the smoothed-subtracted image. +``--flagfrac_lower`` (float, default=0.001) + The fraction of pixels to flag as outliers on the low-flux + side of the smoothed-subtracted image. + +``--flagfrac_upper`` (float, default=0.001) + The fraction of pixels to flag as outliers on the high-flux + side of the smoothed-subtracted image. ``--kernel_size`` (integer, default=15) The size of the kernel to use for the median filter, which is applied diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst index 5f6caeffbf..fe68e21e64 100644 --- a/docs/jwst/badpix_selfcal/description.rst +++ b/docs/jwst/badpix_selfcal/description.rst @@ -43,8 +43,10 @@ the spectral axis. The algorithm proceeds as follows: * The difference between the original background image and the median-filtered background image is taken. The highest- and lowest-flux pixels in this difference image are flagged as bad pixels. The default fraction of pixels to flag is 0.1% of the total number of pixels - on each of the high-flux and low-flux ends of the distribution. This fraction can be adjusted - using the ``flagfrac`` parameter. The total fraction of flagged pixels is thus 2x ``flagfrac``. + on each of the high-flux and low-flux ends of the distribution. These fractions can be adjusted + using the ``flagfrac_lower`` and ``flagfrac_upper`` parameters for the low- and high-flux ends + of the distribution, respectively. The total fraction of flagged pixels is thus + ``flagfrac_lower + flagfrac_upper``. * The bad pixels are flagged in the input data by setting the DQ flag to "OTHER_BAD_PIXEL" and "DO_NOT_USE". * The bad pixels are also flagged in each exposure with ``asn.exptype`` equal to ``background``, diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index d62ebfca90..214eaa1c71 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -35,7 +35,7 @@ class BadpixSelfcalStep(Step): skip = boolean(default=True) """ - def process(self, input, selfcal_list=[]): + def process(self, input, selfcal_list=None, bkg_list=None): """ Flag residual artifacts as bad pixels in the DQ array of a JWST exposure @@ -44,7 +44,13 @@ def process(self, input, selfcal_list=[]): input: JWST data model or association input science data to be corrected, or tuple of (sci, bkg, selfcal) - selfcal_list: list of user-defined ImageModels or filenames to use for selfcal + selfcal_list: list of ImageModels or filenames, default None + exposures to include as part of median background model used to find bad pixels, + but that are not flagged and returned as background exposures + + bkg_list: list of ImageModels or filenames, default None + exposures to include as part of median background model used to find bad pixels, + and that are flagged and returned as background exposures Returns ------- @@ -53,33 +59,34 @@ def process(self, input, selfcal_list=[]): Notes ----- - If selfcal_list is specified manually, it overrides any non-science exposures - in the association file. - If selfcal_list is set to None and an association file is read in, all exposures in the + If an association file is read in, all exposures in the association file, including science, background, and selfcal exposures, are included in the MIN frame from which outliers are detected. - If selfcal_list is set to None and input is a single science exposure, the step will - be skipped with a warning unless the force_single parameter is set True. + If selfcal_list and/or bkg_list are specified manually, they are appended to any + selfcal or background exposures found in the input association file. + If selfcal_list and bkg_list are both set to None and input is + a single science exposure, the step will be skipped with a warning unless + the force_single parameter is set True. In that case, the input exposure will be used as the sole background exposure, i.e., true self-calibration. """ + input_sci, selfcal_list, bkg_list = _parse_inputs(input, selfcal_list, bkg_list) - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(input, selfcal_list) # ensure that there are background exposures to use, otherwise skip the step # unless forced - if (len(selfcal_list) == 0) and (not self.force_single): - log.warning("No selfcal or background exposures provided for self-calibration. \ - Skipping step. If you want to force self-calibration with the science \ - exposure alone (generally not recommended), set force_single=True.") + if (len(selfcal_list + bkg_list) == 0) and (not self.force_single): + log.warning("No selfcal or background exposures provided for self-calibration." + "Skipping step. If you want to force self-calibration with the science" + "exposure alone (generally not recommended), set force_single=True.") input_sci.meta.cal_step.badpix_selfcal = 'SKIPPED' - return input_sci, bkg_list_asn + return input_sci, bkg_list # get the dispersion axis try: dispaxis = input_sci.meta.wcsinfo.dispersion_direction except AttributeError: - log.warning("Dispersion axis not found in input science image metadata.\ - Kernel for self-calibration will be two-dimensional.") + log.warning("Dispersion axis not found in input science image metadata." + "Kernel for self-calibration will be two-dimensional.") dispaxis = None # collapse all selfcal exposures into a single background model @@ -96,15 +103,15 @@ def process(self, input, selfcal_list=[]): input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) # apply the flags to the background data to be passed to background sub step - if len(bkg_list_asn) > 0: - for i, background_model in enumerate(bkg_list_asn): - bkg_list_asn[i] = badpix_selfcal.apply_flags(dm.open(background_model), bad_indices) + if len(bkg_list) > 0: + for i, background_model in enumerate(bkg_list): + bkg_list[i] = badpix_selfcal.apply_flags(dm.open(background_model), bad_indices) input_sci.meta.cal_step.badpix_selfcal = 'COMPLETE' - return input_sci, bkg_list_asn + return input_sci, bkg_list -def _parse_inputs(input, selfcal_list): +def _parse_inputs(input, selfcal_list, bkg_list): """ Parse the input to the step. This is a helper function that is used in the command line interface to the step. @@ -112,9 +119,15 @@ def _parse_inputs(input, selfcal_list): Parameters ---------- input: str - input file or association, or tuple of (sci, bkg, selfcal) + input science exposure or association + + selfcal_list: list of ImageModels or filenames, default None + exposures to include as part of median background model used to find bad pixels, + but that are not flagged and returned as background exposures - selfcal_list: list of user-supplied ImageModels or filenames to use for selfcal + bkg_list: list of ImageModels or filenames, default None + exposures to include as part of median background model used to find bad pixels, + and that are flagged and returned as background exposures Returns ------- @@ -123,44 +136,37 @@ def _parse_inputs(input, selfcal_list): selfcal_list: list of ImageModels or filenames to use for selfcal """ - selfcal_list_user = selfcal_list - - if isinstance(input, tuple): - input_sci = dm.open(input[0]) - selfcal_list = list(input[1]) + list(input[2]) - bkg_list_asn = input[1] + if selfcal_list is None: + selfcal_list = [] + if bkg_list is None: + bkg_list = [] + selfcal_list = selfcal_list + bkg_list - else: - with dm.open(input) as input_data: + with dm.open(input) as input_data: - # find science and background exposures. - if isinstance(input_data, dm.ModelContainer): + # find science and background exposures in association file + if isinstance(input_data, dm.ModelContainer): - sci_models, bkg_list_asn, selfcal_list_asn = split_container_by_asn_exptype( - input_data, exptypes=['science', 'background', 'selfcal']) + sci_models, bkg_list_asn, selfcal_list_asn = split_container_by_asn_exptype( + input_data, exptypes=['science', 'background', 'selfcal']) - selfcal_list = list(bkg_list_asn) + list(selfcal_list_asn) + selfcal_list = selfcal_list + list(bkg_list_asn) + list(selfcal_list_asn) + bkg_list += list(bkg_list_asn) - if len(sci_models) > 1: - raise ValueError("Input data contains multiple science exposures. \ - This is not supported in calwebb_spec2 steps.") - input_sci = sci_models[0] + if len(sci_models) > 1: + raise ValueError("Input data contains multiple science exposures." + "This is not supported in calwebb_spec2 steps.") + input_sci = sci_models[0] - elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): + elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): - input_sci = input_data - bkg_list_asn = [] + input_sci = input_data - else: - raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel.\ - Cannot continue.") + else: + raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel." + "Cannot continue.") - if len(selfcal_list_user) > 0: - selfcal_list = selfcal_list_user - log.warning("selfcal_list provided directly as input, ignoring any other \ - input background and selfcal exposure types, from e.g. an input \ - association file") - return input_sci, selfcal_list, bkg_list_asn + return input_sci, selfcal_list, bkg_list def split_container_by_asn_exptype(container: dm.ModelContainer, exptypes: list) -> list: diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index b9832a53ef..a086cd7d2a 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -164,32 +164,40 @@ def test_input_parsing(asn, sci, background): """Test that the input parsing function correctly identifies the science, background, and selfcal exposures in the association file given association vs imagemodel inputs, and selfcal_list vs not + Note that science exposure gets added to selfcal_list later, not in parse_inputs """ # basic association case. Both background and selfcal get into the list - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(asn, []) + input_sci, selfcal_list, bkg_list = _parse_inputs(asn, [], []) assert isinstance(input_sci, dm.IFUImageModel) - assert len(bkg_list_asn) == 2 + assert len(bkg_list) == 2 assert len(selfcal_list) == 4 + # association with background_list provided + input_sci, selfcal_list, bkg_list = _parse_inputs(asn, [], [background,] * 3) + assert isinstance(input_sci, dm.IFUImageModel) + assert len(bkg_list) == 5 + assert len(selfcal_list) == 7 + # association with selfcal_list provided - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(asn, [background,] * 3) + input_sci, selfcal_list, bkg_list = _parse_inputs(asn, [background,] * 3, []) assert isinstance(input_sci, dm.IFUImageModel) - assert len(bkg_list_asn) == 2 - assert len(selfcal_list) == 3 + assert len(bkg_list) == 2 + assert len(selfcal_list) == 7 # single science exposure - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(sci, []) + input_sci, selfcal_list, bkg_list = _parse_inputs(sci, [], []) assert isinstance(input_sci, dm.IFUImageModel) - assert len(bkg_list_asn) == 0 + assert len(bkg_list) == 0 assert len(selfcal_list) == 0 - # single science exposure with selfcal_list provided - input_sci, selfcal_list, bkg_list_asn = _parse_inputs(sci, [background,] * 3) + # single science exposure with selfcal_list and bkg_list provided + input_sci, selfcal_list, bkg_list = _parse_inputs(sci, [background,] * 3, [background,]*1) assert isinstance(input_sci, dm.IFUImageModel) - assert len(bkg_list_asn) == 0 - assert len(selfcal_list) == 3 + assert len(bkg_list) == 1 + assert len(selfcal_list) == 4 + def test_background_flagger_mrs(background): diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 894847884b..b0d40ef427 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -247,18 +247,16 @@ def process_exposure_product( # Steps whose order is the same for all types of input: # self-calibrate bad/warm pixels, and apply to both background and science # skipped by default for all modes - print(type(members_by_type['background'])) result = self.badpix_selfcal( - (calibrated, members_by_type['background'], members_by_type['selfcal']), + calibrated, members_by_type['selfcal'], members_by_type['background'], ) - if len(result) == 1: + if isinstance(result, datamodels.JwstDataModel): # if step is skipped, unchanged sci exposure is returned - calibrated = result[0] + calibrated = result else: # if step actually occurs, then flagged backgrounds are also returned calibrated, bkg_outlier_flagged = result[0], result[1] members_by_type['background'] = bkg_outlier_flagged - print(type(members_by_type['background'])) # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) calibrated = self.msa_flagging(calibrated) From e6d34cdf8045bd20766f4def24cc0b735cc579e5 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 12 Jun 2024 14:56:01 -0400 Subject: [PATCH 101/687] added spaces to log warnings --- jwst/badpix_selfcal/badpix_selfcal_step.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 214eaa1c71..bfb6a43aaa 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -75,8 +75,8 @@ def process(self, input, selfcal_list=None, bkg_list=None): # ensure that there are background exposures to use, otherwise skip the step # unless forced if (len(selfcal_list + bkg_list) == 0) and (not self.force_single): - log.warning("No selfcal or background exposures provided for self-calibration." - "Skipping step. If you want to force self-calibration with the science" + log.warning("No selfcal or background exposures provided for self-calibration. " + "Skipping step. If you want to force self-calibration with the science " "exposure alone (generally not recommended), set force_single=True.") input_sci.meta.cal_step.badpix_selfcal = 'SKIPPED' return input_sci, bkg_list @@ -85,7 +85,7 @@ def process(self, input, selfcal_list=None, bkg_list=None): try: dispaxis = input_sci.meta.wcsinfo.dispersion_direction except AttributeError: - log.warning("Dispersion axis not found in input science image metadata." + log.warning("Dispersion axis not found in input science image metadata. " "Kernel for self-calibration will be two-dimensional.") dispaxis = None @@ -154,7 +154,7 @@ def _parse_inputs(input, selfcal_list, bkg_list): bkg_list += list(bkg_list_asn) if len(sci_models) > 1: - raise ValueError("Input data contains multiple science exposures." + raise ValueError("Input data contains multiple science exposures. " "This is not supported in calwebb_spec2 steps.") input_sci = sci_models[0] @@ -163,7 +163,7 @@ def _parse_inputs(input, selfcal_list, bkg_list): input_sci = input_data else: - raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel." + raise TypeError("Input data is not a ModelContainer, ImageModel, or IFUImageModel. " "Cannot continue.") return input_sci, selfcal_list, bkg_list From 8cf98d2c816ff5d883688c8e71b9b23b0e93bf23 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 12 Jun 2024 16:16:57 -0400 Subject: [PATCH 102/687] one more round of comments, thanks @melanieclarke --- jwst/badpix_selfcal/badpix_selfcal.py | 7 +++++-- jwst/badpix_selfcal/badpix_selfcal_step.py | 9 ++++++--- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 2c2b19a857..1b6b421019 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -3,6 +3,7 @@ import jwst.datamodels as dm from jwst.outlier_detection.outlier_detection_ifu import medfilt from stdatamodels.jwst.datamodels.dqflags import pixel +import warnings def badpix_selfcal(minimg: np.ndarray, @@ -52,7 +53,9 @@ def badpix_selfcal(minimg: np.ndarray, elif dispaxis == 1: kern_size = (1, kernel_size) - smoothed = medfilt(minimg, kern_size) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") + smoothed = medfilt(minimg, kern_size) minimg_hpf = minimg - smoothed # Flag outliers using percentile cutoff @@ -82,7 +85,7 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d Flagged data model """ - input_model.dq[flagged_indices] += pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] + input_model.dq[flagged_indices] |= pixel["DO_NOT_USE"] + pixel["OTHER_BAD_PIXEL"] input_model.data[flagged_indices] = np.nan input_model.err[flagged_indices] = np.nan diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index bfb6a43aaa..f886b3d4ef 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -1,5 +1,6 @@ +import warnings from ..stpipe import Step from . import badpix_selfcal import numpy as np @@ -64,7 +65,7 @@ def process(self, input, selfcal_list=None, bkg_list=None): are included in the MIN frame from which outliers are detected. If selfcal_list and/or bkg_list are specified manually, they are appended to any selfcal or background exposures found in the input association file. - If selfcal_list and bkg_list are both set to None and input is + If selfcal_list and bkg_list are both set to None and input is a single science exposure, the step will be skipped with a warning unless the force_single parameter is set True. In that case, the input exposure will be used as the sole background exposure, @@ -96,7 +97,9 @@ def process(self, input, selfcal_list=None, bkg_list=None): selfcal_3d = [] for i, selfcal_model in enumerate(selfcal_list): selfcal_3d.append(selfcal_model.data) - minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") + minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) bad_indices = badpix_selfcal.badpix_selfcal(minimg, self.flagfrac_lower, self.flagfrac_upper, self.kernel_size, dispaxis) # apply the flags to the science data @@ -155,7 +158,7 @@ def _parse_inputs(input, selfcal_list, bkg_list): if len(sci_models) > 1: raise ValueError("Input data contains multiple science exposures. " - "This is not supported in calwebb_spec2 steps.") + "This is not supported in calwebb_spec2 steps.") input_sci = sci_models[0] elif isinstance(input_data, dm.IFUImageModel) or isinstance(input_data, dm.ImageModel): From 689ce407bd0a5a6678c57813d706c15fc0fbd3cd Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 13 Jun 2024 11:39:28 -0400 Subject: [PATCH 103/687] fixes in response to @braingram comments --- jwst/badpix_selfcal/badpix_selfcal.py | 5 --- jwst/badpix_selfcal/badpix_selfcal_step.py | 35 +++++++++++++------ .../tests/test_badpix_selfcal.py | 3 +- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 18 +++++++--- 4 files changed, 38 insertions(+), 23 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 1b6b421019..1ad9f1aa79 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -36,11 +36,6 @@ def badpix_selfcal(minimg: np.ndarray, Indices of the flagged pixels, shaped like output from np.where """ - if (flagfrac_lower < 0) or (flagfrac_upper < 0): - raise ValueError("Flagged fraction must be between 0 and 0.5") - if flagfrac_lower + flagfrac_upper >= 1: - raise ValueError("Total flagged fraction (lower plus upper) must be less than 1") - if dispaxis not in [1, 2, None]: raise ValueError("dispaxis must be either 1 or 2, or None.") diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index f886b3d4ef..a027b944b4 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -6,10 +6,6 @@ import numpy as np from jwst import datamodels as dm -import logging -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - __all__ = ["BadpixSelfcalStep"] @@ -26,16 +22,28 @@ class BadpixSelfcalStep(Step): """ class_alias = "badpix_selfcal" - bkg_suffix = "badpix_selfcal" spec = """ - flagfrac_lower = float(default=0.001) #fraction of pixels to flag on the low-flux end - flagfrac_upper = float(default=0.001) #fraction of pixels to flag on the high-flux end - kernel_size = integer(default=15) #size of kernel for median filter + flagfrac_lower = float(default=0.001, min=0.0, max=0.5) #fraction of pixels to flag on the low-flux end + flagfrac_upper = float(default=0.001, min=0.0, max=0.5) #fraction of pixels to flag on the high-flux end + kernel_size = integer(default=15, min=1) #size of kernel for median filter force_single = boolean(default=False) #force single input exposure + save_flagged_bkg = boolean(default=False) #save flagged background exposures to file skip = boolean(default=True) """ + def save_model(self, model, *args, **kwargs): + """Override save_model to suppress index 0 when save_model is True + """ + kwargs["idx"] = None + return Step.save_model(self, model, *args, **kwargs) + + def save_bkg(self, bkg_list, suffix="badpix_selfcal_bkg"): + """Save the background exposures to file with correct indexing + """ + for i, bkg_model in enumerate(bkg_list): + self.save_model(bkg_model, suffix=suffix + f"_{str(i)}") + def process(self, input, selfcal_list=None, bkg_list=None): """ Flag residual artifacts as bad pixels in the DQ array of a JWST exposure @@ -76,7 +84,7 @@ def process(self, input, selfcal_list=None, bkg_list=None): # ensure that there are background exposures to use, otherwise skip the step # unless forced if (len(selfcal_list + bkg_list) == 0) and (not self.force_single): - log.warning("No selfcal or background exposures provided for self-calibration. " + self.log.warning("No selfcal or background exposures provided for self-calibration. " "Skipping step. If you want to force self-calibration with the science " "exposure alone (generally not recommended), set force_single=True.") input_sci.meta.cal_step.badpix_selfcal = 'SKIPPED' @@ -86,14 +94,14 @@ def process(self, input, selfcal_list=None, bkg_list=None): try: dispaxis = input_sci.meta.wcsinfo.dispersion_direction except AttributeError: - log.warning("Dispersion axis not found in input science image metadata. " + self.log.warning("Dispersion axis not found in input science image metadata. " "Kernel for self-calibration will be two-dimensional.") dispaxis = None # collapse all selfcal exposures into a single background model # note that selfcal_list includes the science exposure. This is expected. # all exposures are combined into a single background model using a MIN operation. - selfcal_list = [input_sci] + [dm.open(k) for k in selfcal_list] + selfcal_list = [input_sci] + selfcal_list selfcal_3d = [] for i, selfcal_model in enumerate(selfcal_list): selfcal_3d.append(selfcal_model.data) @@ -110,6 +118,9 @@ def process(self, input, selfcal_list=None, bkg_list=None): for i, background_model in enumerate(bkg_list): bkg_list[i] = badpix_selfcal.apply_flags(dm.open(background_model), bad_indices) + if self.save_flagged_bkg: + self.save_bkg(bkg_list) + input_sci.meta.cal_step.badpix_selfcal = 'COMPLETE' return input_sci, bkg_list @@ -141,8 +152,10 @@ def _parse_inputs(input, selfcal_list, bkg_list): """ if selfcal_list is None: selfcal_list = [] + selfcal_list = [dm.open(selfcal) for selfcal in selfcal_list] if bkg_list is None: bkg_list = [] + bkg_list = [dm.open(bkg) for bkg in bkg_list] selfcal_list = selfcal_list + bkg_list with dm.open(input) as input_data: diff --git a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py index a086cd7d2a..308972ae58 100644 --- a/jwst/badpix_selfcal/tests/test_badpix_selfcal.py +++ b/jwst/badpix_selfcal/tests/test_badpix_selfcal.py @@ -193,13 +193,12 @@ def test_input_parsing(asn, sci, background): assert len(selfcal_list) == 0 # single science exposure with selfcal_list and bkg_list provided - input_sci, selfcal_list, bkg_list = _parse_inputs(sci, [background,] * 3, [background,]*1) + input_sci, selfcal_list, bkg_list = _parse_inputs(sci, [background,] * 3, [background,] * 1) assert isinstance(input_sci, dm.IFUImageModel) assert len(bkg_list) == 1 assert len(selfcal_list) == 4 - def test_background_flagger_mrs(background): """ Ensure the right number of outliers are found, and that diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index a040ac69e5..a65428029d 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -1,6 +1,7 @@ import pytest from astropy.io.fits.diff import FITSDiff from jwst.stpipe import Step +import os @pytest.fixture(scope="module") @@ -10,9 +11,10 @@ def run_pipeline_background(rtdata_module): rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_asn.json") Step.from_cmdline(['calwebb_spec2', rtdata.input, "--steps.badpix_selfcal.save_results=True", + "--steps.badpix_selfcal.save_flagged_bkg=True", "--steps.badpix_selfcal.flagfrac_lower=0.0005", "--steps.badpix_selfcal.skip=False"]) - rtdata.output = "jw01204021001_02101_00004_mirifulong_0_badpix_selfcal.fits" + rtdata.output = "jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits" return rtdata @@ -26,7 +28,7 @@ def run_pipeline_selfcal(rtdata_module): "--steps.badpix_selfcal.save_results=True", "--steps.badpix_selfcal.flagfrac_lower=0.0005", "--steps.badpix_selfcal.skip=False"]) - rtdata.output = "jw01204021001_02101_00004_mirifulong_0_badpix_selfcal.fits" + rtdata.output = "jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits" return rtdata @@ -38,11 +40,17 @@ def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs, request) rtdata = request.getfixturevalue(run_pipeline) - import os - print(os.listdir(".")) + # check the bkg files are saved in the background case, but not in the selfcal case + for idx in range(4): + fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" + if run_pipeline == "run_pipeline_background": + assert os.path.isfile(fname) + os.remove(fname) + elif run_pipeline == "run_pipeline_selfcal": + assert not os.path.isfile(fname) # Get the truth file - rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204-o021_20240127t024203_spec2_00010_asn_0_badpixselfcalstep.fits") + rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From 93574c3d8017d3a94d500800e285f4694535bfa3 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 13 Jun 2024 12:55:37 -0400 Subject: [PATCH 104/687] updated regtest to include intermediate files --- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index a65428029d..8a638e21c3 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -40,18 +40,24 @@ def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs, request) rtdata = request.getfixturevalue(run_pipeline) - # check the bkg files are saved in the background case, but not in the selfcal case + # Get the truth file + rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + # check the bkg files in the background case, but not in the selfcal case for idx in range(4): fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" if run_pipeline == "run_pipeline_background": - assert os.path.isfile(fname) + rtdata.output = fname + rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{fname}") + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() os.remove(fname) + elif run_pipeline == "run_pipeline_selfcal": assert not os.path.isfile(fname) - # Get the truth file - rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") - # Compare the results - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From d620525de6e948730b3bec74fc1aa70c85a6eb38 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 13 Jun 2024 13:42:01 -0400 Subject: [PATCH 105/687] split regtest in two --- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 32 +++++++++++++------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index 8a638e21c3..e8b1effb49 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -34,11 +34,10 @@ def run_pipeline_selfcal(rtdata_module): @pytest.mark.bigdata -@pytest.mark.parametrize("run_pipeline", ["run_pipeline_background", "run_pipeline_selfcal"]) -def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs, request): +def test_miri_mrs_badpix_selfcal(run_pipeline_selfcal, fitsdiff_default_kwargs): """Run a test for MIRI MRS data with dedicated background exposures.""" - rtdata = request.getfixturevalue(run_pipeline) + rtdata = run_pipeline_selfcal # Get the truth file rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") @@ -50,14 +49,25 @@ def test_miri_mrs_badpix_selfcal(run_pipeline, fitsdiff_default_kwargs, request) # check the bkg files in the background case, but not in the selfcal case for idx in range(4): fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" - if run_pipeline == "run_pipeline_background": - rtdata.output = fname - rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{fname}") - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() - os.remove(fname) + assert not os.path.isfile(fname) - elif run_pipeline == "run_pipeline_selfcal": - assert not os.path.isfile(fname) +@pytest.mark.bigdata +def test_miri_mrs_badpix_selfcal_bkg(run_pipeline_background, fitsdiff_default_kwargs): + """Run a test for MIRI MRS data with dedicated background exposures.""" + rtdata = run_pipeline_background + + # Get the truth file + rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + # check the bkg files in the background case, but not in the selfcal case + for idx in range(4): + fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" + truth = rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{fname}") + diff = FITSDiff(fname, truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() \ No newline at end of file From 5a49fcc5c771af39a2c8426b9fda59df3d0f8640 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 13 Jun 2024 13:39:16 -0700 Subject: [PATCH 106/687] JP-3635: Account for pixel area ratio in outlier detection blotting (#8553) --- CHANGES.rst | 3 +++ jwst/outlier_detection/outlier_detection.py | 8 +++++++- jwst/outlier_detection/tests/test_outlier_detection.py | 4 ++++ jwst/resample/resample.py | 8 ++++---- jwst/resample/tests/test_resample_step.py | 4 ++-- 5 files changed, 20 insertions(+), 7 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 375398f4a2..c9ba6302fe 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -171,6 +171,9 @@ outlier_detection to detect outliers in TSO data, with user-defined rolling window width via the ``rolling_window_width`` parameter. [#8473] +- Fixed a bug that led to small total flux offsets between input and blotted + images if the nominal and actual wcs-computed pixel areas were different. [#8553] + pathloss -------- diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index a44a89433f..1e5d3c50c4 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -17,6 +17,7 @@ from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight, calc_gwcs_pixmap from jwst.stpipe import Step +from jwst.resample.resample import compute_image_pixel_area log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -568,7 +569,12 @@ def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0): log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) log.debug("Sci shape: {}".format(blot_img.data.shape)) - pix_ratio = 1 + if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: + input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians + input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) + pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) + else: + pix_ratio = 1.0 log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape)) outsci = np.zeros(blot_img.shape, dtype=np.float32) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 7757ce61dc..40982cd4af 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -141,6 +141,10 @@ def we_many_sci( sci1.var_rnoise = np.zeros(shape) + 1.0 sci1.meta.filename = "foo1_cal.fits" + # add pixel areas + sci1.meta.photometry.pixelarea_steradians = 1.0 + sci1.meta.photometry.pixelarea_arcsecsq = 1.0 + # Make copies with different noise all_sci = [sci1] for i in range(numsci - 1): diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 725ee94bc1..1a9ff734c3 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -114,7 +114,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, self.output_wcs.array_shape = output_shape[::-1] if output_wcs.pixel_area is None: - output_pix_area = _compute_image_pixel_area(self.output_wcs) + output_pix_area = compute_image_pixel_area(self.output_wcs) if output_pix_area is None: raise ValueError( "Unable to compute output pixel area from 'output_wcs'." @@ -250,7 +250,7 @@ def resample_many_to_many(self): if (input_pixflux_area and 'SPECTRAL' not in img.meta.wcs.output_frame.axes_type): img.meta.wcs.array_shape = img.data.shape - input_pixel_area = _compute_image_pixel_area(img.meta.wcs) + input_pixel_area = compute_image_pixel_area(img.meta.wcs) if input_pixel_area is None: raise ValueError( "Unable to compute input pixel area from WCS of input " @@ -336,7 +336,7 @@ def resample_many_to_one(self): if (input_pixflux_area and 'SPECTRAL' not in img.meta.wcs.output_frame.axes_type): img.meta.wcs.array_shape = img.data.shape - input_pixel_area = _compute_image_pixel_area(img.meta.wcs) + input_pixel_area = compute_image_pixel_area(img.meta.wcs) if input_pixel_area is None: raise ValueError( "Unable to compute input pixel area from WCS of input " @@ -805,7 +805,7 @@ def _get_boundary_points(xmin, xmax, ymin, ymax, dx=None, dy=None, shrink=0): return x, y, area, center, b, r, t, l -def _compute_image_pixel_area(wcs): +def compute_image_pixel_area(wcs): """ Computes pixel area in steradians. """ if wcs.array_shape is None: diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index fc9cd101b8..eaf6f15404 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -12,7 +12,7 @@ from jwst.assign_wcs.util import compute_fiducial, compute_scale from jwst.extract_2d import Extract2dStep from jwst.resample import ResampleSpecStep, ResampleStep -from jwst.resample.resample import _compute_image_pixel_area +from jwst.resample.resample import compute_image_pixel_area from jwst.resample.resample_spec import ResampleSpecData @@ -28,7 +28,7 @@ def _set_photom_kwd(im): bb = ((xmin - 0.5, xmax - 0.5), (ymin - 0.5, ymax - 0.5)) im.meta.wcs.bounding_box = bb - mean_pixel_area = _compute_image_pixel_area(im.meta.wcs) + mean_pixel_area = compute_image_pixel_area(im.meta.wcs) if mean_pixel_area: im.meta.photometry.pixelarea_steradians = mean_pixel_area im.meta.photometry.pixelarea_arcsecsq = ( From a8184792b47ccb69768b0183361691e807e52d0b Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 13 Jun 2024 20:37:14 -0400 Subject: [PATCH 107/687] Add check for uncalibrated filters for NIRISS SOSS (#8549) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/associations/lib/dms_base.py | 25 +++++++++++++++++-- jwst/associations/lib/rules_level2b.py | 21 +++++++++++++++- jwst/associations/lib/rules_level3.py | 33 +++++++++++++++++++------- 4 files changed, 70 insertions(+), 12 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c9ba6302fe..6fbf175980 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -46,6 +46,9 @@ associations - Add NIRSpec optical path constraints for TSO associations. [#8537] +- Exclude NIRISS SOSS data taken with uncalibrated filter F277W from spec2 and + tso3 associations. [#8549] + combine_1d ---------- diff --git a/jwst/associations/lib/dms_base.py b/jwst/associations/lib/dms_base.py index 56d8ed921f..7b1f1329f9 100644 --- a/jwst/associations/lib/dms_base.py +++ b/jwst/associations/lib/dms_base.py @@ -136,7 +136,7 @@ 'nrs_brightobj' ] -# Define the valid optical paths vs detector for NIRSpect Fixed-slit Science +# Define the valid optical paths vs detector for NIRSpec Fixed-slit Science # Tuples are (SLIT, GRATING, FILTER, DETECTOR) # All A-slits are represented by SLIT == 'a'. NRS_FSS_VALID_OPTICAL_PATHS = ( @@ -204,7 +204,7 @@ ('g140m', 'flat4', 'nrs2'), ) -# Define the valid optical paths vs detector for NIRSpect IFU Science +# Define the valid optical paths vs detector for NIRSpec IFU Science # Tuples are (GRATING, FILTER, DETECTOR) # The only combinations that result in data on the NRS2 detector are # g140h/f100lp, g235h/f170lp, and g395h/f290lp. @@ -223,6 +223,13 @@ ('g395h', 'f290lp', 'nrs2'), ) + +# Define the uncalibrated filters for NIRISS SOSS +# Data taken with these filters should not be processed beyond +# level 2a. +NIS_SOSS_UNCALIBRATED_FILTERS = ('f277w',) + + # Key that uniquely identifies members. MEMBER_KEY = 'expname' @@ -1158,3 +1165,17 @@ def nrccoron_valid_detector(item): return True else: return True + + +def nissoss_calibrated_filter(item): + """Check that a NIRISS filter is calibrated.""" + _, exp_type = item_getattr(item, ['exp_type']) + if exp_type != 'nis_soss': + return True + + try: + _, filter = item_getattr(item, ['filter']) + except KeyError: + return False + + return filter not in NIS_SOSS_UNCALIBRATED_FILTERS diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index 0ddb850d23..13a2e706c1 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -13,6 +13,7 @@ Constraint_WFSC, format_list, item_getattr, + nissoss_calibrated_filter, nrccoron_valid_detector, nrsfss_valid_detector, nrsifu_valid_detector, @@ -482,7 +483,7 @@ def __init__(self, *args, **kwargs): DMSAttrConstraint( name='exp_type', sources=['exp_type'], - value=('nrs_brightobj') + value='nrs_brightobj' ), SimpleConstraint( value=False, @@ -492,6 +493,24 @@ def __init__(self, *args, **kwargs): ]), ], reduce=Constraint.notany + ), + # Don't allow NIRISS SOSS with uncalibrated filters + Constraint( + [ + Constraint([ + DMSAttrConstraint( + name='exp_type', + sources=['exp_type'], + value='nis_soss' + ), + SimpleConstraint( + value=False, + test=lambda value, item: nissoss_calibrated_filter(item) == value, + force_unique=False + ), + ]), + ], + reduce=Constraint.notany ) ]) diff --git a/jwst/associations/lib/rules_level3.py b/jwst/associations/lib/rules_level3.py index ca6db69a5e..4fb3dd44d5 100644 --- a/jwst/associations/lib/rules_level3.py +++ b/jwst/associations/lib/rules_level3.py @@ -3,12 +3,15 @@ import logging from jwst.associations.registry import RegistryMarker -from jwst.associations.lib.dms_base import (Constraint_TargetAcq, Constraint_TSO, nrsfss_valid_detector, nrsifu_valid_detector) -from jwst.associations.lib.dms_base import (nrccoron_valid_detector) +from jwst.associations.lib.dms_base import ( + Constraint_TargetAcq, Constraint_TSO, + nissoss_calibrated_filter, nrccoron_valid_detector, + nrsfss_valid_detector, nrsifu_valid_detector) from jwst.associations.lib.process_list import ListCategory from jwst.associations.lib.rules_level3_base import * from jwst.associations.lib.rules_level3_base import ( - dms_product_name_sources, dms_product_name_nrsfs_sources, dms_product_name_noopt, dms_product_name_coronimage, + dms_product_name_sources, dms_product_name_nrsfs_sources, + dms_product_name_noopt, dms_product_name_coronimage, format_product ) @@ -881,12 +884,12 @@ def __init__(self, *args, **kwargs): DMSAttrConstraint( name='restricted_grism', sources=['exp_type'], - value=('nrc_tsgrism') + value='nrc_tsgrism' ), DMSAttrConstraint( name='grism_clear', sources=['pupil'], - value=('clear') + value='clear' ), ]), Constraint([ @@ -904,19 +907,31 @@ def __init__(self, *args, **kwargs): ], reduce=Constraint.notany ), - # Don't allow NIRISS SOSS with NINTS=1 in tso3 + # Don't allow NIRISS SOSS with NINTS=1 or uncalibrated filters Constraint( [ Constraint([ DMSAttrConstraint( name='exp_type', sources=['exp_type'], - value=('nis_soss') + value='nis_soss' ), DMSAttrConstraint( name='nints', sources=['nints'], - value=('1') + value='1' + ), + ]), + Constraint([ + DMSAttrConstraint( + name='exp_type', + sources=['exp_type'], + value='nis_soss' + ), + SimpleConstraint( + value=False, + test=lambda value, item: nissoss_calibrated_filter(item) == value, + force_unique=False ), ]), ], @@ -929,7 +944,7 @@ def __init__(self, *args, **kwargs): DMSAttrConstraint( name='exp_type', sources=['exp_type'], - value=('nrs_brightobj') + value='nrs_brightobj' ), SimpleConstraint( value=False, From 7e2d2815e8b476ec03e21f84a510f2d678584cf7 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 14 Jun 2024 11:29:30 -0400 Subject: [PATCH 108/687] responding to @hbushouse comments --- CHANGES.rst | 6 ++++ docs/jwst/badpix_selfcal/arguments.rst | 2 +- jwst/badpix_selfcal/badpix_selfcal.py | 18 +++++----- jwst/badpix_selfcal/badpix_selfcal_step.py | 39 +++++++++++----------- jwst/pipeline/calwebb_spec2.py | 6 +++- 5 files changed, 40 insertions(+), 31 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 2234163dda..2f068a9959 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -46,6 +46,9 @@ associations - Add NIRSpec optical path constraints for TSO associations. [#8537] +- Exclude NIRISS SOSS data taken with uncalibrated filter F277W from spec2 and + tso3 associations. [#8549] + badpix_selfcal -------------- @@ -204,6 +207,9 @@ pipeline virtual ("v") slits and the construction of output file names for each type. [#8442] +- Added new optional step ``badpix_selfcal`` to the ``calwebb_spec2`` to self-calibrate + bad pixels in IFU data. [#8500] + pixel_replace ------------- diff --git a/docs/jwst/badpix_selfcal/arguments.rst b/docs/jwst/badpix_selfcal/arguments.rst index 823516e614..98ee18d54b 100644 --- a/docs/jwst/badpix_selfcal/arguments.rst +++ b/docs/jwst/badpix_selfcal/arguments.rst @@ -14,5 +14,5 @@ The ``badpix_selfcal`` step has the following optional arguments. The size of the kernel to use for the median filter, which is applied in the spectral direction to make the smoothed image. -``--save_flagged_bkgd`` (boolean, default=False) +``--save_flagged_bkg`` (boolean, default=False) Whether to save the flagged background exposures to fits files. diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 1ad9f1aa79..f6724a8465 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -16,23 +16,23 @@ def badpix_selfcal(minimg: np.ndarray, Parameters ---------- - minimg: np.ndarray + minimg : np.ndarray Selfcal data of shape (x, y), i.e., after some operation has already been taken to combine multiple exposures, typically a MIN operation. - flagfrac_lower: float + flagfrac_lower : float Fraction of pixels to flag on the low end - flagfrac_upper: float + flagfrac_upper : float Fraction of pixels to flag on the high end - kernel_size: int + kernel_size : int Size of kernel for median filter - dispaxis: int + dispaxis : int Dispersion axis, either 1 or 2. If None, a two-dimensional median filter is applied. Returns ------- - flagged_indices: np.ndarray + flagged_indices : np.ndarray Indices of the flagged pixels, shaped like output from np.where """ @@ -68,15 +68,15 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d Parameters ---------- - input_model: dm.IFUImageModel + input_model : dm.IFUImageModel Input science data to be corrected - flagged_indices: np.ndarray + flagged_indices : np.ndarray Indices of the flagged pixels, shaped like output from np.where Returns ------- - output_model: dm.IFUImageModel + output_model : dm.IFUImageModel Flagged data model """ diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index a027b944b4..a8cde5d888 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -1,5 +1,3 @@ - - import warnings from ..stpipe import Step from . import badpix_selfcal @@ -24,11 +22,11 @@ class BadpixSelfcalStep(Step): class_alias = "badpix_selfcal" spec = """ - flagfrac_lower = float(default=0.001, min=0.0, max=0.5) #fraction of pixels to flag on the low-flux end - flagfrac_upper = float(default=0.001, min=0.0, max=0.5) #fraction of pixels to flag on the high-flux end - kernel_size = integer(default=15, min=1) #size of kernel for median filter - force_single = boolean(default=False) #force single input exposure - save_flagged_bkg = boolean(default=False) #save flagged background exposures to file + flagfrac_lower = float(default=0.001, min=0.0, max=0.5) # fraction of pixels to flag on the low-flux end + flagfrac_upper = float(default=0.001, min=0.0, max=0.5) # fraction of pixels to flag on the high-flux end + kernel_size = integer(default=15, min=1) # size of kernel for median filter + force_single = boolean(default=False) # force single input exposure + save_flagged_bkg = boolean(default=False) # save flagged background exposures to file skip = boolean(default=True) """ @@ -50,20 +48,20 @@ def process(self, input, selfcal_list=None, bkg_list=None): Parameters ---------- - input: JWST data model or association + input : JWST data model or association input science data to be corrected, or tuple of (sci, bkg, selfcal) - selfcal_list: list of ImageModels or filenames, default None + selfcal_list : list of ImageModels or filenames, default None exposures to include as part of median background model used to find bad pixels, but that are not flagged and returned as background exposures - bkg_list: list of ImageModels or filenames, default None + bkg_list : list of ImageModels or filenames, default None exposures to include as part of median background model used to find bad pixels, and that are flagged and returned as background exposures Returns ------- - output: JWST data model or association + output : JWST data model or association data model with CRs flagged Notes @@ -85,7 +83,8 @@ def process(self, input, selfcal_list=None, bkg_list=None): # unless forced if (len(selfcal_list + bkg_list) == 0) and (not self.force_single): self.log.warning("No selfcal or background exposures provided for self-calibration. " - "Skipping step. If you want to force self-calibration with the science " + "Skipping step.") + self.log.info("If you want to force self-calibration with the science " "exposure alone (generally not recommended), set force_single=True.") input_sci.meta.cal_step.badpix_selfcal = 'SKIPPED' return input_sci, bkg_list @@ -132,23 +131,23 @@ def _parse_inputs(input, selfcal_list, bkg_list): Parameters ---------- - input: str + input : str input science exposure or association - selfcal_list: list of ImageModels or filenames, default None + selfcal_list : list of ImageModels or filenames, default None exposures to include as part of median background model used to find bad pixels, but that are not flagged and returned as background exposures - bkg_list: list of ImageModels or filenames, default None + bkg_list : list of ImageModels or filenames, default None exposures to include as part of median background model used to find bad pixels, and that are flagged and returned as background exposures Returns ------- - input: JWST data model or association + input : JWST data model or association input science data to be corrected - selfcal_list: list of ImageModels or filenames to use for selfcal + selfcal_list : list of ImageModels or filenames to use for selfcal """ if selfcal_list is None: selfcal_list = [] @@ -191,15 +190,15 @@ def split_container_by_asn_exptype(container: dm.ModelContainer, exptypes: list) Parameters ---------- - container: ModelContainer + container : ModelContainer input data - exptype: str + exptype : str exposure type to split on Returns ------- - split_list: list of lists + split_list : list of lists lists of ImageModels """ split_list = [] diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index b0d40ef427..57245439b4 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -245,7 +245,9 @@ def process_exposure_product( raise RuntimeError('Cannot determine WCS.') # Steps whose order is the same for all types of input: - # self-calibrate bad/warm pixels, and apply to both background and science + + # Self-calibrate to flag bad/warm pixels, and apply flags + # to both background and science exposures. # skipped by default for all modes result = self.badpix_selfcal( calibrated, members_by_type['selfcal'], members_by_type['background'], @@ -269,6 +271,8 @@ def process_exposure_product( # science and background exposures. Otherwise, there will be as many `imprint` members as # there are science plus background members. calibrated = self.imprint_subtract(calibrated, members_by_type['imprint']) + + # for each background image subtract an associated leak cal for i, bkg_file in enumerate(members_by_type['background']): bkg_imprint_sub = self.imprint_subtract(bkg_file, members_by_type['imprint']) members_by_type['background'][i] = bkg_imprint_sub From 09fcd5ab32dc16fcc8d50c0770c9600134526cba Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 17 Jun 2024 09:49:49 -0400 Subject: [PATCH 109/687] pin numpy --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 32c2b05a60..8ef1423212 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "crds>=11.17.14", "drizzle>=1.15.0", "gwcs>=0.21.0,<0.22.0", - "numpy>=1.22", + "numpy>=1.22,<2.0", "opencv-python-headless>=4.6.0.66", "photutils>=1.5.0", "psutil>=5.7.2", From dba65ae8e9e7c650eb6aa303566a6fd3d6bf61e6 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 17 Jun 2024 09:50:47 -0400 Subject: [PATCH 110/687] add changelog --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index e19188168f..ff7453565c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -128,6 +128,8 @@ flat_field general ------- +- Require numpy<2.0. [#8565] + - Removed deprecated stdatamodels model types ``DrizProductModel``, ``MIRIRampModel``, and ``MultiProductModel``. [#8388] From 82a810eac7671a41a2c8620494fcf1b57b0a9977 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jun 2024 10:58:10 -0400 Subject: [PATCH 111/687] Update stcal requirement from <1.6.0 to <1.8.0 (#8571) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements-max.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-max.txt b/requirements-max.txt index 39a431d6a6..d6967e5bec 100644 --- a/requirements-max.txt +++ b/requirements-max.txt @@ -1,4 +1,4 @@ # this requirements file applies upper pins on critical upstream dependencies with incompatible functionality / refactoring that has not yet been implemented in this repository -stcal<1.6.0 +stcal<1.8.0 stdatamodels<1.10.0 From 8c27f16cafc7b3af6e440719c79785e8dc5d40f4 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 18 Jun 2024 13:13:50 -0400 Subject: [PATCH 112/687] JP-3102: NIRSpec combined MOS/FS processing (#8467) Co-authored-by: Howard Bushouse --- CHANGES.rst | 59 +++- docs/jwst/data_products/msa_metadata.rst | 73 +++-- docs/jwst/exp_to_source/main.rst | 15 +- docs/jwst/flatfield/main.rst | 21 +- docs/jwst/master_background/description.rst | 26 +- docs/jwst/nsclean/main.rst | 4 + docs/jwst/photom/main.rst | 15 +- docs/jwst/pipeline/calwebb_spec2.rst | 42 +++ docs/jwst/pipeline/calwebb_spec3.rst | 16 + docs/jwst/wavecorr/description.rst | 8 +- jwst/assign_wcs/nirspec.py | 167 ++++++++-- .../tests/data/msa_fs_configuration.fits | Bin 0 -> 2016000 bytes jwst/assign_wcs/tests/test_nirspec.py | 83 ++++- jwst/exp_to_source/exp_to_source.py | 18 +- .../exp_to_source/tests/test_exp_to_source.py | 40 +++ jwst/extract_1d/extract.py | 7 - jwst/extract_2d/nirspec.py | 7 +- jwst/extract_2d/tests/test_nirspec.py | 214 +++++++++++++ jwst/flatfield/flat_field.py | 11 +- jwst/flatfield/tests/test_flatfield.py | 286 ++++++++++++++++++ jwst/lib/wcs_utils.py | 1 - jwst/master_background/expand_to_2d.py | 78 ++++- jwst/master_background/nirspec_utils.py | 78 +++-- .../tests/test_nirspec_corrections.py | 2 +- jwst/nsclean/nsclean.py | 14 +- jwst/photom/photom.py | 5 +- jwst/pipeline/calwebb_spec2.py | 143 ++++++++- jwst/pipeline/calwebb_spec3.py | 7 +- jwst/regtest/test_nirspec_mos_fs_spec2.py | 55 ++++ jwst/regtest/test_nirspec_mos_fs_spec3.py | 39 +++ jwst/srctype/srctype.py | 2 +- jwst/wavecorr/tests/test_wavecorr.py | 9 +- jwst/wavecorr/wavecorr.py | 115 ++++--- 33 files changed, 1422 insertions(+), 238 deletions(-) create mode 100644 jwst/assign_wcs/tests/data/msa_fs_configuration.fits create mode 100644 jwst/extract_2d/tests/test_nirspec.py create mode 100644 jwst/regtest/test_nirspec_mos_fs_spec2.py create mode 100644 jwst/regtest/test_nirspec_mos_fs_spec3.py diff --git a/CHANGES.rst b/CHANGES.rst index ff7453565c..c43eb166a3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -27,6 +27,10 @@ assign_wcs - Update default parameters to increase the accuracy of the SIP approximation in the output FITS WCS. [#8529] +- Added handling for fixed slit sources defined in a MSA metadata file, for combined + NIRSpec MOS and fixed slit observations. Slits are now appended to the data + product in the order they appear in the MSA file. [#8467] + associations ------------ @@ -83,6 +87,10 @@ exp_to_source in order to support changes in `source_id` handling for NIRSpec MOS exposures that contain background and virtual slits. [#8442] +- Update the top-level model exposure type from the slit exposure type, + to support processing for combined NIRSpec MOS and fixed slit + observations. [#8467] + extract_1d ---------- @@ -108,11 +116,19 @@ extract_1d - Fix error in application of aperture correction to variance arrays. [#8530] +- Removed a check for the primary slit for NIRSpec fixed slit mode: + all slits containing point sources are now handled consistently, + whether they are marked primary or not. [#8467] + extract_2d ---------- - Added handling for NIRCam GRISM time series pointing offsets. [#8449] +- Added support for slit names that have string values instead of integer + values, necessary for processing combined NIRSpec MOS and fixed slit + data products. [#8467] + flat_field ---------- @@ -122,9 +138,13 @@ flat_field - Update NIRSpec flatfield code for all modes to ensure SCI=ERR=NaN wherever the DO_NOT_USE flag is set in the DQ array. [#8463] -- Updated the NIRSpec flatfield code to use the new format of the ``wavecorr`` +- Updated the NIRSpec flatfield code to use the new format of the ``wavecorr`` wavelength zero-point corrections for point sources. [#8376] +- Removed a check for the primary slit for NIRSpec fixed slit mode: + all slits containing point sources are now handled consistently, + whether they are marked primary or not. [#8467] + general ------- @@ -139,9 +159,22 @@ lib --- - Updated the ``wcs_utils.get_wavelength`` to use the new format - of the ``wavecorr`` wavelength zero-point corrections for point + of the ``wavecorr`` wavelength zero-point corrections for point sources in NIRSpec slit data. [#8376] +master_background +----------------- + +- Removed a check for the primary slit for NIRSpec fixed slit mode: + all slits containing point sources are now handled consistently, + whether they are marked primary or not. [#8467] + +- Disabled support for master background correction for NIRSpec MOS + slits in the ``master_background``, called in ``calwebb_spec3``. + Master background correction for MOS mode should be performed + via ``master_background_mos``, called in ``calwebb_spec2``. [#8467] + + master_background_mos --------------------- @@ -156,6 +189,10 @@ nsclean sparse matrix to perform linear algebra operations with a diagonal weight matrix. [#8547] +- Added a check for combined NIRSpec MOS and fixed slit products: if fixed + slits are defined in a MOS product, the central fixed slit quadrant + is not automatically masked. [#8467] + outlier_detection ----------------- @@ -198,6 +235,10 @@ photom - Ensure that NaNs in MRS photom files are not replaced with ones by pipeline code for consistency with other modes [#8453] +- Removed a check for the primary slit for NIRSpec fixed slit mode: + all slits containing point sources are now handled consistently, + whether they are marked primary or not. [#8467] + pipeline -------- @@ -215,6 +256,12 @@ pipeline - Added new optional step ``badpix_selfcal`` to the ``calwebb_spec2`` to self-calibrate bad pixels in IFU data. [#8500] +- Added ``calwebb_spec2`` pipeline handling for combined NIRSpec MOS and + fixed slit observations. Steps that require different reference files + for MOS and FS are run twice, first for all MOS slits, then for all + FS slits. Final output products (``cal``, ``s2d``, ``x1d``) contain the + combined products. [#8467] + pixel_replace ------------- @@ -303,7 +350,12 @@ wavecorr - Changed the NIRSpec wavelength correction algorithm to include it in slit WCS models and resampling. Fixed the sign of the wavelength corrections. [#8376] - + +- Added a check for fixed slits that already have source position information, + assigned via a MSA metafile, for combined NIRSpec MOS and fixed slit processing. + Point source position is calculated from dither offsets only for standard + fixed slit processing. [#8467] + wfss_contam ----------- @@ -615,7 +667,6 @@ resample - Changed deprecated ``stpipe.extern.configobj`` to ``astropy.extern.configobj``. [#8320] - residual_fringe --------------- diff --git a/docs/jwst/data_products/msa_metadata.rst b/docs/jwst/data_products/msa_metadata.rst index b251402c80..5173000aa0 100644 --- a/docs/jwst/data_products/msa_metadata.rst +++ b/docs/jwst/data_products/msa_metadata.rst @@ -43,7 +43,7 @@ The overall structure of the MSA FITS file is as follows: +-----+---------------+----------+-----------+--------------------+ | 1 | SHUTTER_IMAGE | IMAGE | float32 | 342 x 730 | +-----+---------------+----------+-----------+--------------------+ -| 2 | SHUTTER_INFO | BINTABLE | N/A | variable x 12 cols | +| 2 | SHUTTER_INFO | BINTABLE | N/A | variable x 13 cols | +-----+---------------+----------+-----------+--------------------+ | 3 | SOURCE_INFO | BINTABLE | N/A | variable x 8 cols | +-----+---------------+----------+-----------+--------------------+ @@ -89,6 +89,8 @@ The structure of the ``SHUTTER_INFO`` table extension is as follows: +-------------------------------+-----------+----------------------+ | PRIMARY_SOURCE | string | Primary source flag | +-------------------------------+-----------+----------------------+ +| FIXED_SLIT | string | Fixed slit name | ++-------------------------------+-----------+----------------------+ - SLITLET_ID: integer ID of each slitlet consisting of one or more open shutters. @@ -102,8 +104,8 @@ The structure of the ``SHUTTER_INFO`` table extension is as follows: quadrant. - SOURCE_ID: unique integer ID for each source in each slitlet, used for matching to an entry in the ``SOURCE_INFO`` table. -- BACKGROUND: boolean indicating whether the shutter is open to background - only or contains a known source. +- BACKGROUND: Y or N. Y indicates that the shutter is open to background + only. - SHUTTER_STATE: OPEN or CLOSED. Generally will always be OPEN, unless the shutter is part of a long slit that is not contiguous. - ESTIMATED_SOURCE_IN_SHUTTER_X/Y: the position of the source within the @@ -111,8 +113,11 @@ The structure of the ``SHUTTER_INFO`` table extension is as follows: and 1,1 is upper-right, as planned in the MPT. - DITHER_POINT_INDEX: integer index of the nod sequence; matches to header keyword `PATT_NUM`. -- PRIMARY_SOURCE: boolean indicating whether the shutter contains the +- PRIMARY_SOURCE: Y or N. Y indicates that the shutter contains the primary science source. +- FIXED_SLIT: string name of a fixed slit containing the source; set to + NONE for MSA slitlets. This column may not appear in older versions of + the MSA metadata files. It is the :ref:`assign_wcs ` step in the :ref:`calwebb_spec2 ` pipeline that opens and loads all @@ -140,29 +145,29 @@ slitlet 2 is comprised of 3 shutters. Because a 3-point nod pattern has been use there are 3 different sets of metadata for each slitlet (one set for each dither/nod position) and hence a total of 9 entries (3 shutters x 3 dithers). -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| Slit | Meta | | | | Src | | | X | Y | Dith | Pri | -| | | | | | | | | | | | | -| ID | ID | Quad | Row | Col | ID | Bkg | State | pos | pos | Pt | Src | -+======+======+======+=====+=====+========+=====+=======+=======+=======+======+=====+ -| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 1 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 155 | 42 | N | OPEN | 0.399 | 0.702 | 1 | Y | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 1 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 154 | 42 | N | OPEN | 0.410 | 0.710 | 2 | Y | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 2 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 2 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 3 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 3 | N | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ -| 2 | 1 | 2 | 10 | 156 | 42 | N | OPEN | 0.389 | 0.718 | 3 | Y | -+------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+ ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| Slit | Meta | | | | Src | | | X | Y | Dith | Pri | Fxd | +| | | | | | | | | | | | | | +| ID | ID | Quad | Row | Col | ID | Bkg | State | pos | pos | Pt | Src | Slit | ++======+======+======+=====+=====+========+=====+=======+=======+=======+======+=====+======+ +| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 1 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 155 | 42 | N | OPEN | 0.399 | 0.702 | 1 | Y | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 1 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 154 | 42 | N | OPEN | 0.410 | 0.710 | 2 | Y | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 2 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 156 | 0 | Y | OPEN | NaN | NaN | 2 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 154 | 0 | Y | OPEN | NaN | NaN | 3 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 155 | 0 | Y | OPEN | NaN | NaN | 3 | N | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ +| 2 | 1 | 2 | 10 | 156 | 42 | N | OPEN | 0.389 | 0.718 | 3 | Y | NONE | ++------+------+------+-----+-----+--------+-----+-------+-------+-------+------+-----+------+ The values in the `slitlet_id` column show that we're only looking at table rows for slitlet 2, all of which come from MSA configuration (`msa_metadata_id`) 1. @@ -229,6 +234,20 @@ from a virtual slitlet with `source_id` = -42 will look like: jw12345-o066_v000000042_nirspec_f170lp_g235m_x1d.fits +Fixed Slits +~~~~~~~~~~~ + +It is possible to plan fixed slit sources alongside standard MOS targets. In this case, +a unique `slitlet_id` is not assigned in the MSA file. Instead, the slit is identified +by the value of the `fixed_slit` column. This value may be set to any of the NIRSpec +fixed slit names used for science: S200A1, S200A2, S400A1, or S1600A1. + +Fixed slit targets must always have `primary_source` = "Y" and `background` = "N". +They will never be extracted as background sources. + +The `shutter_quadrant`, `shutter_row`, and `shutter_column` fields are set to placeholder +values. All other values have the same meaning and values as for MSA slitlets. + The SOURCE_INFO Metadata ------------------------ diff --git a/docs/jwst/exp_to_source/main.rst b/docs/jwst/exp_to_source/main.rst index 0216274adf..2e862079d2 100644 --- a/docs/jwst/exp_to_source/main.rst +++ b/docs/jwst/exp_to_source/main.rst @@ -34,14 +34,21 @@ configuration of MSA slitlets with a source assigned to each slitlet. The source-to-slitlet linkage is carried along in the information contained in the MSA metadata file used during :ref:`calwebb_spec2 ` processing. Each slitlet instance created by the :ref:`extract_2d ` -step stores the source ID (a simple integer number) in the SOURCEID keyword of +step stores the source name in the SRCNAME keyword of the SCI extension header for the slitlet. The ``exp_to_source`` tool uses -the SOURCEID values to sort the data from each input product into an +the SRCNAME values to sort the data from each input product into an appropriate source-based output product. -NIRSpec Fixed-Slit +If fixed slit targets are planned as part of a NIRSpec MOS exposure, they +will also have sources identified by the MSA metadata file. The associated +SRCNAME values are used to sort the data from these slits in the same way the +MSA slitlets are sorted. In this combined mode, the products containing +MOS slitlets are marked with EXP_TYPE = "NRS_MSASPEC" after sorting; the models +containing fixed slits are marked with EXP_TYPE = "NRS_FIXEDSLIT". + +NIRSpec Fixed Slit ^^^^^^^^^^^^^^^^^^ -NIRSpec fixed-slit observations do not have sources identified with each +NIRSpec fixed slit observations do not have sources identified with each slit, so the slit names, e.g. S200A1, S1600A1, etc., are mapped to predefined source ID values, as follows: diff --git a/docs/jwst/flatfield/main.rst b/docs/jwst/flatfield/main.rst index 3cd4111764..f6592b2105 100644 --- a/docs/jwst/flatfield/main.rst +++ b/docs/jwst/flatfield/main.rst @@ -78,12 +78,12 @@ pixel. This interpolation requires knowledge of the dispersion direction, which is read from keyword "DISPAXIS." See the Reference File section for further details. -For NIRSpec Fixed-Slit and MOS exposures, an on-the-fly flat-field is +For NIRSpec Fixed Slit (FS) and MOS exposures, an on-the-fly flat-field is constructed to match each of the slits/slitlets contained in the science exposure. For NIRSpec IFU exposures, a single full-frame flat-field is constructed, which is applied to the entire science image. -NIRSpec NRS_BRIGHTOBJ data are processed just like NIRSpec Fixed-Slit +NIRSpec NRS_BRIGHTOBJ data are processed just like NIRSpec fixed slit data, except that NRS_BRIGHTOBJ data are stored in a CubeModel, rather than a MultiSlitModel. A 2-D flat-field image is constructed on-the-fly as usual, but this image is then divided into each plane of @@ -92,9 +92,9 @@ the 3-D science data arrays. In all cases, there is a step option that allows for saving the on-the-fly flatfield to a file, if desired. -NIRSpec Fixed-Slit Primary Slit +NIRSpec Fixed Slit Primary Slit ------------------------------- -The primary slit in a NIRSpec fixed-slit exposure receives special handling. +The primary slit in a NIRSpec fixed slit exposure receives special handling. If the primary slit, as given by the "FXD_SLIT" keyword value, contains a point source, as given by the "SRCTYPE" keyword, it is necessary to know the flatfield conversion factors for both a point source and a uniform source @@ -104,7 +104,7 @@ is applied to the slit data, but that correction is not appropriate for the background signal contained in the slit, and hence corrections must be applied later in the :ref:`master background ` step. -So in this case the `flatfield` step will compute 2D arrays of conversion +In this case, the `flatfield` step will compute 2D arrays of conversion factors that are appropriate for a uniform source and for a point source, and store those correction factors in the "FLATFIELD_UN" and "FLATFIELD_PS" extensions, respectively, of the output data product. The point source @@ -116,8 +116,15 @@ applied by the :ref:`wavecorr ` step to account for any source mis-centering in the slit and the flatfield conversion factors are wavelength-dependent. A uniform source does not require wavelength corrections and hence the flatfield conversions will differ for point and uniform -sources. Any secondary slits that may be included in a fixed-slit exposure +sources. Any secondary slits that may be included in a fixed slit exposure do not have source centering information available, so the -:ref:`wavecorr ` step is not applied, and hence there's no +:ref:`wavecorr ` step is not applied, and there is no difference between the point source and uniform source flatfield conversions for those slits. + +Fixed slits planned as part of a combined MOS and FS observation are an +exception to this rule. These targets may each be identified as +point sources, with location information for each given in the +:ref:`MSA metadata file `. Point sources in fixed slits planned +this way are treated in the same manner as the primary fixed slit in standard +FS observations. diff --git a/docs/jwst/master_background/description.rst b/docs/jwst/master_background/description.rst index a436ecdc1d..6a5d39b10b 100644 --- a/docs/jwst/master_background/description.rst +++ b/docs/jwst/master_background/description.rst @@ -295,27 +295,25 @@ source centering within the slit, hence slits containing uniform sources receive the same flat-field and photometric calibrations as background spectra and therefore don't require corrections for those two calibrations. Furthermore, the source position in the slit is only known for the primary slit in an exposure, so -even if the secondary slits contain point sources, no wavelength correction can -be applied, and therefore again the flat-field and photometric calibrations are -the same as for background spectra. This means only the pathloss correction -difference between uniform and point sources needs to be accounted for in the -secondary slits. +secondary slits are always handled as extended sources, no wavelength correction is +applied, and therefore again the flat-field, photometric, and pathloss calibrations +are the same as for background spectra. -Therefore if the primary slit (as given by the FXD_SLIT keyword) contains a point source -(as given by the SRCTYPE keyword) the corrections that need to be applied to the 2-D -master background for that slit are: +Fixed slits planned as part of a combined MOS and FS observation are an +exception to this rule. These targets may each be identified as +point sources, with location information for each given in the +:ref:`MSA metadata file `. Point sources in fixed slits planned +this way are treated in the same manner as the primary fixed slit in standard +FS observations. + +Therefore, if a fixed slit contains a point source (as given by the SRCTYPE keyword) +the corrections that need to be applied to the 2-D master background for that slit are: .. math:: bkg(corr) = bkg &* [flatfield(uniform) / flatfield(point)]\\ &* [pathloss(uniform) / pathloss(point)]\\ &* [photom(point) / photom(uniform)] -For secondary slits that contain a point source, the correction applied to the -2-D master background is simply: - -.. math:: - bkg(corr) = bkg * pathloss(uniform) / pathloss(point) - The uniform and point source versions of the flat-field, pathloss, and photom corrections are retrieved from the input :ref:`cal ` product. They are computed and stored there during the execution of each of those steps diff --git a/docs/jwst/nsclean/main.rst b/docs/jwst/nsclean/main.rst index 48a9c615d9..c61e406094 100644 --- a/docs/jwst/nsclean/main.rst +++ b/docs/jwst/nsclean/main.rst @@ -103,6 +103,10 @@ processing MOS and IFU images. The masked region is currently hardwired in the step to image indexes [1:2048, 923:1116], where the indexes are in x, y order and in 1-indexed values. +Note, however, that it is possible to plan one or more fixed slit targets +alongside MSA slitlets in MOS observations. In this situation, the fixed +slit region is not automatically masked. + Left/Right Reference Pixel Columns ---------------------------------- Full-frame images contain 4 columns of reference pixels on the left and diff --git a/docs/jwst/photom/main.rst b/docs/jwst/photom/main.rst index 1d98734cb5..0e0b478b64 100644 --- a/docs/jwst/photom/main.rst +++ b/docs/jwst/photom/main.rst @@ -84,9 +84,9 @@ conversion factor. If the time-dependent coefficients are present in the reference file, the photom step will apply the correction based on the observation date of the exposure being processed. -NIRSpec Fixed-Slit Primary Slit +NIRSpec Fixed Slit Primary Slit ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The primary slit in a NIRSpec fixed-slit exposure receives special handling. +The primary slit in a NIRSpec fixed slit exposure receives special handling. If the primary slit, as given by the "FXD_SLIT" keyword value, contains a point source, as given by the "SRCTYPE" keyword, it is necessary to know the photometric conversion factors for both a point source and a uniform source @@ -96,7 +96,7 @@ is applied to the slit data, but that correction is not appropriate for the background signal contained in the slit, and hence corrections must be applied later in the :ref:`master background ` step. -So in this case the `photom` step will compute 2D arrays of conversion +In this case, the `photom` step will compute 2D arrays of conversion factors that are appropriate for a uniform source and for a point source, and store those correction factors in the "PHOTOM_UN" and "PHOTOM_PS" extensions, respectively, of the output data product. The point source @@ -105,7 +105,7 @@ correction array is also applied to the slit data. Note that this special handling is only needed when the slit contains a point source, because in that case corrections to the wavelength grid are applied by the :ref:`wavecorr ` step to account for any -source mis-centering in the slit and the photometric conversion factors are +source offsets in the slit and the photometric conversion factors are wavelength-dependent. A uniform source does not require wavelength corrections and hence the photometric conversions will differ for point and uniform sources. Any secondary slits that may be included in a fixed-slit exposure @@ -114,6 +114,13 @@ do not have source centering information available, so the difference between the point source and uniform source photometric conversions for those slits. +Fixed slits planned as part of a combined MOS and FS observation are an +exception to this rule. These targets may each be identified as +point sources, with location information for each given in the +:ref:`MSA metadata file `. Point sources in fixed slits planned +this way are all treated in the same manner as the primary fixed slit in standard +FS observations. + Pixel Area Data ^^^^^^^^^^^^^^^ For all instrument modes other than NIRSpec the photom step loads a 2-D pixel diff --git a/docs/jwst/pipeline/calwebb_spec2.rst b/docs/jwst/pipeline/calwebb_spec2.rst index af3284c6c6..d2ad75724b 100644 --- a/docs/jwst/pipeline/calwebb_spec2.rst +++ b/docs/jwst/pipeline/calwebb_spec2.rst @@ -116,6 +116,48 @@ performed on the original (unresampled) data. The :ref:`cube_build ` step. +Combined NIRSpec MOS and FS Exposures +------------------------------------- + +It is possible to observe one or more fixed slit sources as part of a NIRSpec MOS +observation. Fixed slits observed this way are mostly handled as if they were +MOS slitlets: they are assigned a WCS, extracted from the full-frame image, calibrated, +and appended to the output data products. + +However, since FS and MOS modes require different pipeline steps and reference files +at various points in the ``calwebb_spec2`` pipeline, the processing path is not as +straightforward as for a standard MOS exposure. Internal to the pipeline, the data +product is sorted into MOS slits and FS slits. MOS slits are processed together first, +then FS slits are processed through the same steps. At the end of the processing, +the calibrated images and spectra are recombined into final data products containing +all observed slits. + +The detailed processing flow is as follows: + +- Fixed slits containing primary sources are identified in the input + :ref:`MSA metadata file ` in the :ref:`assign_wcs ` + step, via the "fixed_slit" column in the MSA SHUTTER_INFO table. +- All slits (MOS and FS) are processed together through the :ref:`srctype ` step. +- MOS slits are processed together through the :ref:`master_background `, + :ref:`wavecorr `, :ref:`flat_field `, + :ref:`pathloss `, :ref:`barshadow `, and + :ref:`photom ` steps. +- FS slits are processed together through the :ref:`wavecorr `, + :ref:`flat_field `, :ref:`pathloss `, and + :ref:`photom ` steps. If intermediate products from these + steps are saved, they will have an additional "_fs" suffix appended to + their file names. +- MOS and FS slits are recombined and processed together through the + :ref:`resample_spec ` step. +- MOS slits are processed through the :ref:`extract_1d ` step, + then FS slits are processed through the same step. The extracted spectra are + recombined into a final data product. + +The combined, calibrated output product for this mode may be used as input for +the :ref:`calwebb_spec3 ` pipeline. Since that pipeline sorts and +separates the data by source, the fixed slit and MOS targets are independently handled +through all pipeline steps with no further accommodation necessary. + NIRSpec Lamp Exposures ---------------------- diff --git a/docs/jwst/pipeline/calwebb_spec3.rst b/docs/jwst/pipeline/calwebb_spec3.rst index 4331170368..307311bc58 100644 --- a/docs/jwst/pipeline/calwebb_spec3.rst +++ b/docs/jwst/pipeline/calwebb_spec3.rst @@ -51,6 +51,9 @@ to observations of a moving target (TARGTYPE='moving'). :sup:`2`\ The master background subtraction step is applied to NIRSpec MOS exposures in the :ref:`calwebb_spec2 ` pipeline. +WFSS and SOSS Processing +------------------------ + Notice that NIRCam and NIRISS WFSS, as well as NIRISS SOSS data, receive only minimal processing by ``calwebb_spec3``. WFSS 2D input data are reorganized into source-based products by the @@ -66,6 +69,19 @@ obtained in TSO mode. TSO mode NIRISS SOSS exposures should be processed with the :ref:`calwebb_tso3 ` pipeline. +Combined NIRSpec MOS and FS Exposures +------------------------------------- + +It is possible to observe NIRSpec fixed slit targets alongside MSA +slitlets in NIRSpec MOS exposures. In this case, the input files produced by the +:ref:`calwebb_spec2 ` pipeline contain both MOS and FS data. Any +:ref:`master_background ` corrections applied in this pipeline +will operate on the fixed slits only, since background corrections for MOS slitlets should +be applied in the :ref:`calwebb_spec2 ` pipeline. After the sources are +separated in the :ref:`exp_to_source ` step, each data product will contain +only a fixed slit or a MOS target, so all further processing follows the standard flow +for each exposure type. + Arguments --------- diff --git a/docs/jwst/wavecorr/description.rst b/docs/jwst/wavecorr/description.rst index fa7e311d5a..fcaba1f7fe 100644 --- a/docs/jwst/wavecorr/description.rst +++ b/docs/jwst/wavecorr/description.rst @@ -49,7 +49,7 @@ estimate the fractional location of the source within the given slit. Note that this computation can only be performed for the primary slit in the exposure, which is given in the "FXD_SLIT" keyword. The positions of sources in any additional slits cannot be estimated and therefore -the wavelength correction is only applied to the primary slit. +the wavelength correction is only applied to the primary slit.\ :sup:`1` The estimated position of the source within the primary slit (in the dispersion direction) is then used in the same manner as described above @@ -58,3 +58,9 @@ for the primary slit. Upon successful completion of the step, the status keyword "S_WAVCOR" is set to "COMPLETE". + +:sup:`1`\ Note that fixed slits that are planned as part of a combined +MOS and FS observation do have *a priori* estimates of their source +locations, via the :ref:`MSA metadata file`. When available, +these source locations are directly used, instead of recomputing the source +position in the ``wavecorr`` step. diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index 3577a84d57..740782a099 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -35,6 +35,8 @@ log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) +FIXED_SLIT_NUMS = {'NONE': 0, 'S200A1': 1, 'S200A2': 2, + 'S400A1': 3, 'S1600A1': 4, 'S200B1': 5} __all__ = ["create_pipeline", "imaging", "ifu", "slits_wcs", "get_open_slits", "nrs_wcs_set_input", "nrs_ifu_wcs", "get_spectral_order_wrange"] @@ -446,12 +448,11 @@ def get_open_fixed_slits(input_model, slit_y_range=[-.55, .55]): if input_model.meta.instrument.fixed_slit is None: input_model.meta.instrument.fixed_slit = 'NONE' - slit_nums = {'NONE':0, 'S200A1':1, 'S200A2':2, 'S400A1':3, 'S1600A1':4, 'S200B1':5} primary_slit = input_model.meta.instrument.fixed_slit ylow, yhigh = slit_y_range # Slits are defined with hardwired source ID's, based on the assignments - # in the "slit_nums" dictionary. Exact assignments depend on whether the + # in the "FIXED_SLIT_NUMS" dictionary. Exact assignments depend on whether the # slit is the "primary" and hence contains the target of interest. The # source_id for the primary slit is always 1, while source_ids for secondary # slits is a two-digit value, where the first (tens) digit corresponds to @@ -461,15 +462,20 @@ def get_open_fixed_slits(input_model, slit_y_range=[-.55, .55]): # # Slit(Name, ShutterID, DitherPos, Xcen, Ycen, Ymin, Ymax, Quad, SourceID) s2a1 = Slit('S200A1', 0, 0, 0, 0, ylow, yhigh, 5, - 1 if primary_slit=='S200A1' else 10*slit_nums[primary_slit] + 1) + 1 if primary_slit == 'S200A1' + else 10 * FIXED_SLIT_NUMS[primary_slit] + 1) s2a2 = Slit('S200A2', 1, 0, 0, 0, ylow, yhigh, 5, - 1 if primary_slit=='S200A2' else 10*slit_nums[primary_slit] + 2) + 1 if primary_slit == 'S200A2' + else 10 * FIXED_SLIT_NUMS[primary_slit] + 2) s4a1 = Slit('S400A1', 2, 0, 0, 0, ylow, yhigh, 5, - 1 if primary_slit=='S400A1' else 10*slit_nums[primary_slit] + 3) + 1 if primary_slit == 'S400A1' + else 10 * FIXED_SLIT_NUMS[primary_slit] + 3) s16a1 = Slit('S1600A1', 3, 0, 0, 0, ylow, yhigh, 5, - 1 if primary_slit=='S1600A1' else 10*slit_nums[primary_slit] + 4) + 1 if primary_slit == 'S1600A1' + else 10 * FIXED_SLIT_NUMS[primary_slit] + 4) s2b1 = Slit('S200B1', 4, 0, 0, 0, ylow, yhigh, 5, - 1 if primary_slit=='S200B1' else 10*slit_nums[primary_slit] + 5) + 1 if primary_slit == 'S200B1' + else 10 * FIXED_SLIT_NUMS[primary_slit] + 5) # Decide which slits need to be added to this exposure subarray = input_model.meta.subarray.name.upper() @@ -597,37 +603,127 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, log.info(f'Retrieving open MSA slitlets for msa_metadata_id = {msa_metadata_id} ' f'and dither_index = {dither_position}') - # Get the unique slitlet_ids - slitlet_ids_unique = list(set([x['slitlet_id'] for x in msa_data])) + # Sort the MSA rows by slitlet_id + slitlet_sets = {} + for row in msa_data: + # Check for fixed slit: if set, then slitlet_id is null + is_fs = False + try: + fixed_slit = row['fixed_slit'] + if (fixed_slit in FIXED_SLIT_NUMS.keys() + and fixed_slit != 'NONE'): + is_fs = True + except (IndexError, ValueError, KeyError): + # May be old-style MSA file without a fixed_slit column + fixed_slit = None + + if is_fs: + # Fixed slit - use the slit name as the ID + slitlet_id = fixed_slit + else: + # MSA - use the slitlet ID + slitlet_id = row['slitlet_id'] + + # Append the row for the slitlet + if slitlet_id in slitlet_sets: + slitlet_sets[slitlet_id].append(row) + else: + slitlet_sets[slitlet_id] = [row] - # add a margin to the slit y limits + # Add a margin to the slit y limits margin = 0.5 - # Now lets look at each unique slitlet id - for slitlet_id in slitlet_ids_unique: - # Get the rows of shutter info for the current slitlet_id - slitlets_sid = [x for x in msa_data if x['slitlet_id'] == slitlet_id] - open_shutters = [x['shutter_column'] for x in slitlets_sid] + # Now let's look at each unique slitlet id + for slitlet_id, slitlet_rows in slitlet_sets.items(): + # Get the open shutter information from the slitlet rows + open_shutters = [x['shutter_column'] for x in slitlet_rows] # How many shutters in the slitlet are labeled as "main" or "primary"? - n_main_shutter = len([s for s in slitlets_sid if s['primary_source'] == 'Y']) + n_main_shutter = len([s for s in slitlet_rows if s['primary_source'] == 'Y']) + + # Check for fixed slit sources defined in the MSA file + is_fs = [False] * len(slitlet_rows) + for i, slitlet in enumerate(slitlet_rows): + try: + if (slitlet['fixed_slit'] in FIXED_SLIT_NUMS.keys() + and slitlet['fixed_slit'] != 'NONE'): + is_fs[i] = True + except (IndexError, ValueError, KeyError): + # May be old-style MSA file without a fixed_slit column + pass # In the next part we need to calculate, find, or determine 5 things for each slit: # quadrant, xcen, ycen, ymin, ymax - # There are no main shutters: all are background - if n_main_shutter == 0: + # First, check for a fixed slit + if all(is_fs) and len(slitlet_rows) == 1: + # One fixed slit open for the source + slitlet = slitlet_rows[0] + + # Use a standard number for fixed slit shutter id + shutter_id = FIXED_SLIT_NUMS[slitlet_id] - 1 + xcen = ycen = 0 + quadrant = 5 + + # No additional margin for fixed slit bounding boxes + ymin = ylow + ymax = yhigh + + # Source position and id + if n_main_shutter == 1: + # Source is marked primary + source_id = slitlet['source_id'] + source_xpos = slitlet['estimated_source_in_shutter_x'] + source_ypos = slitlet['estimated_source_in_shutter_y'] + log.info(f'Found fixed slit {slitlet_id} with source_id = {source_id}.') + + # Get source info for this slitlet: + # note that slits with a real source assigned have source_id > 0, + # while slits with source_id < 0 contain "virtual" sources + try: + source_name, source_alias, stellarity, source_ra, source_dec = [ + (s['source_name'], s['alias'], s['stellarity'], s['ra'], s['dec']) + for s in msa_source if s['source_id'] == source_id][0] + except IndexError: + # Missing source information: assign a virtual source name + log.warning("Could not retrieve source info from MSA file") + source_name = f"{prog_id}_VRT{slitlet_id}" + source_alias = "VRT{}".format(slitlet_id) + stellarity = 0.0 + source_ra = 0.0 + source_dec = 0.0 + + else: + log.warning(f'Fixed slit {slitlet_id} is not a primary source; ' + f'skipping it.') + continue + + elif any(is_fs): + # Unsupported fixed slit configuration + message = ("For slitlet_id = {}, metadata_id = {}, " + "dither_index = {}".format( + slitlet_id, msa_metadata_id, dither_position)) + log.warning(message) + message = ("MSA configuration file has an unsupported " + "fixed slit configuration.") + log.warning(message) + msa_file.close() + raise MSAFileError(message) + + # Now check for regular MSA slitlets + elif n_main_shutter == 0: + # There are no main shutters: all are background if len(open_shutters) == 1: jmin = jmax = j = open_shutters[0] else: - jmin = min([s['shutter_column'] for s in slitlets_sid]) - jmax = max([s['shutter_column'] for s in slitlets_sid]) + jmin = min([s['shutter_column'] for s in slitlet_rows]) + jmax = max([s['shutter_column'] for s in slitlet_rows]) j = jmin + (jmax - jmin) // 2 ymax = yhigh + margin + (jmax - j) * 1.15 ymin = -(-ylow + margin) + (jmin - j) * 1.15 - quadrant = slitlets_sid[0]['shutter_quadrant'] + quadrant = slitlet_rows[0]['shutter_quadrant'] ycen = j - xcen = slitlets_sid[0]['shutter_row'] # grab the first as they are all the same + xcen = slitlet_rows[0]['shutter_row'] # grab the first as they are all the same shutter_id = xcen + (ycen - 1) * 365 # shutter numbers in MSA file are 1-indexed # Background slits all have source_id=0 in the msa_file, @@ -651,20 +747,21 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, (s['shutter_row'], s['shutter_column'], s['shutter_quadrant'], s['estimated_source_in_shutter_x'], s['estimated_source_in_shutter_y']) - for s in slitlets_sid if s['background'] == 'N'][0] + for s in slitlet_rows if s['background'] == 'N'][0] shutter_id = xcen + (ycen - 1) * 365 # shutter numbers in MSA file are 1-indexed # y-size - jmin = min([s['shutter_column'] for s in slitlets_sid]) - jmax = max([s['shutter_column'] for s in slitlets_sid]) + jmin = min([s['shutter_column'] for s in slitlet_rows]) + jmax = max([s['shutter_column'] for s in slitlet_rows]) j = ycen ymax = yhigh + margin + (jmax - j) * 1.15 ymin = -(-ylow + margin) + (jmin - j) * 1.15 # Get the source_id from the primary shutter entry - for i in range(len(slitlets_sid)): - if slitlets_sid[i]['primary_source'] == 'Y': - source_id = slitlets_sid[i]['source_id'] + source_id = None + for i in range(len(slitlet_rows)): + if slitlet_rows[i]['primary_source'] == 'Y': + source_id = slitlet_rows[i]['source_id'] # Get source info for this slitlet; # note that slits with a real source assigned have source_id > 0, @@ -683,14 +780,15 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, f"assigning virtual source_name={source_name}") if source_id < 0: - log.info(f'Slitlet {slitlet_id} contains virtual source, with source_id={source_id}') + log.info(f'Slitlet {slitlet_id} contains virtual source, ' + f'with source_id={source_id}') # More than 1 main shutter: Not allowed! else: message = ("For slitlet_id = {}, metadata_id = {}, " "and dither_index = {}".format(slitlet_id, msa_metadata_id, dither_position)) log.warning(message) - message = ("MSA configuration file has more than 1 shutter with primary source") + message = "MSA configuration file has more than 1 shutter with primary source" log.warning(message) msa_file.close() raise MSAFileError(message) @@ -711,9 +809,12 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, # Create the shutter_state string all_shutters = _shutter_id_to_str(open_shutters, ycen) - slitlets.append(Slit(slitlet_id, shutter_id, dither_position, xcen, ycen, ymin, ymax, - quadrant, source_id, all_shutters, source_name, source_alias, - stellarity, source_xpos, source_ypos, source_ra, source_dec)) + slit_parameters = (slitlet_id, shutter_id, dither_position, xcen, ycen, ymin, ymax, + quadrant, source_id, all_shutters, source_name, source_alias, + stellarity, source_xpos, source_ypos, source_ra, source_dec) + log.debug(f'Appending slit: {slit_parameters}') + slitlets.append(Slit(*slit_parameters)) + msa_file.close() return slitlets diff --git a/jwst/assign_wcs/tests/data/msa_fs_configuration.fits b/jwst/assign_wcs/tests/data/msa_fs_configuration.fits new file mode 100644 index 0000000000000000000000000000000000000000..5a1f5b6e55be4ba7ca4da17125829f11621e9542 GIT binary patch literal 2016000 zcmeI*O;cN0dH`T`PtQzMDqpM2Zon@}>yv=R`s zBzoGbEV9ccl}-M@)GSl^3q8LgyIE#URhHT0>K+cDVZ>#2 zeVoUux7+o%*;c(<&$@5-8-t^q+`6*-s=A(c2i8~4-kTV?x!Y)UntQFz zNsv?5Dz9pll_zJ!tDPD+=GD~~)o0a}Uc5W&we`(v{;)fz9(p%xwmRKr_n@0sl(ie3 z{k*W|>zziHqh_1!M!nl?Z3oyl&HUzC<@3t=YUKx?ER;Vf-#=YyTFK!4)9UAymF1$ofBfac`{l*^ z>=Jhzemx7(@z^i8wdxzpRs_1#8qBNs@6Llw8Kt*t+K^hM=GKHuW%;>XE+ziG64 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phdu.header['SUBSIZE1'])) + + hdul.append(phdu) + hdul.append(scihdu) + return hdul + + +def create_msa_hdul(): + # Two point sources, one in MSA, one fixed slit. + # Source locations for the fixed slit are placeholders, not realistic. + shutter_data = { + 'slitlet_id': [12, 12, 12, 100], + 'msa_metadata_id': [1, 1, 1, 1], + 'shutter_quadrant': [4, 4, 4, 0], + 'shutter_row': [251, 251, 251, 0], + 'shutter_column': [22, 23, 24, 0], + 'source_id': [1, 1, 1, 2], + 'background': ['Y', 'N', 'Y', 'N'], + 'shutter_state': ['OPEN', 'OPEN', 'OPEN', 'OPEN'], + 'estimated_source_in_shutter_x': [np.nan, 0.18283921, np.nan, 0.5], + 'estimated_source_in_shutter_y': [np.nan, 0.31907734, np.nan, 0.5], + 'dither_point_index': [1, 1, 1, 1], + 'primary_source': ['N', 'Y', 'N', 'Y'], + 'fixed_slit': ['NONE', 'NONE', 'NONE', 'S200A1']} + + source_data = { + 'program': [95065, 95065], + 'source_id': [1, 2], + 'source_name': ['95065_1', '95065_2'], + 'alias': ['2122', '2123'], + 'ra': [53.139904, 53.15], + 'dec': [-27.805002, -27.81], + 'preimage_id': ['95065001_000', '95065001_000'], + 'stellarity': [1.0, 1.0]} + + shutter_table = Table(shutter_data) + source_table = Table(source_data) + + hdul = fits.HDUList() + hdul.append(fits.PrimaryHDU()) + hdul.append(fits.ImageHDU()) + hdul.append(fits.table_to_hdu(shutter_table)) + hdul.append(fits.table_to_hdu(source_table)) + hdul[2].name = 'SHUTTER_INFO' + hdul[3].name = 'SOURCE_INFO' + + return hdul + + +@pytest.fixture +def nirspec_msa_rate(tmp_path): + hdul = create_nirspec_hdul() + hdul[0].header['MSAMETFL'] = str(tmp_path / 'test_msa_01.fits') + filename = str(tmp_path / 'test_nrs_msa_rate.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + + +@pytest.fixture +def nirspec_fs_rate(tmp_path): + hdul = create_nirspec_hdul( + exptype='NRS_FIXEDSLIT', subarray='SUBS200A1', slit='S200A1') + filename = str(tmp_path / 'test_nrs_fs_rate.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + + +@pytest.fixture +def nirspec_bots_rateints(tmp_path): + hdul = create_nirspec_hdul( + exptype='NRS_BRIGHTOBJ', subarray='SUB2048', slit='S1600A1', nint=3) + filename = str(tmp_path / 'test_nrs_bots_rateints.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + + +@pytest.fixture +def nirspec_msa_metfl(tmp_path): + hdul = create_msa_hdul() + filename = str(tmp_path / 'test_msa_01.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + + +def test_extract_2d_nirspec_msa_fs(nirspec_msa_rate, nirspec_msa_metfl): + model = ImageModel(nirspec_msa_rate) + result = AssignWcsStep.call(model) + result = Extract2dStep.call(result) + assert isinstance(result, MultiSlitModel) + + # there should be 2 slits extracted: one MSA, one FS + assert len(result.slits) == 2 + + # the MSA slit has an integer name, slitlet_id matches name + assert result.slits[0].name == '12' + assert result.slits[0].slitlet_id == 12 + assert result.slits[0].data.shape == (31, 1355) + + # the FS slit has a string name, slitlet_id matches shutter ID + assert result.slits[1].name == 'S200A1' + assert result.slits[1].slitlet_id == 0 + assert result.slits[1].data.shape == (45, 1254) + + model.close() + result.close() + + +def test_extract_2d_nirspec_fs(nirspec_fs_rate): + model = ImageModel(nirspec_fs_rate) + result = AssignWcsStep.call(model) + result = Extract2dStep.call(result) + assert isinstance(result, MultiSlitModel) + + # there should be 1 slit extracted: FS, S200A1 + assert len(result.slits) == 1 + + # the FS slit has a string name, slitlet_id matches shutter ID + assert result.slits[0].name == 'S200A1' + assert result.slits[0].slitlet_id == 0 + assert result.slits[0].data.shape == (45, 1254) + + model.close() + result.close() + + +def test_extract_2d_nirspec_bots(nirspec_bots_rateints): + model = CubeModel(nirspec_bots_rateints) + result = AssignWcsStep.call(model) + result = Extract2dStep.call(result) + + # output is a single slit + assert isinstance(result, SlitModel) + + # the BOTS slit has a string name, slitlet_id matches shutter ID + assert result.name == 'S1600A1' + assert result.data.shape == (3, 28, 1300) + + model.close() + result.close() diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 01da31668d..0b653d8d29 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -354,7 +354,6 @@ def nirspec_fs_msa(output_model, f_flat_model, s_flat_model, d_flat_model, dispa """ exposure_type = output_model.meta.exposure.type - primary_slit = output_model.meta.instrument.fixed_slit # Create a list to hold the list of slits. This will eventually be used # to extend the MultiSlitModel.slits attribute. We do it this way to @@ -375,12 +374,12 @@ def nirspec_fs_msa(output_model, f_flat_model, s_flat_model, d_flat_model, dispa if user_supplied_flat is not None: slit_flat = user_supplied_flat.slits[slit_idx] else: - if exposure_type == "NRS_FIXEDSLIT" and \ - slit.name == primary_slit and slit.source_type.upper() == "POINT": + if (exposure_type == "NRS_FIXEDSLIT" + and slit.source_type.upper() == "POINT"): - # For fixed-slit exposures, if this is the primary slit - # and it contains a point source, compute the flat-field - # corrections for both uniform (without wavecorr) and point + # For fixed-slit exposures, if this contains a point source, + # compute the flat-field corrections for both uniform + # (without wavecorr) and point # source (with wavecorr) modes, applying only the point # source version to the data. diff --git a/jwst/flatfield/tests/test_flatfield.py b/jwst/flatfield/tests/test_flatfield.py index 8b0176ae89..a332632bf2 100644 --- a/jwst/flatfield/tests/test_flatfield.py +++ b/jwst/flatfield/tests/test_flatfield.py @@ -1,8 +1,12 @@ import pytest import numpy as np +from astropy.io import fits +from astropy.table import Table from stdatamodels.jwst import datamodels +from jwst.assign_wcs import AssignWcsStep +from jwst.assign_wcs.tests.test_nirspec import create_nirspec_ifu_file from jwst.flatfield import FlatFieldStep from jwst.flatfield.flat_field_step import NRS_IMAGING_MODES, NRS_SPEC_MODES @@ -92,3 +96,285 @@ def test_nirspec_flatfield_step_interface(exptype): data.meta.subarray.ysize = shape[0] FlatFieldStep.call(data) + + +def create_nirspec_flats(shape, msa=False): + flats = [] + for flat_name in ['f', 's', 'd']: + if flat_name == 'f': + f_data = np.full(shape[0], 1.0) + f_err = np.full(shape[0], 0.1) + else: + f_data = np.full(shape[0], 1.0) + f_err = np.full(shape[0], np.nan) + + # make a fast variation table + flat_table = Table({ + 'slit_name': ['ANY'], + 'nelem': [shape[0]], + 'wavelength': [np.arange(1, shape[0] + 1, dtype=float)], + 'data': [f_data], + 'error': [f_err]}) + + fflat_hdul = fits.HDUList([fits.PrimaryHDU()]) + if flat_name == 's' and not msa: + fflat_hdul.append(fits.ImageHDU(data=np.full(shape[-2:], 1.0), name='SCI')) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape[-2:], 0), name='DQ')) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape[-2:], 0.0), name='ERR')) + + elif flat_name == 's' and msa: + fflat_hdul.append(fits.ImageHDU(data=np.full((5, shape[1], shape[2]), 1.0), name='SCI')) + fflat_hdul.append(fits.ImageHDU(data=np.full((5, shape[1], shape[2]), 0), name='DQ')) + fflat_hdul.append(fits.ImageHDU(data=np.full((5, shape[1], shape[2]), 0.0), name='ERR')) + + fflat_hdul.append(fits.table_to_hdu( + Table({'wavelength': np.arange(1, shape[0] + 1)}))) + fflat_hdul[-1].name = 'WAVELENGTH' + + elif flat_name == 'd': + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 1.0), name='SCI')) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 0), name='DQ')) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 0.0), name='ERR')) + fflat_hdul.append(fits.table_to_hdu( + Table({'wavelength': np.arange(1, shape[0] + 1)}))) + fflat_hdul[-1].name = 'WAVELENGTH' + + if flat_name == 'f' and msa: + for quadrant in range(4): + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 1.0), + name='SCI', ver=(quadrant + 1))) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 0), + name='DQ', ver=(quadrant + 1))) + fflat_hdul.append(fits.ImageHDU(data=np.full(shape, 0.0), + name='ERR', ver=(quadrant + 1))) + hdu = fits.table_to_hdu( + Table({'wavelength': [np.arange(1, shape[0] + 1)]})) + hdu.header['EXTNAME'] = 'WAVELENGTH' + hdu.header['EXTVER'] = quadrant + 1 + fflat_hdul.append(hdu) + + hdu = fits.table_to_hdu(flat_table) + hdu.header['EXTNAME'] = 'FAST_VARIATION' + hdu.header['EXTVER'] = quadrant + 1 + fflat_hdul.append(hdu) + + else: + fflat_hdul.append(fits.table_to_hdu(flat_table)) + fflat_hdul[-1].name = 'FAST_VARIATION' + + if msa and flat_name == 'f': + flat = datamodels.NirspecQuadFlatModel(fflat_hdul) + else: + flat = datamodels.NirspecFlatModel(fflat_hdul) + fflat_hdul.close() + + flat.meta.instrument.name = 'NIRSPEC' + flat.meta.subarray.xstart = 1 + flat.meta.subarray.ystart = 1 + flat.meta.subarray.xsize = shape[1] + flat.meta.subarray.ysize = shape[0] + + flats.append(flat) + + return flats + + +def test_nirspec_bots_flat(): + """Test that the interface works for NIRSpec BOTS data""" + shape = (3, 20, 20) + w_shape = (10, 20, 20) + + data = datamodels.SlitModel(shape) + data.meta.instrument.name = 'NIRSPEC' + data.meta.exposure.type = 'NRS_BRIGHTOBJ' + data.meta.subarray.xstart = 1 + data.meta.subarray.ystart = 1 + data.meta.subarray.xsize = shape[1] + data.meta.subarray.ysize = shape[0] + data.xstart = 1 + data.ystart = 1 + data.xsize = shape[1] + data.ysize = shape[0] + data.data += 1 + data.wavelength = np.ones(shape[-2:]) + data.wavelength[:] = np.linspace(1, 5, shape[-1], dtype=float) + + flats = create_nirspec_flats(w_shape) + result = FlatFieldStep.call(data, override_fflat=flats[0], override_sflat=flats[1], + override_dflat=flats[2], override_flat='N/A') + + # null flat, so data is the same, other than nan edges + nn = ~np.isnan(result.data) + assert np.allclose(result.data[nn], data.data[nn]) + + # check that NaNs match in every extension they should + for ext in ['data', 'err', 'var_rnoise', 'var_poisson', 'var_flat']: + test_data = getattr(result, ext) + assert np.all(np.isnan(test_data[~nn])) + assert np.all(result.dq[~nn] | datamodels.dqflags.pixel['DO_NOT_USE']) + + # error is propagated from non-empty fflat error + # (no other additive contribution, scale from data is 1.0) + assert result.var_flat.shape == shape + assert np.allclose(result.var_flat[nn], 0.1 ** 2) + assert result.meta.cal_step.flat_field == 'COMPLETE' + + result.close() + for flat in flats: + flat.close() + + +@pytest.mark.parametrize('srctype', ['POINT', 'EXTENDED']) +def test_nirspec_fs_flat(srctype): + """Test that the interface works for NIRSpec FS data.""" + shape = (20, 20) + w_shape = (10, 20, 20) + + data = datamodels.MultiSlitModel() + data.meta.instrument.name = 'NIRSPEC' + data.meta.exposure.type = 'NRS_FIXEDSLIT' + data.meta.subarray.xstart = 1 + data.meta.subarray.ystart = 1 + data.meta.subarray.xsize = shape[1] + data.meta.subarray.ysize = shape[0] + + data.slits.append(datamodels.SlitModel(shape)) + data.slits[0].data = np.full(shape, 1.0) + data.slits[0].dq = np.full(shape, 0) + data.slits[0].err = np.full(shape, 0.0) + data.slits[0].var_poisson = np.full(shape, 0.0) + data.slits[0].var_rnoise = np.full(shape, 0.0) + data.slits[0].data = np.full(shape, 1.0) + data.slits[0].wavelength = np.ones(shape[-2:]) + data.slits[0].wavelength[:] = np.linspace(1, 5, shape[-1], dtype=float) + data.slits[0].source_type = srctype + data.slits[0].name = 'S200A1' + data.slits[0].xstart = 1 + data.slits[0].ystart = 1 + data.slits[0].xsize = shape[1] + data.slits[0].ysize = shape[0] + + flats = create_nirspec_flats(w_shape) + result = FlatFieldStep.call(data, override_fflat=flats[0], override_sflat=flats[1], + override_dflat=flats[2], override_flat='N/A') + + # null flat, so data is the same, other than nan edges + nn = ~np.isnan(result.slits[0].data) + assert np.allclose(result.slits[0].data[nn], data.slits[0].data[nn]) + + # check that NaNs match in every extension they should + for ext in ['data', 'err', 'var_rnoise', 'var_poisson', 'var_flat']: + test_data = getattr(result.slits[0], ext) + assert np.all(np.isnan(test_data[~nn])) + assert np.all(result.slits[0].dq[~nn] | datamodels.dqflags.pixel['DO_NOT_USE']) + + # error is propagated from non-empty fflat error + # (no other additive contribution, scale from data is 1.0) + assert result.slits[0].var_flat.shape == shape + assert np.allclose(result.slits[0].var_flat[nn], 0.1 ** 2) + assert result.meta.cal_step.flat_field == 'COMPLETE' + + result.close() + for flat in flats: + flat.close() + + +def test_nirspec_msa_flat(): + """Test that the interface works for NIRSpec MSA data.""" + shape = (20, 20) + w_shape = (10, 20, 20) + + data = datamodels.MultiSlitModel() + data.meta.instrument.name = 'NIRSPEC' + data.meta.exposure.type = 'NRS_MSASPEC' + data.meta.subarray.xstart = 1 + data.meta.subarray.ystart = 1 + data.meta.subarray.xsize = shape[1] + data.meta.subarray.ysize = shape[0] + + data.slits.append(datamodels.SlitModel(shape)) + data.slits[0].data = np.full(shape, 1.0) + data.slits[0].dq = np.full(shape, 0) + data.slits[0].err = np.full(shape, 0.0) + data.slits[0].var_poisson = np.full(shape, 0.0) + data.slits[0].var_rnoise = np.full(shape, 0.0) + data.slits[0].data = np.full(shape, 1.0) + data.slits[0].wavelength = np.ones(shape[-2:]) + data.slits[0].wavelength[:] = np.linspace(1, 5, shape[-1], dtype=float) + data.slits[0].source_type = 'UNKNOWN' + data.slits[0].name = '11' + data.slits[0].quadrant = 1 + data.slits[0].xcen = 10 + data.slits[0].ycen = 10 + data.slits[0].xstart = 1 + data.slits[0].ystart = 1 + data.slits[0].xsize = shape[1] + data.slits[0].ysize = shape[0] + + flats = create_nirspec_flats(w_shape, msa=True) + result = FlatFieldStep.call(data, override_fflat=flats[0], override_sflat=flats[1], + override_dflat=flats[2], override_flat='N/A') + + # null flat, so data is the same, other than nan edges + nn = ~np.isnan(result.slits[0].data) + assert np.allclose(result.slits[0].data[nn], data.slits[0].data[nn]) + + # check that NaNs match in every extension they should + for ext in ['data', 'err', 'var_rnoise', 'var_poisson', 'var_flat']: + test_data = getattr(result.slits[0], ext) + assert np.all(np.isnan(test_data[~nn])) + assert np.all(result.slits[0].dq[~nn] | datamodels.dqflags.pixel['DO_NOT_USE']) + + # error is propagated from non-empty fflat error + # (no other additive contribution, scale from data is 1.0) + assert result.slits[0].var_flat.shape == shape + assert np.allclose(result.slits[0].var_flat[nn], 0.1 ** 2) + assert result.meta.cal_step.flat_field == 'COMPLETE' + + result.close() + for flat in flats: + flat.close() + + +@pytest.mark.slow +def test_nirspec_ifu_flat(): + """Test that the interface works for NIRSpec IFU data. + + Larger data and more WCS operations required for testing make + this test take more than a minute, so marking this test 'slow'. + """ + shape = (2048, 2048) + w_shape = (10, 2048, 2048) + + # IFU mode requires WCS information, so make a more realistic model + hdul = create_nirspec_ifu_file(grating='PRISM', filter='CLEAR', + gwa_xtil=0.35986012, gwa_ytil=0.13448857, + gwa_tilt=37.1) + hdul['SCI'].data = np.ones(shape, dtype=float) + + data = datamodels.IFUImageModel(hdul) + data = AssignWcsStep.call(data) + + flats = create_nirspec_flats(w_shape) + result = FlatFieldStep.call(data, override_fflat=flats[0], override_sflat=flats[1], + override_dflat=flats[2], override_flat='N/A') + + # null flat, so data is the same, other than nan edges + nn = ~np.isnan(result.data) + assert np.allclose(result.data[nn], data.data[nn]) + + # check that NaNs match in every extension they should + for ext in ['data', 'err', 'var_rnoise', 'var_poisson', 'var_flat']: + test_data = getattr(result, ext) + assert np.all(np.isnan(test_data[~nn])) + assert np.all(result.dq[~nn] | datamodels.dqflags.pixel['DO_NOT_USE']) + + # error is propagated from non-empty fflat error + # (no other additive contribution, scale from data is 1.0) + assert result.var_flat.shape == shape + assert np.allclose(result.var_flat[nn], 0.1 ** 2) + assert result.meta.cal_step.flat_field == 'COMPLETE' + + result.close() + for flat in flats: + flat.close() diff --git a/jwst/lib/wcs_utils.py b/jwst/lib/wcs_utils.py index 3070756f13..bff72708b1 100644 --- a/jwst/lib/wcs_utils.py +++ b/jwst/lib/wcs_utils.py @@ -41,7 +41,6 @@ def get_wavelengths(model, exp_type="", order=None, use_wavecorr=None): got_wavelength = False wl_array = None - # Evaluate the WCS on the grid of pixel indexes, capturing only the # resulting wavelength values shape = model.data.shape diff --git a/jwst/master_background/expand_to_2d.py b/jwst/master_background/expand_to_2d.py index 34c5871447..de221eab9a 100644 --- a/jwst/master_background/expand_to_2d.py +++ b/jwst/master_background/expand_to_2d.py @@ -18,7 +18,7 @@ WFSS_EXPTYPES = ['NIS_WFSS', 'NRC_WFSS', 'NRC_GRISM', 'NRC_TSGRISM'] -def expand_to_2d(input, m_bkg_spec): +def expand_to_2d(input, m_bkg_spec, allow_mos=False): """Expand a 1-D background to 2-D. Parameters @@ -30,6 +30,14 @@ def expand_to_2d(input, m_bkg_spec): Either the name of a file containing a 1-D background spectrum, or a data model containing such a spectrum. + allow_mos : bool + If True, NIRSpec MOS data is supported. If False, + background is set to 0.0 for any slit marked as exposure + type NRS_MSASPEC. This parameter should be set to True only + for the master_background_mos step in the spec2 pipeline; + MOS data is not supported via the master_background step + in the spec3 pipeline. + Returns ------- background : `~jwst.datamodels.JwstDataModel` @@ -56,15 +64,17 @@ def expand_to_2d(input, m_bkg_spec): # Handle associations, or input ModelContainers if isinstance(input, ModelContainer): - background = bkg_for_container(input, tab_wavelength, tab_background) + background = bkg_for_container(input, tab_wavelength, tab_background, + allow_mos=allow_mos) else: - background = create_bkg(input, tab_wavelength, tab_background) + background = create_bkg(input, tab_wavelength, tab_background, + allow_mos=allow_mos) return background -def bkg_for_container(input, tab_wavelength, tab_background): +def bkg_for_container(input, tab_wavelength, tab_background, allow_mos=False): """Create a 2-D background for a container object. Parameters @@ -78,6 +88,14 @@ def bkg_for_container(input, tab_wavelength, tab_background): tab_background : 1-D ndarray The surf_bright column read from the 1-D background table. + allow_mos : bool + If True, NIRSpec MOS data is supported. If False, + background is set to 0.0 for any slit marked as exposure + type NRS_MSASPEC. This parameter should be set to True only + for the master_background_mos step in the spec2 pipeline; + MOS data is not supported via the master_background step + in the spec3 pipeline. + Returns ------- background : `~jwst.datamodels.ModelContainer` @@ -87,13 +105,14 @@ def bkg_for_container(input, tab_wavelength, tab_background): background = ModelContainer() for input_model in input: - temp = create_bkg(input_model, tab_wavelength, tab_background) + temp = create_bkg(input_model, tab_wavelength, tab_background, + allow_mos=allow_mos) background.append(temp) return background -def create_bkg(input, tab_wavelength, tab_background): +def create_bkg(input, tab_wavelength, tab_background, allow_mos=False): """Create a 2-D background. Parameters @@ -107,6 +126,14 @@ def create_bkg(input, tab_wavelength, tab_background): tab_background : 1-D ndarray The surf_bright column read from the 1-D background table. + allow_mos : bool + If True, NIRSpec MOS data is supported. If False, + background is set to 0.0 for any slit marked as exposure + type NRS_MSASPEC. This parameter should be set to True only + for the master_background_mos step in the spec2 pipeline; + MOS data is not supported via the master_background step + in the spec3 pipeline. + Returns ------- background : `~jwst.datamodels.JwstDataModel` @@ -116,7 +143,8 @@ def create_bkg(input, tab_wavelength, tab_background): # Handle individual NIRSpec FS, NIRSpec MOS if isinstance(input, datamodels.MultiSlitModel): - background = bkg_for_multislit(input, tab_wavelength, tab_background) + background = bkg_for_multislit(input, tab_wavelength, tab_background, + allow_mos=allow_mos) # Handle MIRI LRS elif isinstance(input, datamodels.ImageModel): @@ -134,7 +162,7 @@ def create_bkg(input, tab_wavelength, tab_background): return background -def bkg_for_multislit(input, tab_wavelength, tab_background): +def bkg_for_multislit(input, tab_wavelength, tab_background, allow_mos=False): """Create a 2-D background for a MultiSlitModel. Parameters @@ -148,6 +176,14 @@ def bkg_for_multislit(input, tab_wavelength, tab_background): tab_background : 1-D ndarray The surf_bright column read from the 1-D background table. + allow_mos : bool + If True, NIRSpec MOS data is supported. If False, + background is set to 0.0 for any slit marked as exposure + type NRS_MSASPEC. This parameter should be set to True only + for the master_background_mos step in the spec2 pipeline; + MOS data is not supported via the master_background step + in the spec3 pipeline. + Returns ------- background : `~jwst.datamodels.MultiSlitModel` @@ -162,6 +198,7 @@ def bkg_for_multislit(input, tab_wavelength, tab_background): for (k, slit) in enumerate(input.slits): log.info(f'Expanding background for slit {slit.name}') + wl_array = get_wavelengths(slit, input.meta.exposure.type) if wl_array is None: raise RuntimeError(f"Can't determine wavelengths for {type(slit)}") @@ -184,12 +221,29 @@ def bkg_for_multislit(input, tab_wavelength, tab_background): background.slits[k].dq[mask_limit] = np.bitwise_or(background.slits[k].dq[mask_limit], dqflags.pixel['DO_NOT_USE']) + # Check exposure type for special handling + try: + exp_type = slit.meta.exposure.type + except AttributeError: + exp_type = None + if exp_type is None: + exp_type = input.meta.exposure.type + + # NIRSpec MOS should only have backgrounds assigned in spec2, + # via the master_background_mos step. + if exp_type == 'NRS_MSASPEC' and not allow_mos: + log.warning('Master background subtraction is not supported ' + 'for NIRSpec MOS spectra.') + log.warning('Setting the background to 0.0') + background.slits[k].data[:] = 0.0 + background.slits[k].dq[:] = 0 + continue + # NIRSpec fixed slits need corrections applied to the 2D background # if the slit contains a point source, in order to make the master bkg - # match the calibrated science data in the slit - if input.meta.exposure.type == 'NRS_FIXEDSLIT' and slit.source_type.upper() == 'POINT': - primary = True if slit.name == input.meta.instrument.fixed_slit else False - background.slits[k] = correct_nrs_fs_bkg(background.slits[k], primary) + # match the calibrated science data in the slit. + if exp_type == 'NRS_FIXEDSLIT' and slit.source_type.upper() == 'POINT': + background.slits[k] = correct_nrs_fs_bkg(background.slits[k]) return background diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 77bfa3285c..d29c9da26b 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -1,4 +1,5 @@ import logging +import warnings from stdatamodels.jwst import datamodels @@ -70,7 +71,7 @@ def map_to_science_slits(input_model, master_bkg): # Loop over all input slits, creating 2D master background to # match each 2D slitlet cutout - output_model = expand_to_2d(input_model, master_bkg) + output_model = expand_to_2d(input_model, master_bkg, allow_mos=True) return output_model @@ -166,7 +167,7 @@ def correct_nrs_ifu_bkg(input_model): return input_model -def correct_nrs_fs_bkg(input_model, primary_slit): +def correct_nrs_fs_bkg(input_model): """Apply point source vs. uniform source corrections to a NIRSpec Fixed-Slit 2D master background array. @@ -175,9 +176,6 @@ def correct_nrs_fs_bkg(input_model, primary_slit): input_model : `~jwst.datamodels.SlitModel` The input background data. - primary_slit : bool - Is this the primary slit in the exposure? - Returns ------- input_model : `~jwst.datamodels.SlitModel` @@ -201,46 +199,44 @@ def correct_nrs_fs_bkg(input_model, primary_slit): log.warning('Skipping background updates') return input_model - if primary_slit: - # If processing the primary slit, we also need flatfield and - # photom correction arrays - if 'flatfield_point' in input_model.instance: - ff_point = getattr(input_model, 'flatfield_point') - else: - log.warning('flatfield_point array not found in input') - log.warning('Skipping background updates') - return input_model - - if 'flatfield_uniform' in input_model.instance: - ff_uniform = getattr(input_model, 'flatfield_uniform') - else: - log.warning('flatfield_uniform array not found in input') - log.warning('Skipping background updates') - return input_model - - if 'photom_point' in input_model.instance: - ph_point = getattr(input_model, 'photom_point') - else: - log.warning('photom_point array not found in input') - log.warning('Skipping background updates') - return input_model - - if 'photom_uniform' in input_model.instance: - ph_uniform = getattr(input_model, 'photom_uniform') - else: - log.warning('photom_uniform array not found in input') - log.warning('Skipping background updates') - return input_model - - # Apply the corrections for the primary slit + # If processing the primary slit, we also need flatfield and + # photom correction arrays + if 'flatfield_point' in input_model.instance: + ff_point = getattr(input_model, 'flatfield_point') + else: + log.warning('flatfield_point array not found in input') + log.warning('Skipping background updates') + return input_model + + if 'flatfield_uniform' in input_model.instance: + ff_uniform = getattr(input_model, 'flatfield_uniform') + else: + log.warning('flatfield_uniform array not found in input') + log.warning('Skipping background updates') + return input_model + + if 'photom_point' in input_model.instance: + ph_point = getattr(input_model, 'photom_point') + else: + log.warning('photom_point array not found in input') + log.warning('Skipping background updates') + return input_model + + if 'photom_uniform' in input_model.instance: + ph_uniform = getattr(input_model, 'photom_uniform') + else: + log.warning('photom_uniform array not found in input') + log.warning('Skipping background updates') + return input_model + + # Apply the corrections for the primary slit + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) + warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) input_model.data *= (pl_uniform / pl_point) * \ (ff_uniform / ff_point) * \ (ph_point / ph_uniform) - else: - # Apply the corrections for secondary slits - input_model.data *= (pl_uniform / pl_point) - return input_model diff --git a/jwst/master_background/tests/test_nirspec_corrections.py b/jwst/master_background/tests/test_nirspec_corrections.py index 6e5a4f4276..5eaa41fcc8 100644 --- a/jwst/master_background/tests/test_nirspec_corrections.py +++ b/jwst/master_background/tests/test_nirspec_corrections.py @@ -52,7 +52,7 @@ def test_fs_correction(): photom_point=ph_ps, photom_uniform=ph_un) corrected = input.data * (ff_un / ff_ps) * (pl_un / pl_ps) * (ph_ps / ph_un) - result = correct_nrs_fs_bkg(input, primary_slit=True) + result = correct_nrs_fs_bkg(input) assert np.allclose(corrected, result.data, rtol=1.e-7) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 3117eb624a..bbd3b0d4bf 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -156,8 +156,20 @@ def create_mask(input_model, mask_spectral_regions, n_sigma): mask[nan_pix] = False # If IFU or MOS, mask the fixed-slit area of the image; uses hardwired indexes - if exptype in ['nrs_ifu', 'nrs_msaspec']: + if exptype == 'nrs_ifu': + log.info("Masking the fixed slit region for IFU data.") mask[922:1116, :] = False + elif exptype == 'nrs_msaspec': + # check for any slits defined in the fixed slit quadrant: + # if there is nothing there of interest, mask the whole FS region + slit2msa = input_model.meta.wcs.get_transform('slit_frame', 'msa_frame') + is_fs = [s.quadrant == 5 for s in slit2msa.slits] + if not any(is_fs): + log.info("Masking the fixed slit region for MOS data.") + mask[922:1116, :] = False + else: + log.info("Fixed slits found in MSA definition; " + "not masking the fixed slit region for MOS data.") # Use left/right reference pixel columns (first and last 4). Can only be # applied to data that uses all 2048 columns of the detector. diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py index 7f7a6cc4da..c445d9601f 100644 --- a/jwst/photom/photom.py +++ b/jwst/photom/photom.py @@ -848,9 +848,8 @@ def photom_io(self, tabdata, order=None): slit = self.input.slits[self.slitnum] # The NIRSpec fixed-slit primary slit needs special handling if # it contains a point source - if self.exptype.upper() == 'NRS_FIXEDSLIT' and \ - slit.name == self.input.meta.instrument.fixed_slit and \ - slit.source_type.upper() == 'POINT': + if (self.exptype.upper() == 'NRS_FIXEDSLIT' + and slit.source_type.upper() == 'POINT'): # First, compute 2D array of photom correction values using # uncorrected wavelengths, which is appropriate for a uniform source diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 57245439b4..9ce8c04d61 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -285,6 +285,8 @@ def process_exposure_product( # srctype and wavecorr before flat_field. if exp_type in GRISM_TYPES: calibrated = self._process_grism(calibrated) + elif exp_type == 'NRS_MSASPEC': + calibrated = self._process_nirspec_msa_slits(calibrated) elif exp_type in NRS_SLIT_TYPES: calibrated = self._process_nirspec_slits(calibrated) elif exp_type == 'NIS_SOSS': @@ -365,6 +367,13 @@ def process_exposure_product( x1d = self.photom(x1d) else: self.log.warning("Extract_1d did not return a DataModel - skipping photom.") + elif exp_type == 'NRS_MSASPEC': + # Special handling for MSA spectra, to handle mixed-in + # fixed slits separately + if not self.extract_1d.skip: + x1d = self._extract_nirspec_msa_slits(resampled) + else: + x1d = resampled.copy() else: x1d = resampled.copy() x1d = self.extract_1d(x1d) @@ -524,10 +533,15 @@ def _process_grism(self, data): return calibrated def _process_nirspec_slits(self, data): - """Process NIRSpec + """Process NIRSpec slits. + + This function handles FS, BOTS, and calibration modes. + MOS mode is handled separately, in order to do master + background subtraction and process fixed slits defined in + MSA files. - NIRSpec MOS and FS need srctype and wavecorr before flat_field. - Also have to deal with master background operations. + Note that NIRSpec MOS and FS need srctype and wavecorr before + flat_field. """ calibrated = self.extract_2d(data) calibrated = self.srctype(calibrated) @@ -540,6 +554,76 @@ def _process_nirspec_slits(self, data): return calibrated + def _process_nirspec_msa_slits(self, data): + """Process NIRSpec MSA slits. + + The NRS_MSASPEC exposure type may contain fixed slit definitions + in addition to standard MSA slitlets. These are handled + separately internally to this function, in order to pull the + correct reference files and perform the right algorithms for + each slit type. Processed slits are recombined into a single + model with EXP_TYPE=NRS_MSASPEC on return. + + Note that NIRSpec MOS and FS need srctype and wavecorr before + flat_field. Also have to deal with master background operations. + """ + calibrated = self.extract_2d(data) + calibrated = self.srctype(calibrated) + + # Split the datamodel into 2 pieces: one with MOS slits and + # the other with FS slits + calib_mos = datamodels.MultiSlitModel() + calib_fss = datamodels.MultiSlitModel() + for slit in calibrated.slits: + if slit.quadrant == 5: + slit.meta.exposure.type = "NRS_FIXEDSLIT" + calib_fss.slits.append(slit) + else: + calib_mos.slits.append(slit) + + # First process MOS slits through all remaining steps + calib_mos.update(calibrated) + if len(calib_mos.slits) > 0: + calib_mos = self.master_background_mos(calib_mos) + calib_mos = self.wavecorr(calib_mos) + calib_mos = self.flat_field(calib_mos) + calib_mos = self.pathloss(calib_mos) + calib_mos = self.barshadow(calib_mos) + calib_mos = self.photom(calib_mos) + + # Now repeat for FS slits + if len(calib_fss.slits) > 0: + calib_fss.update(calibrated) + calib_fss.meta.exposure.type = "NRS_FIXEDSLIT" + + # Run each step with an alternate suffix, + # to avoid overwriting previous products if save_results=True + fs_steps = ['wavecorr', 'flat_field', 'pathloss', 'photom'] + for step_name in fs_steps: + # Set suffix + step = getattr(self, step_name) + current_suffix = step.suffix + step.suffix = f'{current_suffix}_fs' + + # Run step + calib_fss = step(calib_fss) + + # Reset suffix + step.suffix = current_suffix + + # Append the FS results to the MOS results + for slit in calib_fss.slits: + calib_mos.slits.append(slit) + + if len(calib_mos.slits) == len(calib_fss.slits): + # update the MOS model with step completion status from the + # FS model, since there were no MOS slits to run + for step in fs_steps: + setattr(calib_mos.meta.cal_step, step, + getattr(calib_fss.meta.cal_step, step)) + + return calib_mos + def _process_niriss_soss(self, data): """Process SOSS @@ -567,3 +651,56 @@ def _process_common(self, data): calibrated = self.residual_fringe(calibrated) # only run on MIRI_MRS data return calibrated + + def _extract_nirspec_msa_slits(self, resampled): + """Extract NIRSpec MSA slits with separate handling for FS slits.""" + + # Check for fixed slits mixed in with MSA spectra: + # they need separate reference files + resamp_mos = datamodels.MultiSlitModel() + resamp_fss = datamodels.MultiSlitModel() + for slit in resampled.slits: + # Quadrant information is not preserved through resampling, + # but MSA slits have numbers for names, so use that to + # distinguish MSA from FS + try: + msa_name = int(slit.name) + except ValueError: + msa_name = None + if msa_name is None: + slit.meta.exposure.type = "NRS_FIXEDSLIT" + resamp_fss.slits.append(slit) + else: + slit.meta.exposure.type = "NRS_MSASPEC" + resamp_mos.slits.append(slit) + resamp_mos.update(resampled) + resamp_fss.update(resampled) + + # Extract the MOS slits + x1d = None + save_x1d = self.extract_1d.save_results + self.extract_1d.save_results = False + if len(resamp_mos.slits) > 0: + self.log.info(f'Extracting {len(resamp_mos.slits)} MSA slitlets') + x1d = self.extract_1d(resamp_mos) + + # Extract the FS slits + if len(resamp_fss.slits) > 0: + self.log.info(f'Extracting {len(resamp_fss.slits)} fixed slits') + resamp_fss.meta.exposure.type = "NRS_FIXEDSLIT" + x1d_fss = self.extract_1d(resamp_fss) + if x1d is None: + x1d = x1d_fss + x1d.meta.exposure.type = "NRS_MSASPEC" + else: + for spec in x1d_fss.spec: + x1d.spec.append(spec) + + # save the composite model + if save_x1d: + self.save_model(x1d, suffix='x1d') + + resamp_mos.close() + resamp_fss.close() + + return x1d diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 667d1229b1..9357903a17 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -189,16 +189,15 @@ def process(self, input): # and potentially also the slit name (for NIRSpec fixed-slit only). if isinstance(source, tuple): source_id, result = source - # NIRSpec fixed-slit data - if result[0].meta.exposure.type == "NRS_FIXEDSLIT": + if exptype == "NRS_FIXEDSLIT": # Output file name is constructed using the source_id and the slit name slit_name = self._create_nrsfs_slit_name(result) srcid = f's{source_id:>09s}' self.output_file = format_product(output_file, source_id=srcid, slit_name=slit_name) # NIRSpec MOS/MSA data - elif result[0].meta.exposure.type == "NRS_MSASPEC": + elif exptype == "NRS_MSASPEC": # Construct the specially formatted source_id to use in the output file # name that separates source, background, and virtual slits srcid = self._create_nrsmos_source_id(result) @@ -368,4 +367,4 @@ def save_model(model, **kwargs): return result - return save_model \ No newline at end of file + return save_model diff --git a/jwst/regtest/test_nirspec_mos_fs_spec2.py b/jwst/regtest/test_nirspec_mos_fs_spec2.py new file mode 100644 index 0000000000..82dd419888 --- /dev/null +++ b/jwst/regtest/test_nirspec_mos_fs_spec2.py @@ -0,0 +1,55 @@ +import pytest + +from astropy.io.fits.diff import FITSDiff + +from jwst.stpipe import Step + + +@pytest.fixture(scope="module") +def run_pipeline(rtdata_module): + """Run the calwebb_spec2 pipeline on a single NIRSpec MOS/FS exposure.""" + + rtdata = rtdata_module + + # Get the MSA metadata file referenced in the input exposure + rtdata.get_data("nirspec/mos/jw02674004001_01_msa.fits") + + # Get the input ASN file and exposures + rtdata.get_data("nirspec/mos/jw02674004001_03101_00001_nrs1_rate.fits") + + # Run the calwebb_spec2 pipeline; save results from intermediate steps + args = ["calwebb_spec2", rtdata.input, + "--steps.assign_wcs.save_results=true", + "--steps.msa_flagging.save_results=true", + "--steps.master_background_mos.save_results=true", + "--steps.extract_2d.save_results=true", + "--steps.srctype.save_results=true", + "--steps.wavecorr.save_results=true", + "--steps.flat_field.save_results=true", + "--steps.pathloss.save_results=true", + "--steps.barshadow.save_results=true"] + Step.from_cmdline(args) + + return rtdata + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", [ + "assign_wcs", "msa_flagging", "extract_2d", "srctype", + "master_background_mos", "wavecorr", "flat_field", "pathloss", "barshadow", + "wavecorr_fs", "flat_field_fs", "pathloss_fs", + "cal", "s2d", "x1d"]) +def test_nirspec_mos_fs_spec2(run_pipeline, fitsdiff_default_kwargs, suffix): + """Regression test for calwebb_spec2 on a NIRSpec MOS/FS exposure.""" + + # Run the pipeline and retrieve outputs + rtdata = run_pipeline + output = f"jw02674004001_03101_00001_nrs1_{suffix}.fits" + rtdata.output = output + + # Get the truth files + rtdata.get_truth("truth/test_nirspec_mos_fs_spec2/" + output) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() diff --git a/jwst/regtest/test_nirspec_mos_fs_spec3.py b/jwst/regtest/test_nirspec_mos_fs_spec3.py new file mode 100644 index 0000000000..33e82e5a2c --- /dev/null +++ b/jwst/regtest/test_nirspec_mos_fs_spec3.py @@ -0,0 +1,39 @@ +import pytest +from astropy.io.fits.diff import FITSDiff + +from jwst.stpipe import Step + + +@pytest.fixture(scope="module") +def run_pipeline(rtdata_module): + """Run calwebb_spec3 on NIRSpec MOS data.""" + rtdata = rtdata_module + rtdata.get_asn("nirspec/mos/jw02674-o004_20240305t054741_spec3_00001_asn.json") + + # Run the calwebb_spec3 pipeline on the association + args = ["calwebb_spec3", rtdata.input] + Step.from_cmdline(args) + + return rtdata + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) +@pytest.mark.parametrize("source_id", ["b000000003", "b000000004", "b000000048", + "b000000052", "s000001354", "s000012105", + "s000034946"]) +def test_nirspec_mos_fs_spec3(run_pipeline, suffix, source_id, fitsdiff_default_kwargs): + """Check results of calwebb_spec3""" + rtdata = run_pipeline + + output = f"jw02674-o004_{source_id}_nirspec_f290lp-g395m_{suffix}.fits" + rtdata.output = output + rtdata.get_truth(f"truth/test_nirspec_mos_fs_spec3/{output}") + + # Adjust tolerance for machine precision with float32 drizzle code + if suffix == "s2d": + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 1e-5 + + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() diff --git a/jwst/srctype/srctype.py b/jwst/srctype/srctype.py index 46f192e533..8448f1109f 100644 --- a/jwst/srctype/srctype.py +++ b/jwst/srctype/srctype.py @@ -131,7 +131,7 @@ def set_source_type(input_model, source_type=None): # NIRSpec fixed-slit is a special case: Apply the source type # determined above to only the primary slit (the one in which # the target is located). Set all other slits to the default - # value, which for NRS_FIXEDSLIT is 'POINT'. + # value, which for NRS_FIXEDSLIT is 'EXTENDED'. default_type = 'EXTENDED' primary_slit = input_model.meta.instrument.fixed_slit log.debug(f' primary_slit = {primary_slit}') diff --git a/jwst/wavecorr/tests/test_wavecorr.py b/jwst/wavecorr/tests/test_wavecorr.py index d2e3d08ce6..85e74a0d0a 100644 --- a/jwst/wavecorr/tests/test_wavecorr.py +++ b/jwst/wavecorr/tests/test_wavecorr.py @@ -191,8 +191,9 @@ def test_mos_slit_status(): im_src.slits[0].source_type = 'EXTENDED' im_wave = WavecorrStep.call(im_src) - # check that the step is recorded as completed - assert im_wave.meta.cal_step.wavecorr == 'COMPLETE' + # check that the step is recorded as skipped, + # since no slits were corrected + assert im_wave.meta.cal_step.wavecorr == 'SKIPPED' # check that the step is listed as skipped for extended mos sources assert im_wave.slits[0].meta.cal_step.wavecorr == 'SKIPPED' @@ -248,7 +249,7 @@ def test_wavecorr_fs(): assert_allclose(result.slits[0].source_xpos, 0.127111, atol=1e-6) slit = result.slits[0] - source_xpos = wavecorr.get_source_xpos(slit, slit.meta.wcs, lam=2) + source_xpos = wavecorr.get_source_xpos(slit) assert_allclose(result.slits[0].source_xpos, source_xpos, atol=1e-6) mean_correction = np.abs(src_result.slits[0].wavelength - result.slits[0].wavelength) @@ -261,7 +262,7 @@ def test_wavecorr_fs(): corrected_wavelength = wavecorr.compute_wavelength(slit.meta.wcs, x, y) assert_allclose(slit.wavelength, corrected_wavelength) - # test the roundtripping on the wavelength correction transform + # test the round-tripping on the wavelength correction transform ref_name = result.meta.ref_file.wavecorr.name freference = datamodels.WaveCorrModel(reference_uri_to_cache_path(ref_name, im.crds_observatory)) diff --git a/jwst/wavecorr/wavecorr.py b/jwst/wavecorr/wavecorr.py index 46a92513b2..d514355397 100644 --- a/jwst/wavecorr/wavecorr.py +++ b/jwst/wavecorr/wavecorr.py @@ -64,44 +64,48 @@ def do_correction(input_model, wavecorr_file): output_model = input_model.copy() - # Get the primary slit for a FS exposure - if exp_type == 'NRS_FIXEDSLIT': - primary_slit = input_model.meta.instrument.fixed_slit - if primary_slit is None or primary_slit == 'NONE': - log.warning('Primary slit name not found in input') - log.warning('Skipping wavecorr correction') - input_model.meta.cal_step.wavecorr = 'SKIPPED' - return input_model - # For BRIGHTOBJ, operate on the single SlitModel if isinstance(input_model, datamodels.SlitModel): if _is_point_source(input_model, exp_type): apply_zero_point_correction(output_model, wavecorr_file) else: - # For FS only work on the primary slit + # For FS only work on point source slits with + # position information + corrected = False if exp_type == 'NRS_FIXEDSLIT': + primary_slit = input_model.meta.instrument.fixed_slit for slit in output_model.slits: - if slit.name == primary_slit: - if not hasattr(slit.meta, "dither"): - log.warning('meta.dither is not populated for the primary slit') - log.warning('Skipping wavecorr correction') - input_model.meta.cal_step.wavecorr = 'SKIPPED' - break - if slit.meta.dither.x_offset is None or slit.meta.dither.y_offset is None: - log.warning('dither.x(y)_offset values are None for primary slit') - log.warning('Skipping wavecorr correction') - input_model.meta.cal_step.wavecorr = 'SKIPPED' - break - if _is_point_source(slit, exp_type): - completed = apply_zero_point_correction(slit, wavecorr_file) - if completed: - output_model.meta.cal_step.wavecorr = 'COMPLETE' - else: # pragma: no cover - log.warning(f'Corrections are not invertible for slit {slit.name}') + if _is_point_source(slit, exp_type): + # If fixed slit was not defined via MSA file, + # it must have dither information to find the + # source position, and it must be the primary slit + if not _is_msa_fixed_slit(slit): + if slit.name != primary_slit: + log.warning(f'Skipping wavecorr correction for ' + f'non-primary slit {slit.name}') + continue + if not hasattr(slit.meta, "dither"): + log.warning('meta.dither is not populated for the primary slit') log.warning('Skipping wavecorr correction') - output_model.meta.cal_step.wavecorr = 'SKIPPED' + continue + if slit.meta.dither.x_offset is None or slit.meta.dither.y_offset is None: + log.warning('dither.x(y)_offset values are None for primary slit') + log.warning('Skipping wavecorr correction') + input_model.meta.cal_step.wavecorr = 'SKIPPED' + continue + completed = apply_zero_point_correction(slit, wavecorr_file) + if completed: + corrected = True + slit.meta.cal_step.wavecorr = 'COMPLETE' + else: # pragma: no cover + log.warning(f'Corrections are not invertible for slit {slit.name}') + log.warning('Skipping wavecorr correction') + slit.meta.cal_step.wavecorr = 'SKIPPED' - break + if corrected: + output_model.meta.cal_step.wavecorr = 'COMPLETE' + else: + output_model.meta.cal_step.wavecorr = 'SKIPPED' # For MOS work on all slits containing a point source else: @@ -110,14 +114,18 @@ def do_correction(input_model, wavecorr_file): completed = apply_zero_point_correction(slit, wavecorr_file) if completed: slit.meta.cal_step.wavecorr = 'COMPLETE' + corrected = True else: # pragma: no cover log.warning(f'Corrections are not invertible for slit {slit.name}') log.warning('Skipping wavecorr correction') slit.meta.cal_step.wavecorr = 'SKIPPED' else: slit.meta.cal_step.wavecorr = 'SKIPPED' - - output_model.meta.cal_step.wavecorr = 'COMPLETE' + + if corrected: + output_model.meta.cal_step.wavecorr = 'COMPLETE' + else: + output_model.meta.cal_step.wavecorr = 'SKIPPED' return output_model @@ -142,9 +150,15 @@ def apply_zero_point_correction(slit, reffile): # Get the source position in the slit and set the aperture name if slit.meta.exposure.type in ['NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ']: - # pass lam = 2 microns - # needed for wavecorr with fixed slits - source_xpos = get_source_xpos(slit, slit_wcs, lam=2) + # Check for fixed slits defined via MSA files in + # MOS/FS combination processing: they should not have their + # source position overridden by dither keywords + if _is_msa_fixed_slit(slit): + # get the planned source position + source_xpos = slit.source_xpos + else: + # get the source position from the dither offsets + source_xpos = get_source_xpos(slit) aperture_name = slit.name else: source_xpos = slit.source_xpos @@ -154,11 +168,9 @@ def apply_zero_point_correction(slit, reffile): lam = slit.wavelength.copy() * 1e-6 dispersion = compute_dispersion(slit.meta.wcs) - wave2wavecorr = calculate_wavelength_correction_transform(lam, - dispersion, - reffile, - source_xpos, - aperture_name) + wave2wavecorr = calculate_wavelength_correction_transform( + lam, dispersion, reffile, source_xpos, aperture_name) + # wave2wavecorr should not be None for real data if wave2wavecorr is None: # pragma: no cover completed = False @@ -309,6 +321,25 @@ def compute_wavelength(wcs, xpix=None, ypix=None): return lam +def _is_msa_fixed_slit(slit): + """ + Determine if a fixed slit source was defined via a MSA file. + + Parameters + ---------- + slit : `~stdatamodels.jwst.transforms.models.Slit` + A slit object. + """ + # Fixed slits defined via MSA files in MOS/FS combination + # processing will have a non-empty source name + if (not hasattr(slit, 'source_name') + or slit.source_name is None + or slit.source_name == ""): + return False + else: + return True + + def _is_point_source(slit, exp_type): """ Determine if a source is a point source. @@ -343,7 +374,7 @@ def _is_point_source(slit, exp_type): return result -def get_source_xpos(slit, slit_wcs, lam): +def get_source_xpos(slit): """ Compute the source position within the slit for a NIRSpec fixed slit. @@ -351,10 +382,6 @@ def get_source_xpos(slit, slit_wcs, lam): ---------- slit : `~jwst.datamodels.SlitModel` The slit object. - slit_wcs : `~gwcs.wcs.WCS` - The WCS object for this slit. - lam : float - Wavelength in microns. Returns ------- From ac8bb0abf4ac7a2541a05359d1cf9e9be5cedf57 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 18 Jun 2024 16:19:52 -0400 Subject: [PATCH 113/687] JP-3659: Fix for output WCS when first data set is empty (#8562) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 +++ jwst/resample/resample_spec.py | 21 +++++++++++++++++---- 2 files changed, 20 insertions(+), 4 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c43eb166a3..9a7ee97b79 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -315,6 +315,9 @@ resample_spec - Removed the use of the `drizpars` reference file [#8546] +- Fix a bug resulting in incorrect output spectral WCS for NIRSpec data when + the first input data set is filled with zero or NaN. [#8562] + residual_fringe --------------- diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 71413ec9a3..e462fb6f2e 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -120,13 +120,26 @@ def build_nirspec_output_wcs(self, refmodel=None): if refmodel: all_wcs.insert(0, refmodel.meta.wcs) else: - refmodel = self.input_models[0] + # Use the first model with any good data as the reference model + for model in self.input_models: + dq_mask = resample_utils.build_mask(model.dq, self.good_bits) + good = np.isfinite(model.data) & (model.data != 0) & dq_mask + if np.any(good) and refmodel is None: + refmodel = model + break + + # If no good data was found, use the first model. + if refmodel is None: + refmodel = self.input_models[0] # make a copy of the data array for internal manipulation refmodel_data = refmodel.data.copy() + # renormalize to the minimum value, for best results when # computing the weighted mean below - refmodel_data -= np.nanmin(refmodel_data) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") + refmodel_data -= np.nanmin(refmodel_data) # save the wcs of the reference model refwcs = refmodel.meta.wcs @@ -145,8 +158,8 @@ def build_nirspec_output_wcs(self, refmodel=None): sd = s * refmodel_data ld = lam * refmodel_data good_s = np.isfinite(sd) - if np.any(good_s): - total = np.sum(refmodel_data[good_s]) + total = np.sum(refmodel_data[good_s]) + if np.any(good_s) and total != 0: wmean_s = np.sum(sd[good_s]) / total wmean_l = np.sum(ld[good_s]) / total else: From d668926287477894e980af1ec271e229c0fda4fa Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 18 Jun 2024 10:11:30 -0400 Subject: [PATCH 114/687] remove requirements-max.txt --- JenkinsfileRT | 2 +- requirements-max.txt | 4 ---- 2 files changed, 1 insertion(+), 5 deletions(-) delete mode 100644 requirements-max.txt diff --git a/JenkinsfileRT b/JenkinsfileRT index edb7b393d5..9ee1443b3b 100644 --- a/JenkinsfileRT +++ b/JenkinsfileRT @@ -74,7 +74,7 @@ bc0.conda_ver = '22.11.1' bc0.conda_packages = [ "python=${python_version}", ] -bc0.pip_reqs_files = ['requirements-dev-st.txt', 'requirements-max.txt', 'requirements-sdp.txt'] +bc0.pip_reqs_files = ['requirements-dev-st.txt', 'requirements-sdp.txt'] bc0.build_cmds = [ "pip install certifi -U --force-reinstall", "pip install -e .[test,sdp] --no-cache-dir", diff --git a/requirements-max.txt b/requirements-max.txt deleted file mode 100644 index d6967e5bec..0000000000 --- a/requirements-max.txt +++ /dev/null @@ -1,4 +0,0 @@ -# this requirements file applies upper pins on critical upstream dependencies with incompatible functionality / refactoring that has not yet been implemented in this repository - -stcal<1.8.0 -stdatamodels<1.10.0 From 10501f22def0460b15df65cd5261601c6a71d7cf Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 20 Jun 2024 09:07:52 -0400 Subject: [PATCH 115/687] remove end in ami log messages (#8568) --- jwst/ami/leastsqnrm.py | 45 +++++++++++++++++++++--------------------- jwst/ami/nrm_core.py | 2 +- 2 files changed, 23 insertions(+), 24 deletions(-) diff --git a/jwst/ami/leastsqnrm.py b/jwst/ami/leastsqnrm.py index 87f011ed63..a798ada376 100644 --- a/jwst/ami/leastsqnrm.py +++ b/jwst/ami/leastsqnrm.py @@ -541,29 +541,28 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): flatimg = img.reshape(np.shape(img)[0] * np.shape(img)[1]) flatdqm = dqm.reshape(np.shape(img)[0] * np.shape(img)[1]) - log.info("fringefitting.leastsqnrm.matrix_operations(): ", end="") - log.info(f"\n\timg {img.shape:} \n\tdqm {dqm.shape:}", end="") + log.info("fringefitting.leastsqnrm.matrix_operations(): ") + log.info(f"\timg {img.shape:} \n\tdqm {dqm.shape:}") log.info( - f"\n\tL x W = {img.shape[0]:d} x {img.shape[1]:d} = {img.shape[0] * img.shape[1]:d}", - end="", + f"\tL x W = {img.shape[0]:d} x {img.shape[1]:d} = {img.shape[0] * img.shape[1]:d}", ) - log.info(f"\n\tflatimg {flatimg.shape:}", end="") - log.info(f"\n\tflatdqm {flatdqm.shape:}", end="") + log.info(f"\tflatimg {flatimg.shape:}") + log.info(f"\tflatdqm {flatdqm.shape:}") - log.info("\n\ttype(dqm)", type(dqm), end="") + log.info("\n\ttype(dqm) %s", type(dqm)) if dqm is not None: nanlist = np.where(flatdqm) # where DO_NOT_USE up. else: nanlist = (np.array(()),) # shouldn't occur w/MAST JWST data - log.info(f"\n\ttype(nanlist) {type(nanlist):}, len={len(nanlist):}", end="") - log.info(f"\n\tnumber of nanlist pixels: {len(nanlist[0]):d} items", end="") - log.info(f"\n\t{len(nanlist[0]):d} DO_NOT_USE pixels found in data slice", end="") + log.info(f"\ttype(nanlist) {type(nanlist):}, len={len(nanlist):}") + log.info(f"\tnumber of nanlist pixels: {len(nanlist[0]):d} items") + log.info(f"\t{len(nanlist[0]):d} DO_NOT_USE pixels found in data slice") flatimg = np.delete(flatimg, nanlist) - log.info(f"\n\tflatimg {flatimg.shape:} after deleting {len(nanlist[0]):d}", end="") + log.info(f"\tflatimg {flatimg.shape:} after deleting {len(nanlist[0]):d}") if flux is not None: flatimg = flux * flatimg / flatimg.sum() @@ -573,13 +572,13 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): np.shape(model)[0] * np.shape(model)[1], np.shape(model)[2] ) flatmodel = np.zeros((len(flatimg), np.shape(model)[2])) - log.info(f"\n\tflatmodel_nan {flatmodel_nan.shape:}", end="") - log.info(f"\n\tflatmodel {flatmodel.shape:}", end="") + log.info(f"\tflatmodel_nan {flatmodel_nan.shape:}") + log.info(f"\tflatmodel {flatmodel.shape:}") log.info( - f"\n\tdifference {flatmodel_nan.shape[0] - flatmodel.shape[0]:}", end="" + f"\tdifference {flatmodel_nan.shape[0] - flatmodel.shape[0]:}" ) - log.info("flat model dimensions ", np.shape(flatmodel)) - log.info("flat image dimensions ", np.shape(flatimg)) + log.info("flat model dimensions %s", np.shape(flatmodel)) + log.info("flat image dimensions %s", np.shape(flatimg)) for fringe in range(np.shape(model)[2]): flatmodel[:, fringe] = np.delete(flatmodel_nan[:, fringe], nanlist) @@ -602,13 +601,13 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): res = np.insert(res, naninsert, np.nan) res = res.reshape(img.shape[0], img.shape[1]) - log.info("model flux", flux) - log.info("data flux", flatimg.sum()) - log.info("flat model dimensions ", np.shape(flatmodel)) - log.info("model transpose dimensions ", np.shape(flatmodeltransp)) - log.info("flat image dimensions ", np.shape(flatimg)) - log.info("transpose * image data dimensions", np.shape(data_vector)) - log.info("flat img * transpose dimensions", np.shape(inverse)) + log.info("model flux %s", flux) + log.info("data flux %s", flatimg.sum()) + log.info("flat model dimensions %s", np.shape(flatmodel)) + log.info("model transpose dimensions %s", np.shape(flatmodeltransp)) + log.info("flat image dimensions %s", np.shape(flatimg)) + log.info("transpose * image data dimensions %s", np.shape(data_vector)) + log.info("flat img * transpose dimensions %s", np.shape(inverse)) if linfit: try: diff --git a/jwst/ami/nrm_core.py b/jwst/ami/nrm_core.py index b65ce3059d..e8e54b68f7 100755 --- a/jwst/ami/nrm_core.py +++ b/jwst/ami/nrm_core.py @@ -233,4 +233,4 @@ def fit_fringes_single_integration(self, slc): nrm.create_modelpsf() # model now stored as nrm.modelpsf, also nrm.residual self.nrm = nrm # this gets updated with each slice - return nrm # to fit_fringes_all, where the output model will be created from list of nrm objects \ No newline at end of file + return nrm # to fit_fringes_all, where the output model will be created from list of nrm objects From 5a89d783f20b18b16fb3e255703a791a1d9ac8cf Mon Sep 17 00:00:00 2001 From: Joseph Hunkeler Date: Fri, 21 Jun 2024 08:15:24 -0400 Subject: [PATCH 116/687] Do not set conda version in Jenkinsfile(s) (#8585) --- JenkinsfileRT | 1 - JenkinsfileRT_dev | 1 - 2 files changed, 2 deletions(-) diff --git a/JenkinsfileRT b/JenkinsfileRT index 9ee1443b3b..e155c83ab1 100644 --- a/JenkinsfileRT +++ b/JenkinsfileRT @@ -70,7 +70,6 @@ bc0 = new BuildConfig() bc0.nodetype = 'jwst' bc0.name = 'stable-deps' bc0.env_vars = env_vars -bc0.conda_ver = '22.11.1' bc0.conda_packages = [ "python=${python_version}", ] diff --git a/JenkinsfileRT_dev b/JenkinsfileRT_dev index 99c4d56476..2f88a1d81a 100644 --- a/JenkinsfileRT_dev +++ b/JenkinsfileRT_dev @@ -70,7 +70,6 @@ bc0 = new BuildConfig() bc0.nodetype = 'jwst' bc0.name = 'unstable-deps' bc0.env_vars = env_vars -bc0.conda_ver = '22.11.1' bc0.conda_packages = [ "python=${python_version}", ] From 33e3e672622a4fa37e1d2ed0c2f4e6a334cc7b77 Mon Sep 17 00:00:00 2001 From: David Law Date: Fri, 21 Jun 2024 17:01:44 -0400 Subject: [PATCH 117/687] JP-3618: Add intermediate LRS slit wcs reference frame (#8475) Co-authored-by: Howard Bushouse --- CHANGES.rst | 3 + jwst/assign_wcs/miri.py | 134 +++++++++++++++++++++++++++++++--------- 2 files changed, 107 insertions(+), 30 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 9a7ee97b79..ab5791a3a7 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -27,6 +27,9 @@ assign_wcs - Update default parameters to increase the accuracy of the SIP approximation in the output FITS WCS. [#8529] +- Update MIRI LRS WCS code to introduce an intermediate alpha-beta slit reference frame + between pixel coordinates and the v2/v3 frame. [#8475] + - Added handling for fixed slit sources defined in a MSA metadata file, for combined NIRSpec MOS and fixed slit observations. Slits are now appended to the data product in the order they appear in the MSA file. [#8467] diff --git a/jwst/assign_wcs/miri.py b/jwst/assign_wcs/miri.py index f8f4372687..9949428392 100644 --- a/jwst/assign_wcs/miri.py +++ b/jwst/assign_wcs/miri.py @@ -170,6 +170,15 @@ def lrs(input_model, reference_files): # Define the various coordinate frames. # Original detector frame detector = cf.Frame2D(name='detector', axes_order=(0, 1), unit=(u.pix, u.pix)) + + # Intermediate slit frame + alpha_beta = cf.Frame2D(name='alpha_beta_spatial', axes_order=(0, 1), + unit=(u.arcsec, u.arcsec), axes_names=('alpha', 'beta')) + spec_local = cf.SpectralFrame(name='alpha_beta_spectral', axes_order=(2,), + unit=(u.micron,), axes_names=('lambda',)) + miri_focal = cf.CompositeFrame([alpha_beta, spec_local], name='alpha_beta') + + # Spectral component spec = cf.SpectralFrame(name='spec', axes_order=(2,), unit=(u.micron,), axes_names=('lambda',)) # v2v3 spatial component @@ -193,7 +202,8 @@ def lrs(input_model, reference_files): world = cf.CompositeFrame(name="world", frames=[icrs, spec]) # Create the transforms - dettotel = lrs_distortion(input_model, reference_files) + dettoabl = lrs_xytoabl(input_model, reference_files) + abltov2v3l = lrs_abltov2v3l(input_model, reference_files) v2v3tosky = pointing.v23tosky(input_model) teltosky = v2v3tosky & models.Identity(1) @@ -205,7 +215,8 @@ def lrs(input_model, reference_files): ) & models.Identity(1) # Put the transforms together into a single pipeline - pipeline = [(detector, dettotel), + pipeline = [(detector, dettoabl), + (miri_focal, abltov2v3l), (v2v3, va_corr), (v2v3vacorr, teltosky), (world, None)] @@ -213,11 +224,11 @@ def lrs(input_model, reference_files): return pipeline -def lrs_distortion(input_model, reference_files): +def lrs_xytoabl(input_model, reference_files): """ - The LRS-FIXEDSLIT and LRS-SLITLESS WCS pipeline. + The first part of LRS-FIXEDSLIT and LRS-SLITLESS WCS pipeline. - Transform from subarray (x, y) to (v2, v3, lambda) using + Transform from subarray (x, y) to (alpha, beta, lambda) using the "specwcs" and "distortion" reference files. """ @@ -249,6 +260,11 @@ def lrs_distortion(input_model, reference_files): # Transform to slitless subarray from full array zero_point = subarray2full.inverse(zero_point[0], zero_point[1]) + # Figure out the typical along-slice pixel scale at the center of the slit + v2_cen, v3_cen = subarray_dist(zero_point[0], zero_point[1]) + v2_off, v3_off = subarray_dist(zero_point[0] + 1, zero_point[1]) + pscale = np.sqrt(np.power(v2_cen - v2_off, 2) + np.power(v3_cen - v3_off,2)) + # In the lrsdata reference table, X_center,y_center,wavelength describe the location of the # centroid trace along the detector in pixels relative to nominal location. # x0,y0(ul) x1,y1 (ur) x2,y2(lr) x3,y3(ll) define corners of the box within which the distortion @@ -275,18 +291,6 @@ def lrs_distortion(input_model, reference_files): bb_sub = ((input_model.meta.subarray.xstart - 1 + 4 - 0.5, input_model.meta.subarray.xsize - 1 + 0.5), (np.floor(y2.min() + zero_point[1]) - 0.5, np.ceil(y0.max() + zero_point[1]) + 0.5)) - # Find the ROW of the zero point - row_zero_point = zero_point[1] - - # The inputs to the "detector_to_v2v3" transform are - # - the indices in x spanning the entire image row - # - y is the y-value of the zero point - # This is equivalent of making a vector of x, y locations for - # every pixel in the reference row - const1d = models.Const1D(row_zero_point) - const1d.inverse = models.Const1D(row_zero_point) - det_to_v2v3 = models.Identity(1) & const1d | subarray_dist - # Now deal with the fact that the spectral trace isn't perfectly up and down along detector. # This information is contained in the xcenter/ycenter values in the CDP table, but we'll handle it # as a simple x shift using a linear fit to this relation provided by the CDP. @@ -330,34 +334,104 @@ def lrs_distortion(input_model, reference_files): ymodel = models.Mapping([1], n_inputs=2) # What is the effective XY as a function of subarray x,y? xymodel = models.Mapping((0, 1, 0, 1)) | xmodel & ymodel + # What is the alpha as a function of slit XY? + alphamodel = models.Mapping([0], n_inputs=2) | models.Shift(-zero_point[0]) | models.Polynomial1D(1, c0=0, c1=pscale) + # What is the alpha,beta as a function of slit XY? (beta is always zero) + abmodel = models.Mapping((0, 1, 0)) | alphamodel & models.Const1D(0) + # Define a shift by the reference point and immediately back again # This doesn't do anything effectively, but it stores the reference point for later use in pathloss reftransform = models.Shift(-zero_point[0]) & models.Shift(-zero_point[1]) | models.Shift(+zero_point[0]) & models.Shift(+zero_point[1]) # Put the transforms together - xytov2v3 = reftransform | xymodel | det_to_v2v3 + xytoab = reftransform | xymodel | abmodel - # Construct the full distortion model (xsub,ysub -> v2,v3,wavelength) + # Construct the full distortion model (xsub,ysub -> alpha,beta,wavelength) lrs_wav_model = models.Mapping([1], n_inputs=2) | wavemodel - dettotel = models.Mapping((0, 1, 0, 1)) | xytov2v3 & lrs_wav_model + dettoabl = models.Mapping((0, 1, 0, 1)) | xytoab & lrs_wav_model - # Construct the inverse distortion model (v2,v3,wavelength -> xsub,ysub) - # Go from v2,v3 to slit-x - v2v3_to_xdet = det_to_v2v3.inverse | models.Mapping([0], n_inputs=2) + # Construct the inverse distortion model (alpha,beta,wavelength -> xsub,ysub) + # Go from alpha to slit-X + slitxmodel = models.Polynomial1D(1, c0=0, c1=1/pscale) | models.Shift(zero_point[0]) # Go from lambda to real y lam_to_y = wavemodel.inverse # Go from slit-x and real y to real-x backwards = models.Mapping([0], n_inputs=2) + (models.Mapping([1], n_inputs=2) | dxmodel) - # Go from v2,v3,lam to real x - aa = v2v3_to_xdet & lam_to_y | backwards - # Go from v2,v3,lam to real y + + # Go from alpha,beta,lam to real x + aa = models.Mapping((0, 2)) | slitxmodel & lam_to_y | backwards + # Go from alpha,beta,lam to real y bb = models.Mapping([2], n_inputs=3) | lam_to_y - # Go from v2,v3,lam, to real x,y - dettotel.inverse = models.Mapping((0, 1, 2, 0, 1, 2)) | aa & bb + # Go from alpha,beta,lam, to real x,y + dettoabl.inverse = models.Mapping((0, 1, 2, 0, 1, 2)) | aa & bb # Bounding box is the subarray bounding box, because we're assuming subarray coordinates passed in - dettotel.bounding_box = bb_sub[::-1] + dettoabl.bounding_box = bb_sub[::-1] + + return dettoabl + +def lrs_abltov2v3l(input_model, reference_files): + """ + The second part of LRS-FIXEDSLIT and LRS-SLITLESS WCS pipeline. + + Transform from (alpha, beta, lambda) to (v2, v3, lambda) using + the "specwcs" and "distortion" reference files. + + """ + + # subarray to full array transform + subarray2full = subarray_transform(input_model) + + # full array to v2v3 transform for the ordinary imager + with DistortionModel(reference_files['distortion']) as dist: + distortion = dist.model + + # Combine models to create subarray to v2v3 distortion + if subarray2full is not None: + subarray_dist = subarray2full | distortion + else: + subarray_dist = distortion + + ref = fits.open(reference_files['specwcs']) + + with ref: + # Get the zero point from the reference data. + # The zero_point is X, Y (which should be COLUMN, ROW) + # These are 1-indexed in CDP-7 (i.e., SIAF convention) so must be converted to 0-indexed + if input_model.meta.exposure.type.lower() == 'mir_lrs-fixedslit': + zero_point = ref[0].header['imx'] - 1, ref[0].header['imy'] - 1 + elif input_model.meta.exposure.type.lower() == 'mir_lrs-slitless': + zero_point = ref[0].header['imxsltl'] - 1, ref[0].header['imysltl'] - 1 + # Transform to slitless subarray from full array + zero_point = subarray2full.inverse(zero_point[0], zero_point[1]) - return dettotel + # Figure out the typical along-slice pixel scale at the center of the slit + v2_cen, v3_cen = subarray_dist(zero_point[0], zero_point[1]) + v2_off, v3_off = subarray_dist(zero_point[0] + 1, zero_point[1]) + pscale = np.sqrt(np.power(v2_cen - v2_off, 2) + np.power(v3_cen - v3_off,2)) + + # Go from alpha to slit-X + slitxmodel = models.Polynomial1D(1, c0=0, c1=1 / pscale) | models.Shift(zero_point[0]) + # Go from beta to slit-Y (row_zero_point plus some offset) + # Beta should always be zero unless using in a pseudo-ifu mode + slitymodel = models.Polynomial1D(1, c0=0, c1=1 / pscale) | models.Shift(zero_point[1]) + # Go from alpha-beta to slit xy, and onward to v2v3 + ab_to_v2v3 = slitxmodel & slitymodel | subarray_dist + # Put it together to pass through wavelength + abl_to_v2v3l = models.Mapping((0, 1, 2)) | ab_to_v2v3 & models.Identity(1) + + # Define the inverse transform + # Go from slit X to alpha + alphamodel = models.Shift(-zero_point[0]) | models.Polynomial1D(1, c0=0, c1=pscale) + # Go from slit Y to beta + betamodel = models.Shift(-zero_point[1]) | models.Polynomial1D(1, c0=0, c1=pscale) + # Go from v2,v3 to slit-x,slit-y + v2v3_to_xydet = subarray_dist.inverse + # Go from v2,v3 to alpha, beta + aa = v2v3_to_xydet | alphamodel & betamodel + # Go from v2,v3,lambda to alpha,beta,lambda + abl_to_v2v3l.inverse = models.Mapping((0,1,2)) | aa & models.Identity(1) + + return abl_to_v2v3l def ifu(input_model, reference_files): """ From 95c8eb5ce89288f3ca056417c31c0bc527329f44 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Sat, 22 Jun 2024 08:40:20 -0400 Subject: [PATCH 118/687] unpin stsci.imagestats (#8577) Co-authored-by: Howard Bushouse --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 8ef1423212..b5f89bf2fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,7 @@ dependencies = [ "stdatamodels>=1.10.1,<1.11.0", "stpipe>=0.5.2,<0.6.0", "stsci.image>=2.3.5", - "stsci.imagestats>=1.6.3,<1.7.0", + "stsci.imagestats>=1.6.3", "synphot>=1.2", "tweakwcs>=0.8.6", "asdf-astropy>=0.3.0", From c79c4aedb255de3f99357ab35c650459454d8b39 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Mon, 24 Jun 2024 08:03:43 -0400 Subject: [PATCH 119/687] JP-3660: extract1d _coalesce_bounds fix (#8586) Co-authored-by: Timothy Brandt Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 ++++ jwst/extract_1d/extract1d.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index ab5791a3a7..27f019d4b3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -119,6 +119,10 @@ extract_1d - Fix error in application of aperture correction to variance arrays. [#8530] +- Fix error in ``_coalesce_bounds`` that returned incorrect spectral or background + extraction region when one set of pixel limits is entirely contained within + another [#8586] + - Removed a check for the primary slit for NIRSpec fixed slit mode: all slits containing point sources are now handled consistently, whether they are marked primary or not. [#8467] diff --git a/jwst/extract_1d/extract1d.py b/jwst/extract_1d/extract1d.py index f0c3a60d95..e083497cd6 100644 --- a/jwst/extract_1d/extract1d.py +++ b/jwst/extract_1d/extract1d.py @@ -802,7 +802,7 @@ def _coalesce_bounds(segments): pint = cint[-1] interval = intervals.pop(0) if interval[0] <= pint[1]: - pint[1] = interval[1] + pint[1] = max(interval[1], pint[1]) continue cint.append(interval) From 9939a491d11ceb7230b8fc7fba2d7e97baca0611 Mon Sep 17 00:00:00 2001 From: David Law Date: Mon, 24 Jun 2024 11:09:51 -0400 Subject: [PATCH 120/687] Update MIRI MRS background documentation table (#8582) Co-authored-by: Howard Bushouse --- CHANGES.rst | 6 ++++++ docs/jwst/background_subtraction/main.rst | 4 ++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 27f019d4b3..fa4650f4e5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -56,6 +56,12 @@ associations - Exclude NIRISS SOSS data taken with uncalibrated filter F277W from spec2 and tso3 associations. [#8549] +background_subtraction +---------------------- + +- Clarified MIRI MRS default/optional background subtraction steps in the + documentation pages. [#8582] + badpix_selfcal -------------- diff --git a/docs/jwst/background_subtraction/main.rst b/docs/jwst/background_subtraction/main.rst index 9aa995fca2..2dade7dea9 100644 --- a/docs/jwst/background_subtraction/main.rst +++ b/docs/jwst/background_subtraction/main.rst @@ -98,9 +98,9 @@ when the available data support multiple methods. +--------------------------+---------------+-------------------+-----------------------------+ | **MIRI MRS:** | | | | +--------------------------+---------------+-------------------+-----------------------------+ -| Dedicated background | Default | Optional | | +| Dedicated background | Optional | Default | | +--------------------------+---------------+-------------------+-----------------------------+ -| Nodded point source | Default | Optional | | +| Nodded point source | Optional | Optional | | +--------------------------+---------------+-------------------+-----------------------------+ | User supplied | | Default | | +--------------------------+---------------+-------------------+-----------------------------+ From aea285f8ac8a373d21a5b1b99c300a659ce83e76 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 24 Jun 2024 08:12:24 -0700 Subject: [PATCH 121/687] JP-3662: skip photom step for SOSS full-frame non-TSO exposure (#8575) --- CHANGES.rst | 6 ++++++ jwst/photom/photom.py | 3 +++ jwst/photom/tests/test_photom.py | 15 +++++++++++++++ jwst/pipeline/calwebb_spec2.py | 6 +++--- 4 files changed, 27 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index fa4650f4e5..00baa2337f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -252,6 +252,9 @@ photom all slits containing point sources are now handled consistently, whether they are marked primary or not. [#8467] +- Added a hook to bypass the ``photom`` step when the ``extract_1d`` step + was bypassed for non-TSO NIRISS SOSS exposures. [#8575] + pipeline -------- @@ -275,6 +278,9 @@ pipeline FS slits. Final output products (``cal``, ``s2d``, ``x1d``) contain the combined products. [#8467] +- Added a hook to skip ``photom`` step when the ``extract_1d`` step was skipped + for NIRISS SOSS data [#8575]. + pixel_replace ------------- diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py index c445d9601f..7628582e36 100644 --- a/jwst/photom/photom.py +++ b/jwst/photom/photom.py @@ -425,6 +425,9 @@ def calc_niriss(self, ftab): raise DataModelTypeError(f"Unexpected input data model type for NIRISS: {self.input.__class__.__name__}") elif self.exptype in ['NIS_SOSS']: + if isinstance(self.input, datamodels.ImageModel): + raise DataModelTypeError(f"Unexpected input data model type for NIRISS: {self.input.__class__.__name__}") + for spec in self.input.spec: self.specnum += 1 self.order = spec.spectral_order diff --git a/jwst/photom/tests/test_photom.py b/jwst/photom/tests/test_photom.py index 43c7d5e601..2fc0f5b688 100644 --- a/jwst/photom/tests/test_photom.py +++ b/jwst/photom/tests/test_photom.py @@ -1357,6 +1357,21 @@ def test_expected_failure_niriss_cubemodel(): ds.calc_niriss(None) +def test_expected_failure_soss_imagemodel(): + """ + Test that passing a CubeModel to calc_niriss raises an exception + This occurs when extract_1d step is skipped, e.g. for NIRISS SOSS data + in FULL subarray. + """ + + input_model = create_input('NIRISS', 'NIS', 'NIS_SOSS', + filter='CLEAR', pupil='GR700XD') + ds = photom.DataSet(input_model) + ds.input = datamodels.ImageModel() + with pytest.raises(photom.DataModelTypeError): + ds.calc_niriss(None) + + def test_miri_mrs(): """Test calc_miri, MRS data""" diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 9ce8c04d61..9f3004f4a3 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -362,11 +362,11 @@ def process_exposure_product( x1d = self.extract_1d(x1d) # Possible that no fit was possible - if so, skip photom - if x1d is not None: + if (x1d is None) or (x1d.meta.cal_step.extract_1d == "SKIPPED"): + self.log.warning("Extract_1d did not return a DataModel - skipping photom.") + else: self.photom.save_results = self.save_results x1d = self.photom(x1d) - else: - self.log.warning("Extract_1d did not return a DataModel - skipping photom.") elif exp_type == 'NRS_MSASPEC': # Special handling for MSA spectra, to handle mixed-in # fixed slits separately From 2ee252f11918c8b11942a3469fb5904fd2f22d04 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 24 Jun 2024 12:11:09 -0400 Subject: [PATCH 122/687] increase stdatamodels and stpipe versions --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b5f89bf2fa..b451a677a1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,8 +35,8 @@ dependencies = [ "scipy>=1.9.3", "spherical-geometry>=1.2.22", "stcal>=1.7.1,<1.8.0", - "stdatamodels>=1.10.1,<1.11.0", - "stpipe>=0.5.2,<0.6.0", + "stdatamodels>=2.0.0,<2.1.0", + "stpipe>=0.6.0,<0.7.0", "stsci.image>=2.3.5", "stsci.imagestats>=1.6.3", "synphot>=1.2", From b1baedb6434b6535ee761b8599802669674e806c Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 24 Jun 2024 12:12:15 -0400 Subject: [PATCH 123/687] add changelog --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 00baa2337f..ce926ae34b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -168,6 +168,8 @@ general - Increase minimum required scipy. [#8441] +- Increase minimum required stdatamodels and stpipe. [#8592] + lib --- From 60305f0842df8b5db0e8c8de77812926b825335a Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 20 Jun 2024 16:19:22 -0400 Subject: [PATCH 124/687] added check for skipping extract_1d --- jwst/pipeline/calwebb_tso3.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index 91ad8b505d..703d4a937d 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -154,17 +154,19 @@ def process(self, input): x1d_result.int_times[row[0] - 1] = row # SOSS F277W may return None - don't bother with that - if result is not None: - if input_exptype == 'NIS_SOSS': - # SOSS data have yet to be photometrically calibrated - # Calibrate 1D spectra here. - result = self.photom(result) + if (result is None) or (result.meta.cal_step.extract_1d == "SKIPPED"): + continue - x1d_result.spec.extend(result.spec) + if input_exptype == 'NIS_SOSS': + # SOSS data have yet to be photometrically calibrated + # Calibrate 1D spectra here. + result = self.photom(result) - # perform white-light photometry on 1d extracted data - self.log.info("Performing white-light photometry ...") - phot_result_list.append(self.white_light(result)) + x1d_result.spec.extend(result.spec) + + # perform white-light photometry on 1d extracted data + self.log.info("Performing white-light photometry ...") + phot_result_list.append(self.white_light(result)) # Update some metadata from the association x1d_result.meta.asn.pool_name = input_models.meta.asn_table.asn_pool @@ -172,7 +174,11 @@ def process(self, input): # Save the final x1d Multispec model x1d_result.meta.cal_step.pixel_replace = state - self.save_model(x1d_result, suffix='x1dints') + if len(x1d_result.spec) == 0: + self.log.warning("extract_1d step could not be completed for any integrations") + self.log.warning("x1dints products will not be created.") + else: + self.save_model(x1d_result, suffix='x1dints') # Done with all the inputs input_models.close() From be142087f946e1a5030ed4b5698aaa89c49070d9 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 21 Jun 2024 09:09:01 -0400 Subject: [PATCH 125/687] added changelog entry --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 00baa2337f..7dfe4b4de6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -280,6 +280,9 @@ pipeline - Added a hook to skip ``photom`` step when the ``extract_1d`` step was skipped for NIRISS SOSS data [#8575]. + +- Added hook to the ``calwebb_tso3`` pipeline to skip all subsequent steps + if the ``extract_1d`` step is skipped. [#8583] pixel_replace ------------- From f0960936f6520891a4b0324fba3ec6021bb74e08 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Tue, 25 Jun 2024 11:02:54 -0400 Subject: [PATCH 126/687] bump GWCS pin to `>=0.21.0,<0.23.0` (#8598) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index b451a677a1..11dcffc077 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ dependencies = [ "BayesicFitting>=3.0.1", "crds>=11.17.14", "drizzle>=1.15.0", - "gwcs>=0.21.0,<0.22.0", + "gwcs>=0.21.0,<0.23.0", "numpy>=1.22,<2.0", "opencv-python-headless>=4.6.0.66", "photutils>=1.5.0", From 801d24a74a0f479ac0603ebff67df7639f094e29 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 25 Jun 2024 11:43:02 -0400 Subject: [PATCH 127/687] remove unused nlow nhigh parameters from outlier detection (#8595) Co-authored-by: Howard Bushouse --- docs/jwst/outlier_detection/arguments.rst | 8 -------- docs/jwst/outlier_detection/outlier_detection_imaging.rst | 2 -- docs/jwst/outlier_detection/outlier_detection_tso.rst | 4 +--- jwst/outlier_detection/outlier_detection_step.py | 4 ---- 4 files changed, 1 insertion(+), 17 deletions(-) diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index a2900bcbd6..7e86003214 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -24,14 +24,6 @@ that control the behavior of the processing: Any floating-point value, given as a string, is valid. A value of 'INDEF' will use the last zero weight flux. -``--nlow`` (integer, default=0) - The number of low values in each pixel stack to ignore - when computing the median value. - -``--nhigh`` (integer, default=0) - The number of high values in each pixel stack to ignore - when computing the median value. - ``--maskpt`` (float, default=0.7) The percent of maximum weight to use as lower-limit for valid data; valid values go from 0.0 to 1.0. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index c4cf5eceba..d1b86b28f8 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -69,8 +69,6 @@ Specifically, this routine performs the following operations: * The median image is created by combining all grouped mosaic images or non-resampled input data (as planes in a ModelContainer) pixel-by-pixel. - * The ``nlow`` and ``nhigh`` parameters specify how many low and high values - to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when resampled. diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst index 2f74611e32..0df852ea00 100644 --- a/docs/jwst/outlier_detection/outlier_detection_tso.rst +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -21,8 +21,6 @@ a few variations to accomodate the nature of these 3D data. * The ``rolling_window_width`` parameter specifies the number of integrations over which to compute the median. The default is 25. If the number of integrations is less than or equal to ``rolling_window_width``, a simple median is used instead. - * The ``nlow`` and ``nhigh`` parameters specify how many low and high values - to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when the median is taken. @@ -38,4 +36,4 @@ a few variations to accomodate the nature of these 3D data. #. The input data model DQ arrays are updated with the mask of detected outliers. -.. automodapi:: jwst.outlier_detection.outlier_detection_tso \ No newline at end of file +.. automodapi:: jwst.outlier_detection.outlier_detection_tso diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 8add2f32a9..6aa786cc91 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -55,8 +55,6 @@ class OutlierDetectionStep(Step): pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel fillval = string(default='INDEF') - nlow = integer(default=0) - nhigh = integer(default=0) maskpt = float(default=0.7) snr = string(default='5.0 4.0') scale = string(default='1.2 0.7') @@ -107,8 +105,6 @@ def process(self, input_data): 'pixfrac': self.pixfrac, 'kernel': self.kernel, 'fillval': self.fillval, - 'nlow': self.nlow, - 'nhigh': self.nhigh, 'maskpt': self.maskpt, 'snr': self.snr, 'scale': self.scale, From 4e15c70b6fd7d2ecb4a65f11edd113c0d7a2df55 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 25 Jun 2024 13:20:55 -0400 Subject: [PATCH 128/687] JP-3593: Fix 2nd/3rd group saturation for NIRSpec IRS2 (#8593) Co-authored-by: Maria Pena-Guerrero Co-authored-by: Maria Co-authored-by: Howard Bushouse --- CHANGES.rst | 5 +++++ docs/jwst/saturation/description.rst | 7 ++++++ jwst/saturation/saturation.py | 27 ++++++++++++++++++++++++ jwst/saturation/tests/test_saturation.py | 9 ++++++-- 4 files changed, 46 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index ad495e735a..4f51f1ce17 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -347,6 +347,11 @@ residual_fringe - Use DQ plane to exclude pixels marked as DO_NOT_USE in correction. [#8381] +saturation +---------- + +- Adds a check for saturation bias in group 2 for IRS2 mode nframes > 1. [#8593] + tweakreg -------- diff --git a/docs/jwst/saturation/description.rst b/docs/jwst/saturation/description.rst index c5ea5193cc..9a6e29cfcf 100644 --- a/docs/jwst/saturation/description.rst +++ b/docs/jwst/saturation/description.rst @@ -34,6 +34,13 @@ The "NO_SAT_CHECK" flag is propagated to the PIXELDQ array in the output science data to indicate which pixels fall into this category. +For grouped data, this step also looks for and flags data that saturated during +the second group but did not trigger the normal saturation threshold due to the +grouped data averaging. This requires that the third group be saturated, and +the first group sufficiently low that the third group would not have been expected +to saturate (i.e., flagging due to cosmic rays but not sources). +At present, this is only implemented for NIRSpec IRS2 data. + .. _charge_migration: Charge Migration diff --git a/jwst/saturation/saturation.py b/jwst/saturation/saturation.py index fe81f8d370..f76b6fec66 100644 --- a/jwst/saturation/saturation.py +++ b/jwst/saturation/saturation.py @@ -117,6 +117,8 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): nints = data.shape[0] ngroups = data.shape[1] detector = input_model.meta.instrument.detector + nframes = input_model.meta.exposure.nframes + read_pattern = [[x + 1 + groupstart * nframes for x in range(nframes)] for groupstart in range(ngroups)] # create a mask of the appropriate size irs2_mask = x_irs2.make_mask(input_model) @@ -160,6 +162,31 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): irs2_mask, detector) # check for saturation flag_temp = np.where(sci_temp >= sat_thresh, SATURATED, 0) + if group == 2: + # Identify groups which we wouldn't expect to saturate by the third group, + # on the basis of the first group + scigp1 = x_irs2.from_irs2(data[ints, 0, :, :], irs2_mask, detector) + mask = scigp1 / np.mean(read_pattern[0]) * read_pattern[2][-1] < sat_thresh + + # Identify groups with suspiciously large values in the second group + scigp2 = x_irs2.from_irs2(data[ints, 1, :, :], irs2_mask, detector) + mask &= scigp2 > sat_thresh / len(read_pattern[1]) + + # Identify groups that are saturated in the third group + gp3mask = np.where(flag_temp & SATURATED, True, False) + mask &= gp3mask + + # Flag the 2nd group for the pixels passing that gauntlet in the 3rd group + dq_temp = np.zeros_like(mask,dtype='uint8') + dq_temp[mask] = SATURATED + # flag any pixels that border saturated pixels + if n_pix_grow_sat > 0: + dq_temp = adjacency_sat(dq_temp, SATURATED, n_pix_grow_sat) + # set the flags in dq array for group 2, i.e. index 1 + x_irs2.to_irs2(flagarray, dq_temp, irs2_mask, detector) + np.bitwise_or(groupdq[ints, 1, ...], flagarray, + groupdq[ints, 1, ...]) + # check for A/D floor flaglow_temp = np.where(sci_temp <= 0, AD_FLOOR | DONOTUSE, 0) diff --git a/jwst/saturation/tests/test_saturation.py b/jwst/saturation/tests/test_saturation.py index be74671006..c39997107b 100644 --- a/jwst/saturation/tests/test_saturation.py +++ b/jwst/saturation/tests/test_saturation.py @@ -28,8 +28,8 @@ def test_basic_saturation_flagging(setup_nrc_cube): # Add ramp values up to the saturation limit data.data[0, 0, 5, 5] = 0 data.data[0, 1, 5, 5] = 20000 - data.data[0, 2, 5, 5] = 40000 - data.data[0, 3, 5, 5] = 60000 # Signal reaches saturation limit + data.data[0, 2, 5, 5] = 60000 # Signal reaches saturation limit + data.data[0, 3, 5, 5] = 60000 data.data[0, 4, 5, 5] = 62000 # Set saturation value in the saturation model @@ -386,6 +386,7 @@ def _cube(ngroups, nrows, ncols): data_model.meta.subarray.xsize = ncols data_model.meta.subarray.ysize = nrows data_model.meta.exposure.ngroups = ngroups + data_model.meta.exposure.nframes = 3 data_model.meta.instrument.name = 'NIRCAM' data_model.meta.instrument.detector = 'NRCA1' data_model.meta.observation.date = '2017-10-01' @@ -432,6 +433,8 @@ def _cube(xstart, ystart, ngroups, nrows, ncols): data_model.meta.subarray.xsize = ncols data_model.meta.subarray.ystart = ystart data_model.meta.subarray.ysize = nrows + data_model.meta.exposure.ngroups = ngroups + data_model.meta.exposure.nframes = 3 # create a saturation model for the saturation step saturation_model = SaturationModel((1032, 1024)) @@ -477,6 +480,8 @@ def _cube(): data_model.meta.exposure.nrs_normal = 16 data_model.meta.exposure.nrs_reference = 4 data_model.meta.exposure.readpatt = 'NRSIRS2RAPID' + data_model.meta.exposure.nframes = 3 + data_model.meta.exposure.ngroups = 5 # create a saturation model for the saturation step saturation_model = SaturationModel((2048, 2048)) From 70ad1aa3bd3fe68ec1c9306617d446abf0e49d03 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 25 Jun 2024 14:22:18 -0400 Subject: [PATCH 129/687] JP-3523: Use zero background instead of NaN background where undefined (#8597) --- CHANGES.rst | 3 +++ jwst/master_background/expand_to_2d.py | 21 ++++----------------- 2 files changed, 7 insertions(+), 17 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 4f51f1ce17..aa82041af5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -189,6 +189,9 @@ master_background Master background correction for MOS mode should be performed via ``master_background_mos``, called in ``calwebb_spec2``. [#8467] +- Use zero values for master background outside the background + wavelength range instead of NaN to avoid NaN-ing out entire + sets of science data when backgrounds are missing. [#8597] master_background_mos --------------------- diff --git a/jwst/master_background/expand_to_2d.py b/jwst/master_background/expand_to_2d.py index de221eab9a..4148569c67 100644 --- a/jwst/master_background/expand_to_2d.py +++ b/jwst/master_background/expand_to_2d.py @@ -319,8 +319,6 @@ def bkg_for_ifu_image(input, tab_wavelength, tab_background): background = input.copy() background.data[:, :] = 0. - min_wave = np.amin(tab_wavelength) - max_wave = np.amax(tab_wavelength) if input.meta.instrument.name.upper() == "NIRSPEC": list_of_wcs = nirspec.nrs_ifu_wcs(input) @@ -329,17 +327,9 @@ def bkg_for_ifu_image(input, tab_wavelength, tab_background): wl_array = ifu_wcs(x, y)[2] wl_array[np.isnan(wl_array)] = -1. - # mask wavelengths not covered by the master background - mask_limit = (wl_array > max_wave) | (wl_array < min_wave) - wl_array[mask_limit] = -1 - - # mask_limit is indices into each WCS slice grid. Need them in - # full frame coordinates, so we use x and y, which are full-frame - full_frame_ind = y[mask_limit].astype(int), x[mask_limit].astype(int) + # Anywhere science pixels are beyond the wavelength range of the background, zero + # background will be used. # TODO - add another DQ Flag something like NO_BACKGROUND when we have space in dqflags - background.dq[full_frame_ind] = np.bitwise_or(background.dq[full_frame_ind], - dqflags.pixel['DO_NOT_USE']) - bkg_surf_bright = np.interp(wl_array, tab_wavelength, tab_background, left=0., right=0.) background.data[y.astype(int), x.astype(int)] = bkg_surf_bright.copy() @@ -356,13 +346,10 @@ def bkg_for_ifu_image(input, tab_wavelength, tab_background): # first remove the nans from wl_array and replace with -1 mask = np.isnan(wl_array) wl_array[mask] = -1. - # next look at the limits of the wavelength table - mask_limit = (wl_array > max_wave) | (wl_array < min_wave) - wl_array[mask_limit] = -1 + # Anywhere science pixels are beyond the wavelength range of the background, zero + # background will be used. # TODO - add another DQ Flag something like NO_BACKGROUND when we have space in dqflags - background.dq[mask_limit] = np.bitwise_or(background.dq[mask_limit], - dqflags.pixel['DO_NOT_USE']) bkg_surf_bright = np.interp(wl_array, tab_wavelength, tab_background, left=0., right=0.) background.data[:, :] = bkg_surf_bright.copy() From 346e703418925722d824f175baefffdd8ca2d222 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 25 Jun 2024 11:57:09 -0700 Subject: [PATCH 130/687] JP-3648: Move NIRSpec FS point source position assignment out of wavecorr (#8569) --- CHANGES.rst | 12 +++ docs/jwst/extract_2d/main.rst | 20 ++++ docs/jwst/wavecorr/description.rst | 23 +---- jwst/extract_2d/nirspec.py | 75 ++++++++++++++ jwst/extract_2d/tests/test_nirspec.py | 20 +++- jwst/srctype/srctype.py | 4 + jwst/srctype/tests/test_srctype.py | 8 ++ jwst/wavecorr/tests/test_wavecorr.py | 60 +++++------ jwst/wavecorr/wavecorr.py | 143 ++++---------------------- jwst/wavecorr/wavecorr_step.py | 21 ++-- 10 files changed, 203 insertions(+), 183 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index aa82041af5..326776417a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -141,6 +141,9 @@ extract_2d - Added support for slit names that have string values instead of integer values, necessary for processing combined NIRSpec MOS and fixed slit data products. [#8467] + +- Assign slit ``source_xpos`` and ``source_ypos`` attributes here instead of + in ``wavecorr`` for NIRSpec FS data. [#8569] flat_field ---------- @@ -350,6 +353,12 @@ residual_fringe - Use DQ plane to exclude pixels marked as DO_NOT_USE in correction. [#8381] +srctype +------- + +- Reset ``source_xpos`` and ``source_ypos`` values to zero for extended sources + in NIRSpec FS data to enable assigning those attributes in ``extract_2d``. [#8569] + saturation ---------- @@ -391,6 +400,9 @@ wavecorr Point source position is calculated from dither offsets only for standard fixed slit processing. [#8467] +- Assign slit ``source_xpos`` and ``source_ypos`` attributes in ``extract_2d`` + instead of in ``wavecorr`` for NIRSpec FS data. [#8569] + wfss_contam ----------- diff --git a/docs/jwst/extract_2d/main.rst b/docs/jwst/extract_2d/main.rst index e138f6271a..4cc262b987 100644 --- a/docs/jwst/extract_2d/main.rst +++ b/docs/jwst/extract_2d/main.rst @@ -69,6 +69,26 @@ corresponding to the FITS keywords "SLTNAME", "SLTSTRT1", "SLTSIZE1", "SLTSTRT2", and "SLTSIZE2." Keyword "DISPAXIS" (dispersion direction) will be copied from the input file to each of the output cutout images. +The "SRCXPOS" and "SRCYPOS" keywords in the SCI extension header of each slitlet +are also populated with estimates of the source +x (dispersion) and y (cross-dispersion) location within the slitlet. +For MOS data, these values are taken from the :ref:`MSA metadata file`. +Fixed slit data do not have an *a priori* estimate of the source +location within a given slit, so the estimated source location is +computed by the ``extract_2d`` step. It uses the target coordinates in +conjunction with the aperture reference point in V2/V3 space to +estimate the fractional location of the source within the given slit. +Note that this computation can only be performed for the primary slit +in the exposure, which is given in the "FXD_SLIT" keyword. The positions +of sources in any additional slits cannot be estimated and therefore +are set to 0.0 (the center of the slit).\ :sup:`1` + +:sup:`1`\ Note that fixed slits that are planned as part of a combined +MOS and FS observation do have *a priori* estimates of their source +locations, via the :ref:`MSA metadata file`. When available, +these source locations are directly used, instead of recomputing the source +position from the WCS information. + NIRCam and NIRISS WFSS ++++++++++++++++++++++ diff --git a/docs/jwst/wavecorr/description.rst b/docs/jwst/wavecorr/description.rst index fcaba1f7fe..b558132338 100644 --- a/docs/jwst/wavecorr/description.rst +++ b/docs/jwst/wavecorr/description.rst @@ -41,26 +41,13 @@ produces a new wavelength corrected slit frame, ``wavecorr_frame``. NIRSpec Fixed Slit (FS) ----------------------- -Fixed slit data do not have an *a priori* estimate of the source -location within a given slit, so the estimated source location is -computed by the ``wavecorr`` step. It uses the target coordinates in -conjunction with the aperture reference point in V2/V3 space to -estimate the fractional location of the source within the given slit. -Note that this computation can only be performed for the primary slit -in the exposure, which is given in the "FXD_SLIT" keyword. The positions -of sources in any additional slits cannot be estimated and therefore -the wavelength correction is only applied to the primary slit.\ :sup:`1` -The estimated position of the source within the primary slit (in the -dispersion direction) is then used in the same manner as described above +The source position within the primary slit is estimated based on the +target coordinates and aperture reference point during the +:ref:`extract_2d ` step. This estimated position (in the +dispersion direction) is used in the same manner as described above for MOS slitlets to update the slit WCS and compute corrected wavelengths -for the primary slit. +for the primary slit only. Upon successful completion of the step, the status keyword "S_WAVCOR" is set to "COMPLETE". - -:sup:`1`\ Note that fixed slits that are planned as part of a combined -MOS and FS observation do have *a priori* estimates of their source -locations, via the :ref:`MSA metadata file`. When available, -these source locations are directly used, instead of recomputing the source -position in the ``wavecorr`` step. diff --git a/jwst/extract_2d/nirspec.py b/jwst/extract_2d/nirspec.py index c29ab427c2..a134231df8 100644 --- a/jwst/extract_2d/nirspec.py +++ b/jwst/extract_2d/nirspec.py @@ -8,6 +8,7 @@ from gwcs import wcstools from stdatamodels.jwst import datamodels +from stdatamodels.jwst.transforms import models as trmodels from ..assign_wcs import nirspec from ..assign_wcs import util @@ -61,6 +62,13 @@ def nrs_extract2d(input_model, slit_name=None): slit = open_slits[0] output_model, xlo, xhi, ylo, yhi = process_slit(input_model, slit, exp_type) set_slit_attributes(output_model, slit, xlo, xhi, ylo, yhi) + try: + get_source_xpos(output_model) + except DitherMetadataError as e: + log.warning(str(e)) + log.warning("Setting source position in slit to 0.0, 0.0") + output_model.source_ypos = 0.0 + output_model.source_xpos = 0.0 if 'world' in input_model.meta.wcs.available_frames: orig_s_region = output_model.meta.wcsinfo.s_region.strip() util.update_s_region_nrs_slit(output_model) @@ -82,6 +90,20 @@ def nrs_extract2d(input_model, slit_name=None): # The overall subarray offset is recorded in model.meta.subarray. set_slit_attributes(new_model, slit, xlo, xhi, ylo, yhi) + if (new_model.meta.exposure.type.lower() == 'nrs_fixedslit'): + if (slit.name == input_model.meta.instrument.fixed_slit): + try: + get_source_xpos(new_model) + except DitherMetadataError as e: + log.warning(str(e)) + log.warning("Setting source position in slit to 0.0, 0.0") + new_model.source_ypos = 0.0 + new_model.source_xpos = 0.0 + else: + # ensure nonsense data never end up in non-primary slits + new_model.source_ypos = 0.0 + new_model.source_xpos = 0.0 + # Update the S_REGION keyword value for the extracted slit if 'world' in input_model.meta.wcs.available_frames: util.update_s_region_nrs_slit(new_model) @@ -266,3 +288,56 @@ def extract_slit(input_model, slit, exp_type): new_model.meta.wcs = slit_wcs return new_model, xlo, xhi, ylo, yhi + + +class DitherMetadataError(Exception): + pass + + +def get_source_xpos(slit): + """ + Compute the source position within the slit for a NIRSpec fixed slit. + + Parameters + ---------- + slit : `~jwst.datamodels.SlitModel` + The slit object. + + Returns + ------- + xpos : float + X coordinate of the source as a fraction of the slit size. + """ + + if not hasattr(slit.meta, "dither"): + raise DitherMetadataError('meta.dither is not populated for the primary slit; ' + 'Failed to estimate source position in slit.') + + if slit.meta.dither.x_offset is None or slit.meta.dither.y_offset is None: + raise DitherMetadataError('meta.dither.x(y)_offset values are None for primary slit; ' + 'Failed to estimate source position in slit.') + + xoffset = slit.meta.dither.x_offset # in arcsec + yoffset = slit.meta.dither.y_offset # in arcsec + v2ref = slit.meta.wcsinfo.v2_ref # in arcsec + v3ref = slit.meta.wcsinfo.v3_ref # in arcsec + v3idlyangle = slit.meta.wcsinfo.v3yangle # in deg + vparity = slit.meta.wcsinfo.vparity + + idl2v23 = trmodels.IdealToV2V3(v3idlyangle, v2ref, v3ref, vparity) + log.debug("wcsinfo: {0}, {1}, {2}, {3}".format(v2ref, v3ref, v3idlyangle, vparity)) + # Compute the location in V2,V3 [in arcsec] + xv, yv = idl2v23(xoffset, yoffset) + log.info(f'xoffset, yoffset, {xoffset}, {yoffset}') + + # Position in the virtual slit + wavelength = 2.0 # microns, but it doesn't make any difference here + xpos_slit, ypos_slit, lam_slit = slit.meta.wcs.get_transform('v2v3', 'slit_frame')( + xv, yv, wavelength) + # Update slit.source_xpos, slit.source_ypos + slit.source_xpos = xpos_slit + slit.source_ypos = ypos_slit + log.debug('Source X/Y position in V2V3: {0}, {1}'.format(xv, yv)) + log.info('Source X/Y position in the slit: {0}, {1}'.format(xpos_slit, ypos_slit)) + + return xpos_slit diff --git a/jwst/extract_2d/tests/test_nirspec.py b/jwst/extract_2d/tests/test_nirspec.py index ab6c2747de..536bea0246 100644 --- a/jwst/extract_2d/tests/test_nirspec.py +++ b/jwst/extract_2d/tests/test_nirspec.py @@ -11,6 +11,7 @@ # WCS keywords, borrowed from NIRCam grism tests WCS_KEYS = {'wcsaxes': 2, 'ra_ref': 53.1490299775, 'dec_ref': -27.8168745624, 'v2_ref': 86.103458, 'v3_ref': -493.227512, 'roll_ref': 45.04234459270135, + 'v3i_yang': 0.0, 'vparity': -1, 'crpix1': 1024.5, 'crpix2': 1024.5, 'crval1': 53.1490299775, 'crval2': -27.8168745624, 'cdelt1': 1.81661111111111e-05, 'cdelt2': 1.8303611111111e-05, @@ -39,6 +40,8 @@ def create_nirspec_hdul(detector='NRS1', grating='G395M', filter_name='F290LP', phdu.header['EXP_TYPE'] = exptype phdu.header['PATT_NUM'] = 1 phdu.header['SUBARRAY'] = subarray + phdu.header['XOFFSET'] = 0.0 + phdu.header['YOFFSET'] = 0.0 if subarray == 'SUBS200A1': phdu.header['SUBSIZE1'] = 2048 phdu.header['SUBSIZE2'] = 64 @@ -182,8 +185,10 @@ def test_extract_2d_nirspec_msa_fs(nirspec_msa_rate, nirspec_msa_metfl): def test_extract_2d_nirspec_fs(nirspec_fs_rate): model = ImageModel(nirspec_fs_rate) - result = AssignWcsStep.call(model) - result = Extract2dStep.call(result) + model_wcs = AssignWcsStep.call(model) + + + result = Extract2dStep.call(model_wcs) assert isinstance(result, MultiSlitModel) # there should be 1 slit extracted: FS, S200A1 @@ -194,7 +199,18 @@ def test_extract_2d_nirspec_fs(nirspec_fs_rate): assert result.slits[0].slitlet_id == 0 assert result.slits[0].data.shape == (45, 1254) + # ensure x_offset, y_offset become zero when dither information is missing + model_wcs.meta.dither = None + result = Extract2dStep.call(model_wcs) + assert result.slits[0].source_xpos == 0.0 + assert result.slits[0].source_ypos == 0.0 + model_wcs.meta.dither = {"x_offset": None, "y_offset": None} + result = Extract2dStep.call(model_wcs) + assert result.slits[0].source_xpos == 0.0 + assert result.slits[0].source_ypos == 0.0 + model.close() + model_wcs.close() result.close() diff --git a/jwst/srctype/srctype.py b/jwst/srctype/srctype.py index 8448f1109f..97ca5057c6 100644 --- a/jwst/srctype/srctype.py +++ b/jwst/srctype/srctype.py @@ -138,6 +138,10 @@ def set_source_type(input_model, source_type=None): for slit in input_model.slits: if slit.name == primary_slit: slit.source_type = src_type + # Ensure x,y position reset to zero if source type is EXTENDED + if src_type == 'EXTENDED': + slit.source_xpos = 0.0 + slit.source_ypos = 0.0 else: slit.source_type = default_type log.debug(f' slit {slit.name} = {slit.source_type}') diff --git a/jwst/srctype/tests/test_srctype.py b/jwst/srctype/tests/test_srctype.py index 29632e3cc3..57cba8187f 100644 --- a/jwst/srctype/tests/test_srctype.py +++ b/jwst/srctype/tests/test_srctype.py @@ -191,6 +191,14 @@ def test_nrs_fixedslit(): assert result.slits[1].source_type == 'POINT' assert result.slits[2].source_type == 'EXTENDED' + # Check that source_xpos and source_ypos are reset to zero for primary slit + # if the source is EXTENDED + input.slits[1].source_xpos = 0.5 + input.slits[1].source_ypos = -0.5 + result = srctype.set_source_type(input, "EXTENDED") + assert result.slits[1].source_xpos == 0.0 + assert result.slits[1].source_ypos == 0.0 + def test_valid_user_spec(): """ Overwrite source_type with valid user-specified value diff --git a/jwst/wavecorr/tests/test_wavecorr.py b/jwst/wavecorr/tests/test_wavecorr.py index 85e74a0d0a..9f1f00ae29 100644 --- a/jwst/wavecorr/tests/test_wavecorr.py +++ b/jwst/wavecorr/tests/test_wavecorr.py @@ -108,47 +108,45 @@ def test_skipped(): # Test an error is raised if assign_wcs or extract_2d were not run. im.meta.exposure.type = 'NRS_FIXEDSLIT' - with pytest.raises(AttributeError): + with pytest.raises(ValueError): WavecorrStep.call(im) outa = AssignWcsStep.call(im) - oute = Extract2dStep.call(outa) - outs = SourceTypeStep.call(oute) - - # Test step is skipped if no coverage in CRDS - outs.slits[0].meta.observation.date = '2001-08-03' - outw = WavecorrStep.call(outs) - assert out.meta.cal_step.wavecorr == "SKIPPED" - outs.meta.observation.date = '2017-08-03' - outw = WavecorrStep.call(outs) - # Primary name not set - assert out.meta.cal_step.wavecorr == "SKIPPED" + # wcs and other metadata necessary for a good (not skipped) result + dither = {'x_offset': 0.0, 'y_offset': 0.0} + outa.meta.dither = dither + outa.meta.wcsinfo.v3yangle = 138.78 + outa.meta.wcsinfo.vparity = -1 + outa.meta.wcsinfo.v2_ref = 321.87 + outa.meta.wcsinfo.v3_ref = -477.94 + outa.meta.wcsinfo.roll_ref = 15.1234 + outa.meta.instrument.fixed_slit = "S400A1" - outs.meta.instrument.fixed_slit = "S400A1" + oute = Extract2dStep.call(outa) - # Test step is skipped if meta.dither is not populated - outw = WavecorrStep.call(outs) - assert out.meta.cal_step.wavecorr == "SKIPPED" + # ensure the source position is set as expected by extract2d + ind = np.nonzero([s.name == 'S400A1' for s in oute.slits])[0].item() + source_pos = (0.004938526981283373, -0.02795306204991911) + assert_allclose((oute.slits[ind].source_xpos, oute.slits[ind].source_ypos),source_pos) - dither = {'x_offset': 0.0, 'y_offset': 0.0} - ind = np.nonzero([s.name == 'S400A1' for s in outs.slits])[0].item() - outs.slits[ind].meta.dither = dither - outs.slits[ind].meta.wcsinfo.v3yangle = 138.78 - outs.slits[ind].meta.wcsinfo.vparity = -1 - outs.slits[ind].meta.wcsinfo.v2_ref = 321.87 - outs.slits[ind].meta.wcsinfo.v3_ref = -477.94 - outs.slits[ind].meta.wcsinfo.roll_ref = 15.1234 - - # Test step is skipped if source is "EXTENDED" - outw = WavecorrStep.call(outs) - assert out.meta.cal_step.wavecorr == "SKIPPED" + outs = SourceTypeStep.call(oute) + # primary slit is found by source type step to be EXTENDED + # which means its source position is reset to (0, 0) + # hack it here to be a point source with the same offset as before here outs.slits[ind].source_type = 'POINT' + outs.slits[ind].source_xpos = source_pos[0] + outs.slits[ind].source_ypos = source_pos[1] + + # Test step is skipped if no coverage in CRDS + outs.meta.observation.date = '2001-08-03' outw = WavecorrStep.call(outs) + assert outw.meta.cal_step.wavecorr == "SKIPPED" + outs.meta.observation.date = '2017-08-03' - source_pos = (0.004938526981283373, -0.02795306204991911) - assert_allclose((outw.slits[ind].source_xpos, outw.slits[ind].source_ypos),source_pos) + # Run the step for real + outw = WavecorrStep.call(outs) # Test if the corrected wavelengths are not monotonically increasing @@ -249,8 +247,6 @@ def test_wavecorr_fs(): assert_allclose(result.slits[0].source_xpos, 0.127111, atol=1e-6) slit = result.slits[0] - source_xpos = wavecorr.get_source_xpos(slit) - assert_allclose(result.slits[0].source_xpos, source_xpos, atol=1e-6) mean_correction = np.abs(src_result.slits[0].wavelength - result.slits[0].wavelength) assert_allclose(np.nanmean(mean_correction), 0.003, atol=.001) diff --git a/jwst/wavecorr/wavecorr.py b/jwst/wavecorr/wavecorr.py index d514355397..d84071a13c 100644 --- a/jwst/wavecorr/wavecorr.py +++ b/jwst/wavecorr/wavecorr.py @@ -17,7 +17,7 @@ FS data: ``input_model.meta.dither.x_offset``, populated by SDP, is used. The value is in the telescope Ideal frame. To calculate the slit position, -it is transformed Ideal --> V2, V3 --> slit_frame. +it is transformed Ideal --> V2, V3 --> slit_frame in the extract_2d step. The interpolation of the lookup table uses the absolute fractional offset in x. This can be computed in two ways. The above transform gives the absolute @@ -36,7 +36,6 @@ from astropy.modeling.mappings import Identity from stdatamodels.jwst import datamodels -from stdatamodels.jwst.transforms import models as trmodels log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -69,63 +68,24 @@ def do_correction(input_model, wavecorr_file): if _is_point_source(input_model, exp_type): apply_zero_point_correction(output_model, wavecorr_file) else: - # For FS only work on point source slits with - # position information corrected = False - if exp_type == 'NRS_FIXEDSLIT': - primary_slit = input_model.meta.instrument.fixed_slit - for slit in output_model.slits: - if _is_point_source(slit, exp_type): - # If fixed slit was not defined via MSA file, - # it must have dither information to find the - # source position, and it must be the primary slit - if not _is_msa_fixed_slit(slit): - if slit.name != primary_slit: - log.warning(f'Skipping wavecorr correction for ' - f'non-primary slit {slit.name}') - continue - if not hasattr(slit.meta, "dither"): - log.warning('meta.dither is not populated for the primary slit') - log.warning('Skipping wavecorr correction') - continue - if slit.meta.dither.x_offset is None or slit.meta.dither.y_offset is None: - log.warning('dither.x(y)_offset values are None for primary slit') - log.warning('Skipping wavecorr correction') - input_model.meta.cal_step.wavecorr = 'SKIPPED' - continue - completed = apply_zero_point_correction(slit, wavecorr_file) - if completed: - corrected = True - slit.meta.cal_step.wavecorr = 'COMPLETE' - else: # pragma: no cover - log.warning(f'Corrections are not invertible for slit {slit.name}') - log.warning('Skipping wavecorr correction') - slit.meta.cal_step.wavecorr = 'SKIPPED' - - if corrected: - output_model.meta.cal_step.wavecorr = 'COMPLETE' + for slit in output_model.slits: + if _is_point_source(slit, exp_type): + completed = apply_zero_point_correction(slit, wavecorr_file) + if completed: + corrected = True + slit.meta.cal_step.wavecorr = 'COMPLETE' + else: # pragma: no cover + log.warning(f'Corrections are not invertible for slit {slit.name}') + log.warning('Skipping wavecorr correction') + slit.meta.cal_step.wavecorr = 'SKIPPED' else: - output_model.meta.cal_step.wavecorr = 'SKIPPED' + slit.meta.cal_step.wavecorr = 'SKIPPED' - # For MOS work on all slits containing a point source + if corrected: + output_model.meta.cal_step.wavecorr = 'COMPLETE' else: - for slit in output_model.slits: - if _is_point_source(slit, exp_type): - completed = apply_zero_point_correction(slit, wavecorr_file) - if completed: - slit.meta.cal_step.wavecorr = 'COMPLETE' - corrected = True - else: # pragma: no cover - log.warning(f'Corrections are not invertible for slit {slit.name}') - log.warning('Skipping wavecorr correction') - slit.meta.cal_step.wavecorr = 'SKIPPED' - else: - slit.meta.cal_step.wavecorr = 'SKIPPED' - - if corrected: - output_model.meta.cal_step.wavecorr = 'COMPLETE' - else: - output_model.meta.cal_step.wavecorr = 'SKIPPED' + output_model.meta.cal_step.wavecorr = 'SKIPPED' return output_model @@ -147,21 +107,12 @@ def apply_zero_point_correction(slit, reffile): """ log.info(f'slit name {slit.name}') slit_wcs = slit.meta.wcs - - # Get the source position in the slit and set the aperture name + + # Retrieve the source position and aperture name from metadata + source_xpos = slit.source_xpos if slit.meta.exposure.type in ['NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ']: - # Check for fixed slits defined via MSA files in - # MOS/FS combination processing: they should not have their - # source position overridden by dither keywords - if _is_msa_fixed_slit(slit): - # get the planned source position - source_xpos = slit.source_xpos - else: - # get the source position from the dither offsets - source_xpos = get_source_xpos(slit) aperture_name = slit.name else: - source_xpos = slit.source_xpos # For the MSA the aperture name is "MOS" aperture_name = "MOS" @@ -321,25 +272,6 @@ def compute_wavelength(wcs, xpix=None, ypix=None): return lam -def _is_msa_fixed_slit(slit): - """ - Determine if a fixed slit source was defined via a MSA file. - - Parameters - ---------- - slit : `~stdatamodels.jwst.transforms.models.Slit` - A slit object. - """ - # Fixed slits defined via MSA files in MOS/FS combination - # processing will have a non-empty source name - if (not hasattr(slit, 'source_name') - or slit.source_name is None - or slit.source_name == ""): - return False - else: - return True - - def _is_point_source(slit, exp_type): """ Determine if a source is a point source. @@ -372,42 +304,3 @@ def _is_point_source(slit, exp_type): log.info("Unknown source type") return result - - -def get_source_xpos(slit): - """ - Compute the source position within the slit for a NIRSpec fixed slit. - - Parameters - ---------- - slit : `~jwst.datamodels.SlitModel` - The slit object. - - Returns - ------- - xpos : float - X coordinate of the source as a fraction of the slit size. - """ - xoffset = slit.meta.dither.x_offset # in arcsec - yoffset = slit.meta.dither.y_offset # in arcsec - v2ref = slit.meta.wcsinfo.v2_ref # in arcsec - v3ref = slit.meta.wcsinfo.v3_ref # in arcsec - v3idlyangle = slit.meta.wcsinfo.v3yangle # in deg - vparity = slit.meta.wcsinfo.vparity - - idl2v23 = trmodels.IdealToV2V3(v3idlyangle, v2ref, v3ref, vparity) - log.debug("wcsinfo: {0}, {1}, {2}, {3}".format(v2ref, v3ref, v3idlyangle, vparity)) - # Compute the location in V2,V3 [in arcsec] - xv, yv = idl2v23(xoffset, yoffset) - log.info(f'xoffset, yoffset, {xoffset}, {yoffset}') - - # Position in the virtual slit - xpos_slit, ypos_slit, lam_slit = slit.meta.wcs.get_transform('v2v3', 'slit_frame')( - xv, yv, 2) - # Update slit.source_xpos, slit.source_ypos - slit.source_xpos = xpos_slit - slit.source_ypos = ypos_slit - log.debug('Source X/Y position in V2V3: {0}, {1}'.format(xv, yv)) - log.info('Source X/Y position in the slit: {0}, {1}'.format(xpos_slit, ypos_slit)) - - return xpos_slit diff --git a/jwst/wavecorr/wavecorr_step.py b/jwst/wavecorr/wavecorr_step.py index 6caa9432c3..53a6f0dd6e 100755 --- a/jwst/wavecorr/wavecorr_step.py +++ b/jwst/wavecorr/wavecorr_step.py @@ -43,13 +43,11 @@ def process(self, step_input): # Check for existence of WCS if isinstance(input_model, datamodels.SlitModel): - if not (hasattr(input_model.meta, 'wcs') and input_model.meta.wcs is not None): - raise AttributeError("Input model does not have a WCS object; assign_wcs should " - "be run before wavecorr.") + _check_slit_metadata_attributes(input_model) + elif isinstance(input_model, datamodels.MultiSlitModel): + _check_slit_metadata_attributes(input_model.slits[0]) else: - if not (hasattr(input_model.slits[0].meta, 'wcs') and input_model.slits[0].meta.wcs is not None): - raise AttributeError("Input model does not have a WCS object; assign_wcs should " - "be run before wavecorr.") + raise ValueError(f"Input model must be a SlitModel or MultiSlitModel, not {type(input_model)}") # Get the reference file reffile = self.get_reference_file(input_model, 'wavecorr') @@ -64,3 +62,14 @@ def process(self, step_input): output_model = wavecorr.do_correction(input_model, reffile) return output_model + + +def _check_slit_metadata_attributes(slit): + + if not hasattr(slit.meta, 'wcs') and slit.meta.wcs is not None: + raise AttributeError("Input model does not have a WCS object; assign_wcs should " + "be run before wavecorr.") + + if not hasattr(slit, 'source_xpos'): + raise AttributeError("Input model does not have source_xpos attribute; " + "extract_2d should be run before wavecorr.") \ No newline at end of file From 1bd2bc6fdbc1b6f22078540687bc6c8296f5fe67 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 26 Jun 2024 08:02:37 -0400 Subject: [PATCH 131/687] Revert "remove unused nlow nhigh parameters from outlier detection (#8595)" This reverts commit 801d24a74a0f479ac0603ebff67df7639f094e29. --- docs/jwst/outlier_detection/arguments.rst | 8 ++++++++ docs/jwst/outlier_detection/outlier_detection_imaging.rst | 2 ++ docs/jwst/outlier_detection/outlier_detection_tso.rst | 4 +++- jwst/outlier_detection/outlier_detection_step.py | 4 ++++ 4 files changed, 17 insertions(+), 1 deletion(-) diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index 7e86003214..a2900bcbd6 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -24,6 +24,14 @@ that control the behavior of the processing: Any floating-point value, given as a string, is valid. A value of 'INDEF' will use the last zero weight flux. +``--nlow`` (integer, default=0) + The number of low values in each pixel stack to ignore + when computing the median value. + +``--nhigh`` (integer, default=0) + The number of high values in each pixel stack to ignore + when computing the median value. + ``--maskpt`` (float, default=0.7) The percent of maximum weight to use as lower-limit for valid data; valid values go from 0.0 to 1.0. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index d1b86b28f8..c4cf5eceba 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -69,6 +69,8 @@ Specifically, this routine performs the following operations: * The median image is created by combining all grouped mosaic images or non-resampled input data (as planes in a ModelContainer) pixel-by-pixel. + * The ``nlow`` and ``nhigh`` parameters specify how many low and high values + to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when resampled. diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst index 0df852ea00..2f74611e32 100644 --- a/docs/jwst/outlier_detection/outlier_detection_tso.rst +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -21,6 +21,8 @@ a few variations to accomodate the nature of these 3D data. * The ``rolling_window_width`` parameter specifies the number of integrations over which to compute the median. The default is 25. If the number of integrations is less than or equal to ``rolling_window_width``, a simple median is used instead. + * The ``nlow`` and ``nhigh`` parameters specify how many low and high values + to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when the median is taken. @@ -36,4 +38,4 @@ a few variations to accomodate the nature of these 3D data. #. The input data model DQ arrays are updated with the mask of detected outliers. -.. automodapi:: jwst.outlier_detection.outlier_detection_tso +.. automodapi:: jwst.outlier_detection.outlier_detection_tso \ No newline at end of file diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 6aa786cc91..8add2f32a9 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -55,6 +55,8 @@ class OutlierDetectionStep(Step): pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel fillval = string(default='INDEF') + nlow = integer(default=0) + nhigh = integer(default=0) maskpt = float(default=0.7) snr = string(default='5.0 4.0') scale = string(default='1.2 0.7') @@ -105,6 +107,8 @@ def process(self, input_data): 'pixfrac': self.pixfrac, 'kernel': self.kernel, 'fillval': self.fillval, + 'nlow': self.nlow, + 'nhigh': self.nhigh, 'maskpt': self.maskpt, 'snr': self.snr, 'scale': self.scale, From 619cb3bbf6e6dd122ad807cf73705c5c81c49c06 Mon Sep 17 00:00:00 2001 From: Maria Date: Wed, 26 Jun 2024 11:01:10 -0400 Subject: [PATCH 132/687] JP-3592: Improve runtime for MIRI emicorr step (#8589) Co-authored-by: David Law Co-authored-by: Howard Bushouse --- CHANGES.rst | 10 +++ docs/jwst/emicorr/arguments.rst | 3 + jwst/emicorr/emicorr.py | 123 ++++++++++------------------- jwst/emicorr/emicorr_step.py | 5 +- jwst/emicorr/tests/test_emicorr.py | 22 +----- 5 files changed, 59 insertions(+), 104 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 326776417a..2c05d9c5f7 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -89,6 +89,16 @@ documentation - Added documentation for NIRCam GRISM time series pointing offsets. [#8449] +emicorr +-------- + +- Improved running time by introducing a new parameter, use_n_cycles, that can + be modified by the user and has a default of 3. Now the user can either provide + the number of integrations to phase or the number of cycles to calculate the + number of integrations necessary to phase. Additionally fix a bug in applying + emicorr to data with nints > 1, and improve runtime in the phase amplitude + loop. [#8589] + exp_to_source ------------- diff --git a/docs/jwst/emicorr/arguments.rst b/docs/jwst/emicorr/arguments.rst index c84a6c0617..bb0510fb14 100644 --- a/docs/jwst/emicorr/arguments.rst +++ b/docs/jwst/emicorr/arguments.rst @@ -25,3 +25,6 @@ The ``emicorr`` step has the following step-specific arguments. This is to tell the code to do correction for the frequencies in the list with a reference file created on-the-fly instead of CRDS. +``--use_n_cycles`` (integer, default=3) + Number of cycles to use to calculate the phase. To use all + integrations, set to None. diff --git a/jwst/emicorr/emicorr.py b/jwst/emicorr/emicorr.py index 16691ef16f..dd8c31897a 100644 --- a/jwst/emicorr/emicorr.py +++ b/jwst/emicorr/emicorr.py @@ -4,7 +4,7 @@ import numpy as np import logging - +from astropy.stats import sigma_clipped_stats as scs from stdatamodels.jwst import datamodels log = logging.getLogger(__name__) @@ -93,6 +93,7 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): nbins = pars['nbins'] scale_reference = pars['scale_reference'] onthefly_corr_freq = pars['onthefly_corr_freq'] + use_n_cycles = pars['use_n_cycles'] output_model = apply_emicorr(input_model, emicorr_model, onthefly_corr_freq, save_onthefly_reffile, @@ -100,7 +101,8 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): user_supplied_reffile=user_supplied_reffile, nints_to_phase=nints_to_phase, nbins_all=nbins, - scale_reference=scale_reference + scale_reference=scale_reference, + use_n_cycles=use_n_cycles ) return output_model @@ -109,7 +111,8 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): def apply_emicorr(input_model, emicorr_model, onthefly_corr_freq, save_onthefly_reffile, save_intermediate_results=False, user_supplied_reffile=None, - nints_to_phase=None, nbins_all=None, scale_reference=True): + nints_to_phase=None, nbins_all=None, scale_reference=True, + use_n_cycles=3): """ -> NOTE: This is translated from IDL code fix_miri_emi.pro @@ -166,6 +169,9 @@ def apply_emicorr(input_model, emicorr_model, scale_reference : bool If True, the reference wavelength will be scaled to the data's phase amplitude + use_n_cycles : int + Only use N cycles to calculate the phase to reduce code running time + Returns ------- output_model : JWST data model @@ -216,10 +222,6 @@ def apply_emicorr(input_model, emicorr_model, # Initialize the output model as a copy of the input output_model = input_model.copy() nints, ngroups, ny, nx = np.shape(output_model.data) - if nints_to_phase is None: - nints_to_phase = nints - elif nints_to_phase > nints: - nints_to_phase = nints # create the dictionary to store the frequencies and corresponding phase amplitudes if save_intermediate_results and save_onthefly_reffile is not None: @@ -265,6 +267,20 @@ def apply_emicorr(input_model, emicorr_model, # e.g. ((1./390.625) / 10e-6) = 256.0 pix and ((1./218.52055) / 10e-6) = 457.62287 pix period_in_pixels = (1./frequency) / 10.0e-6 + if nints_to_phase is None and use_n_cycles is None: # user wats to use all integrations + # use all integrations + nints_to_phase = nints + elif nints_to_phase is None and use_n_cycles is not None: # user wants to use nints_to_phase + # Calculate how many integrations you need to get that many cycles for + # a given frequency (rounding up to the nearest Nintegrations) + nints_to_phase = (use_n_cycles * period_in_pixels) / (frameclocks * ngroups) + nints_to_phase = int(np.ceil(nints_to_phase)) + elif nints_to_phase is not None and use_n_cycles == 3: + # user wants to use nints_to_phase because default value for use_n_cycles is 3 + # make sure to never use more integrations than data has + if nints_to_phase > nints: + nints_to_phase = nints + start_time, ref_pix_sample = 0, 3 # Need colstop for phase calculation in case of last refpixel in a row. Technically, @@ -274,7 +290,7 @@ def apply_emicorr(input_model, emicorr_model, colstop = int( xsize/4 + xstart - 1 ) log.info('doing phase calculation per integration') - for ninti in range(nints_to_phase): + for ninti in range(nints): log.debug(' Working on integration: {}'.format(ninti+1)) # Remove source signal and fixed bias from each integration ramp @@ -332,7 +348,7 @@ def apply_emicorr(input_model, emicorr_model, # point to the first pixel of the next frame (add "end-of-frame" pad) start_time += extra_rowclocks - # Convert "times" to phase each integration. Nothe times has units of + # Convert "times" to phase each integration. Note that times has units of # number of 10us from the first data pixel in this integration, so to # convert to phase, divide by the waveform *period* in float pixels phase_this_int = times_this_int / period_in_pixels @@ -365,12 +381,15 @@ def apply_emicorr(input_model, emicorr_model, # Define the binned waveform amplitude (pa = phase amplitude) pa = np.arange(nbins, dtype=float) # keep track of n per bin to check for low n - nu = np.arange(nbins) + nb_over_nbins = [nb/nbins for nb in range(nbins)] + nbp1_over_nbins = [(nb + 1)/nbins for nb in range(nbins)] + # Construct a phase map and dd map for only the nints_to_phase + phase_temp = phaseall[0:nints_to_phase,:,:,:] + dd_temp = dd_all[0:nints_to_phase,:,:,:] for nb in range(nbins): - u = np.where((phaseall > nb/nbins) & (phaseall <= (nb + 1)/nbins) & (np.isfinite(dd_all))) - nu[nb] = phaseall[u].size + u = np.where((phase_temp > nb_over_nbins[nb]) & (phase_temp <= nbp1_over_nbins[nb])) # calculate the sigma-clipped mean - dmean, _, _, _ = iter_stat_sig_clip(dd_all[u]) + dmean,_,_ = scs(dd_temp[u]) pa[nb] = dmean # amplitude in this bin pa -= np.median(pa) @@ -431,15 +450,21 @@ def apply_emicorr(input_model, emicorr_model, # Interleave (straight copy) into 4 amps noise = np.ones((nints, ngroups, ny, nx)) # same size as input data + noise_x = np.arange(nx4) * 4 for k in range(4): - noise_x = np.arange(nx4)*4 + k - noise[..., noise_x] = dd_noise + noise[:, :, :, noise_x + k] = dd_noise # Subtract EMI noise from the input data log.info('Subtracting EMI noise from data') corr_data = orig_data - noise output_model.data = corr_data + # clean up + del data + del dd_all + del times_this_int + del phaseall + if save_intermediate_results and save_onthefly_reffile is not None: if 'FAST' in readpatt: freqs_dict = {readpatt: freqs2correct} @@ -642,74 +667,6 @@ def get_frequency_info(freqs_names_vals, frequency_name): break return freq_number, phase_amplitudes - -def iter_stat_sig_clip(data, sigrej=3.0, maxiter=10): - """ Compute the mean, mediand and/or sigma of data with iterative sigma clipping. - This funtion is based on djs_iterstat.pro (authors therein) - - Parameters - ---------- - data : numpy array - - sigrej : float - Sigma for rejection - - maxiter: int - Maximum number of sigma rejection iterations - - Returns - ------- - dmean : float - Computed mean - - dmedian : float - Computed median - - dsigma : float - Computed sigma - - dmask : numpy array - Mask set to 1 for good points, and 0 for rejected points - """ - # special cases of 0 or 1 data points - ngood = np.size(data) - dmean, dmedian, dsigma, dmask = 0.0, 0.0, 0.0, np.zeros(np.shape(data)) - if ngood == 0: - log.debug('No data points for sigma clipping') - return dmean, dmedian, dsigma, dmask - elif ngood == 1: - log.debug('Only 1 data point for sigma clipping') - return dmean, dmedian, dsigma, dmask - - # Compute the mean + standard deviation of the entire data array, - # these values will be returned if there are fewer than 2 good points. - dmask = np.ones(ngood, dtype='b') + 1 - dmean = np.sum(data * dmask) / ngood - dsigma = np.sqrt(sum((data - dmean)**2) / (ngood - 1)) - iiter = 1 - - # Iteratively compute the mean + stdev, updating the sigma-rejection thresholds - # each iteration - nlast = -1 - while iiter < maxiter and nlast != ngood and ngood >= 2: - loval = dmean - sigrej * dsigma - hival = dmean + sigrej * dsigma - nlast = ngood - - dmask[data < loval] = 0 - dmask[data > hival] = 0 - ngood = sum(dmask) - - if ngood >= 2: - dmean = np.sum(data*dmask) / ngood - dsigma = np.sqrt( sum((data - dmean)**2 * dmask) / (ngood - 1) ) - dsigma = dsigma - - iiter += 1 - - return dmean, dmedian, dsigma, dmask - - def rebin(arr, newshape): """Rebin an array to a new shape. diff --git a/jwst/emicorr/emicorr_step.py b/jwst/emicorr/emicorr_step.py index 06106200de..27d4966098 100755 --- a/jwst/emicorr/emicorr_step.py +++ b/jwst/emicorr/emicorr_step.py @@ -24,11 +24,13 @@ class EmiCorrStep(Step): scale_reference = boolean(default=True) # If True, the reference wavelength will be scaled to the data's phase amplitude skip = boolean(default=True) # Skip the step onthefly_corr_freq = float_list(default=None) # Frequencies to use for correction + use_n_cycles = integer(default=3) # Use N cycles to calculate the phase, to use all integrations set to None """ reference_file_types = ['emicorr'] def process(self, input): + # Open the input data model with datamodels.open(input) as input_model: # Catch the cases to skip @@ -52,7 +54,8 @@ def process(self, input): 'nints_to_phase': self.nints_to_phase, 'nbins': self.nbins, 'scale_reference': self.scale_reference, - 'onthefly_corr_freq': self.onthefly_corr_freq + 'onthefly_corr_freq': self.onthefly_corr_freq, + 'use_n_cycles': self.use_n_cycles } # Get the reference file diff --git a/jwst/emicorr/tests/test_emicorr.py b/jwst/emicorr/tests/test_emicorr.py index 5a64f22807..f7678ea332 100644 --- a/jwst/emicorr/tests/test_emicorr.py +++ b/jwst/emicorr/tests/test_emicorr.py @@ -69,7 +69,8 @@ def test_do_correction(): 'nints_to_phase': None, 'nbins': None, 'scale_reference': True, - 'onthefly_corr_freq': None + 'onthefly_corr_freq': None, + 'use_n_cycles': None } save_onthefly_reffile = None outmdl = emicorr.do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars) @@ -127,25 +128,6 @@ def test_minmed(): assert compare_arr.all() == medimg.all() -def test_iter_stat_sig_clip(): - data = np.ones((5, 5, 5, 5)) - data[0, 0, 0, 0] = 0.55 - data[1, 1, 1, 1] = 1.11 - data[2, 2, 2, 2] = 2.22 - data[3, 3, 3, 3] = 3.33 - data[4, 4, 4, 4] = 4.44 - u = np.where(data > 1) - dmean, dmedian, dsigma, dmask = emicorr.iter_stat_sig_clip(data[u]) - - compare_mean, compare_median = 2.7750000000000004, 0.0 - compare_sigma, compare_mask = 1.326703756361177, np.ones(np.size(data), dtype='b')+1 - - assert compare_mean == dmean - assert compare_median == dmedian - assert compare_sigma == dsigma - assert compare_mask.all() == dmask.all() - - def test_rebin(): data = np.ones(10) data[1] = 0.55 From 27d92c98e192076402c324c39caaac9d67d2a03e Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:04:03 -0400 Subject: [PATCH 133/687] Added support for reuse of aperture correction objects in multiple integration data --- jwst/extract_1d/extract.py | 68 +++++++++++++++++++++++++++----------- 1 file changed, 48 insertions(+), 20 deletions(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 32442c8f83..ff64a602c6 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3736,6 +3736,13 @@ def create_extraction(extract_ref_dict, use_source_posn = False log.info(f"Setting use_source_posn to False for source type {source_type}") + # Turn off use_source_posn if working on non-primary NRS fixed slits + if is_multiple_slits: + if exp_type == 'NRS_FIXEDSLIT' and slitname != slit.meta.instrument.fixed_slit: + use_source_posn = False + log.info("Can only compute source location for primary NIRSpec slit,") + log.info("so setting use_source_posn to False") + if photom_has_been_run: pixel_solid_angle = meta_source.meta.photometry.pixelarea_steradians if pixel_solid_angle is None: @@ -3783,6 +3790,8 @@ def create_extraction(extract_ref_dict, log.info(f"Beginning loop over {shape[0]} integrations ...") integrations = range(shape[0]) + ra_last = dec_last = wl_last = None + for integ in integrations: try: ra, dec, wavelength, temp_flux, f_var_poisson, f_var_rnoise, \ @@ -3896,27 +3905,46 @@ def create_extraction(extract_ref_dict, else: wl = wavelength.min() - if isinstance(input_model, datamodels.ImageModel): - apcorr = select_apcorr(input_model)( - input_model, - apcorr_ref_model.apcorr_table, - apcorr_ref_model.sizeunit, - location=(ra, dec, wl) - ) - else: - match_kwargs = {'location': (ra, dec, wl)} - if exp_type in ['NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ']: - match_kwargs['slit'] = slitname - - apcorr = select_apcorr(input_model)( - input_model, - apcorr_ref_model.apcorr_table, - apcorr_ref_model.sizeunit, - slit_name=slitname, - **match_kwargs - ) - apcorr.apply(spec.spec_table) + # See whether we can reuse the previous aperture correction + # object. If so, just apply the pre-computed correction to + # save a ton of time. + if ra == ra_last and dec == dec_last and wl == wl_last and apcorr.tabulated_correction is not None: + # re-use the last aperture correction + apcorr.apply_tabulated_correction(spec.spec_table) + else: + if isinstance(input_model, datamodels.ImageModel): + apcorr = select_apcorr(input_model)( + input_model, + apcorr_ref_model.apcorr_table, + apcorr_ref_model.sizeunit, + location=(ra, dec, wl) + ) + else: + match_kwargs = {'location': (ra, dec, wl)} + if exp_type in ['NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ']: + match_kwargs['slit'] = slitname + + apcorr = select_apcorr(input_model)( + input_model, + apcorr_ref_model.apcorr_table, + apcorr_ref_model.sizeunit, + slit_name=slitname, + **match_kwargs + ) + # Attempt to tabulate the aperture correction for later use. + # If this fails, fall back on the old method. + try: + apcorr.tabulate_correction(spec.spec_table) + print(apcorr.tabulated_correction) + except: + apcorr.apply(spec.spec_table) + + # Save previous ra, dec, wavelength in case we can reuse + # the aperture correction object. + ra_last = ra + dec_last = dec + wl_last = wl output_model.spec.append(spec) if log_increment > 0 and (integ + 1) % log_increment == 0: From cf9ed07a46dc21d0b10848f88f50d13df14f76c1 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:04:42 -0400 Subject: [PATCH 134/687] Added methods to enable the reuse of aperature corrections --- jwst/extract_1d/apply_apcorr.py | 46 +++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index d39ba45235..79ae9a9b39 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -73,6 +73,7 @@ def __init__(self, input_model: DataModel, apcorr_table: fits.FITS_rec, sizeunit self.reference = self._reduce_reftable() self._convert_size_units() self.apcorr_func = self.approximate() + self.tabulated_correction = None def _convert_size_units(self): """If the SIZE or Radius column is in units of arcseconds, convert to pixels.""" @@ -227,6 +228,51 @@ def _approx_func(wavelength: float, size: float, pixel_phase: float) -> RectBiva def measure_phase(self): # Future method in determining pixel phase pass + def tabulate_correction(self, spec_table: fits.FITS_rec): + """Tabulate the interpolated aperture correction value. This will save time when applying it later, especially if it is to be applied to multiple integrations. Modifies self.tabulated_correction. + + Parameters + ---------- + spec_table : `~fits.FITS_rec` + Table of aperture corrections values from apcorr reference file. + + """ + + coefs = [] + for row in spec_table: + try: + correction = self.apcorr_func(row['wavelength'], row['npixels'], self.phase) + except ValueError: + correction = None # Some input wavelengths might not be supported (especially at the ends of the range) + + if correction: + coefs += [correction.item()] + else: + coefs += [1] + + self.tabulated_correction = np.asarray(coefs) + + def apply_tabulated_correction(self, spec_table: fits.FITS_rec): + """Apply self.tabulated_correction values to source-related extraction results in-place. + + Parameters + ---------- + spec_table : `~fits.FITS_rec` + Table of aperture corrections values from apcorr reference file. + + """ + + if self.tabulated_correction is None: + raise ValueError("Cannot call apply_tabulated_correction without first calling tabulate_correction") + + flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') + var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', + 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') + for col in flux_cols_to_correct: + spec_table[col] *= self.tabulated_correction + for col in var_cols_to_correct: + spec_table[col] *= self.tabulated_correction**2 + def apply(self, spec_table: fits.FITS_rec): """Apply interpolated aperture correction value to source-related extraction results in-place. From ff0b1eaa4e3291140dbfce8996d9e9f3ca269241 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:09:00 -0400 Subject: [PATCH 135/687] Added notes for changes to the aperture correction --- CHANGES.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 4f51f1ce17..3a773e9ec7 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -133,6 +133,13 @@ extract_1d all slits containing point sources are now handled consistently, whether they are marked primary or not. [#8467] +- Added functionality to the phase-based aperture correction object to support + reuse of aperture correction objects across multiple integrations [#] + +- Changed extract.py to attempt to tabulate and reuse an aperture correction + object in integrations after the first one. This can save a very large + amount of time in BOTS reductions [#] + extract_2d ---------- From 0913a1c64a9f980e21ae109ea37ad3e3bd5ca9d6 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:13:57 -0400 Subject: [PATCH 136/687] Updated github issue number --- CHANGES.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3a773e9ec7..9451eea497 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -134,11 +134,11 @@ extract_1d whether they are marked primary or not. [#8467] - Added functionality to the phase-based aperture correction object to support - reuse of aperture correction objects across multiple integrations [#] + reuse of aperture correction objects across multiple integrations [#8609] - Changed extract.py to attempt to tabulate and reuse an aperture correction object in integrations after the first one. This can save a very large - amount of time in BOTS reductions [#] + amount of time in BOTS reductions [#8609] extract_2d ---------- From 5df4b46fe06ad849522453f739ac06c6d2a034fa Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:28:27 -0400 Subject: [PATCH 137/687] Fixed bugs in the calling of the tabulated aperture correction --- jwst/extract_1d/extract.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index ff64a602c6..6e75dcd237 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3790,7 +3790,7 @@ def create_extraction(extract_ref_dict, log.info(f"Beginning loop over {shape[0]} integrations ...") integrations = range(shape[0]) - ra_last = dec_last = wl_last = None + ra_last = dec_last = wl_last = apcorr = None for integ in integrations: try: @@ -3905,10 +3905,16 @@ def create_extraction(extract_ref_dict, else: wl = wavelength.min() + apcorr_available = False + if apcorr is not None: + if hasattr(apcorr, 'tabulated_correction'): + if apcorr.tabulated_correction is not None: + apcorr_available = True + # See whether we can reuse the previous aperture correction # object. If so, just apply the pre-computed correction to # save a ton of time. - if ra == ra_last and dec == dec_last and wl == wl_last and apcorr.tabulated_correction is not None: + if ra == ra_last and dec == dec_last and wl == wl_last and apcorr_available: # re-use the last aperture correction apcorr.apply_tabulated_correction(spec.spec_table) @@ -3936,7 +3942,7 @@ def create_extraction(extract_ref_dict, # If this fails, fall back on the old method. try: apcorr.tabulate_correction(spec.spec_table) - print(apcorr.tabulated_correction) + apcorr.apply_tabulated_correction(spec.spec_table) except: apcorr.apply(spec.spec_table) From fb50e418ec867d3c6c528ab08e9be1591a361db1 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 14:33:25 -0400 Subject: [PATCH 138/687] Fixed bare except statement --- jwst/extract_1d/extract.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 6e75dcd237..1e4db226f0 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3943,7 +3943,8 @@ def create_extraction(extract_ref_dict, try: apcorr.tabulate_correction(spec.spec_table) apcorr.apply_tabulated_correction(spec.spec_table) - except: + except AttributeError: + log.info("Falling back on old .apply method of aperture correction.") apcorr.apply(spec.spec_table) # Save previous ra, dec, wavelength in case we can reuse From 90f4d57772e0a77ade2d46cd8c40fe11799395ab Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 26 Jun 2024 15:34:20 -0400 Subject: [PATCH 139/687] deprecate nlow nhigh, fix incorrect spec values in docs (#8603) Co-authored-by: Howard Bushouse --- CHANGES.rst | 4 ++ docs/jwst/outlier_detection/arguments.rst | 64 ++++++++++--------- .../outlier_detection_imaging.rst | 2 - .../outlier_detection_tso.rst | 4 +- .../outlier_detection_step.py | 4 +- 5 files changed, 40 insertions(+), 38 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 2c05d9c5f7..4b7c096009 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -253,6 +253,10 @@ outlier_detection - Fixed a bug that led to small total flux offsets between input and blotted images if the nominal and actual wcs-computed pixel areas were different. [#8553] +- Deprecate ``nlow`` and ``nhigh`` parameters, which no longer have an effect. [#8603] + +- Fix errors in documentation describing arguments. [#8603] + pathloss -------- diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index a2900bcbd6..b2acf485d4 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -1,80 +1,81 @@ .. _outlier_detection_step_args: +For more details about step arguments (including datatypes, possible values +and defaults) see :py:obj:`jwst.outlier_detection.OutlierDetectionStep.spec`. + Step Arguments for Non-IFU data =============================== The `outlier_detection` step for non-IFU data has the following optional arguments that control the behavior of the processing: -``--weight_type`` (string, default='exptime') - The type of data weighting to use during resampling; - options are 'exptime', 'error', and 'None'. +``--weight_type`` + The type of data weighting to use during resampling. -``--pixfrac`` (float, default=1.0) +``--pixfrac`` The pixel fraction used during resampling; valid values go from 0.0 to 1.0. -``--kernel`` (string, default='square') +``--kernel`` The form of the kernel function used to distribute flux onto a - resampled image. Options are 'square', 'turbo', 'point', and - 'lanczos'. + resampled image. -``--fillval`` (string, default='INDEF') +``--fillval`` The value to assign to resampled image pixels that have zero weight or do not receive any flux from any input pixels during drizzling. Any floating-point value, given as a string, is valid. A value of 'INDEF' will use the last zero weight flux. -``--nlow`` (integer, default=0) - The number of low values in each pixel stack to ignore - when computing the median value. +``--nlow`` + Deprecated and has no effect. This parameter will be removed + in a future version. -``--nhigh`` (integer, default=0) - The number of high values in each pixel stack to ignore - when computing the median value. +``--nhigh`` + Deprecated and has no effect. This parameter will be removed + in a future version. -``--maskpt`` (float, default=0.7) +``--maskpt`` The percent of maximum weight to use as lower-limit for valid data; valid values go from 0.0 to 1.0. -``--snr`` (string, default='4.0 3.0') +``--snr`` The signal-to-noise values to use for bad pixel identification. Since cosmic rays often extend across several pixels the user must specify two cut-off values for determining whether a pixel should be masked: the first for detecting the primary cosmic ray, and the second (typically lower threshold) for masking lower-level bad pixels adjacent to those found in the first pass. Valid values are a pair of - floating-point values in a single string. + floating-point values in a single string (for example "5.0 4.0"). -``--scale`` (string, default='0.5 0.4') +``--scale`` The scaling factor applied to derivative used to identify bad pixels. Since cosmic rays often extend across several pixels the user must specify two cut-off values for determining whether a pixel should be masked: the first for detecting the primary cosmic ray, and the second (typically lower threshold) for masking lower-level bad pixels adjacent to those found in the first pass. Valid values are a pair of - floating-point values in a single string. + floating-point values in a single string (for example "1.2 0.7"). -``--backg`` (float, default=0.0) +``--backg`` User-specified background value to apply to the median image. -``--rolling_window_width`` (int, default=25) +``--rolling_window_width`` Number of integrations over which to take the median when using rolling-window median for TSO observations. -``--save_intermediate_results`` (boolean, default=False) +``--save_intermediate_results`` Specifies whether or not to save any intermediate products created during step processing. -``--resample_data`` (boolean, default=True) +``--resample_data`` Specifies whether or not to resample the input images when performing outlier detection. -``--good_bits`` (string, default="~DO_NOT_USE") +``--good_bits`` The DQ bit values from the input image DQ arrays that should be considered 'good' when building the weight mask. See DQ flag :ref:`dq_parameter_specification` for details. -``--allowed_memory`` (float, default=None) +``--allowed_memory`` Specifies the fractional amount of free memory to allow when creating the resampled image. If ``None``, the environment variable ``DMODEL_ALLOWED_MEMORY`` is used. If not defined, no @@ -84,7 +85,7 @@ that control the behavior of the processing: For example, if set to ``0.5``, only resampled images that use less than half the available memory can be created. -``--in_memory`` (boolean, default=False) +``--in_memory`` Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when needed and all intermediate files are stored on disk, rather than in memory. @@ -94,19 +95,20 @@ Step Arguments for IFU data The `outlier_detection` step for IFU data has the following optional arguments that control the behavior of the processing: -``--kernel_size`` (string, default='7 7') +``--kernel_size`` The size of the kernel to use to normalize the pixel differences. The kernel size - must only contain odd values. + must only contain odd values. Valid values are a pair of ints in a single string + (for example "7 7"). -``--threshold_percent`` (float, default=99.8) +``--threshold_percent`` The threshold (in percent) of the normalized minimum pixel difference used to identify bad pixels. Pixels with a normalized minimum pixel difference above this percentage are flagged as a outlier. -``--save_intermediate_results`` (boolean, default=False) +``--save_intermediate_results`` Specifies whether or not to save any intermediate products created during step processing. -``--in_memory`` (boolean, default=False) +``--in_memory`` Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when needed and all intermediate files are stored on disk, rather than in memory. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index c4cf5eceba..d1b86b28f8 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -69,8 +69,6 @@ Specifically, this routine performs the following operations: * The median image is created by combining all grouped mosaic images or non-resampled input data (as planes in a ModelContainer) pixel-by-pixel. - * The ``nlow`` and ``nhigh`` parameters specify how many low and high values - to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when resampled. diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst index 2f74611e32..0df852ea00 100644 --- a/docs/jwst/outlier_detection/outlier_detection_tso.rst +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -21,8 +21,6 @@ a few variations to accomodate the nature of these 3D data. * The ``rolling_window_width`` parameter specifies the number of integrations over which to compute the median. The default is 25. If the number of integrations is less than or equal to ``rolling_window_width``, a simple median is used instead. - * The ``nlow`` and ``nhigh`` parameters specify how many low and high values - to ignore when computing the median for any given pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when the median is taken. @@ -38,4 +36,4 @@ a few variations to accomodate the nature of these 3D data. #. The input data model DQ arrays are updated with the mask of detected outliers. -.. automodapi:: jwst.outlier_detection.outlier_detection_tso \ No newline at end of file +.. automodapi:: jwst.outlier_detection.outlier_detection_tso diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 8add2f32a9..cd633a42c2 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -55,8 +55,8 @@ class OutlierDetectionStep(Step): pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel fillval = string(default='INDEF') - nlow = integer(default=0) - nhigh = integer(default=0) + nlow = integer(default=0) # DEPRECATED this setting has no effect and will be removed + nhigh = integer(default=0) # DEPRECATED this setting has no effect and will be removed maskpt = float(default=0.7) snr = string(default='5.0 4.0') scale = string(default='1.2 0.7') From c7fe27ff390f80467e61d83fc1f2196c4c608138 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 26 Jun 2024 14:55:41 -0700 Subject: [PATCH 140/687] JP-3671: make OLS_C the default algorithm for ramp fitting (#8607) --- CHANGES.rst | 2 ++ docs/jwst/ramp_fitting/arguments.rst | 2 +- jwst/ramp_fitting/ramp_fit_step.py | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 4b7c096009..7ce19344dd 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -327,6 +327,8 @@ ramp_fitting fitting or "OLS_C" to use the C extension implementation of ramp fitting. [#8503] +- Made "OLS_C" the default algorithm for ramp fitting. [#8607] + refpix ------ diff --git a/docs/jwst/ramp_fitting/arguments.rst b/docs/jwst/ramp_fitting/arguments.rst index 12f646a16a..26a746a6b8 100644 --- a/docs/jwst/ramp_fitting/arguments.rst +++ b/docs/jwst/ramp_fitting/arguments.rst @@ -5,7 +5,7 @@ The ramp fitting step has the following optional arguments that can be set by th * ``--algorithm``: A string to select the desired algorithm. The available values are "OLS" to select the python implementation of the Ordinary Least Squares algorithm and "OLS_C" to select the C extension - implementation of OLS. The algorithm defaults to "OLS". + implementation of OLS. The algorithm defaults to "OLS_C". * ``--save_opt``: A True/False value that specifies whether to write the optional output product. Default is False. diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index b28339ee55..baf0e95aa8 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -385,7 +385,7 @@ class RampFitStep(Step): class_alias = "ramp_fit" spec = """ - algorithm = option('OLS', 'OLS_C', default='OLS') # Can be 'OLS_C' to select the C extension + algorithm = option('OLS', 'OLS_C', default='OLS_C') # 'OLS' and 'OLS_C' use the same underlying algorithm, but OLS_C is implemented in C int_name = string(default='') save_opt = boolean(default=False) # Save optional output opt_name = string(default='') From 3a25c2ab618171d7b0232b00d2da28bd84e62d66 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Wed, 26 Jun 2024 18:55:47 -0400 Subject: [PATCH 141/687] release metadata for B11.0rc1 (#8604) Co-authored-by: Howard Bushouse --- CHANGES.rst | 7 +++- CITATION.cff | 4 +- README.md | 1 + requirements-sdp.txt | 95 ++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 104 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 7ce19344dd..c18dce5a16 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,4 +1,9 @@ -1.14.1 (unreleased) +1.15.1 (unreleased) +=================== + +- + +1.15.0 (2024-06-26) =================== ami diff --git a/CITATION.cff b/CITATION.cff index 3bcda2d468..7f4b7b3a47 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -75,7 +75,7 @@ authors: given-names: "Maria" orcid: "https://orcid.org/0000-0003-2314-3453" title: "JWST Calibration Pipeline" -version: 1.14.0 +version: 1.15.0 doi: 10.5281/zenodo.7038885 -date-released: 2024-03-29 +date-released: 2024-06-26 url: "https://github.com/spacetelescope/jwst" diff --git a/README.md b/README.md index f62f9df79f..b57f62af8a 100644 --- a/README.md +++ b/README.md @@ -216,6 +216,7 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| +| 1.15.0 | B11.0rc1 | TBD | 1241 | 2024-06-26 | TBD | First release candidate for B11.0 | | 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | | 1.13.4 | | | 1185 | 2024-01-25 | | PyPI-only release for external users | | 1.13.3 | B10.1 | 2023.4.0 | 1181 | 2024-01-05 | | Final release candidate for B10.1 | diff --git a/requirements-sdp.txt b/requirements-sdp.txt index a110a4b011..8ce369a67c 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,3 +14,98 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt +alabaster==0.7.16 +asdf==3.2.0 +asdf-astropy==0.6.1 +asdf_coordinates_schemas==0.3.0 +asdf_standard==1.1.1 +asdf_transform_schemas==0.5.0 +asdf_wcs_schemas==0.4.0 +astropy==6.1.1 +astropy-iers-data==0.2024.6.24.0.31.11 +attrs==23.2.0 +Babel==2.15.0 +BayesicFitting==3.2.1 +certifi==2024.6.2 +charset-normalizer==3.3.2 +ci-watson==0.6.2 +colorama==0.4.6 +contourpy==1.2.1 +coverage==7.5.4 +crds==11.17.25 +cycler==0.12.1 +docutils==0.21.2 +drizzle==1.15.2 +et-xmlfile==1.1.0 +filelock==3.15.4 +fonttools==4.53.0 +future==1.0.0 +gwcs==0.21.0 +idna==3.7 +imageio==2.34.2 +imagesize==1.4.1 +importlib_metadata==8.0.0 +iniconfig==2.0.0 +Jinja2==3.1.4 +jmespath==1.0.1 +jplephem==2.22 +jsonschema==4.22.0 +jsonschema-specifications==2023.12.1 +kiwisolver==1.4.5 +lazy_loader==0.4 +lxml==5.2.2 +MarkupSafe==2.1.5 +matplotlib==3.9.0 +networkx==3.3 +numpy==1.26.4 +numpydoc==1.7.0 +opencv-python-headless==4.10.0.84 +openpyxl==3.1.4 +packaging==24.1 +Parsley==1.3 +photutils==1.12.0 +pillow==10.3.0 +pluggy==1.5.0 +poppy==1.1.1 +psutil==6.0.0 +pyerfa==2.0.1.4 +Pygments==2.18.0 +pyparsing==3.1.2 +pysiaf==0.22.0 +pytest==8.2.2 +pytest-cov==5.0.0 +pytest-doctestplus==1.2.1 +python-dateutil==2.9.0.post0 +PyYAML==6.0.1 +readchar==4.1.0 +referencing==0.35.1 +requests==2.32.3 +requests-mock==1.12.1 +rpds-py==0.18.1 +ruff==0.4.10 +scikit-image==0.24.0 +scipy==1.14.0 +semantic-version==2.10.0 +six==1.16.0 +snowballstemmer==2.2.0 +spherical_geometry==1.3.2 +Sphinx==7.3.7 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +stcal==1.7.2 +stdatamodels==2.0.0 +stpipe==0.6.0 +stsci.image==2.3.9 +stsci.imagestats==1.8.3 +stsci.stimage==0.2.9 +synphot==1.4.0 +tabulate==0.9.0 +tifffile==2024.6.18 +tweakwcs==0.8.7 +urllib3==2.2.2 +wiimatch==0.3.2 +zipp==3.19.2 From d5ec16ed9a60e76d0669dccd05bf95204538c75b Mon Sep 17 00:00:00 2001 From: zacharyburnett Date: Wed, 26 Jun 2024 19:02:12 -0400 Subject: [PATCH 142/687] clear SDP requirements --- requirements-sdp.txt | 95 -------------------------------------------- 1 file changed, 95 deletions(-) diff --git a/requirements-sdp.txt b/requirements-sdp.txt index 8ce369a67c..a110a4b011 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,98 +14,3 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt -alabaster==0.7.16 -asdf==3.2.0 -asdf-astropy==0.6.1 -asdf_coordinates_schemas==0.3.0 -asdf_standard==1.1.1 -asdf_transform_schemas==0.5.0 -asdf_wcs_schemas==0.4.0 -astropy==6.1.1 -astropy-iers-data==0.2024.6.24.0.31.11 -attrs==23.2.0 -Babel==2.15.0 -BayesicFitting==3.2.1 -certifi==2024.6.2 -charset-normalizer==3.3.2 -ci-watson==0.6.2 -colorama==0.4.6 -contourpy==1.2.1 -coverage==7.5.4 -crds==11.17.25 -cycler==0.12.1 -docutils==0.21.2 -drizzle==1.15.2 -et-xmlfile==1.1.0 -filelock==3.15.4 -fonttools==4.53.0 -future==1.0.0 -gwcs==0.21.0 -idna==3.7 -imageio==2.34.2 -imagesize==1.4.1 -importlib_metadata==8.0.0 -iniconfig==2.0.0 -Jinja2==3.1.4 -jmespath==1.0.1 -jplephem==2.22 -jsonschema==4.22.0 -jsonschema-specifications==2023.12.1 -kiwisolver==1.4.5 -lazy_loader==0.4 -lxml==5.2.2 -MarkupSafe==2.1.5 -matplotlib==3.9.0 -networkx==3.3 -numpy==1.26.4 -numpydoc==1.7.0 -opencv-python-headless==4.10.0.84 -openpyxl==3.1.4 -packaging==24.1 -Parsley==1.3 -photutils==1.12.0 -pillow==10.3.0 -pluggy==1.5.0 -poppy==1.1.1 -psutil==6.0.0 -pyerfa==2.0.1.4 -Pygments==2.18.0 -pyparsing==3.1.2 -pysiaf==0.22.0 -pytest==8.2.2 -pytest-cov==5.0.0 -pytest-doctestplus==1.2.1 -python-dateutil==2.9.0.post0 -PyYAML==6.0.1 -readchar==4.1.0 -referencing==0.35.1 -requests==2.32.3 -requests-mock==1.12.1 -rpds-py==0.18.1 -ruff==0.4.10 -scikit-image==0.24.0 -scipy==1.14.0 -semantic-version==2.10.0 -six==1.16.0 -snowballstemmer==2.2.0 -spherical_geometry==1.3.2 -Sphinx==7.3.7 -sphinxcontrib-applehelp==1.0.8 -sphinxcontrib-devhelp==1.0.6 -sphinxcontrib-htmlhelp==2.0.5 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==1.0.7 -sphinxcontrib-serializinghtml==1.1.10 -stcal==1.7.2 -stdatamodels==2.0.0 -stpipe==0.6.0 -stsci.image==2.3.9 -stsci.imagestats==1.8.3 -stsci.stimage==0.2.9 -synphot==1.4.0 -tabulate==0.9.0 -tifffile==2024.6.18 -tweakwcs==0.8.7 -urllib3==2.2.2 -wiimatch==0.3.2 -zipp==3.19.2 From 807f17f0dd4c58bc831f148ea6d045dff7b09ecf Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 14:59:04 -0400 Subject: [PATCH 143/687] fix ruff style check --- tox.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tox.ini b/tox.ini index ed50f485f5..efead36d3a 100644 --- a/tox.ini +++ b/tox.ini @@ -19,7 +19,7 @@ skip_install = true deps = ruff commands = - ruff . {posargs} + ruff check . {posargs} [testenv:check-security] description = run bandit to check security compliance From 064208c9db3c4fbb255d80dd9f206d4203a3e55f Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 15:10:15 -0400 Subject: [PATCH 144/687] add noqa for new style check failure --- jwst/lib/engdb_direct.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/lib/engdb_direct.py b/jwst/lib/engdb_direct.py index 4b6e7b47ba..39ddf977ce 100644 --- a/jwst/lib/engdb_direct.py +++ b/jwst/lib/engdb_direct.py @@ -81,7 +81,7 @@ def __init__(self, base_url=None, default_format='dict', **service_kwargs): response.raise_for_status() @property - def default_format(self): + def default_format(self): # noqa: F811 return self._default_format @default_format.setter From c869c228bd99c1588fe3b6a2c091ea6a3c19f072 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 28 Jun 2024 14:38:07 -0400 Subject: [PATCH 145/687] remove non-working or out-dated scripts --- jwst/scripts/assign_wcs.py | 50 -- jwst/scripts/coron.py | 65 --- jwst/scripts/cube_build.py | 64 --- jwst/scripts/dark_current.py | 63 --- jwst/scripts/data_generate.py | 103 ---- jwst/scripts/dqinit.py | 50 -- jwst/scripts/flatfieldcorr.py | 70 --- jwst/scripts/fringecorr.py | 18 - jwst/scripts/ipc.py | 62 --- jwst/scripts/jump.py | 64 --- jwst/scripts/linearitycorr.py | 65 --- jwst/scripts/make_header.py | 798 ----------------------------- jwst/scripts/outlier_detection.py | 60 --- jwst/scripts/persistencecorr.py | 64 --- jwst/scripts/photomcorr.py | 65 --- jwst/scripts/rampfitcorr.py | 58 --- jwst/scripts/refpix.py | 49 -- jwst/scripts/resample.py | 64 --- jwst/scripts/saturationcorr.py | 49 -- jwst/scripts/straylight.py | 63 --- jwst/scripts/superbias.py | 52 -- jwst/scripts/tests/test_scripts.py | 21 - 22 files changed, 2017 deletions(-) delete mode 100755 jwst/scripts/assign_wcs.py delete mode 100755 jwst/scripts/coron.py delete mode 100755 jwst/scripts/cube_build.py delete mode 100755 jwst/scripts/dark_current.py delete mode 100755 jwst/scripts/data_generate.py delete mode 100755 jwst/scripts/dqinit.py delete mode 100755 jwst/scripts/flatfieldcorr.py delete mode 100755 jwst/scripts/fringecorr.py delete mode 100755 jwst/scripts/ipc.py delete mode 100755 jwst/scripts/jump.py delete mode 100755 jwst/scripts/linearitycorr.py delete mode 100755 jwst/scripts/make_header.py delete mode 100755 jwst/scripts/outlier_detection.py delete mode 100755 jwst/scripts/persistencecorr.py delete mode 100755 jwst/scripts/photomcorr.py delete mode 100755 jwst/scripts/rampfitcorr.py delete mode 100755 jwst/scripts/refpix.py delete mode 100755 jwst/scripts/resample.py delete mode 100755 jwst/scripts/saturationcorr.py delete mode 100755 jwst/scripts/straylight.py delete mode 100755 jwst/scripts/superbias.py diff --git a/jwst/scripts/assign_wcs.py b/jwst/scripts/assign_wcs.py deleted file mode 100755 index ce0e131a5b..0000000000 --- a/jwst/scripts/assign_wcs.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import sys - -from stpipe import cmdline - -# LOCAL -from jwst import assign_wcs - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % assign_wcs.__version__) - sys.exit(0) - - cmdline.step_script(assign_wcs.AssignWcsStep) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/coron.py b/jwst/scripts/coron.py deleted file mode 100755 index c43ccce8f3..0000000000 --- a/jwst/scripts/coron.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import coron -from jwst.coron import klip - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % coron.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do Coronagraphic KLIP processing on Level 2b FITS files""" - ) - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 2b FITS file.""" - ) - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, help="""Output KLIP file""" - ) - - args = parser.parse_args() - - klip_a = klip.DataSet(args.infile[0], args.outfile[0]) - klip_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/cube_build.py b/jwst/scripts/cube_build.py deleted file mode 100755 index 53977ce944..0000000000 --- a/jwst/scripts/cube_build.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import cube_build - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % cube_build.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Create a Spectral Cube using Level 2a FITS files""" - ) - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 2a FITS file.""" - ) - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, help="""Output Spectral Cube file""" - ) - - args = parser.parse_args() - - sl_p = cube_build.cube_build.DataSet(args.infile[0], args.outfile[0]) - sl_p.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/dark_current.py b/jwst/scripts/dark_current.py deleted file mode 100755 index a80ea7e0c9..0000000000 --- a/jwst/scripts/dark_current.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import dark_current -from stcal.dark_current import dark_sub - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % dark_current.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser("""Do a Dark Subtraction on Level 1b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 1b FITS file.""" - ) - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, help="""Output Dark corrected file""" - ) - - args = parser.parse_args() - - dc_a = dark_sub.DataSet(args.infile[0], args.outfile[0]) - dc_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/data_generate.py b/jwst/scripts/data_generate.py deleted file mode 100755 index 26f42bd997..0000000000 --- a/jwst/scripts/data_generate.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2010-2011 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys -import warnings - -# LOCAL -from jwst import fits_generator - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % fits_generator.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Adds data to a raw FITS file to correspond to a given file type.""" - ) - parser.add_argument( - 'files', - metavar='file', - nargs='+', - help="""A list of files, containing 1 input fits file, and 0 or - more data files to pull data from. Most output file types - require at least a target and program data file.""", - ) - parser.add_argument( - '--type', - '-t', - nargs=1, - default=None, - help="""Output file type. If not provided, the result will be a level - 1B FITS file of the appropriate instrument type, determined - automatically from the input FITS file.""", - ) - parser.add_argument( - '--output', - '-o', - nargs=1, - default=None, - help="""Output filename. If not provided, a default one will be - generated based on the content.""", - ) - parser.add_argument( - '--clobber', - '-c', - action='store_true', - help='Overwrite the output file if it exists.', - ) - parser.add_argument( - '--verify', '-v', action='store_true', help='Verify the output after generation' - ) - - args = parser.parse_args() - - if args.type is not None: - filetype = args.type[0] - else: - filetype = None - - hdulist = fits_generator.generate(args.files, filetype=filetype, verify=args.verify) - filename = args.output - if filename is None: - try: - filename = fits_generator.guess_filename(hdulist) - except ValueError as e: - warnings.warn(str(e)) - filename = 'output.fits' - hdulist.writeto(filename, clobber=args.clobber, output_verify='silentfix') - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/dqinit.py b/jwst/scripts/dqinit.py deleted file mode 100755 index 9afab4f9cd..0000000000 --- a/jwst/scripts/dqinit.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2010-2011 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import sys - -# LOCAL -from jwst import dq_init -from jwst.dq_init.dq_init_step import DQInitStep -from stpipe import cmdline - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('Version %s\n' % dq_init.__version__) - sys.exit(0) - - cmdline.step_script(DQInitStep) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/flatfieldcorr.py b/jwst/scripts/flatfieldcorr.py deleted file mode 100755 index 4110a45a6a..0000000000 --- a/jwst/scripts/flatfieldcorr.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -import jwst.flatfield as ff -from jwst.flatfield import flat_field - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % ff.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do a Flat Field Correction on Level 1b FITS files""" - ) - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 1b FITS file.""" - ) - - parser.add_argument('offsets', metavar='offsets', nargs=1, help="""Offsets.""") - - parser.add_argument( - 'outfile', - metavar='outfile', - nargs=1, - help="""Output Flat Field corrected file""", - ) - - args = parser.parse_args() - - ff_a = flat_field.DataSet(args.infile[0], args.offsets[0], args.outfile[0]) - ff_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/fringecorr.py b/jwst/scripts/fringecorr.py deleted file mode 100755 index 3c10316da8..0000000000 --- a/jwst/scripts/fringecorr.py +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env python - -# ExampleStep - -# Import the custom step -from jwst.flatfield import flat_field_step - -# Import stpipe.cmdline -from stpipe import cmdline - - -def main(): - # Pass the step class to cmdline.step_script - cmdline.step_script(flat_field_step) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/ipc.py b/jwst/scripts/ipc.py deleted file mode 100755 index 1f70d7035e..0000000000 --- a/jwst/scripts/ipc.py +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import ipc - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % ipc.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser("""Do IPC correction on Level 1b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 1b FITS file.""" - ) - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, help="""Output IPC corrected file""" - ) - - args = parser.parse_args() - - ipc_a = ipc.ipc_corr.DataSet(args.infile[0], args.outfile[0]) - ipc_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/jump.py b/jwst/scripts/jump.py deleted file mode 100755 index 27d55ce58f..0000000000 --- a/jwst/scripts/jump.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import jump -from stcal.jump import twopoint_difference - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % jump.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do jump detection on Level 1b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 1b FITS file.""") - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, - help="""Output jump detected file""") - - args = parser.parse_args() - - jd_a = twopoint_difference.DataSet(args.infile[0], args.outfile[0]) - jd_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/linearitycorr.py b/jwst/scripts/linearitycorr.py deleted file mode 100755 index 8ec9f9be30..0000000000 --- a/jwst/scripts/linearitycorr.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import linearity - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % linearity.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser("""Do a Linearity Correction""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, help="""Input Level 1b FITS file.""" - ) - - parser.add_argument( - 'outfile', - metavar='outfile', - nargs=1, - help="""Output linearity corrected file""", - ) - - args = parser.parse_args() - - ff_p = linearity.linearity.DataSet(args.infile[0], args.outfile[0]) - ff_p.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/make_header.py b/jwst/scripts/make_header.py deleted file mode 100755 index e0af6b8baa..0000000000 --- a/jwst/scripts/make_header.py +++ /dev/null @@ -1,798 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2018 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -import argparse - -import re -import inspect -import numpy as np -from os import path as os_path -from asdf import ValidationError -from asdf.tags.core import ndarray - -from stdatamodels import validate -from stdatamodels import properties -from stdatamodels import schema as mschema -from stdatamodels.jwst.datamodels import (ImageModel, IFUImageModel) - -from ..pipeline import (GuiderPipeline, DarkPipeline, Ami3Pipeline, - Tso3Pipeline, Coron3Pipeline, - Detector1Pipeline, Image2Pipeline, - Image3Pipeline, Spec2Pipeline, Spec3Pipeline) - -DEBUG = 0 - -comment = re.compile(r'#.*') -continued = re.compile(r'\\\s*$') -assignment = re.compile(r'=(?!=)') - -single_quote = re.compile(r"'[^']+'") -double_quote = re.compile(r'"[^"]+"') -triple_quote = re.compile(r'""".*?"""', flags=re.DOTALL) - -relative_import = re.compile(r'\s*from\s+\.+[\.\w]*\s+import') - - -def _main(instrument, mode, level): - """ - Main procedure for make_header - """ - cls = get_class(instrument, mode) - im = cls() - - defaults = set_default_values(im, instrument, mode, level) - pipeline = choose_pipeline(defaults) - - add_metadata(im, defaults, pipeline) - fill_extensions(im, defaults) - - return im - - -def add_metadata(im, defaults, pipeline): - """ - Add metadata values to match values used in pipeline - """ - keywords = undefined_metadata(pipeline, 'meta') - for keyword in keywords: - if keyword == 'meta': - pass - elif keyword == 'meta.model_type': - pass - elif keyword == 'meta.wcs': - set_value(im, defaults, 'meta.wcsinfo') - elif not another_instrument(defaults, im, keyword): - set_value(im, defaults, keyword) - additional_metadata(im, defaults, keyword) - - -def additional_metadata(im, defaults, keyword): - """ - Add additional metadata items from the same group - """ - groups = [] - - groups.append(set(('meta.subarray.name', - 'meta.subarray.xstart', - 'meta.subarray.ystart', - 'meta.subarray.xsize', - 'meta.subarray.ysize'))) - - groups.append(set(('meta.instrument.name', - 'meta.instrument.band', - 'meta.instrument.channel', - 'meta.instrument.module', - 'meta.instrument.detector', - 'meta.instrument.filter', - 'meta.instrument.grating', - 'meta.instrument.pupil', - 'meta.exposure.readpatt'))) - - groups.append(set(('meta.asn.pool_name', - 'meta.asn.table_name'))) - - groups.append(set(('meta.target.catalog_name', - 'meta.target.ra', - 'meta.target.dec'))) - - groups.append(set(('meta.exposure.start_time', - 'meta.exposure.mid_time', - 'meta.exposure.end_time', - 'meta.exposure.exposure_time'))) - - groups.append(set(('meta.exposure.frame_divisor', - 'meta.exposure.frame_time', - 'meta.exposure.groupgap', - 'meta.exposure.group_time', - 'meta.exposure.integration_time', - 'meta.exposure.nframes', - 'meta.exposure.ngroups', - 'meta.exposure.nints', - 'meta.exposure.nresets_at_start', - 'meta.exposure.nresets_between_ints'))) - - for group in groups: - if keyword in group: - for additional_key in group: - if not another_instrument(defaults, im, additional_key): - if additional_key != keyword: - set_value(im, defaults, additional_key) - - -def another_instrument(defaults, im, keyword): - """ - Return True if metadata item is for - another instrument or exposure type - """ - modes = ((('NIRCAM',), 'NIRCAM'), - (('NIRSPEC',), 'NIRSPEC'), - (('NIRSPEC',), 'IRS2'), - (('MIRI',), 'MIRI'), - (('FGS',), 'FGS'), - (('NIRISS',), 'NIRISS'), - (('NRC_FOCUS',), 'WFSC'), - (('NRS_IFU',), 'IFU'), - (('NRS_MSASPEC',), 'MSA'), - (('MIR_MRS',), 'MRS'), - (('MIR_LRS-FIXEDSLIT', 'MIR_LRS-SLITLESS'), 'LRS'), - ) - - instrument = defaults['meta.instrument.name'] - exposure_type = defaults['meta.exposure.type'] - - schema = im._schema - for attr in keyword.split('.'): - schema = properties._get_schema_for_property(schema, attr) - if schema: - title = schema.get('title', '').upper() - for mode in modes: - (mode_name, mode_value) = mode - if not (instrument in mode_name or - exposure_type in mode_name): - if title.find(mode_value) >= 0: - return True - else: - break - - return False - - -def build_exposure_type(instrument, mode): - """ - Build the exposure type from the instrument name and mode - """ - abbreviations = {'FGS': 'FGS', 'MIRI': 'MIR', 'NIRISS': 'NIS', - 'NIRCAM': 'NRC', 'NIRSPEC': 'NRS'} - - spec_mode = {'FGS': '', 'MIRI': 'MRS', 'NIRISS': 'WFSS', - 'NIRCAM': 'WFSS', 'NIRSPEC': 'FIXEDSLIT'} - - abbrev = abbreviations[instrument] - if mode[0:4] == 'IMAG': - mode = 'IMAGE' - mode = '_'.join((abbrev, mode)) - elif mode[0:4] == 'SPEC': - mode = spec_mode[instrument] - mode = '_'.join((abbrev, mode)) - - return mode - - -def choose_pipeline(defaults): - """ - Select the pipeline class from the exposure type and level - """ - mode = {'FGS_ACQ1': 1, 'FGS_ACQ2': 1, 'FGS_DARK': 2, - 'FGS_FINEGUIDE': 1, 'FGS_FOCUS': 6, 'FGS_ID-IMAGE': 1, - 'FGS_ID-STACK': 1, 'FGS_IMAGE': 6, 'FGS_INTFLAT': 0, - 'FGS_SKYFLAT': 6, 'FGS_TRACK': 1, 'MIR_IMAGE': 6, - 'MIR_TACQ': 0, 'MIR_LYOT': 5, 'MIR_4QPM': 5, - 'MIR_LRS-FIXEDSLIT': 9, 'MIR_LRS-SLITLESS': 4, 'MIR_MRS': 9, - 'MIR_DARK': 2, 'MIR_FLAT-IMAGE': 6, 'MIR_FLATIMAGE': 6, - 'MIR_FLAT-MRS': 6, 'MIR_FLATMRS': 6, 'MIR_CORONCAL': 7, - 'NIS_AMI': 3, 'NIS_DARK': 2, 'NIS_FOCUS': 6, 'NIS_IMAGE': 6, - 'NIS_LAMP': 0, 'NIS_SOSS': 4, 'NIS_TACQ': 6, - 'NIS_TACONFIRM': 6, 'NIS_WFSS': 9, 'N/A': 6, 'ANY': 6, - 'NRC_IMAGE': 6, 'NRC_GRISM': 9, 'NRC_TACQ': 6, - 'NRC_CORON': 5, 'NRC_FOCUS': 6, 'NRC_DARK': 2, 'NRC_FLAT': 6, - 'NRC_LED': 0, 'NRC_TACONFIRM': 6, 'NRC_TSIMAGE': 4, - 'NRC_WFSS': 9, 'NRC_TSGRISM': 4, 'NRS_AUTOFLAT': 0, - 'NRS_AUTOWAVE': 0, 'NRS_BRIGHTOBJ': 4, 'NRS_CONFIRM': 0, - 'NRS_DARK': 2, 'NRS_FIXEDSLIT': 9, 'NRS_FOCUS': 0, 'NRS_IFU': 9, - 'NRS_IMAGE': 6, 'NRS_LAMP': 0, 'NRS_MIMF': 0, 'NRS_MSASPEC': 9, - 'NRS_MSATA': 0, 'NRS_TACONFIRM': 0, 'NRS_TACQ': 0, 'NRS_TASLIT': 9, - 'NRS_WATA': 0} - - pipelines = [None, GuiderPipeline, DarkPipeline, - Ami3Pipeline, Tso3Pipeline, Coron3Pipeline, - Detector1Pipeline, Image2Pipeline, Image3Pipeline, - Detector1Pipeline, Spec2Pipeline, Spec3Pipeline] - - exposure_type = defaults['meta.exposure.type'] - index = mode[exposure_type] - if index == 0: - raise ValueError("Pipeline not defined for mode " + - exposure_type) - if index > 5: - level = defaults['meta.pipeline_level'] - index += level - 1 - - return pipelines[index] - - -def drop_prefix(string, name): - """ - Drop the model name preceding the metadata keyword - """ - parts = name.split('.') - for i, part in enumerate(parts): - if part == string: - return '.'.join(parts[i:]) - return '' - - -def drop_suffix(name): - """ - Drop a method name from the keyword name - """ - parts = name.split('.') - while parts: - if parts[-1][0] != '_' and parts[-1][-1] != '(': - break - parts.pop() - - return '.'.join(parts) - - -def fill_extensions(im, defaults): - """ - Set image extensions to their default values - """ - def fill_hdu(subschema, path, combiner, shape, recurse): - if 'fits_hdu' not in subschema: - return - - dtype = subschema.get('datatype') - if dtype is None: - return - - ndim = subschema.get('ndim') - if ndim and ndim != len(shape): - return - - dtype = ndarray.asdf_datatype_to_numpy_dtype(dtype) - - keyword = '.'.join(path) - default = subschema.get('default', 0.0) - im[keyword] = np.full(shape, default, dtype=dtype) - - shape = get_shape(im, defaults) - mschema.walk_schema(im._schema, fill_hdu, shape) - - -def get_class(instrument, mode): - """ - Get the model class from the exposure type - """ - instrument = instrument.upper() - mode = mode.upper() - exposure_type = build_exposure_type(instrument, mode) - - if exposure_type in ('NRS_IFU', 'MIR_MRS'): - cls = IFUImageModel - else: - cls = ImageModel - return cls - - -def get_package(obj): - """ - Get the package name associated with an object - """ - if inspect.isclass(obj): - path = obj.__module__ - elif inspect.isfunction(obj): - path = obj.__module__ - elif inspect.ismodule(obj): - path = obj.__name__ - else: - path = '' - path = path.split('.') - - # Do not pop the module name if the source - # comes from an `_init_.py` file. - source_file = inspect.getfile(obj) - if os_path.basename(source_file) != '__init__.py': - path.pop() - - return '.'.join(path) - - -def get_shape(im, defaults): - """ - Get the shape of the image - """ - shape = im.shape - if not shape or shape[0] == 0: - xsize = defaults['meta.subarray.xsize'] - ysize = defaults['meta.subarray.ysize'] - shape = (xsize, ysize) - - return shape - - -def get_subschema(im, keyword): - """ - Get the subschema for a keyword or None if not found - """ - schema = im._schema - for attr in keyword.split('.'): - schema = properties._get_schema_for_property(schema, attr) - if schema is None: - break - return schema - - -def import_objects(package, line): - """ - Run an import command and save any objects generated by it - """ - line = line.lstrip() - local_variables = {} - global_variables = {'__package__': package} - try: - exec(line, global_variables, local_variables) # nosec - except ImportError: - pass - - objects = [] - for value in local_variables.values(): - if (inspect.isclass(value) or inspect.isfunction(value) or - inspect.ismodule(value)): - objects.append(value) - return objects - - -def log_file(filename): - """ - Log files checked to a log file - """ - if DEBUG: - path = filename.split('/') - try: - jpos = path.index('jwst') - except ValueError: - jpos = 0 - filename = '/'.join(path[jpos:]) - print("Searching " + '/'.join(path[jpos:])) - - -def log_results(title, strings): - """ - Write intermediate results to a log file - """ - if DEBUG: - print("\n=== %s ===\n" % title) - for s in strings: - print(s) - - -def next_line(fd): - """ - Read the next line from a file, including any continuations - """ - qcount = 0 - pcount = 0 - more = True - long_line = '' - - while more: - more = False - line = fd.readline() - if line: - qcount += line.count('"""') - qcount = qcount % 2 - if qcount > 0: - more = True - - else: - line = unquote(line) - if continued.search(line): - more = True - - pcount += line.count('(') - pcount -= line.count(')') - if pcount > 0: - more = True - - long_line += line - - return unquote(long_line) - - -def search_file(source_file, package, string, seen): - """ - Search a file for lines containing a string - """ - matches = [] - with open(source_file, 'r') as fd: - while True: - line = next_line(fd) - if not line: - break - - import_match = relative_import.match(line) - if import_match: - for obj in import_objects(package, line): - new_matches = search_source_lines(obj, string, seen) - matches.extend(new_matches) - elif line.find(string) >= 0: - matches.append(line) - return matches - - -def search_source_lines(obj, string, seen): - """ - Search all files contained in a pipeline for a string - """ - matches = [] - package = get_package(obj) - if package: - source_file = inspect.getfile(obj) - if source_file not in seen: - seen.add(source_file) - if source_file.endswith('.py'): - log_file(source_file) - new_matches = search_file(source_file, package, - string, seen) - matches.extend(new_matches) - return matches - - -def set_default_values(im, instrument, mode, level): - """ - Set the default values used to set header keywords - """ - defaults = {} - set_standard_defaults(im, defaults, instrument, mode, level) - set_pipeline_defaults(im, defaults) - set_wcs_defaults(im, defaults) - return defaults - - -def set_pipeline_defaults(im, defaults): - """ - Set header keyword defaults associated with a pipeline - """ - generic_defaults = {'meta.telescope': 'JWST', - 'meta.subarray.name': 'FULL', - 'meta.subarray.xstart': 1, - 'meta.subarray.ystart': 1, - 'meta.target.catalog_name': 'SMC', - 'meta.target.ra': 5.3196, - 'meta.target.dec': -72.98605, - 'meta.target.type': 'FIXED', - 'meta.target.source_type': 'EXTENDED' - } - - nircam_defaults = {'meta.subarray.xsize': 2048, - 'meta.subarray.ysize': 2048, - 'meta.subarray.fastaxis': 1, - 'meta.subarray.slowaxis': -2, - 'meta.instrument.channel': 'SHORT', - 'meta.instrument.module': 'B', - 'meta.instrument.detector': 'NRCB1', - 'meta.instrument.filter': 'F115W', - 'meta.instrument.pupil': 'CLEAR' - } - - nirspec_defaults = {'meta.subarray.xsize': 2048, - 'meta.subarray.ysize': 2048, - 'meta.subarray.fastaxis': 1, - 'meta.subarray.slowaxis': 2, - 'meta.instrument.detector': 'NRS1', - 'meta.instrument.filter': 'F100LP', - 'meta.instrument.grating': 'MIRROR', - 'meta.exposure.readpatt': 'NRSRAPID' - } - - miri_defaults = {'meta.subarray.xsize': 1032, - 'meta.subarray.ysize': 1024, - 'meta.subarray.fastaxis': 1, - 'meta.subarray.slowaxis': 2, - 'meta.subarray.name': 'FULL', - 'meta.instrument.channel': '34', - 'meta.instrument.band': 'SHORT', - 'meta.instrument.detector': 'MIRIMAGE', - 'meta.instrument.filter': 'N/A', - } - - fgs_defaults = {'meta.subarray.xsize': 2048, - 'meta.subarray.ysize': 2048, - 'meta.subarray.fastaxis': 2, - 'meta.subarray.slowaxis': -1, - 'meta.instrument.detector': 'GUIDER2' - } - - niriss_defaults = {'meta.subarray.xsize': 2048, - 'meta.subarray.ysize': 2048, - 'meta.subarray.fastaxis': -2, - 'meta.subarray.slowaxis': -1, - 'meta.instrument.detector': 'NIS', - 'meta.instrument.filter': 'F380M' - } - - any_defaults = {} - - instrument_defaults = {'NIRCAM': nircam_defaults, - 'NIRSPEC': nirspec_defaults, - 'MIRI': miri_defaults, - 'FGS': fgs_defaults, - 'NIRISS': niriss_defaults, - 'ANY': any_defaults - } - - mir_slitless_defaults = {'meta.subarray.name': 'SLITLESSPRISM', - 'meta.subarray.xstart': 1, - 'meta.subarray.ystart': 321, - 'meta.subarray.xsize': 68, - 'meta.subarray.ysize': 1024 - } - - mir_fixedslit_defaults = {'meta.instrument.detector': 'MIRIFULONG'} - - mir_mrs_defaults = {'meta.instrument.detector': 'MIRIFULONG'} - - exposure_type_defaults = {'MIR_LRS-SLITLESS': mir_slitless_defaults, - 'MIR_LRS-FIXEDSLIT': mir_fixedslit_defaults, - 'MIR_MRS': mir_mrs_defaults - } - - defaults.update(generic_defaults) - instrument = defaults['meta.instrument.name'] - defaults.update(instrument_defaults[instrument]) - exposure_type = defaults['meta.exposure.type'] - exposure_deltas = exposure_type_defaults.get(exposure_type) - if exposure_deltas is not None: - defaults.update(exposure_deltas) - - shape = im.shape - if shape: - if shape[0] != 0: - defaults['meta.subarray.xsize'] = shape[0] - if shape[1] != 0: - defaults['meta.subarray.ysize'] = shape[1] - - -def set_standard_defaults(im, defaults, instrument, mode, level): - """ - Set keyword defaults from instrument, mode, and level - """ - instrument = instrument.upper() - if validate_value(im, 'meta.instrument.name', instrument): - defaults['meta.instrument.name'] = instrument - else: - raise ValueError('Unrecognized instrument name: ' + instrument) - - if defaults['meta.instrument.name'] == 'ANY': - mode = 'ANY' - else: - mode = mode.upper() - if validate_value(im, 'meta.exposure.type', mode): - defaults['meta.exposure.type'] = mode - else: - value = build_exposure_type(instrument, mode) - if not validate_value(im, 'meta.exposure.type', value): - value = None - - if value is None: - raise ValueError('Unrecognized mode: ' + mode) - else: - defaults['meta.exposure.type'] = value - - try: - value = int(level) - except ValueError: - match = re.search(r'(\d+)', level) - if match: - value = int(match.group(1)) - else: - value = 0 - if value < 1 or value > 3: - raise ValueError('Unrecognized level: ' + level) - else: - defaults['meta.pipeline_level'] = value - - -def set_value(im, defaults, keyword): - """ - Set a value for a header keyword from defaults and data type - """ - schema = get_subschema(im, keyword) - val = None - if schema: - if keyword in defaults: - val = defaults[keyword] - elif 'default' in schema: - val = schema['default'] - elif 'enum' in schema: - for choice in ('N/A', 'NONE', 'ANY'): - try: - i = schema['enum'].index(choice) - break - except ValueError: - i = 0 - val = schema['enum'][i] - elif 'type' in schema: - typ = schema['type'] - if typ == 'string': - if keyword.startswith('meta.cal_step'): - val = 'COMPLETE' - else: - val = '' - elif typ == 'number': - val = 0.0 - elif typ == 'integer': - val = 1 - elif typ == 'boolean': - val = True - elif typ == 'object': - for field in schema['properties']: - subkey = '.'.join((keyword, field)) - set_value(im, defaults, subkey) - else: - val = None - else: - val = None - - if val is not None: - im[keyword] = val - - -def set_wcs_defaults(im, defaults): - """ - Set default values for the wcs header keywords - """ - wcs_defaults = { - 'crval1': 5.3196, 'crval2': -72.98605, - 'ctype1': 'RA---TAN', 'ctype2': 'DEC--TAN', - 'cunit1': 'deg', 'cunit2': 'deg', - 'cdelt1': 5.5E-06, 'cdelt2': 5.5E-06} - - shape = get_shape(im, defaults) - for i in (1, 2): - name = "crpix%d" % i - wcs_defaults[name] = 0.5 * shape[i - 1] - - for j in (1, 2): - name = "pc%d_%d" % (i, j) - if i == j: - wcs_defaults[name] = 1.0 - else: - wcs_defaults[name] = 0.0 - - for name in wcs_defaults: - wcs_name = 'meta.wcsinfo.' + name - defaults[wcs_name] = wcs_defaults[name] - - -def undefined_metadata(module, string): - """ - Find the metadata keywords that are undefined in the pipeline - """ - name_set = [] - for i in range(2): - name_set.append(set()) - - pat = re.compile(r'([\w\.]+' + string + r'[\w\.]+\(?)') - seen = set() - matched = search_source_lines(module, - '.' + string + '.', - seen) - log_results("matched lines", matched) - - for line in matched: - sides = assignment.split(line) - if len(sides) == 1: - sides.append(sides[0][:]) - sides[0] = '' - - j = 1 - for i in range(len(sides), 0, -1): - pat_match = pat.search(sides[i - 1]) - if pat_match is not None: - for name in pat_match.groups(): - if name is not None: - name = drop_suffix(name) - name_set[j].add(name) - j = 0 - - log_results("lhs metadata", name_set[0]) - log_results("rhs metadata", name_set[1]) - - undefined = set() - for name in name_set[1]: - name = drop_prefix(string, name) - if name: - undefined.add(name) - - log_results("undefined metadata", undefined) - return undefined - - -def unquote(line): - """ - Remove quoted strings and comments from a line - """ - line = triple_quote.sub('', line) - line = double_quote.sub('""', line) - line = single_quote.sub("''", line) - line = comment.sub('', line) - return line - - -def validate_value(im, name, value): - """ - Test a value against the schema to see if it is valid - """ - valid = True - schema = get_subschema(im, name) - if schema: - try: - validate._check_value(value, schema, im) - except ValidationError: - valid = False - return valid - - -def main(): - long_description = """ - Create an image the contains the header keywords needed to run - the JWST pipeline for a specified instrument, observing mode, and - pipeline level. These three are specified by the first three arguments - to the command. The fourth and final argument is the filename that the - image will be saved as. - """ - - parser = argparse.ArgumentParser(description=long_description) - parser.add_argument('instrument', help='The instrument name') - parser.add_argument('mode', help='\'image\' or \'spectrum\' or an exposure type') - parser.add_argument('level', help='The pipeline level: 1, 2, or 3') - parser.add_argument('filename', help='The output image with the headers') - args = parser.parse_args() - - im = _main(args.instrument, args.mode, args.level) - im.save(args.filename) - im.close() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/outlier_detection.py b/jwst/scripts/outlier_detection.py deleted file mode 100755 index 38d102d33e..0000000000 --- a/jwst/scripts/outlier_detection.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -from jwst import jump - -# LOCAL -from jwst.outlier_detection import outlier_detection - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % jump.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do outlier detection on Level 2b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input ASN file for Level 2b products.""") - - args = parser.parse_args() - - outlier_detection.do_detection(args.infile[0], args.outfile[0]) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/persistencecorr.py b/jwst/scripts/persistencecorr.py deleted file mode 100755 index 14b40f6557..0000000000 --- a/jwst/scripts/persistencecorr.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -import jwst.persistence as pc -from jwst.persistence import persistence - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % pc.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do a Persistence Correction""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 1b FITS file.""") - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, - help="""Output Persistence corrected file""") - - args = parser.parse_args() - - ff_p = persistence.DataSet(args.infile[0], args.outfile[0]) - ff_p.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/photomcorr.py b/jwst/scripts/photomcorr.py deleted file mode 100755 index 58bd2453c4..0000000000 --- a/jwst/scripts/photomcorr.py +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse - -# LOCAL -from jwst import photom as pc -from jwst.photom import photom - - -def main(): - import sys - - if '--version' in sys.argv: - sys.stdout.write('%s\n' % pc.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do a Photom on Level 1b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 1b FITS file.""") - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, - help="""Output Photom file""") - - args = parser.parse_args() - - ff_a = photom.DataSet(args.infile[0], args.outfile[0]) - ff_a.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/rampfitcorr.py b/jwst/scripts/rampfitcorr.py deleted file mode 100755 index e72adf2820..0000000000 --- a/jwst/scripts/rampfitcorr.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -import jwst.ramp_fitting as rf -from stcal.ramp_fitting import ramp_fit - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % rf.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do a Ramp Fitting""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 1b FITS file.""") - - args = parser.parse_args() - ramp_fit.ramp_fit(args.infile[0], ramp_fit.BUFSIZE) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/refpix.py b/jwst/scripts/refpix.py deleted file mode 100755 index a012ffb759..0000000000 --- a/jwst/scripts/refpix.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2010-2011 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -import sys - -from stpipe import cmdline - -from jwst import refpix -from jwst.refpix import refpix_step - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % refpix.__version__) - sys.exit(0) - # Pass the step class to cmdline.step_script - cmdline.step_script(refpix_step.RefPixStep) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/resample.py b/jwst/scripts/resample.py deleted file mode 100755 index d4829f062d..0000000000 --- a/jwst/scripts/resample.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - - -# LOCAL -from jwst.resample import resample -from jwst import jump - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % jump.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do image resampling on Level 2b FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 2b FITS file.""") - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, - help="""Output resampled file""") - - args = parser.parse_args() - - resample.do_drizzle(args.infile[0], args.outfile[0]) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/saturationcorr.py b/jwst/scripts/saturationcorr.py deleted file mode 100755 index 7ce52feb08..0000000000 --- a/jwst/scripts/saturationcorr.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -import sys - -from stpipe import cmdline - -import jwst.saturation as saturation -from jwst.saturation import saturation_step - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % saturation.__version__) - sys.exit(0) - # Pass the step class to cmdline.step_script - cmdline.step_script(saturation_step.SaturationStep) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/straylight.py b/jwst/scripts/straylight.py deleted file mode 100755 index 5d647da0d4..0000000000 --- a/jwst/scripts/straylight.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import argparse -import sys - -# LOCAL -from jwst import straylight - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % straylight.__version__) - sys.exit(0) - - parser = argparse.ArgumentParser( - """Do a Straylight correction on Level 2a FITS files""") - - parser.add_argument( - 'infile', metavar='infile', nargs=1, - help="""Input Level 2a FITS file.""") - - parser.add_argument( - 'outfile', metavar='outfile', nargs=1, - help="""Output straylight corrected file""") - - args = parser.parse_args() - - sl_p = straylight.straylight.DataSet(args.infile[0], args.outfile[0]) - sl_p.do_all() - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/superbias.py b/jwst/scripts/superbias.py deleted file mode 100755 index 781c848dc3..0000000000 --- a/jwst/scripts/superbias.py +++ /dev/null @@ -1,52 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2011-2012 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -# STDLIB -import sys - -from stpipe import cmdline - -# LOCAL -from jwst import superbias -from jwst.superbias import superbias_step - - -def main(): - if '--version' in sys.argv: - sys.stdout.write('%s\n' % superbias.__version__) - sys.exit(0) - - # Pass the step class to cmdline.step_script - cmdline.step_script(superbias_step.SuperBiasStep) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/tests/test_scripts.py b/jwst/scripts/tests/test_scripts.py index 9c7b2529c3..18e702b597 100644 --- a/jwst/scripts/tests/test_scripts.py +++ b/jwst/scripts/tests/test_scripts.py @@ -7,37 +7,16 @@ 'asn_edit', 'asn_gather', 'asn_make_pool', - 'assign_wcs', 'collect_pipeline_cfgs', - 'coron', 'create_data', - 'cube_build', - 'dark_current', - 'data_generate', - 'dqinit', - 'flatfieldcorr', - 'fringecorr', - 'ipc', - 'jump', - 'linearitycorr', - 'make_header', 'okify_regtests', - 'outlier_detection', - 'persistencecorr', - 'photomcorr', 'pointing_summary', - 'rampfitcorr', - 'refpix', - 'resample', - 'saturationcorr', 'schema_editor', 'schemadoc', 'set_telescope_pointing', 'set_telescope_pointing.py', 'set_velocity_aberration', 'set_velocity_aberration.py', - 'straylight', - 'superbias', 'v1_calculate', 'verify_install_requires', 'world_coords', From fd9913f0139ec9a733a18259538bf683d4777ffb Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 1 Jul 2024 17:07:52 -0400 Subject: [PATCH 146/687] update changelog --- CHANGES.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index c18dce5a16..16445f3a5b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,7 +1,11 @@ 1.15.1 (unreleased) =================== -- +scripts +------- + +- Removed many non-working and out-dated scripts. Including + many scripts that were replaced by ``strun``. [#8619] 1.15.0 (2024-06-26) =================== From 2b69c3a467a4dcf044af72ed0dc34f9d0ceb92da Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Wed, 3 Jul 2024 11:13:05 -0400 Subject: [PATCH 147/687] add `1.14.1` release to README table (#8617) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b57f62af8a..0c79d6c7a5 100644 --- a/README.md +++ b/README.md @@ -217,6 +217,7 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| | 1.15.0 | B11.0rc1 | TBD | 1241 | 2024-06-26 | TBD | First release candidate for B11.0 | +| 1.14.1 | | | 1238 | 2024-06-27 | | PyPI-only release for external users | | 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | | 1.13.4 | | | 1185 | 2024-01-25 | | PyPI-only release for external users | | 1.13.3 | B10.1 | 2023.4.0 | 1181 | 2024-01-05 | | Final release candidate for B10.1 | From d02ac2563b13fca96075d411266f6a8c62ba07ce Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 12:19:14 -0400 Subject: [PATCH 148/687] minor fixes to tso test --- .../tests/test_outlier_detection.py | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 40982cd4af..af139b5e33 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -346,18 +346,10 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): im = we_many_sci( numsci=numsci, background=bkg, sigma=sig, signal=signal, exptype=exptype, tsovisit=tsovisit ) - cube_data = np.array([i.data for i in im]) - cube_err = np.array([i.err for i in im]) - cube_dq = np.array([i.dq for i in im]) - cube_var_noise = np.array([i.var_rnoise for i in im]) - cube = datamodels.CubeModel(data=cube_data, err=cube_err, dq=cube_dq, var_noise=cube_var_noise) - - # update metadata of cube to match the first image - cube.meta = im[0].meta # Drop a weak CR on the science array cr_timestep = 5 - cube.data[cr_timestep, 12, 12] = bkg + sig * 10 + im[cr_timestep].data[12, 12] = bkg + sig * 10 # make time variability that has larger total amplitude than # the CR signal but deviations frame-by-frame are smaller @@ -366,10 +358,20 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): model.data[7, 7] += real_time_variability[i] model.err[7, 7] = np.sqrt(sig ** 2 + model.data[7, 7]) + cube_data = np.array([i.data for i in im]) + cube_err = np.array([i.err for i in im]) + cube_dq = np.array([i.dq for i in im]) + cube_var_noise = np.array([i.var_rnoise for i in im]) + cube = datamodels.CubeModel(data=cube_data, err=cube_err, dq=cube_dq, var_noise=cube_var_noise) + + # update metadata of cube to match the first image + cube.meta = im[0].meta + result = OutlierDetectionStep.call(cube, rolling_window_width=rolling_window_width) # Make sure nothing changed in SCI array - np.testing.assert_allclose(cube.data, result.data) + for i, model in enumerate(im): + np.testing.assert_allclose(model.data, result.data[i]) # Verify source is not flagged assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) From ccf871a479dad57b0a1dc235ea385ed145325d1f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 13:45:30 -0400 Subject: [PATCH 149/687] refactor msa fail/nofail and association duplicate tests to try to make them not flakey --- .../tests/test_level3_duplicate.py | 22 ++++++++--------- jwst/regtest/test_nirspec_exceptions.py | 24 +++++++++++++++---- jwst/tests/helpers.py | 22 +++++++++++++++++ 3 files changed, 52 insertions(+), 16 deletions(-) diff --git a/jwst/associations/tests/test_level3_duplicate.py b/jwst/associations/tests/test_level3_duplicate.py index 7fe76609ad..3f6e6c1274 100644 --- a/jwst/associations/tests/test_level3_duplicate.py +++ b/jwst/associations/tests/test_level3_duplicate.py @@ -11,9 +11,10 @@ from jwst.associations import (AssociationPool, generate) from jwst.associations.main import Main from jwst.associations.lib.utilities import constrain_on_candidates +from jwst.tests.helpers import LogWatcher -def test_duplicate_names(caplog): +def test_duplicate_names(monkeypatch): """ For Level 3 association, there should be no association with the same product name. @@ -22,18 +23,15 @@ def test_duplicate_names(caplog): constrain_all_candidates = constrain_on_candidates(None) rules = registry_level3_only(global_constraints=constrain_all_candidates) - caplog.clear() - logger = logging.getLogger('jwst.associations') - propagate = logger.propagate - logger.propagate = True - try: - with caplog.at_level(logging.WARNING): - asns = generate(pool, rules) - finally: - logger.propagate = propagate + # watch for an error log message, we don't use caplog here because + # something in the test suite messes up the logging during some runs + # (probably stpipe or the association generator) and causes caplog + # to sometimes miss the message + watcher = LogWatcher("Following associations have the same product name but significant differences") + monkeypatch.setattr(logging.getLogger('jwst.associations.lib.prune'), "warning", watcher) + asns = generate(pool, rules) - # There should only be one association left. - assert "Following associations have the same product name but significant differences" in caplog.text + watcher.assert_seen() def test_duplicate_generate(): diff --git a/jwst/regtest/test_nirspec_exceptions.py b/jwst/regtest/test_nirspec_exceptions.py index 73a0c70981..f28bc36761 100644 --- a/jwst/regtest/test_nirspec_exceptions.py +++ b/jwst/regtest/test_nirspec_exceptions.py @@ -1,13 +1,15 @@ +import logging import pytest import subprocess from jwst.stpipe import Step from jwst.assign_wcs.util import NoDataOnDetectorError from jwst.pipeline import Spec2Pipeline +from jwst.tests.helpers import LogWatcher @pytest.mark.bigdata -def test_nirspec_missing_msa_fail(rtdata, fitsdiff_default_kwargs, caplog): +def test_nirspec_missing_msa_fail(rtdata, fitsdiff_default_kwargs, monkeypatch): """ Test of calwebb_spec2 pipeline performed on NIRSpec MSA exposure that's missing an MSAMETFL. Exception should be raised. @@ -19,14 +21,21 @@ def test_nirspec_missing_msa_fail(rtdata, fitsdiff_default_kwargs, caplog): # Run the calwebb_spec2 pipeline args = ["calwebb_spec2", rtdata.input] + # watch for an error log message, we don't use caplog here because + # something in the test suite messes up the logging during some runs + # (probably stpipe or the association generator) and causes caplog + # to sometimes miss the message + watcher = LogWatcher('Missing MSA meta (MSAMETFL) file') + monkeypatch.setattr(logging.getLogger('jwst.assign_wcs.nirspec'), "error", watcher) + with pytest.raises(Exception): Step.from_cmdline(args) - assert 'Missing MSA meta (MSAMETFL) file' in caplog.text + watcher.assert_seen() @pytest.mark.bigdata -def test_nirspec_missing_msa_nofail(rtdata, fitsdiff_default_kwargs, caplog): +def test_nirspec_missing_msa_nofail(rtdata, fitsdiff_default_kwargs, monkeypatch): """ Test of calwebb_spec2 pipeline performed on NIRSpec MSA exposure that's missing an MSAMETFL. Exception should NOT be raised. @@ -40,9 +49,16 @@ def test_nirspec_missing_msa_nofail(rtdata, fitsdiff_default_kwargs, caplog): rtdata.input, '--fail_on_exception=False'] + # watch for an error log message, we don't use caplog here because + # something in the test suite messes up the logging during some runs + # (probably stpipe or the association generator) and causes caplog + # to sometimes miss the message + watcher = LogWatcher('Missing MSA meta (MSAMETFL) file') + monkeypatch.setattr(logging.getLogger('jwst.assign_wcs.nirspec'), "error", watcher) + Step.from_cmdline(args) - assert 'Missing MSA meta (MSAMETFL) file' in caplog.text + watcher.assert_seen() @pytest.mark.bigdata diff --git a/jwst/tests/helpers.py b/jwst/tests/helpers.py index 96eae9edf1..6cef376f6b 100644 --- a/jwst/tests/helpers.py +++ b/jwst/tests/helpers.py @@ -37,3 +37,25 @@ def word_precision_check(str1, str2, length=5): else: return True return False + + +class LogWatcher: + """ + The pytest caplog fixture only works for the root + logger. We use all sorts of loggers which can lead + to random test failures with caplog. This class + can be monkeypatched onto a logger method to + check for a specific message. + """ + def __init__(self, message): + self.seen = False + self.message = message + + def __call__(self, *args, **kwargs): + if not args or not isinstance(args[0], str): + return + if self.message in args[0]: + self.seen = True + + def assert_seen(self): + assert self.seen, f"{self.message} not in logs" From 71fd2bd356750f00296a61e5dd4cf1067e6a8d75 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 26 Jun 2024 15:33:49 -0400 Subject: [PATCH 150/687] Removed code that had been deleted in #8467 --- jwst/extract_1d/extract.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 1e4db226f0..089de5be84 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3736,12 +3736,6 @@ def create_extraction(extract_ref_dict, use_source_posn = False log.info(f"Setting use_source_posn to False for source type {source_type}") - # Turn off use_source_posn if working on non-primary NRS fixed slits - if is_multiple_slits: - if exp_type == 'NRS_FIXEDSLIT' and slitname != slit.meta.instrument.fixed_slit: - use_source_posn = False - log.info("Can only compute source location for primary NIRSpec slit,") - log.info("so setting use_source_posn to False") if photom_has_been_run: pixel_solid_angle = meta_source.meta.photometry.pixelarea_steradians From 04e575856514918a0e52764594d7a3ed7ffaca60 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 3 Jul 2024 21:16:04 -0400 Subject: [PATCH 151/687] Removed extra newline --- jwst/extract_1d/extract.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 089de5be84..1d3adcc114 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3736,7 +3736,6 @@ def create_extraction(extract_ref_dict, use_source_posn = False log.info(f"Setting use_source_posn to False for source type {source_type}") - if photom_has_been_run: pixel_solid_angle = meta_source.meta.photometry.pixelarea_steradians if pixel_solid_angle is None: From 9fd1d7902bf4157cc9021995233d7b56491b01b9 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Wed, 3 Jul 2024 21:22:29 -0400 Subject: [PATCH 152/687] Added comments and log info for both tabulated and standalone aperture corrections. --- jwst/extract_1d/extract.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 1d3adcc114..b52476aca0 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3898,6 +3898,9 @@ def create_extraction(extract_ref_dict, else: wl = wavelength.min() + # Determine whether we have a tabulated aperature correction + # available to save time. + apcorr_available = False if apcorr is not None: if hasattr(apcorr, 'tabulated_correction'): @@ -3936,8 +3939,9 @@ def create_extraction(extract_ref_dict, try: apcorr.tabulate_correction(spec.spec_table) apcorr.apply_tabulated_correction(spec.spec_table) + log.info("Tabulating aperture correction for use in multiple integrations.") except AttributeError: - log.info("Falling back on old .apply method of aperture correction.") + log.info("Computing aperture correction.") apcorr.apply(spec.spec_table) # Save previous ra, dec, wavelength in case we can reuse From 5b0ebb8c41b672428811b8b73b5c08c843265136 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:31:10 -0400 Subject: [PATCH 153/687] Update CHANGES.rst Co-authored-by: Melanie Clarke --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 1964c1e549..9c37eb5f56 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -149,7 +149,7 @@ extract_1d whether they are marked primary or not. [#8467] - Added functionality to the phase-based aperture correction object to support - reuse of aperture correction objects across multiple integrations [#8609] + reuse of aperture correction objects across multiple integrations. [#8609] - Changed extract.py to attempt to tabulate and reuse an aperture correction object in integrations after the first one. This can save a very large From 506816697ee08a17bf1eb77f9dba59ba5c74f5f0 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:31:20 -0400 Subject: [PATCH 154/687] Update CHANGES.rst Co-authored-by: Melanie Clarke --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 9c37eb5f56..af6893588c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -153,7 +153,7 @@ extract_1d - Changed extract.py to attempt to tabulate and reuse an aperture correction object in integrations after the first one. This can save a very large - amount of time in BOTS reductions [#8609] + amount of time in BOTS reductions. [#8609] extract_2d ---------- From 7d51a40f2ec161247e6e11ed86d96453216658ce Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:34:05 -0400 Subject: [PATCH 155/687] Update jwst/extract_1d/extract.py Indenting saving ra, dec, wl to apply only where wavelength is defined Co-authored-by: Melanie Clarke --- jwst/extract_1d/extract.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index b52476aca0..27464aad8c 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3944,11 +3944,11 @@ def create_extraction(extract_ref_dict, log.info("Computing aperture correction.") apcorr.apply(spec.spec_table) - # Save previous ra, dec, wavelength in case we can reuse - # the aperture correction object. - ra_last = ra - dec_last = dec - wl_last = wl + # Save previous ra, dec, wavelength in case we can reuse + # the aperture correction object. + ra_last = ra + dec_last = dec + wl_last = wl output_model.spec.append(spec) if log_increment > 0 and (integ + 1) % log_increment == 0: From b93bb281e2d9723d4ebab55a6321c95e6492d959 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:34:27 -0400 Subject: [PATCH 156/687] Update jwst/extract_1d/extract.py Co-authored-by: Melanie Clarke --- jwst/extract_1d/extract.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 27464aad8c..859b472106 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3898,7 +3898,7 @@ def create_extraction(extract_ref_dict, else: wl = wavelength.min() - # Determine whether we have a tabulated aperature correction + # Determine whether we have a tabulated aperture correction # available to save time. apcorr_available = False From 45344ddc406a9bb2ab7903b0b395a36cce436df6 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:34:36 -0400 Subject: [PATCH 157/687] Update jwst/extract_1d/apply_apcorr.py Co-authored-by: Melanie Clarke --- jwst/extract_1d/apply_apcorr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index 79ae9a9b39..3661b5652a 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -267,7 +267,7 @@ def apply_tabulated_correction(self, spec_table: fits.FITS_rec): flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', - 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') + 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') for col in flux_cols_to_correct: spec_table[col] *= self.tabulated_correction for col in var_cols_to_correct: From 9674cf542f5658213a2b35169cab9f0c42710f66 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Fri, 5 Jul 2024 14:48:24 -0400 Subject: [PATCH 158/687] Splitting error message over two lines --- jwst/extract_1d/apply_apcorr.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index 3661b5652a..507f5ab895 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -263,7 +263,8 @@ def apply_tabulated_correction(self, spec_table: fits.FITS_rec): """ if self.tabulated_correction is None: - raise ValueError("Cannot call apply_tabulated_correction without first calling tabulate_correction") + raise ValueError("Cannot call apply_tabulated_correction without first " + "calling tabulate_correction") flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', From 47c9a7f50b7ae49657b2c3a138fdd84b18fa51d4 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 5 Jul 2024 14:51:52 -0400 Subject: [PATCH 159/687] Update jwst/extract_1d/apply_apcorr.py Split docstring for tabulate_correction Co-authored-by: Melanie Clarke --- jwst/extract_1d/apply_apcorr.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index 507f5ab895..1d6eeebe20 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -229,7 +229,10 @@ def measure_phase(self): # Future method in determining pixel phase pass def tabulate_correction(self, spec_table: fits.FITS_rec): - """Tabulate the interpolated aperture correction value. This will save time when applying it later, especially if it is to be applied to multiple integrations. Modifies self.tabulated_correction. + """Tabulate the interpolated aperture correction value. + + This will save time when applying it later, especially if it is to be applied to multiple integrations. + Modifies self.tabulated_correction. Parameters ---------- From fd85c8381354b218bd672751d55eb7f1d5dc0275 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Fri, 5 Jul 2024 15:52:00 -0400 Subject: [PATCH 160/687] Refactored tabulated aperture correction into apcorr.apply() --- jwst/extract_1d/extract.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 859b472106..03b36b8968 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -3912,7 +3912,7 @@ def create_extraction(extract_ref_dict, # save a ton of time. if ra == ra_last and dec == dec_last and wl == wl_last and apcorr_available: # re-use the last aperture correction - apcorr.apply_tabulated_correction(spec.spec_table) + apcorr.apply(spec.spec_table, use_tabulated=True) else: if isinstance(input_model, datamodels.ImageModel): @@ -3938,7 +3938,7 @@ def create_extraction(extract_ref_dict, # If this fails, fall back on the old method. try: apcorr.tabulate_correction(spec.spec_table) - apcorr.apply_tabulated_correction(spec.spec_table) + apcorr.apply(spec.spec_table, use_tabulated=True) log.info("Tabulating aperture correction for use in multiple integrations.") except AttributeError: log.info("Computing aperture correction.") From 69a10bc7b6d0d625600cc9a647c63346835740a2 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Fri, 5 Jul 2024 15:52:10 -0400 Subject: [PATCH 161/687] Refactored tabulated aperture correction into apcorr.apply() --- jwst/extract_1d/apply_apcorr.py | 59 ++++++++++++++------------------- 1 file changed, 25 insertions(+), 34 deletions(-) diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index 1d6eeebe20..daf9f67588 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -255,52 +255,43 @@ def tabulate_correction(self, spec_table: fits.FITS_rec): self.tabulated_correction = np.asarray(coefs) - def apply_tabulated_correction(self, spec_table: fits.FITS_rec): - """Apply self.tabulated_correction values to source-related extraction results in-place. - - Parameters - ---------- - spec_table : `~fits.FITS_rec` - Table of aperture corrections values from apcorr reference file. - - """ - - if self.tabulated_correction is None: - raise ValueError("Cannot call apply_tabulated_correction without first " - "calling tabulate_correction") - - flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') - var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', - 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') - for col in flux_cols_to_correct: - spec_table[col] *= self.tabulated_correction - for col in var_cols_to_correct: - spec_table[col] *= self.tabulated_correction**2 - - def apply(self, spec_table: fits.FITS_rec): + def apply(self, spec_table: fits.FITS_rec, use_tabulated=False): """Apply interpolated aperture correction value to source-related extraction results in-place. Parameters ---------- spec_table : `~fits.FITS_rec` Table of aperture corrections values from apcorr reference file. + use_tabulated : bool, Optional + Use self.tabulated_correction to perform the aperture correction? + Default False (recompute correction from scratch). """ flux_cols_to_correct = ('flux', 'flux_error', 'surf_bright', 'sb_error') var_cols_to_correct = ('flux_var_poisson', 'flux_var_rnoise', 'flux_var_flat', 'sb_var_poisson', 'sb_var_rnoise', 'sb_var_flat') + + if use_tabulated: + if self.tabulated_correction is None: + raise ValueError("Cannot call apply_tabulated_correction without first " + "calling tabulate_correction") - for row in spec_table: - try: - correction = self.apcorr_func(row['wavelength'], row['npixels'], self.phase) - except ValueError: - correction = None # Some input wavelengths might not be supported (especially at the ends of the range) - - if correction: - for col in flux_cols_to_correct: - row[col] *= correction.item() - for col in var_cols_to_correct: - row[col] *= correction.item() * correction.item() + for col in flux_cols_to_correct: + spec_table[col] *= self.tabulated_correction + for col in var_cols_to_correct: + spec_table[col] *= self.tabulated_correction**2 + else: + for row in spec_table: + try: + correction = self.apcorr_func(row['wavelength'], row['npixels'], self.phase) + except ValueError: + correction = None # Some input wavelengths might not be supported (especially at the ends of the range) + + if correction: + for col in flux_cols_to_correct: + row[col] *= correction.item() + for col in var_cols_to_correct: + row[col] *= correction.item() * correction.item() class ApCorrRadial(ApCorrBase): From d4bd628cf527ae3b0bd33a191cdb778f0f532293 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Mon, 8 Jul 2024 15:26:28 -0400 Subject: [PATCH 162/687] metadata for release `1.15.1` (`B11.0rc2`) (#8629) --- CHANGES.rst | 12 +++++- CITATION.cff | 4 +- README.md | 1 + pyproject.toml | 2 +- requirements-sdp.txt | 95 ++++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 110 insertions(+), 4 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index c18dce5a16..9acb8a2de2 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,8 +1,18 @@ -1.15.1 (unreleased) +1.15.2 (unreleased) =================== - +1.15.1 (2024-07-08) +=================== + +ramp_fitting +------------ + +- Fix bugs in the C algorithm Poisson variance calculation when provided with an average dark current. [stcal#269] + +- Use the C extension with multiprocessing. [stcal#268] + 1.15.0 (2024-06-26) =================== diff --git a/CITATION.cff b/CITATION.cff index 7f4b7b3a47..350a0a0af2 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -75,7 +75,7 @@ authors: given-names: "Maria" orcid: "https://orcid.org/0000-0003-2314-3453" title: "JWST Calibration Pipeline" -version: 1.15.0 +version: 1.15.1 doi: 10.5281/zenodo.7038885 -date-released: 2024-06-26 +date-released: 2024-07-08 url: "https://github.com/spacetelescope/jwst" diff --git a/README.md b/README.md index 0c79d6c7a5..a83e87bd3a 100644 --- a/README.md +++ b/README.md @@ -216,6 +216,7 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| +| 1.15.1 | B11.0rc2 | TBD | 1242 | 2024-07-08 | TBD | Second release candidate for B11.0 | | 1.15.0 | B11.0rc1 | TBD | 1241 | 2024-06-26 | TBD | First release candidate for B11.0 | | 1.14.1 | | | 1238 | 2024-06-27 | | PyPI-only release for external users | | 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | diff --git a/pyproject.toml b/pyproject.toml index 11dcffc077..aa865e5197 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal>=1.7.1,<1.8.0", + "stcal>=1.7.3,<1.8.0", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", "stsci.image>=2.3.5", diff --git a/requirements-sdp.txt b/requirements-sdp.txt index a110a4b011..976fbe3c65 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,3 +14,98 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt +alabaster==0.7.16 +asdf==3.2.0 +asdf-astropy==0.6.1 +asdf_coordinates_schemas==0.3.0 +asdf_standard==1.1.1 +asdf_transform_schemas==0.5.0 +asdf_wcs_schemas==0.4.0 +astropy==6.1.1 +astropy-iers-data==0.2024.7.8.0.31.19 +attrs==23.2.0 +Babel==2.15.0 +BayesicFitting==3.2.1 +certifi==2024.7.4 +charset-normalizer==3.3.2 +ci-watson==0.6.2 +colorama==0.4.6 +contourpy==1.2.1 +coverage==7.5.4 +crds==11.17.25 +cycler==0.12.1 +docutils==0.21.2 +drizzle==1.15.2 +et-xmlfile==1.1.0 +filelock==3.15.4 +fonttools==4.53.1 +future==1.0.0 +gwcs==0.21.0 +idna==3.7 +imageio==2.34.2 +imagesize==1.4.1 +importlib_metadata==8.0.0 +iniconfig==2.0.0 +Jinja2==3.1.4 +jmespath==1.0.1 +jplephem==2.22 +jsonschema==4.22.0 +jsonschema-specifications==2023.12.1 +kiwisolver==1.4.5 +lazy_loader==0.4 +lxml==5.2.2 +MarkupSafe==2.1.5 +matplotlib==3.9.1 +networkx==3.3 +numpy==1.26.4 +numpydoc==1.7.0 +opencv-python-headless==4.10.0.84 +openpyxl==3.1.5 +packaging==24.1 +Parsley==1.3 +photutils==1.13.0 +pillow==10.4.0 +pluggy==1.5.0 +poppy==1.1.1 +psutil==6.0.0 +pyerfa==2.0.1.4 +Pygments==2.18.0 +pyparsing==3.1.2 +pysiaf==0.22.0 +pytest==8.2.2 +pytest-cov==5.0.0 +pytest-doctestplus==1.2.1 +python-dateutil==2.9.0.post0 +PyYAML==6.0.1 +readchar==4.1.0 +referencing==0.35.1 +requests==2.32.3 +requests-mock==1.12.1 +rpds-py==0.19.0 +ruff==0.5.1 +scikit-image==0.24.0 +scipy==1.14.0 +semantic-version==2.10.0 +six==1.16.0 +snowballstemmer==2.2.0 +spherical_geometry==1.3.2 +Sphinx==7.3.7 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +stcal==1.7.3 +stdatamodels==2.0.0 +stpipe==0.6.0 +stsci.image==2.3.9 +stsci.imagestats==1.8.3 +stsci.stimage==0.2.9 +synphot==1.4.0 +tabulate==0.9.0 +tifffile==2024.7.2 +tweakwcs==0.8.7 +urllib3==2.2.2 +wiimatch==0.3.2 +zipp==3.19.2 From 033b81756c2cc04c0a38930d1c88b3c931586bd7 Mon Sep 17 00:00:00 2001 From: zacharyburnett Date: Mon, 8 Jul 2024 15:27:07 -0400 Subject: [PATCH 163/687] clear SDP requirements after release --- requirements-sdp.txt | 95 -------------------------------------------- 1 file changed, 95 deletions(-) diff --git a/requirements-sdp.txt b/requirements-sdp.txt index 976fbe3c65..a110a4b011 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,98 +14,3 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt -alabaster==0.7.16 -asdf==3.2.0 -asdf-astropy==0.6.1 -asdf_coordinates_schemas==0.3.0 -asdf_standard==1.1.1 -asdf_transform_schemas==0.5.0 -asdf_wcs_schemas==0.4.0 -astropy==6.1.1 -astropy-iers-data==0.2024.7.8.0.31.19 -attrs==23.2.0 -Babel==2.15.0 -BayesicFitting==3.2.1 -certifi==2024.7.4 -charset-normalizer==3.3.2 -ci-watson==0.6.2 -colorama==0.4.6 -contourpy==1.2.1 -coverage==7.5.4 -crds==11.17.25 -cycler==0.12.1 -docutils==0.21.2 -drizzle==1.15.2 -et-xmlfile==1.1.0 -filelock==3.15.4 -fonttools==4.53.1 -future==1.0.0 -gwcs==0.21.0 -idna==3.7 -imageio==2.34.2 -imagesize==1.4.1 -importlib_metadata==8.0.0 -iniconfig==2.0.0 -Jinja2==3.1.4 -jmespath==1.0.1 -jplephem==2.22 -jsonschema==4.22.0 -jsonschema-specifications==2023.12.1 -kiwisolver==1.4.5 -lazy_loader==0.4 -lxml==5.2.2 -MarkupSafe==2.1.5 -matplotlib==3.9.1 -networkx==3.3 -numpy==1.26.4 -numpydoc==1.7.0 -opencv-python-headless==4.10.0.84 -openpyxl==3.1.5 -packaging==24.1 -Parsley==1.3 -photutils==1.13.0 -pillow==10.4.0 -pluggy==1.5.0 -poppy==1.1.1 -psutil==6.0.0 -pyerfa==2.0.1.4 -Pygments==2.18.0 -pyparsing==3.1.2 -pysiaf==0.22.0 -pytest==8.2.2 -pytest-cov==5.0.0 -pytest-doctestplus==1.2.1 -python-dateutil==2.9.0.post0 -PyYAML==6.0.1 -readchar==4.1.0 -referencing==0.35.1 -requests==2.32.3 -requests-mock==1.12.1 -rpds-py==0.19.0 -ruff==0.5.1 -scikit-image==0.24.0 -scipy==1.14.0 -semantic-version==2.10.0 -six==1.16.0 -snowballstemmer==2.2.0 -spherical_geometry==1.3.2 -Sphinx==7.3.7 -sphinxcontrib-applehelp==1.0.8 -sphinxcontrib-devhelp==1.0.6 -sphinxcontrib-htmlhelp==2.0.5 -sphinxcontrib-jsmath==1.0.1 -sphinxcontrib-qthelp==1.0.7 -sphinxcontrib-serializinghtml==1.1.10 -stcal==1.7.3 -stdatamodels==2.0.0 -stpipe==0.6.0 -stsci.image==2.3.9 -stsci.imagestats==1.8.3 -stsci.stimage==0.2.9 -synphot==1.4.0 -tabulate==0.9.0 -tifffile==2024.7.2 -tweakwcs==0.8.7 -urllib3==2.2.2 -wiimatch==0.3.2 -zipp==3.19.2 From 11040c877e3a4ca227352e62f08482d0a1cbc917 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 9 Jul 2024 16:24:41 -0400 Subject: [PATCH 164/687] add Brett Graham to citation (#8627) --- CITATION.cff | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CITATION.cff b/CITATION.cff index 350a0a0af2..f34b4c8c9a 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -74,6 +74,9 @@ authors: - family-names: "Pena-Guerrero" given-names: "Maria" orcid: "https://orcid.org/0000-0003-2314-3453" +- family-names: "Graham" + given-names: "Brett" + orcid: "https://orcid.org/0000-0001-6315-4507" title: "JWST Calibration Pipeline" version: 1.15.1 doi: 10.5281/zenodo.7038885 From 766070ba7e3efb7cb7cb2ce440127b180965df9e Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 10 Jul 2024 06:27:57 -0700 Subject: [PATCH 165/687] JP-3631: remove direct setting of self.skip within calibration steps (#8600) --- CHANGES.rst | 25 +++++++- jwst/cube_build/cube_build_step.py | 9 ++- .../master_background_mos_step.py | 11 ++-- .../master_background_step.py | 9 +-- jwst/master_background/nirspec_utils.py | 3 +- .../tests/test_nirspec_corrections.py | 2 +- jwst/pipeline/calwebb_spec2.py | 5 +- jwst/pipeline/calwebb_spec3.py | 3 +- jwst/pixel_replace/pixel_replace_step.py | 5 +- jwst/stpipe/__init__.py | 3 +- jwst/stpipe/core.py | 28 --------- jwst/stpipe/tests/test_utilities.py | 31 +++++++++- jwst/stpipe/utilities.py | 61 +++++++++++++++++++ jwst/tweakreg/tweakreg_step.py | 27 +++----- 14 files changed, 151 insertions(+), 71 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 9acb8a2de2..72b9f369e5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,7 +1,30 @@ 1.15.2 (unreleased) =================== -- +cube_build +---------- + +- Removed direct setting of the ``self.skip`` attribute from within the step + itself. [#8600] + +master_background +----------------- + +- Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit + as a background slit, instead of ``"bkg"`` only. [#8600] + +stpipe +------ + +- Removed setting of the `self.skip` attribute in the `record_step_status()` function; + added a `query_step_status()` function to use as an alternative to checking + `self.skip`. [#8600] + +tweakreg +-------- + +- Removed direct setting of the ``self.skip`` attribute from within the step + itself. [#8600] 1.15.1 (2024-07-08) =================== diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 15a9dde45d..7801169ba7 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -3,7 +3,7 @@ from jwst.datamodels import ModelContainer -from ..stpipe import Step +from ..stpipe import Step, record_step_status from . import cube_build from . import ifu_cube from . import data_types @@ -396,14 +396,13 @@ def process(self, input): if key in cube.meta.wcsinfo.instance: del cube.meta.wcsinfo.instance[key] if status_cube == 1: - self.skip = True + record_step_status(cube_container, "cube_build", success=False) + else: + record_step_status(cube_container, "cube_build", success=True) t1 = time.time() self.log.debug(f'Time to build all cubes {t1-t0}') - if status_cube == 1: - self.skip = True - input_table.close() return cube_container # ****************************************************************************** diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 252155a3c4..45c641ff5c 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -1,5 +1,6 @@ """Master Background Pipeline for applying Master Background to NIRSpec MOS data""" from stpipe.step import preserve_step_pars +from jwst.stpipe import record_step_status from stdatamodels.jwst import datamodels @@ -108,7 +109,7 @@ def process(self, data): if not self.force_subtract and \ 'COMPLETE' in [data_model.meta.cal_step.back_sub, data_model.meta.cal_step.master_background]: self.log.info('Background subtraction has already occurred. Skipping.') - self.record_step_status(data, 'master_background', False) + record_step_status(data, 'master_background', success=False) return data if self.user_background: @@ -123,11 +124,11 @@ def process(self, data): num_bkg, num_src = self._classify_slits(data_model) if num_bkg == 0: self.log.warning('No background slits available for creating master background. Skipping') - self.record_step_status(data, 'master_background', False) + record_step_status(data, 'master_background', False) return data elif num_src == 0: self.log.warning('No source slits for applying master background. Skipping') - self.record_step_status(data, 'master_background', False) + record_step_status(data, 'master_background', False) return data self.log.info('Calculating master background') @@ -136,14 +137,14 @@ def process(self, data): # Check that a master background was actually determined. if master_background is None: self.log.info('No master background could be calculated. Skipping.') - self.record_step_status(data, 'master_background', False) + record_step_status(data, 'master_background', False) return data # Now apply the de-calibrated background to the original science result = nirspec_utils.apply_master_background(data_model, mb_multislit, inverse=self.inverse) # Mark as completed and setup return data - self.record_step_status(result, 'master_background', True) + record_step_status(result, 'master_background', True) self.correction_pars = { 'masterbkg_1d': master_background, 'masterbkg_2d': mb_multislit diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 76efbbff64..1ea83855ee 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -4,6 +4,7 @@ from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer +from jwst.stpipe import record_step_status from ..stpipe import Step from ..combine_1d.combine1d import combine_1d_spectra @@ -63,7 +64,7 @@ def process(self, input): # First check if we should even do the subtraction. If not, bail. if not self._do_sub: result = input_data.copy() - self.record_step_status(result, 'master_background', success=False) + record_step_status(result, 'master_background', success=False) return result # Check that data is a supported datamodel. If not, bail. @@ -78,7 +79,7 @@ def process(self, input): "Input %s of type %s cannot be handled. Step skipped.", input, type(input) ) - self.record_step_status(result, 'master_background', success=False) + record_step_status(result, 'master_background', success=False) return result # If user-supplied master background, subtract it @@ -159,7 +160,7 @@ def process(self, input): "Input %s of type %s cannot be handled without user-supplied background. Step skipped.", input, type(input) ) - self.record_step_status(result, 'master_background', success=False) + record_step_status(result, 'master_background', success=False) return result # Save the computed background if requested by user @@ -167,7 +168,7 @@ def process(self, input): self.save_model(master_background, suffix='masterbg1d', force=True, asn_id=asn_id) self.save_model(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) - self.record_step_status(result, 'master_background', success=True) + record_step_status(result, 'master_background', success=True) return result diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index d29c9da26b..153bf9f504 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -254,7 +254,8 @@ def is_background_msa_slit(slit): bool True if the slit is background; False if it is not. """ - if "BKG" in str(slit.source_name).upper(): + name = str(slit.source_name).upper() + if ("BKG" in name) or ("BACKGROUND" in name): return True else: return False diff --git a/jwst/master_background/tests/test_nirspec_corrections.py b/jwst/master_background/tests/test_nirspec_corrections.py index 5eaa41fcc8..9be59fd1e4 100644 --- a/jwst/master_background/tests/test_nirspec_corrections.py +++ b/jwst/master_background/tests/test_nirspec_corrections.py @@ -59,7 +59,7 @@ def test_fs_correction(): @pytest.mark.parametrize('name,status', [('BKG101', True), ('bkg101', True), - ('background_101', False), ('101', False), + ('background_101', True), ('101', False), (None, False)]) def test_is_background(name, status): """Test check for background slit.""" diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 9f3004f4a3..d75f44dc72 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -5,6 +5,7 @@ import numpy as np from stdatamodels.jwst import datamodels +from jwst.stpipe import query_step_status from ..assign_wcs.util import NoDataOnDetectorError from ..lib.exposure_types import is_nrs_ifu_flatlamp, is_nrs_ifu_linelamp, is_nrs_slit_linelamp @@ -332,7 +333,7 @@ def process_exposure_product( # as DO_NOT_USE or NON_SCIENCE. resampled = self.pixel_replace(resampled) resampled = self.cube_build(resampled) - if not self.cube_build.skip: + if query_step_status(resampled, "cube_build") == 'COMPLETE': self.save_model(resampled[0], suffix='s3d') elif exp_type in ['MIR_LRS-SLITLESS']: resampled = calibrated.copy() @@ -346,7 +347,7 @@ def process_exposure_product( # as DO_NOT_USE or NON_SCIENCE. resampled = self.pixel_replace(resampled) # Extract a 1D spectrum from the 2D/3D data - if exp_type in ['MIR_MRS', 'NRS_IFU'] and self.cube_build.skip: + if exp_type in ['MIR_MRS', 'NRS_IFU'] and query_step_status(resampled, "cube_build") == 'SKIPPED': # Skip extract_1d for IFU modes where no cube was built self.extract_1d.skip = True diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 9357903a17..23b9902a7c 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -6,6 +6,7 @@ from stdatamodels.jwst import datamodels from jwst.datamodels import SourceModelContainer +from jwst.stpipe import query_step_status from ..associations.lib.rules_level3_base import format_product from ..exp_to_source import multislit_to_container @@ -148,7 +149,7 @@ def process(self, input): # If the step is skipped, do the container splitting that # would've been done in master_background - if self.master_background.skip: + if query_step_status(source_models, "master_background") == 'SKIPPED': source_models, bkg_models = split_container(input_models) # we don't need the background members bkg_models.close() diff --git a/jwst/pixel_replace/pixel_replace_step.py b/jwst/pixel_replace/pixel_replace_step.py index 3efd7d2318..2c9b117e10 100644 --- a/jwst/pixel_replace/pixel_replace_step.py +++ b/jwst/pixel_replace/pixel_replace_step.py @@ -2,6 +2,7 @@ from functools import partial from ..stpipe import Step +from jwst.stpipe import record_step_status from .. import datamodels from .pixel_replace import PixelReplacement @@ -107,7 +108,7 @@ def process(self, input): replacement = PixelReplacement(model, **pars) replacement.replace() output_model[i] = replacement.output - self.record_step_status(output_model[i], 'pixel_replace', success=True) + record_step_status(output_model[i], 'pixel_replace', success=True) return output_model # ________________________________________ # calewbb_spec2 case - single input model @@ -117,6 +118,6 @@ def process(self, input): result = input_model.copy() replacement = PixelReplacement(result, **pars) replacement.replace() - self.record_step_status(replacement.output, 'pixel_replace', success=True) + record_step_status(replacement.output, 'pixel_replace', success=True) result = replacement.output return result diff --git a/jwst/stpipe/__init__.py b/jwst/stpipe/__init__.py index 637a36d0a6..8e30727f86 100644 --- a/jwst/stpipe/__init__.py +++ b/jwst/stpipe/__init__.py @@ -1,4 +1,5 @@ from .core import JwstStep as Step, JwstPipeline as Pipeline +from .utilities import record_step_status, query_step_status -__all__ = ['Step', 'Pipeline'] +__all__ = ['Step', 'Pipeline', 'record_step_status', 'query_step_status'] diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 01ca6921ad..2c86df37c8 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -1,7 +1,6 @@ """ JWST-specific Step and Pipeline base classes. """ -from collections.abc import Sequence from stdatamodels.jwst.datamodels import JwstDataModel from stdatamodels.jwst import datamodels @@ -89,33 +88,6 @@ def finalize_result(self, result, reference_files_used): if self.parent is None: log.info(f"Results used CRDS context: {result.meta.ref_file.crds.context_used}") - def record_step_status(self, datamodel, cal_step, success=True): - """Record whether or not a step completed in meta.cal_step - - Parameters - ---------- - datamodel : `~jwst.datamodels.JwstDataModel` instance - This is the datamodel or container of datamodels to modify in place - - cal_step : str - The attribute in meta.cal_step for recording the status of the step - - success : bool - If True, then 'COMPLETE' is recorded. If False, then 'SKIPPED' - """ - if success: - status = 'COMPLETE' - else: - status = 'SKIPPED' - self.skip = True - - if isinstance(datamodel, Sequence): - for model in datamodel: - model.meta.cal_step._instance[cal_step] = status - else: - datamodel.meta.cal_step._instance[cal_step] = status - - # TODO: standardize cal_step naming to point to the official step name def remove_suffix(self, name): return remove_suffix(name) diff --git a/jwst/stpipe/tests/test_utilities.py b/jwst/stpipe/tests/test_utilities.py index 0f35087181..0a1d543d48 100644 --- a/jwst/stpipe/tests/test_utilities.py +++ b/jwst/stpipe/tests/test_utilities.py @@ -1,9 +1,11 @@ """Test utility funcs""" from stpipe.utilities import resolve_step_class_alias +from jwst.stpipe import record_step_status, query_step_status -from jwst.stpipe.utilities import all_steps +from jwst.stpipe.utilities import all_steps, NOT_SET import jwst.pipeline import jwst.step +from jwst import datamodels as dm # All steps available to users should be represented in one @@ -25,3 +27,30 @@ def test_resolve_step_class_alias(): if pipeline_class.class_alias is not None: assert resolve_step_class_alias(pipeline_class.class_alias) == full_class_name assert resolve_step_class_alias(full_class_name) == full_class_name + + +def test_record_query_step_status(): + + # single model case + model = dm.MultiSpecModel() + record_step_status(model, 'test_step', success=True) + assert query_step_status(model, 'test_step') == 'COMPLETE' + + # modelcontainer case where all skipped + model2 = dm.ModelContainer() + model2.append(dm.MultiSpecModel()) + model2.append(dm.MultiSpecModel()) + record_step_status(model2, 'test_step', success=False) + assert model2[0].meta.cal_step._instance['test_step'] == 'SKIPPED' + assert model2[1].meta.cal_step._instance['test_step'] == 'SKIPPED' + assert query_step_status(model2, 'test_step') == 'SKIPPED' + + # modelcontainer case with at least one complete + # currently the zeroth is checked, so this should return SKIPPED + model2.append(dm.MultiSpecModel()) + model2[2].meta.cal_step._instance['test_step'] = 'COMPLETE' + assert query_step_status(model2, 'test_step') == 'SKIPPED' + + # test query not set + model3 = dm.MultiSpecModel() + assert query_step_status(model3, 'test_step') == NOT_SET diff --git a/jwst/stpipe/utilities.py b/jwst/stpipe/utilities.py index f75af200b7..67a4769113 100644 --- a/jwst/stpipe/utilities.py +++ b/jwst/stpipe/utilities.py @@ -35,6 +35,7 @@ import logging import os import re +from collections.abc import Sequence # Configure logging logger = logging.getLogger(__name__) @@ -51,6 +52,9 @@ 'SystemCall', ] +NOT_SET = "NOT SET" +COMPLETE = "COMPLETE" +SKIPPED = "SKIPPED" def all_steps(): """List all classes subclassed from Step @@ -144,3 +148,60 @@ def folder_traverse(folder_path, basename_regex='.+', path_exclude_regex='^$'): for file in files: if basename_regex.match(file): yield os.path.join(root, file) + + +def record_step_status(datamodel, cal_step, success=True): + """Record whether or not a step completed in meta.cal_step + + Parameters + ---------- + datamodel : `~jwst.datamodels.JwstDataModel` instance + This is the datamodel or container of datamodels to modify in place + + cal_step : str + The attribute in meta.cal_step for recording the status of the step + + success : bool + If True, then 'COMPLETE' is recorded. If False, then 'SKIPPED' + """ + if success: + status = COMPLETE + else: + status = SKIPPED + + if isinstance(datamodel, Sequence): + for model in datamodel: + model.meta.cal_step._instance[cal_step] = status + else: + datamodel.meta.cal_step._instance[cal_step] = status + + # TODO: standardize cal_step naming to point to the official step name + + +def query_step_status(datamodel, cal_step): + """Query the status of a step in meta.cal_step + + Parameters + ---------- + datamodel : `~jwst.datamodels.JwstDataModel` or `~jwst.datamodels.ModelContainer` instance + The datamodel or container of datamodels to check + + cal_step : str + The attribute in meta.cal_step to check + + Returns + ------- + status : str + The status of the step in meta.cal_step, typically 'COMPLETE' or 'SKIPPED' + + Notes + ----- + In principle, a step could set the COMPLETE status for only some subset + of models, so checking the zeroth model instance may not always be correct. + However, this is not currently done in the pipeline. This function should be + updated to accommodate that use-case as needed. + """ + if isinstance(datamodel, Sequence): + return getattr(datamodel[0].meta.cal_step, cal_step, NOT_SET) + else: + return getattr(datamodel.meta.cal_step, cal_step, NOT_SET) diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index d928cb166a..f0d7d68600 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -16,6 +16,7 @@ from tweakwcs.matchutils import XYXYMatch from jwst.datamodels import ModelContainer +from jwst.stpipe import record_step_status # LOCAL from ..stpipe import Step @@ -204,10 +205,8 @@ def process(self, input): self.log.warning("At least two exposures are required for image " "alignment.") self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - self.skip = True - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - return input + record_step_status(images, "tweakreg", success=False) + return images # === start processing images === @@ -311,10 +310,8 @@ def process(self, input): self.log.warning("At least two exposures are required for " "image alignment.") self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" + record_step_status(images, "tweakreg", success=False) if not align_to_abs_refcat: - self.skip = True return images local_align_failed = True else: @@ -329,9 +326,7 @@ def process(self, input): "matched to a single reference source. Try to " "adjust 'tolerance' and/or 'separation' parameters.") self.log.warning("Skipping 'TweakRegStep'...") - self.skip = True - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" + record_step_status(images, "tweakreg", success=False) return images else: raise e @@ -341,9 +336,7 @@ def process(self, input): self.log.warning("Skipping relative alignment (stage 1)...") else: self.log.warning("Skipping 'TweakRegStep'...") - self.skip = True - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" + record_step_status(images, "tweakreg", success=False) return images if align_to_abs_refcat: @@ -461,9 +454,7 @@ def process(self, input): ) if local_align_failed or n_groups == 1: self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - self.skip = True + record_step_status(images, "tweakreg", success=False) return images else: raise e @@ -482,9 +473,7 @@ def process(self, input): ) if local_align_failed or n_groups == 1: self.log.warning("Skipping 'TweakRegStep'...") - self.skip = True - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" + record_step_status(images, "tweakreg", success=False) return images else: raise e From bc255f4cffa7a887ab3d1eed7ebfe82b856a4c67 Mon Sep 17 00:00:00 2001 From: Joseph Hunkeler Date: Wed, 10 Jul 2024 13:18:38 -0400 Subject: [PATCH 166/687] RTD: Restore tags for all branches (#8633) --- .readthedocs.yaml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 15010cb781..3d1fb60bc5 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -20,6 +20,11 @@ build: os: ubuntu-22.04 tools: python: mambaforge-4.10 + jobs: + post_checkout: + - git fetch --unshallow || true + - git config remote.origin.fetch '+refs/heads/*:refs/remotes/origin/*' || true + - git fetch --all --tags || true conda: environment: docs/rtd_environment.yaml From bc8099ef51f94e421d0f25f3ffb5929419a433ad Mon Sep 17 00:00:00 2001 From: Joseph Hunkeler Date: Fri, 12 Jul 2024 13:47:16 -0400 Subject: [PATCH 167/687] Adding version information to the landing page (#8642) --- docs/index.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index 4a1ed4350f..a27a560828 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -8,6 +8,10 @@ :width: 400 :align: center + +**Version**: |release| + + Welcome to the documentation for `jwst`. This package contains the Python software suite for the James Webb Space Telescope (JWST) calibration pipeline, which processes data from all JWST instruments by applying various corrections to From 24cbfd70cb73fd7e64672a2f66beaa835c4a81fa Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 12 Jul 2024 17:09:31 -0400 Subject: [PATCH 168/687] aligning only the first integration of the science exposure --- jwst/coron/imageregistration.py | 42 ++++++++++++++++----------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 475d91da50..b63ee9f9bb 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -216,26 +216,26 @@ def align_models(reference, target, mask): output_model = QuadModel(quad_shape) output_model.update(target) - # Loop over all integrations of the science exposure - for k in range(nrefslices): - - # Compute the shifts of the PSF ("target") images relative to - # the science ("reference") image in this integration, and apply - # the shifts to the PSF images - d, shifts = align_array( - reference.data[k].astype(np.float64), - target.data.astype(np.float64), - mask.data) - output_model.data[k] = d - - # Apply the same shifts to the PSF error arrays, if they exist - if target.err is not None: - output_model.err[k] = fourier_imshift( - target.err.astype(np.float64), - -shifts) - - # TODO: in the future we need to add shifts and other info (such as - # slice ID from the reference image to which target was aligned) - # to output cube metadata (or property). + # Do alignment with only the first integration of the science exposure + k = 0 + + # Compute the shifts of the PSF ("target") images relative to + # the science ("reference") image in this integration, and apply + # the shifts to the PSF images + d, shifts = align_array( + reference.data[k].astype(np.float64), + target.data.astype(np.float64), + mask.data) + output_model.data[k] = d + + # Apply the same shifts to the PSF error arrays, if they exist + if target.err is not None: + output_model.err[k] = fourier_imshift( + target.err.astype(np.float64), + -shifts) + + # TODO: in the future we need to add shifts and other info (such as + # slice ID from the reference image to which target was aligned) + # to output cube metadata (or property). return output_model From ebf9fcc09afc16f80c274884700550874fa5f908 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 12 Jul 2024 17:13:10 -0400 Subject: [PATCH 169/687] added PR number --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 72b9f369e5..561be56c95 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ 1.15.2 (unreleased) =================== +align_refs +---------- + +- Only aligning the first integration of the science exposure. [#8643] + cube_build ---------- From 5a22bbf53c2ef11fab7b3b96db3edcd763302cdd Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 12 Jul 2024 17:20:46 -0400 Subject: [PATCH 170/687] updating test --- jwst/coron/tests/test_coron.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index 377486d97f..141c2c52c8 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -183,8 +183,8 @@ def test_align_models(): truth_results_sub = np.array( [ [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], - [[10.053974, 11.1953335, 11.993213], [10.36224, 10.805556, 10.274276]], - [[9.988604, 11.33026, 11.968155], [10.024722, 10.971058, 10.108071]], + [[0., 0., 0.], [0., 0., 0.]], + [[0., 0., 0.], [0., 0., 0.]], ] ) From 2d15511cab1a6ffbe72b3bab135fa2998e243371 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 12 Jul 2024 17:24:34 -0400 Subject: [PATCH 171/687] updating the docs accordingly --- docs/jwst/align_refs/description.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/jwst/align_refs/description.rst b/docs/jwst/align_refs/description.rst index b0f99e9bc0..e1bf2d3f76 100644 --- a/docs/jwst/align_refs/description.rst +++ b/docs/jwst/align_refs/description.rst @@ -8,10 +8,10 @@ The ``align_refs`` step is one of the coronagraphic-specific steps in the ``coro sub-package that is part of Stage 3 :ref:`calwebb_coron3 ` processing. It computes offsets between science target images and reference PSF images, and shifts the PSF images into -alignment. This is performed on a per-integration basis for both the science target -data and the reference PSF data. Each integration contained in the stacked PSF data +alignment. This is performed on the first integration for both the science target +data and the reference PSF data. The first integration contained in the stacked PSF data (the result of the :ref:`stack_refs `) step is -aligned to each integration within a given science target exposure. +aligned to the first integration within a given science target exposure. This results in a new product for each science target exposure that contains a stack of individual PSF images that have been aligned to each integration in the science target exposure. From dd76184e82c1e863bce22d02de093f9bfe12f16f Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Mon, 15 Jul 2024 13:13:07 -0400 Subject: [PATCH 172/687] move `ruff` and `bandit` checks to `pre-commit` (#8646) Co-authored-by: Brett Graham --- .github/workflows/ci.yml | 8 +++++-- .pre-commit-config.yaml | 29 +++++++++++++++++++++++++ jwst/msaflagopen/tests/test_msa_open.py | 2 +- pyproject.toml | 2 +- tox.ini | 19 +--------------- 5 files changed, 38 insertions(+), 22 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4d67ea3583..abb96ccb00 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -20,13 +20,17 @@ concurrency: cancel-in-progress: true jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + - uses: pre-commit/action@v3.0.1 check: uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 with: default_python: "3.12" envs: | - - linux: check-style - - linux: check-security - linux: check-dependencies crds_contexts: uses: spacetelescope/crds/.github/workflows/contexts.yml@master diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000000..6550323ab8 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,29 @@ +exclude: ".*\\.asdf$" + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-added-large-files + - id: check-ast + - id: check-case-conflict + - id: check-yaml + args: ["--unsafe"] + - id: check-toml + - id: check-merge-conflict + - id: check-symlinks + - id: debug-statements + - id: detect-private-key + # - id: end-of-file-fixer + # - id: trailing-whitespace + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.5.2' + hooks: + - id: ruff + args: ["--fix"] + # - id: ruff-format + - repo: https://github.com/PyCQA/bandit + rev: 1.7.9 + hooks: + - id: bandit + args: ["-r", "-ll", "-x", "jwst/*test*,jwst/**/*test*,jwst/fits_generator", "jwst"] diff --git a/jwst/msaflagopen/tests/test_msa_open.py b/jwst/msaflagopen/tests/test_msa_open.py index 50e6379984..131c5eba46 100644 --- a/jwst/msaflagopen/tests/test_msa_open.py +++ b/jwst/msaflagopen/tests/test_msa_open.py @@ -103,7 +103,7 @@ def test_create_slitlets(): for slit in result: # Test the returned data type and fields. - assert type(slit) == Slit + assert isinstance(slit, Slit) assert slit._fields == slit_fields diff --git a/pyproject.toml b/pyproject.toml index aa865e5197..2cf29c2660 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -284,7 +284,7 @@ exclude = [ "build", ] lint.ignore = [ - "E741", + "E741", # ambiguous variable name (O/0, l/I, etc.) ] line-length = 130 diff --git a/tox.ini b/tox.ini index efead36d3a..8813c9998a 100644 --- a/tox.ini +++ b/tox.ini @@ -1,6 +1,6 @@ [tox] envlist = - check-{style,security,dependencies} + check-dependencies test{,-oldestdeps,-stdevdeps,-devdeps,-sdpdeps}{,-pyargs,-warnings,-regtests,-cov}-xdist build-{docs,dist} @@ -13,22 +13,6 @@ envlist = # tox -l -v # -[testenv:check-style] -description = check code style, e.g. with flake8 -skip_install = true -deps = - ruff -commands = - ruff check . {posargs} - -[testenv:check-security] -description = run bandit to check security compliance -skip_install = true -deps = - bandit>=1.7 -commands = - bandit -r -ll -x jwst/*test*,jwst/**/*test*,jwst/fits_generator jwst - [testenv:check-dependencies] description = verify that install_requires in setup.cfg has correct dependencies # `extras` needs to be empty to check modules without additional dependencies @@ -92,4 +76,3 @@ description = invoke sphinx-build to build the HTML docs extras = docs commands = sphinx-build -W docs docs/_build - From 8af184cef5fe4553ae704cd1932d494beca07272 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 16 Jul 2024 05:46:20 -0700 Subject: [PATCH 173/687] JP-3687: fix outlier_detection failures when run outside pipeline (#8645) Co-authored-by: Melanie Clarke --- CHANGES.rst | 6 ++++++ jwst/outlier_detection/outlier_detection.py | 2 ++ 2 files changed, 8 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 72b9f369e5..256562695e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -13,6 +13,12 @@ master_background - Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit as a background slit, instead of ``"bkg"`` only. [#8600] +outlier_detection +----------------- + +- Fixed failures due to a missing ``wcs.array_shape`` attribute when the + ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] + stpipe ------ diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 1e5d3c50c4..92cbdc6db4 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -569,6 +569,8 @@ def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0): log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) log.debug("Sci shape: {}".format(blot_img.data.shape)) + # Set array shape, needed to compute image pixel area + blot_img.meta.wcs.array_shape = blot_img.shape if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) From bcfcbd962d992ead69edc3071721b7b3e965d7af Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 16 Jul 2024 11:06:26 -0400 Subject: [PATCH 174/687] parameter needed in function call --- jwst/regtest/test_miri_image.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index c8aa65dd67..f7237c3d7f 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -164,7 +164,7 @@ def test_miri_image3_catalog(run_image3, rtdata_module, diff_astropy_tables): rtdata.output = "jw01024-o001_t002_miri_f770w_cat.ecsv" rtdata.get_truth("truth/test_miri_image_stages/jw01024-o001_t002_miri_f770w_cat.ecsv") - assert diff_astropy_tables(rtdata.output, rtdata.truth, rtol=1e-4) + assert diff_astropy_tables(rtdata.output, rtdata.truth, rtol=1e-3, atol=1e-4) @pytest.mark.bigdata From 136f1d99c61540538793dcde0785a6e99fe0e287 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 16 Jul 2024 11:11:55 -0400 Subject: [PATCH 175/687] added PR number --- CHANGES.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 256562695e..ae3ab2d61f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -19,6 +19,12 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +regtest +------- + +- An missing parameter in the function call was preventing the tolerance + to be updated in test_miri_image3_catalog. [#8653] + stpipe ------ From 11cdda33d7f79cd5e2ae6aa30ba10ae1cd32ff79 Mon Sep 17 00:00:00 2001 From: AlexKurek Date: Wed, 17 Jul 2024 16:06:17 +0200 Subject: [PATCH 176/687] Remove some unused modules (#8630) Co-authored-by: Ned Molter --- jwst/associations/asn_edit.py | 9 ++++----- jwst/associations/association.py | 1 - jwst/associations/pool.py | 2 +- jwst/fits_generator/main.py | 1 - jwst/fits_generator/objects.py | 3 --- jwst/fits_generator/template.py | 1 - 6 files changed, 5 insertions(+), 12 deletions(-) diff --git a/jwst/associations/asn_edit.py b/jwst/associations/asn_edit.py index 5af158435d..87b5adb4c0 100644 --- a/jwst/associations/asn_edit.py +++ b/jwst/associations/asn_edit.py @@ -1,5 +1,4 @@ import os -import sys import json import warnings import os.path as op @@ -190,9 +189,9 @@ def _lookup(asn, filename, ignore_suffix=False): found = [] path = _path(filename) basename = op.basename(path) - (root, ext) = op.splitext(basename) + (root, _) = op.splitext(basename) if ignore_suffix: - search_key, separator = suffix.remove_suffix(root) + search_key, _ = suffix.remove_suffix(root) else: search_key = basename @@ -201,8 +200,8 @@ def _lookup(asn, filename, ignore_suffix=False): expname = member.get('expname') if expname: if ignore_suffix: - (root, ext) = op.splitext(expname) - match_key, separator = suffix.remove_suffix(root) + (root, _) = op.splitext(expname) + match_key, _ = suffix.remove_suffix(root) else: match_key = expname if search_key == match_key: diff --git a/jwst/associations/association.py b/jwst/associations/association.py index 17a9f7ac4f..f5d681954d 100644 --- a/jwst/associations/association.py +++ b/jwst/associations/association.py @@ -1,5 +1,4 @@ from collections.abc import MutableMapping -from copy import deepcopy from datetime import datetime, timezone import json import jsonschema diff --git a/jwst/associations/pool.py b/jwst/associations/pool.py index 3d625f57fb..48311695bf 100644 --- a/jwst/associations/pool.py +++ b/jwst/associations/pool.py @@ -4,7 +4,7 @@ from collections import UserDict from astropy.io.ascii import convert_numpy -from astropy.table import Row, Table +from astropy.table import Table __all__ = ['AssociationPool'] diff --git a/jwst/fits_generator/main.py b/jwst/fits_generator/main.py index eabfc12d91..b38f9da324 100644 --- a/jwst/fits_generator/main.py +++ b/jwst/fits_generator/main.py @@ -28,7 +28,6 @@ # DAMAGE. # THIRD-PARTY -from astropy.io import fits as pyfits # STDLIB import glob diff --git a/jwst/fits_generator/objects.py b/jwst/fits_generator/objects.py index fef3fffde9..f905d145f3 100644 --- a/jwst/fits_generator/objects.py +++ b/jwst/fits_generator/objects.py @@ -28,10 +28,7 @@ # DAMAGE. # STDLIB -import datetime -import os import re -import warnings # THIRD-PARTY from astropy.io import fits as pyfits diff --git a/jwst/fits_generator/template.py b/jwst/fits_generator/template.py index d986fc64c3..5f422975d9 100644 --- a/jwst/fits_generator/template.py +++ b/jwst/fits_generator/template.py @@ -28,7 +28,6 @@ # DAMAGE. # STDLIB -from operator import itemgetter import os import re From 7a29796fd62fe00e10f73f0b716d7cba5191b8b3 Mon Sep 17 00:00:00 2001 From: Joseph Hunkeler Date: Wed, 17 Jul 2024 16:19:18 -0400 Subject: [PATCH 177/687] RTD: Clear the dirty git index flag (#8644) Co-authored-by: Nadia Dencheva --- .readthedocs.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3d1fb60bc5..6dff1248d4 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -25,6 +25,8 @@ build: - git fetch --unshallow || true - git config remote.origin.fetch '+refs/heads/*:refs/remotes/origin/*' || true - git fetch --all --tags || true + pre_install: + - git update-index --assume-unchanged docs/rtd_environment.yaml docs/conf.py conda: environment: docs/rtd_environment.yaml From 1ef982df94a25f23b1cf69c2fea36a905b8cc375 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 17 Jul 2024 18:12:13 -0400 Subject: [PATCH 178/687] JP-3634: Fix for custom WCS unit test (#8620) Co-authored-by: Nadia Dencheva --- jwst/resample/tests/test_resample_step.py | 27 ++++++++++++++++++----- 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index eaf6f15404..f9cfa56ef3 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -546,18 +546,20 @@ def test_custom_wcs_resample_imaging(nircam_rate, ratio, rotation, crpix, crval, 'output_shape2, match', [((1205, 1100), True), ((1222, 1111), False), (None, True)] ) -@pytest.mark.xfail(reason="Empty output region, unclear what this is meant to test.") def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, tmp_path): + + # make some data with a WCS and some random values + im = AssignWcsStep.call(nircam_rate, sip_approx=False) + rng = np.random.default_rng(seed=77) + im.data[:, :] = rng.random(im.data.shape) + crpix = (600, 550) - crval = (50, 77) + crval = (22.04, 11.98) rotation = 15 ratio = 0.7 - im = AssignWcsStep.call(nircam_rate, sip_approx=False) - # first pass - create a reference output WCS: - im.data[:, :] = np.random.random(im.data.shape) result = ResampleStep.call( im, output_shape=(1205, 1100), @@ -567,7 +569,9 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, pixel_scale_ratio=ratio ) + # make sure results are nontrivial data1 = result.data + assert not np.all(np.isnan(data1)) refwcs = str(tmp_path / "resample_refwcs.asdf") result.meta.wcs.bounding_box = [(-0.5, 1204.5), (-0.5, 1099.5)] @@ -580,6 +584,7 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, ) data2 = result.data + assert not np.all(np.isnan(data2)) if output_shape2 is not None: assert data2.shape == output_shape2[::-1] @@ -587,7 +592,17 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, if match: # test output image shape assert data1.shape == data2.shape - assert np.allclose(data1, data2) + assert np.allclose(data1, data2, equal_nan=True) + + # make sure pixel values are similar, accounting for scale factor + # (assuming inputs are in surface brightness units) + iscale = np.sqrt(im.meta.photometry.pixelarea_steradians + / compute_image_pixel_area(im.meta.wcs)) + input_mean = np.nanmean(im.data) + output_mean_1 = np.nanmean(data1) + output_mean_2 = np.nanmean(data2) + assert np.isclose(input_mean * iscale**2, output_mean_1, atol=1e-4) + assert np.isclose(input_mean * iscale**2, output_mean_2, atol=1e-4) @pytest.mark.parametrize('ratio', [1.3, 1]) From a8e772bc41b4d4429058f436e4197ec1025e12bd Mon Sep 17 00:00:00 2001 From: Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com> Date: Thu, 18 Jul 2024 18:54:25 +0700 Subject: [PATCH 179/687] docs(README): link CRDS PUB (#8658) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a83e87bd3a..e830ffedfe 100644 --- a/README.md +++ b/README.md @@ -150,7 +150,7 @@ Need other useful packages in your development environment? **Note: As of November 10, 2022, the process of deprecating the CRDS PUB Server will start. For details, refer to the [CRDS PUB Server Freeze -and Deprecation page](https://jwst-pipeline.readthedocs.io/en/stable/jwst/pub_deprecation.html#pub-deprecation)** +and Deprecation page](https://jwst-pipeline.readthedocs.io/en/stable/jwst/user_documentation/pub_deprecation.html#crds-pub-server-freeze-and-deprecation)** CRDS is the system that manages the reference files needed to run the pipeline. From 68e8fccfc0665ce22573330678bd6c40a419743d Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Thu, 18 Jul 2024 09:38:11 -0400 Subject: [PATCH 180/687] lint `jwst/fits_generator` and `jwst/associations` (#8652) Co-authored-by: Ned Molter --- jwst/associations/asn_gather.py | 1 - jwst/associations/generator/generate.py | 2 +- jwst/associations/lib/association_rules.py | 2 +- jwst/associations/lib/constraint.py | 2 -- jwst/associations/lib/diff.py | 3 +- jwst/associations/lib/dms_base.py | 1 - jwst/associations/lib/lv2_v1_to_v2.py | 2 -- jwst/associations/lib/rules_level2_base.py | 8 ++--- jwst/associations/lib/rules_level2b.py | 29 +++++++++++++------ jwst/associations/lib/rules_level3.py | 8 ++++- jwst/associations/lib/rules_level3_base.py | 2 -- jwst/associations/lib/update_path.py | 6 ++-- .../associations/tests/data/asn_rules_set1.py | 2 +- .../associations/tests/data/asn_rules_set2.py | 2 +- jwst/associations/tests/data/dms_rules.py | 2 -- jwst/associations/tests/data/rules_basic.py | 2 -- .../tests/data/rules_level3_set1.py | 2 -- .../tests/data/rules_level3_set2.py | 2 -- jwst/associations/tests/helpers.py | 2 -- jwst/associations/tests/test_asn_names.py | 2 -- jwst/associations/tests/test_associations.py | 3 -- jwst/associations/tests/test_level3_basics.py | 2 +- .../tests/test_level3_duplicate.py | 1 - jwst/associations/tests/test_level3_wfscmb.py | 6 +--- jwst/associations/tests/test_pool.py | 1 - jwst/associations/tests/test_process_list.py | 2 +- jwst/associations/tests/test_registry.py | 4 --- jwst/fits_generator/input_file_types.py | 6 ++-- jwst/fits_generator/main.py | 2 +- jwst/fits_generator/objects.py | 2 +- jwst/fits_generator/proposalparser.py | 1 - jwst/fits_generator/template.py | 4 +-- jwst/fits_generator/verifiers.py | 2 +- pyproject.toml | 26 +++++++++-------- 34 files changed, 67 insertions(+), 77 deletions(-) diff --git a/jwst/associations/asn_gather.py b/jwst/associations/asn_gather.py index f374ce48f1..5485a37ff5 100644 --- a/jwst/associations/asn_gather.py +++ b/jwst/associations/asn_gather.py @@ -50,7 +50,6 @@ def asn_gather(association, destination=None, exp_types=None, exclude_types=None The copied association. """ from .load_as_asn import LoadAsAssociation - from . import Association exclude_types = exclude_types if exclude_types is not None else [] source_asn_path = Path(association) diff --git a/jwst/associations/generator/generate.py b/jwst/associations/generator/generate.py index 333bf17eef..1994efb5a4 100644 --- a/jwst/associations/generator/generate.py +++ b/jwst/associations/generator/generate.py @@ -49,7 +49,7 @@ def generate(pool, rules, version_id=None, finalize=True): documentation for a full description. """ associations = [] - if type(version_id) is bool: + if isinstance(version_id, bool): version_id = make_timestamp() process_queue = ProcessQueueSorted([ ProcessList( diff --git a/jwst/associations/lib/association_rules.py b/jwst/associations/lib/association_rules.py index c9c1f5cd2a..a179d5a815 100644 --- a/jwst/associations/lib/association_rules.py +++ b/jwst/associations/lib/association_rules.py @@ -4,7 +4,7 @@ ----- These associations are specifically defined for use in DMS. """ -from jwst.associations import Association +from jwst.associations import Association # noqa F401 from jwst.associations.registry import RegistryMarker from jwst.associations.lib import (rules_level2b, rules_level3) diff --git a/jwst/associations/lib/constraint.py b/jwst/associations/lib/constraint.py index 7ae0f34e45..ed3c3d1c35 100644 --- a/jwst/associations/lib/constraint.py +++ b/jwst/associations/lib/constraint.py @@ -3,11 +3,9 @@ import abc import collections from copy import deepcopy -from functools import wraps from itertools import chain import logging import re -import typing from .process_list import ListCategory, ProcessList from .utilities import ( diff --git a/jwst/associations/lib/diff.py b/jwst/associations/lib/diff.py index 2d002bdbc5..f5d0c4523d 100644 --- a/jwst/associations/lib/diff.py +++ b/jwst/associations/lib/diff.py @@ -389,7 +389,8 @@ def compare_product_membership(left, right, strict_expname=True): # Determine how to compare expnames if strict_expname: - munge_expname = lambda expname: expname + def munge_expname(expname): + return expname else: munge_expname = exposure_name diff --git a/jwst/associations/lib/dms_base.py b/jwst/associations/lib/dms_base.py index 7b1f1329f9..d3b885174f 100644 --- a/jwst/associations/lib/dms_base.py +++ b/jwst/associations/lib/dms_base.py @@ -2,7 +2,6 @@ from jwst.associations.lib.counter import Counter from jwst.associations.exceptions import ( - AssociationNotAConstraint, AssociationNotValidError, ) from jwst.associations.lib.acid import ACIDMixin diff --git a/jwst/associations/lib/lv2_v1_to_v2.py b/jwst/associations/lib/lv2_v1_to_v2.py index 2f91490991..1216b71ea9 100644 --- a/jwst/associations/lib/lv2_v1_to_v2.py +++ b/jwst/associations/lib/lv2_v1_to_v2.py @@ -1,10 +1,8 @@ """Convert old-style Level2 associations to new style""" import argparse -from glob import glob import json import logging import os.path as path -import re from ...lib.suffix import remove_suffix diff --git a/jwst/associations/lib/rules_level2_base.py b/jwst/associations/lib/rules_level2_base.py index 8821b00da9..def32a8f2a 100644 --- a/jwst/associations/lib/rules_level2_base.py +++ b/jwst/associations/lib/rules_level2_base.py @@ -8,7 +8,6 @@ splitext ) import re -import warnings from jwst.associations import ( Association, @@ -22,7 +21,6 @@ ) from jwst.associations.lib.dms_base import ( Constraint_TargetAcq, - CORON_EXP_TYPES, DMSAttrConstraint, DMSBaseMixin, IMAGE2_NONSCIENCE_EXP_TYPES, @@ -788,9 +786,11 @@ def __init__(self, association=None): # If an association is not provided, the check for original # exposure type is ignored. if association is None: - sources = lambda item: 'not imprint' + def sources(item): + return 'not imprint' else: - sources = lambda item: association.original_exposure_type + def sources(item): + return association.original_exposure_type super(Constraint_Imprint_Special, self).__init__( [ diff --git a/jwst/associations/lib/rules_level2b.py b/jwst/associations/lib/rules_level2b.py index 13a2e706c1..480640d112 100644 --- a/jwst/associations/lib/rules_level2b.py +++ b/jwst/associations/lib/rules_level2b.py @@ -1,29 +1,40 @@ """Association Definitions: DMS Level2b product associations """ -from collections import deque import logging -import re -from jwst.associations.exceptions import AssociationNotValidError from jwst.associations.lib.rules_level2_base import AsnMixin_Lv2WFSS, Constraint_Imprint_Special from jwst.associations.registry import RegistryMarker from jwst.associations.lib.constraint import (Constraint, SimpleConstraint) from jwst.associations.lib.dms_base import ( Constraint_TSO, Constraint_WFSC, - format_list, - item_getattr, nissoss_calibrated_filter, nrccoron_valid_detector, nrsfss_valid_detector, nrsifu_valid_detector, nrslamp_valid_detector, ) -from jwst.associations.lib.member import Member from jwst.associations.lib.process_list import ListCategory -from jwst.associations.lib.utilities import (getattr_from_list, getattr_from_list_nofail) -from jwst.associations.lib.rules_level2_base import * -from jwst.associations.lib.rules_level3_base import DMS_Level3_Base +from jwst.associations.lib.rules_level2_base import ( + ASN_SCHEMA, # noqa F401 + AsnMixin_Lv2Image, + AsnMixin_Lv2Spectral, + AsnMixin_Lv2Nod, + AsnMixin_Lv2Special, + DMSLevel2bBase, + DMSAttrConstraint, + Utility, # noqa F401 + Constraint_Mode, + Constraint_Base, + Constraint_Background, + Constraint_Single_Science, + Constraint_Image_Science, + Constraint_Image_Nonscience, + Constraint_Special, + Constraint_Spectral_Science, + Constraint_Imprint, + Constraint_Target, +) __all__ = [ 'Asn_Lv2CoronAsRate', diff --git a/jwst/associations/lib/rules_level3.py b/jwst/associations/lib/rules_level3.py index 4fb3dd44d5..ac64e1128e 100644 --- a/jwst/associations/lib/rules_level3.py +++ b/jwst/associations/lib/rules_level3.py @@ -8,7 +8,13 @@ nissoss_calibrated_filter, nrccoron_valid_detector, nrsfss_valid_detector, nrsifu_valid_detector) from jwst.associations.lib.process_list import ListCategory -from jwst.associations.lib.rules_level3_base import * +from jwst.associations.lib.rules_level3_base import ( + ASN_SCHEMA, # noqa F401 + AsnMixin_Science, AsnMixin_AuxData, AsnMixin_Coronagraphy, AsnMixin_Spectrum, + DMS_Level3_Base, DMSAttrConstraint, + Utility, # noqa F401 + Constraint, SimpleConstraint, Constraint_Optical_Path, Constraint_Target, Constraint_Image, Constraint_MSA, Constraint_IFU +) from jwst.associations.lib.rules_level3_base import ( dms_product_name_sources, dms_product_name_nrsfs_sources, dms_product_name_noopt, dms_product_name_coronimage, diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index 756ca3e3a6..b16a444aed 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -29,9 +29,7 @@ from jwst.associations.lib.counter import Counter from jwst.associations.lib.dms_base import ( _EMPTY, - ACQ_EXP_TYPES, Constraint_TargetAcq, - CORON_EXP_TYPES, DMSAttrConstraint, DMSBaseMixin, IMAGE2_SCIENCE_EXP_TYPES, diff --git a/jwst/associations/lib/update_path.py b/jwst/associations/lib/update_path.py index 95605719fd..6f26ee565d 100644 --- a/jwst/associations/lib/update_path.py +++ b/jwst/associations/lib/update_path.py @@ -44,7 +44,8 @@ def update_key_value(obj, target, func_args, mod_func=None): the function. """ if mod_func is None: - mod_func = lambda value, args: args + def mod_func(value, args): + return args if hasattr(obj, 'items'): for key, value in obj.items(): @@ -116,7 +117,8 @@ def test_update_key_value_mod_func(): obj = deepcopy(_test_obj) target = 'a' new_value = 'changed' - mod_func = lambda v, suffix: v + suffix + def mod_func(v, suffix): + return v + suffix update_key_value(obj, target, (new_value,), mod_func=mod_func) assert_values = [ mod_func(value, new_value) diff --git a/jwst/associations/tests/data/asn_rules_set1.py b/jwst/associations/tests/data/asn_rules_set1.py index 8d68807fa7..c35bc53ad5 100644 --- a/jwst/associations/tests/data/asn_rules_set1.py +++ b/jwst/associations/tests/data/asn_rules_set1.py @@ -4,4 +4,4 @@ ----- These associations are specifically defined for use in DMS. """ -from jwst.associations.tests.data.rules_level3_set1 import * +from jwst.associations.tests.data.rules_level3_set1 import * # noqa F403 diff --git a/jwst/associations/tests/data/asn_rules_set2.py b/jwst/associations/tests/data/asn_rules_set2.py index fb68b8f8b3..fb42f313c3 100644 --- a/jwst/associations/tests/data/asn_rules_set2.py +++ b/jwst/associations/tests/data/asn_rules_set2.py @@ -4,4 +4,4 @@ ----- These associations are specifically defined for use in DMS. """ -from jwst.associations.tests.data.rules_level3_set2 import * +from jwst.associations.tests.data.rules_level3_set2 import * # noqa F403 diff --git a/jwst/associations/tests/data/dms_rules.py b/jwst/associations/tests/data/dms_rules.py index f138347416..bf94bd898c 100644 --- a/jwst/associations/tests/data/dms_rules.py +++ b/jwst/associations/tests/data/dms_rules.py @@ -1,6 +1,4 @@ # To avoid relative imports and mimic actual usage -import os -import sys from jwst.associations import Association from jwst.associations.association import finalize as general_asn_finalize diff --git a/jwst/associations/tests/data/rules_basic.py b/jwst/associations/tests/data/rules_basic.py index 4059acc50b..c93022aa7c 100644 --- a/jwst/associations/tests/data/rules_basic.py +++ b/jwst/associations/tests/data/rules_basic.py @@ -1,7 +1,5 @@ """Association Rules: Basic """ -import os -import sys from jwst.associations import Association from jwst.associations.registry import RegistryMarker diff --git a/jwst/associations/tests/data/rules_level3_set1.py b/jwst/associations/tests/data/rules_level3_set1.py index 06487de7d1..320e5aa823 100644 --- a/jwst/associations/tests/data/rules_level3_set1.py +++ b/jwst/associations/tests/data/rules_level3_set1.py @@ -1,7 +1,5 @@ """Association Definitions: DMS Level3 product associations """ -import os -import sys from jwst.associations import Association from jwst.associations.registry import RegistryMarker diff --git a/jwst/associations/tests/data/rules_level3_set2.py b/jwst/associations/tests/data/rules_level3_set2.py index 0a17affd2d..ff97265891 100644 --- a/jwst/associations/tests/data/rules_level3_set2.py +++ b/jwst/associations/tests/data/rules_level3_set2.py @@ -1,7 +1,5 @@ """Association Definitions: DMS Level3 product associations """ -import os -import sys from jwst.associations import Association from jwst.associations.registry import RegistryMarker diff --git a/jwst/associations/tests/helpers.py b/jwst/associations/tests/helpers.py index e976c1007c..11878d4dbe 100644 --- a/jwst/associations/tests/helpers.py +++ b/jwst/associations/tests/helpers.py @@ -1,7 +1,6 @@ """Helpers for tests.""" from collections import namedtuple from contextlib import contextmanager -from copy import copy from glob import glob import os import pytest @@ -12,7 +11,6 @@ from jwst.associations import (AssociationRegistry, AssociationPool, generate) from jwst.associations.lib.counter import Counter -from jwst.associations.lib.diff import compare_asns from jwst.associations.lib.utilities import is_iterable diff --git a/jwst/associations/tests/test_asn_names.py b/jwst/associations/tests/test_asn_names.py index 204893c7df..a668a211c7 100644 --- a/jwst/associations/tests/test_asn_names.py +++ b/jwst/associations/tests/test_asn_names.py @@ -1,11 +1,9 @@ import os import pytest import re -import warnings from jwst.associations.tests import helpers -from jwst import associations from jwst.associations import generate from jwst.associations import load_asn from jwst.associations.lib.utilities import constrain_on_candidates diff --git a/jwst/associations/tests/test_associations.py b/jwst/associations/tests/test_associations.py index e95e5962a7..58129967cd 100644 --- a/jwst/associations/tests/test_associations.py +++ b/jwst/associations/tests/test_associations.py @@ -8,9 +8,6 @@ AssociationError, AssociationRegistry, generate) -from jwst.associations.registry import ( - import_from_file, -) from jwst.associations.lib.dms_base import DMSAttrConstraint diff --git a/jwst/associations/tests/test_level3_basics.py b/jwst/associations/tests/test_level3_basics.py index bc92aba922..6cf77e82e5 100644 --- a/jwst/associations/tests/test_level3_basics.py +++ b/jwst/associations/tests/test_level3_basics.py @@ -47,7 +47,7 @@ def test_targacq(pool_file): assert len(asns) > 0 for asn in asns: # Ignore reprocessed asn's with only science - if not asn['asn_rule'] in ["Asn_Lv3SpecAux", "Asn_Lv3NRSIFUBackground"]: + if asn['asn_rule'] not in ["Asn_Lv3SpecAux", "Asn_Lv3NRSIFUBackground"]: for product in asn['products']: exptypes = [ member['exptype'].lower() diff --git a/jwst/associations/tests/test_level3_duplicate.py b/jwst/associations/tests/test_level3_duplicate.py index 3f6e6c1274..58c415063b 100644 --- a/jwst/associations/tests/test_level3_duplicate.py +++ b/jwst/associations/tests/test_level3_duplicate.py @@ -1,6 +1,5 @@ """Test for duplication/missing associations""" import logging -import pytest from jwst.associations.tests.helpers import ( level3_rule_path, diff --git a/jwst/associations/tests/test_level3_wfscmb.py b/jwst/associations/tests/test_level3_wfscmb.py index 0e71dbacb7..108855aecc 100644 --- a/jwst/associations/tests/test_level3_wfscmb.py +++ b/jwst/associations/tests/test_level3_wfscmb.py @@ -1,9 +1,6 @@ -import pytest -from pprint import pprint from jwst.associations.tests.helpers import ( - level3_rule_path, mkstemp_pool_file, t_path, ) @@ -91,5 +88,4 @@ def test_level3_wfscmb_jitter_suppression(tmp_path): generated = Main.cli(cmd_args) jitter = get_jitter_not_jitter(pool_path) - jitter_present_in_asn = jitter_present(jitter, generated.associations) - assert jitter_present_in_asn == False + assert not jitter_present(jitter, generated.associations) diff --git a/jwst/associations/tests/test_pool.py b/jwst/associations/tests/test_pool.py index 91f12571df..b054619021 100644 --- a/jwst/associations/tests/test_pool.py +++ b/jwst/associations/tests/test_pool.py @@ -1,4 +1,3 @@ -import os from jwst.associations.tests.helpers import t_path from jwst.associations import AssociationPool diff --git a/jwst/associations/tests/test_process_list.py b/jwst/associations/tests/test_process_list.py index 4e7c8d35fe..474ad84b32 100644 --- a/jwst/associations/tests/test_process_list.py +++ b/jwst/associations/tests/test_process_list.py @@ -5,7 +5,7 @@ t_path ) -from jwst.associations.lib.process_list import * +from jwst.associations.lib.process_list import ProcessItem, ProcessList, ProcessQueue, ProcessQueueSorted, ListCategory def test_item(): diff --git a/jwst/associations/tests/test_registry.py b/jwst/associations/tests/test_registry.py index 8bd59e5982..9a31062389 100644 --- a/jwst/associations/tests/test_registry.py +++ b/jwst/associations/tests/test_registry.py @@ -1,10 +1,6 @@ """Test the Registry""" -import sys -from copy import deepcopy -from tempfile import NamedTemporaryFile from jwst.associations.lib.keyvalue_registry import KeyValueRegistry -from jwst.associations.registry import import_from_file import pytest diff --git a/jwst/fits_generator/input_file_types.py b/jwst/fits_generator/input_file_types.py index fac71ab820..163ea9dd28 100644 --- a/jwst/fits_generator/input_file_types.py +++ b/jwst/fits_generator/input_file_types.py @@ -351,7 +351,8 @@ def is_nirspec_fm1(hdulist): """Returns True if the file is NIRSpec FM1 data. These files have a DATE-OBS in the first half of 2011""" instrument = hdulist[0].header['INSTRUME'] - if instrument != 'NIRSPEC': return False + if instrument != 'NIRSPEC': + return False date = hdulist[0].header['DATE-OBS'] year = date[:4] target_year = '2011' @@ -365,7 +366,8 @@ def is_nircam_fm1(hdulist): """Returns True is the file is NIRCAM FM1 data. These files have a DATE-OBS after the first half of 2012""" instrument = hdulist[0].header['INSTRUME'] - if instrument != 'NIRCAM': return False + if instrument != 'NIRCAM': + return False date = hdulist[0].header['DATE-OBS'] year = int(date[:4]) target_year = 2012.5 diff --git a/jwst/fits_generator/main.py b/jwst/fits_generator/main.py index b38f9da324..88f4cc8f84 100644 --- a/jwst/fits_generator/main.py +++ b/jwst/fits_generator/main.py @@ -276,7 +276,7 @@ def close(self): return stream.getvalue() else: try: - import docutils + import docutils # noqa F401 except ImportError: raise ImportError("docutils is required for HTML output") diff --git a/jwst/fits_generator/objects.py b/jwst/fits_generator/objects.py index f905d145f3..f081536713 100644 --- a/jwst/fits_generator/objects.py +++ b/jwst/fits_generator/objects.py @@ -565,6 +565,6 @@ def describe(self, stream, state): assert isinstance(hdudef, HDU) state.hdu = i state.depth += 1 - hdu = hdudef.describe(stream, state) + hdudef.describe(stream, state) state.depth -= 1 describe.__doc__ = Object.describe.__doc__ diff --git a/jwst/fits_generator/proposalparser.py b/jwst/fits_generator/proposalparser.py index 09a35b6b97..ca22ffd7e7 100644 --- a/jwst/fits_generator/proposalparser.py +++ b/jwst/fits_generator/proposalparser.py @@ -49,7 +49,6 @@ def get_detectors(filename): ExposureNum = 0 for exposure in activity: ExposureNum = ExposureNum + 1 - DetectorNum = 0 for detector in exposure: subarray = detector.find('subarray').text base = detector.find('base').text diff --git a/jwst/fits_generator/template.py b/jwst/fits_generator/template.py index 5f422975d9..362106ec3d 100644 --- a/jwst/fits_generator/template.py +++ b/jwst/fits_generator/template.py @@ -39,7 +39,7 @@ from . import generators from . import util from . import verifiers -from pyparsing import * +from pyparsing import ParserElement class TemplateParserBase: # This parser is a hybrid between hand-written parsing and @@ -416,7 +416,7 @@ def _add_card(self, header, definition, comment): if len(definition): try: value = eval(definition[2]) - except Exception as e: + except Exception: raise self._raise( "Can't parse as literal '%s'" % definition[2]) key = definition[0] diff --git a/jwst/fits_generator/verifiers.py b/jwst/fits_generator/verifiers.py index 9b0837e1be..1e6877de08 100644 --- a/jwst/fits_generator/verifiers.py +++ b/jwst/fits_generator/verifiers.py @@ -95,7 +95,7 @@ def is_cds(val): raise ValueError( "Expected integer value for CDS") - day = (value & 0xffff000000000000) >> (32 + 16) + day = (value & 0xffff000000000000) >> (32 + 16) # noqa F841 millis = (value & 0xffffffff0000) >> 16 submillis = (value & 0xffff) diff --git a/pyproject.toml b/pyproject.toml index 2cf29c2660..55e35d0728 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -277,30 +277,32 @@ write_to = "jwst/_version.py" exclude = [ "jwst/extern", "docs", - "jwst/associations", - "jwst/fits_generator", ".tox", ".eggs", "build", ] lint.ignore = [ "E741", # ambiguous variable name (O/0, l/I, etc.) + "E722", # Do not use bare `except` ] line-length = 130 [tool.ruff.lint.per-file-ignores] -"jwst/ramp_fitting/tests/compare_cr_navg_files.py" = [ - "E402", +"jwst/**/__init__.py" = [ + "E402", # module-level import not at top of file + "F401", # unused import + "F403", # import with * + "F405", # ambiguous import from * ] -"jwst/ramp_fitting/tests/compare_crs.py" = [ - "E402", +"jwst/fits_generator/*" = [ + "F821", # undefined name ] -"jwst/ramp_fitting/tests/compare_cr_files.py" = [ - "E402", +"jwst/fits_generator/tests/*" = [ + "F841", # unused variable ] -"jwst/ramp_fitting/tests/create_cube.py" = [ - "E402", +"jwst/associations/tests*" = [ + "F841", # unused variable ] -"jwst/ramp_fitting/tests/mc_3d.py" = [ - "E402", +"jwst/ramp_fitting/tests/*" = [ + "E402", # module-level import not at top of file ] From 2b0a030a7cf744bb736c32b013886939ecb4ba98 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Thu, 18 Jul 2024 09:54:07 -0400 Subject: [PATCH 181/687] unlink deleted scripts from build metadata (#8659) --- pyproject.toml | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 55e35d0728..523c454aca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,39 +72,18 @@ asn_from_list = "jwst.associations.asn_from_list:main" asn_gather = "jwst.scripts.asn_gather:main" asn_generate = "jwst.associations.main:main" asn_make_pool = "jwst.scripts.asn_make_pool:main" -assign_wcs = "jwst.scripts.assign_wcs:main" collect_pipeline_cfgs = "jwst.scripts.collect_pipeline_cfgs:main" -coron = "jwst.scripts.coron:main" create_data = "jwst.scripts.create_data:main" csvconvert = "jwst.csv_tools.csvconvert:CSVConvertScript" -cube_build = "jwst.scripts.cube_build:main" -dark_current = "jwst.scripts.dark_current:main" -data_generate = "jwst.scripts.data_generate:main" -dqinit = "jwst.scripts.dqinit:main" exp_to_source = "jwst.exp_to_source.main:Main" -flatfieldcorr = "jwst.scripts.flatfieldcorr:main" -fringecorr = "jwst.scripts.fringecorr:main" -ipc = "jwst.scripts.ipc:main" -jump = "jwst.scripts.jump:main" -linearitycorr = "jwst.scripts.linearitycorr:main" -make_header = "jwst.scripts.make_header:main" okify_regtests = "jwst.scripts.okify_regtests:main" -outlier_detection = "jwst.scripts.outlier_detection:main" -persistencecorr = "jwst.scripts.persistencecorr:main" -photomcorr = "jwst.scripts.photomcorr:main" pointing_summary = "jwst.scripts.pointing_summary:main" -rampfitcorr = "jwst.scripts.rampfitcorr:main" -refpix = "jwst.scripts.refpix:main" -resample = "jwst.scripts.resample:main" -saturationcorr = "jwst.scripts.saturationcorr:main" schema_editor = "jwst.scripts.schema_editor:main" schemadoc = "jwst.scripts.schemadoc:main" set_telescope_pointing = "jwst.scripts.set_telescope_pointing:main" "set_telescope_pointing.py" = "jwst.scripts.set_telescope_pointing:deprecated_name" set_velocity_aberration = "jwst.scripts.set_velocity_aberration:main" "set_velocity_aberration.py" = "jwst.scripts.set_velocity_aberration:deprecated_name" -straylight = "jwst.scripts.straylight:main" -superbias = "jwst.scripts.superbias:main" v1_calculate = "jwst.scripts.v1_calculate:main" verify_install_requires = "jwst.scripts.verify_install_requires:main" world_coords = "jwst.scripts.world_coords:main" From de39de07b6f0380860caa86e8485666b4cef35c2 Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Thu, 18 Jul 2024 13:47:46 -0400 Subject: [PATCH 182/687] JP-3636: Fix crash in tweakreg due to MalformedPolygonError (#8657) Co-authored-by: Nadia Dencheva --- CHANGES.rst | 26 ++++++++++++++++---------- pyproject.toml | 2 +- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b5e9fecd8b..c581f1cb4c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -23,14 +23,14 @@ master_background outlier_detection ----------------- -- Fixed failures due to a missing ``wcs.array_shape`` attribute when the +- Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] stpipe ------ - Removed setting of the `self.skip` attribute in the `record_step_status()` function; - added a `query_step_status()` function to use as an alternative to checking + added a `query_step_status()` function to use as an alternative to checking `self.skip`. [#8600] tweakreg @@ -39,6 +39,12 @@ tweakreg - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] +- Updated requirement for ``tweakwcs`` to version ``0.8.8`` which fixes a crash + in the ``tweakreg`` step due to a ``MalformedPolygonError`` exception being + raised by the ``spherical_geometry`` package for some data + sets. [#8657, spacetelescope/tweakwcs#205] + + 1.15.1 (2024-07-08) =================== @@ -182,7 +188,7 @@ extract_1d - Add propagation of uncertainty when annular backgrounds are subtracted from source spectra during IFU spectral extraction. [#8515] -- Add propagation of background uncertainty when background is subtracted from +- Add propagation of background uncertainty when background is subtracted from source spectra during non-IFU spectral extraction. [#8532] - Fix error in application of aperture correction to variance arrays. [#8530] @@ -190,7 +196,7 @@ extract_1d - Fix error in ``_coalesce_bounds`` that returned incorrect spectral or background extraction region when one set of pixel limits is entirely contained within another [#8586] - + - Removed a check for the primary slit for NIRSpec fixed slit mode: all slits containing point sources are now handled consistently, whether they are marked primary or not. [#8467] @@ -203,7 +209,7 @@ extract_2d - Added support for slit names that have string values instead of integer values, necessary for processing combined NIRSpec MOS and fixed slit data products. [#8467] - + - Assign slit ``source_xpos`` and ``source_ypos`` attributes here instead of in ``wavecorr`` for NIRSpec FS data. [#8569] @@ -312,8 +318,8 @@ outlier_detection pathloss -------- -- Updated pathloss calculations for NIRSpec fixed slit mode to use the appropriate - wavelengths for point and uniform sources if the ``wavecorr`` wavelength +- Updated pathloss calculations for NIRSpec fixed slit mode to use the appropriate + wavelengths for point and uniform sources if the ``wavecorr`` wavelength zero-point corrections for point sources have been applied. [#8376] photom @@ -354,7 +360,7 @@ pipeline - Added a hook to skip ``photom`` step when the ``extract_1d`` step was skipped for NIRISS SOSS data [#8575]. - + - Added hook to the ``calwebb_tso3`` pipeline to skip all subsequent steps if the ``extract_1d`` step is skipped. [#8583] @@ -424,7 +430,7 @@ residual_fringe srctype ------- -- Reset ``source_xpos`` and ``source_ypos`` values to zero for extended sources +- Reset ``source_xpos`` and ``source_ypos`` values to zero for extended sources in NIRSpec FS data to enable assigning those attributes in ``extract_2d``. [#8569] saturation @@ -470,7 +476,7 @@ wavecorr - Assign slit ``source_xpos`` and ``source_ypos`` attributes in ``extract_2d`` instead of in ``wavecorr`` for NIRSpec FS data. [#8569] - + wfss_contam ----------- diff --git a/pyproject.toml b/pyproject.toml index 523c454aca..b422963bf4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,7 @@ dependencies = [ "stsci.image>=2.3.5", "stsci.imagestats>=1.6.3", "synphot>=1.2", - "tweakwcs>=0.8.6", + "tweakwcs>=0.8.8", "asdf-astropy>=0.3.0", "wiimatch>=0.2.1", "packaging>20.0", From c3f84993f6f7175566932e2483b32e33980d5836 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 19 Jul 2024 16:10:06 -0400 Subject: [PATCH 183/687] fixing brain freeze --- jwst/coron/imageregistration.py | 34 +++++++++++++++++---------------- jwst/coron/tests/test_coron.py | 4 ++-- 2 files changed, 20 insertions(+), 18 deletions(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index b63ee9f9bb..130a4550aa 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -127,7 +127,7 @@ def fourier_imshift(image, shift): return offset -def align_array(reference, target, mask=None): +def align_array(reference, target, mask=None, useshift=None): """ Computes shifts between target image (or image "slices") and the reference image and re-aligns input images to the target. @@ -216,26 +216,28 @@ def align_models(reference, target, mask): output_model = QuadModel(quad_shape) output_model.update(target) - # Do alignment with only the first integration of the science exposure - k = 0 - # Compute the shifts of the PSF ("target") images relative to - # the science ("reference") image in this integration, and apply - # the shifts to the PSF images - d, shifts = align_array( - reference.data[k].astype(np.float64), + # the science ("reference") image in the first integration + _, shifts = align_array( + reference.data[0].astype(np.float64), target.data.astype(np.float64), mask.data) - output_model.data[k] = d - # Apply the same shifts to the PSF error arrays, if they exist - if target.err is not None: - output_model.err[k] = fourier_imshift( - target.err.astype(np.float64), + # Loop over all integrations of the science exposure + for k in range(nrefslices): + + # Apply the shifts to the PSF images + output_model.data[k] = fourier_imshift( + target.data.astype(np.float64), -shifts) - # TODO: in the future we need to add shifts and other info (such as - # slice ID from the reference image to which target was aligned) - # to output cube metadata (or property). + # Apply the same shifts to the PSF error arrays, if they exist + if target.err is not None: + output_model.err[k] = fourier_imshift( + target.err.astype(np.float64), + -shifts) + # TODO: in the future we need to add shifts and other info (such as + # slice ID from the reference image to which target was aligned) + # to output cube metadata (or property). return output_model diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index 141c2c52c8..c33b56fa60 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -183,8 +183,8 @@ def test_align_models(): truth_results_sub = np.array( [ [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], - [[0., 0., 0.], [0., 0., 0.]], - [[0., 0., 0.], [0., 0., 0.]], + [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], + [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], ] ) From 320e31eb5e507af979e825105a15b3abd9f4e424 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 19 Jul 2024 16:13:28 -0400 Subject: [PATCH 184/687] removing unused var --- jwst/coron/imageregistration.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 130a4550aa..b32ff56adc 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -127,7 +127,7 @@ def fourier_imshift(image, shift): return offset -def align_array(reference, target, mask=None, useshift=None): +def align_array(reference, target, mask=None): """ Computes shifts between target image (or image "slices") and the reference image and re-aligns input images to the target. From 9fe1c1b904fceeae261e8071f3ad51dd46711c9f Mon Sep 17 00:00:00 2001 From: Maria Date: Tue, 23 Jul 2024 15:22:17 -0400 Subject: [PATCH 185/687] Update CHANGES.rst Co-authored-by: Melanie Clarke --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index a170f725e0..4b16762260 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -4,7 +4,7 @@ align_refs ---------- -- Only aligning the first integration of the science exposure. [#8643] +- Compute alignment shifts from the first integration of the science exposure only. [#8643] scripts ------- From bba2289b56f5c9c2b4c90be7a43ad5738d4b8b7f Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 23 Jul 2024 15:38:45 -0400 Subject: [PATCH 186/687] removing unnecessary computations --- docs/jwst/align_refs/description.rst | 3 ++- jwst/coron/imageregistration.py | 17 +++++++++++------ 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/docs/jwst/align_refs/description.rst b/docs/jwst/align_refs/description.rst index e1bf2d3f76..f5e21ae8e2 100644 --- a/docs/jwst/align_refs/description.rst +++ b/docs/jwst/align_refs/description.rst @@ -8,7 +8,8 @@ The ``align_refs`` step is one of the coronagraphic-specific steps in the ``coro sub-package that is part of Stage 3 :ref:`calwebb_coron3 ` processing. It computes offsets between science target images and reference PSF images, and shifts the PSF images into -alignment. This is performed on the first integration for both the science target +alignment. This is performed from the first integration and applied to all the +subsequent ones for both the science target data and the reference PSF data. The first integration contained in the stacked PSF data (the result of the :ref:`stack_refs `) step is aligned to the first integration within a given science target exposure. diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index b32ff56adc..38eb4c068b 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -127,7 +127,7 @@ def fourier_imshift(image, shift): return offset -def align_array(reference, target, mask=None): +def align_array(reference, target, mask=None, return_aligned=True): """ Computes shifts between target image (or image "slices") and the reference image and re-aligns input images to the target. @@ -160,21 +160,26 @@ def align_array(reference, target, mask=None): if len(target.shape) == 2: shifts = align_fourierLSQ(reference, target, mask=mask) - aligned = fourier_imshift(target, -shifts) + if return_aligned: + aligned = fourier_imshift(target, -shifts) elif len(target.shape) == 3: nslices = target.shape[0] shifts = np.empty((nslices, 3), dtype=float) - aligned = np.empty_like(target) + if return_aligned: + aligned = np.empty_like(target) for m in range(nslices): sh = align_fourierLSQ(reference, target[m], mask=mask) shifts[m, :] = sh - aligned[m, :, :] = fourier_imshift(target[m], -sh) + if return_aligned: + aligned[m, :, :] = fourier_imshift(target[m], -sh) else: raise ValueError("Input target image must be either a 2D or 3D array.") + if not return_aligned: + return shifts return aligned, shifts @@ -218,10 +223,10 @@ def align_models(reference, target, mask): # Compute the shifts of the PSF ("target") images relative to # the science ("reference") image in the first integration - _, shifts = align_array( + shifts = align_array( reference.data[0].astype(np.float64), target.data.astype(np.float64), - mask.data) + mask=mask.data, return_aligned=False) # Loop over all integrations of the science exposure for k in range(nrefslices): From 10d9f912c401b0361541e28a3f349e6b5640e180 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Tue, 23 Jul 2024 15:48:33 -0400 Subject: [PATCH 187/687] Implemented new subarray mode for NSClean --- jwst/nsclean/lib.py | 22 +++++--- jwst/nsclean/nsclean.py | 114 ++++++++++++++++++++++++++++++++++++---- 2 files changed, 120 insertions(+), 16 deletions(-) diff --git a/jwst/nsclean/lib.py b/jwst/nsclean/lib.py index f348c85431..08feaa045e 100644 --- a/jwst/nsclean/lib.py +++ b/jwst/nsclean/lib.py @@ -1,3 +1,4 @@ +#-*- python -*- import numpy as np @@ -134,7 +135,10 @@ def fit(self, data): # Get data and weights for this line d = data[y][self.mask[y]] # unmasked (useable) data - p = np.diag(self.P[y][self.mask[y]]) # Weights + # The line below uses a vector to represent a diagonal weight matrix. + # Multiplications by this vector later on may be viewed as + # equivalent formulations to multiplication by diag(p). + p = self.P[y][self.mask[y]] # Weights # If none of the pixels in this line is useable (all masked out), # skip and move on to the next line. @@ -164,7 +168,7 @@ def fit(self, data): # Compute the Moore-Penrose inverse of A = P*B. # $A^+ = (A^H A)^{-1} A^H$ - A = np.matmul(p, B) + A = B*p[:, np.newaxis] AH = np.conjugate(A.transpose()) # Hermitian transpose of A pinv_PB = np.matmul(np.linalg.inv(np.matmul(AH, A)), AH) @@ -172,7 +176,7 @@ def fit(self, data): # The way that we have done it, this multiplies the input data by the # number of samples used for the fit. rfft = np.zeros(self.nx//2 + 1, dtype=np.complex64) - rfft[:k.shape[1]] = np.matmul(np.matmul(pinv_PB, p), d) + rfft[:k.shape[1]] = np.matmul(pinv_PB, p*d) # Numpy requires that the forward transform multiply # the data by n. Correct normalization. @@ -515,7 +519,7 @@ def fit(self, return_fit=False, weight_fit=False): return(rfft) - def clean(self, weight_fit=True): + def clean(self, weight_fit=True, return_model=False): """ Clean the data @@ -524,6 +528,9 @@ def clean(self, weight_fit=True): weight_fit : bool Use weighted least squares as described in the NSClean paper. Otherwise, it is a simple unweighted fit. + return_model : bool + Return the fitted model rather than the corrected data? + Default False (return the corrected data, not the model). Returns ------- @@ -531,5 +538,8 @@ def clean(self, weight_fit=True): The cleaned data array. """ self.fit(weight_fit=weight_fit) # Fit the background model - self.data -= self.model # Overwrite data with cleaned data - return(self.data) + if return_model: + return self.model + else: + self.data -= self.model # Overwrite data with cleaned data + return(self.data) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 3117eb624a..e68240f373 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -1,6 +1,6 @@ +#-*- python -*- import logging import numpy as np - import gwcs from astropy.stats import sigma_clipped_stats @@ -216,7 +216,8 @@ def clean_full_frame(detector, image, mask): return cleaned_image -def clean_subarray(detector, image, mask): +def clean_subarray(detector, image, mask, npix_iter=512, + exclude_outliers=True, sigrej=4, minfrac=0.05): """Clean a subarray image. Parameters @@ -230,6 +231,22 @@ def clean_subarray(detector, image, mask): mask : 2D bool array The mask that indicates which pixels are to be used in fitting. + npix_iter : int + Number of pixels to process simultaneously. Default 512. Should + be at least a few hundred to access sub-kHz frequencies in areas + where most pixels are available for fitting. Previous default + behavior corresponds to npix_iter of infinity. + + exclude_outliers : bool + Find and mask outliers in the fit? Default True + + sigrej : float + Number of sigma to clip when identifying outliers. Default 4. + + minpct : float + Minimum fraction of pixels locally available in the mask in + order to attempt a correction. Default 0.05 (i.e., 5%). + Returns ------- cleaned_image : 2D float array @@ -245,15 +262,92 @@ def clean_subarray(detector, image, mask): image = image.transpose()[::-1] mask = mask.transpose()[::-1] - # Instantiate the cleaner - cleaner = NSCleanSubarray(image, mask) + # We must do the masking of discrepant pixels here: it just + # doesn't work if we wait and do it in the cleaner. This is + # basically copied from lib.py. Use a robust estimator for + # standard deviation, then exclude discrepant pixels and their + # four nearest neighbors from the fit. + + if exclude_outliers: + med = np.median(image[mask]) + std = 1.4825*np.median(np.abs((image - med)[mask])) + outlier = mask&(np.abs(image - med) > sigrej*std) + + mask = mask&(~outlier) + + # also get four nearest neighbors of flagged pixels + mask[1:] = mask[1:]&(~outlier[:-1]) + mask[:-1] = mask[:-1]&(~outlier[1:]) + mask[:, 1:] = mask[:, 1:]&(~outlier[:, :-1]) + mask[:, :-1] = mask[:, :-1]&(~outlier[:, 1:]) + + + # Used to determine the fitting intervals along the slow scan + # direction. Pre-pend a zero so that we sum_mask[i] is equal + # to np.sum(mask[:i], axis=1). + + sum_mask = np.array([0] + list(np.cumsum(np.sum(mask, axis=1)))) + + i1 = 0 + i1_vals = [] + di_list = [] + models = [] + while i1 < image.shape[0] - 1: + + # Want npix_iter available pixels in this section. If + # there are fewer than 1.5*npix_iter available pixels in + # the rest of the image, just go to the end. + + for k in range(i1 + 1, image.shape[0] + 1): + if sum_mask[k] - sum_mask[i1] > npix_iter and sum_mask[-1] - sum_mask[i1] > 1.5*npix_iter: + break + + di = k - i1 + + i1_vals += [i1] + di_list += [di] + + # Fit this section only if at least minpct% of the pixels + # are available for finding the background. Don't flag + # outliers section-by-section; we have to do that earlier + # over the full array to get reliable values for the mean + # and standard deviation. + + if np.mean(mask[i1:i1 + di]) > minfrac: + cleaner = NSCleanSubarray(image[i1:i1 + di], mask[i1:i1 + di], + exclude_outliers=False) + models += [cleaner.clean(return_model=True)] + else: + log.warning("Insufficient reference pixels for NSClean around " + "row %d; no correction will be made here." % (i1)) + models += [np.zeros(image[i1:i1 + di].shape)] - # Clean the image - try: - cleaned_image = cleaner.clean() - except np.linalg.LinAlgError: - log.warning("Error cleaning image; step will be skipped") - return None + # If we have reached the end of the array, we are finished. + if k == image.shape[0]: + break + + # Step forward by half an interval so that we have + # overlapping fitting regions. + + i1 += max(int(np.round(di/2)), 1) + + model = image*0 + tot_wgt = image*0 + + # When we combine different corrections computed over + # different intervals, each one the highest weight towards the + # center of its interval and less weight towards the edge. + # Use nonzero weights everywhere so that if only one + # correction is available it gets unit weight when we + # normalize. + + for i in range(len(models)): + wgt = 1.001 - np.abs(np.linspace(-1, 1, di_list[i]))[:, np.newaxis] + model[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt*models[i] + tot_wgt[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt + + model /= tot_wgt + cleaned_image = image - model # Restore the cleaned image to the science frame if detector == "NRS1": From d3a562a4504b65548894d38269b57e56380138f2 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 23 Jul 2024 15:51:46 -0400 Subject: [PATCH 188/687] fixing docs --- docs/jwst/align_refs/description.rst | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/jwst/align_refs/description.rst b/docs/jwst/align_refs/description.rst index f5e21ae8e2..7c97d670c1 100644 --- a/docs/jwst/align_refs/description.rst +++ b/docs/jwst/align_refs/description.rst @@ -9,14 +9,18 @@ sub-package that is part of Stage 3 :ref:`calwebb_coron3 ` proce It computes offsets between science target images and reference PSF images, and shifts the PSF images into alignment. This is performed from the first integration and applied to all the -subsequent ones for both the science target -data and the reference PSF data. The first integration contained in the stacked PSF data +subsequent ones for both the science target data and the reference PSF data. +Each integration contained in the stacked PSF data (the result of the :ref:`stack_refs `) step is aligned to the first integration within a given science target exposure. This results in a new product for each science target exposure that contains a stack -of individual PSF images that have been aligned to each integration in the science +of individual PSF images that have been aligned to the first integration in the science target exposure. +The simplification of aligning to the first science integration is due to flight data +showing minimal drifts during an observation in line-of-sight pointing, or in PSF +properties. + Shifts between each PSF and target image are computed using the ``scipy.optimize.leastsq`` function. A 2D mask, supplied via a PSFMASK reference file, is used to indicate pixels to ignore when performing the minimization in the From 2f4410fe8d8636b674ff13f8eb4284997329cba4 Mon Sep 17 00:00:00 2001 From: Maria Date: Tue, 23 Jul 2024 15:59:34 -0400 Subject: [PATCH 189/687] Update CHANGES.rst removed comment since it does not affect behavior. --- CHANGES.rst | 6 ------ 1 file changed, 6 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index d1ae7d9548..c581f1cb4c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -26,12 +26,6 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] -regtest -------- - -- An missing parameter in the function call was preventing the tolerance - to be updated in test_miri_image3_catalog. [#8653] - stpipe ------ From b36e70cfad490aeba3bce01c5c45d69d934531e0 Mon Sep 17 00:00:00 2001 From: Maria Date: Wed, 24 Jul 2024 14:17:23 -0400 Subject: [PATCH 190/687] Update docs/jwst/align_refs/description.rst Co-authored-by: Melanie Clarke --- docs/jwst/align_refs/description.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/align_refs/description.rst b/docs/jwst/align_refs/description.rst index 7c97d670c1..a502265a3c 100644 --- a/docs/jwst/align_refs/description.rst +++ b/docs/jwst/align_refs/description.rst @@ -8,7 +8,7 @@ The ``align_refs`` step is one of the coronagraphic-specific steps in the ``coro sub-package that is part of Stage 3 :ref:`calwebb_coron3 ` processing. It computes offsets between science target images and reference PSF images, and shifts the PSF images into -alignment. This is performed from the first integration and applied to all the +alignment. The alignment shifts are computed from the first integration and applied to all the subsequent ones for both the science target data and the reference PSF data. Each integration contained in the stacked PSF data (the result of the :ref:`stack_refs `) step is From d9a59d210b30f64c720120fc369b8ce575db866c Mon Sep 17 00:00:00 2001 From: Maria Date: Wed, 24 Jul 2024 14:17:32 -0400 Subject: [PATCH 191/687] Update docs/jwst/align_refs/description.rst Co-authored-by: Melanie Clarke --- docs/jwst/align_refs/description.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/align_refs/description.rst b/docs/jwst/align_refs/description.rst index a502265a3c..cf5fa313c5 100644 --- a/docs/jwst/align_refs/description.rst +++ b/docs/jwst/align_refs/description.rst @@ -17,8 +17,8 @@ This results in a new product for each science target exposure that contains a s of individual PSF images that have been aligned to the first integration in the science target exposure. -The simplification of aligning to the first science integration is due to flight data -showing minimal drifts during an observation in line-of-sight pointing, or in PSF +Note that aligning to the first science integration is sufficient because flight data +shows that there are minimal drifts during an observation in line-of-sight pointing, or in PSF properties. Shifts between each PSF and target image are computed using the From 08e34a943217b965bd8164e2652e930ceb5228b3 Mon Sep 17 00:00:00 2001 From: David Law Date: Thu, 25 Jul 2024 11:28:15 -0400 Subject: [PATCH 192/687] Ensure EMICORR correction is never NaN --- jwst/emicorr/emicorr.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jwst/emicorr/emicorr.py b/jwst/emicorr/emicorr.py index dd8c31897a..1f10c52085 100644 --- a/jwst/emicorr/emicorr.py +++ b/jwst/emicorr/emicorr.py @@ -454,6 +454,9 @@ def apply_emicorr(input_model, emicorr_model, for k in range(4): noise[:, :, :, noise_x + k] = dd_noise + # Safety catch; anywhere the noise value is not finite, set it to zero + noise[np.isfinite(noise) == False] = 0 + # Subtract EMI noise from the input data log.info('Subtracting EMI noise from data') corr_data = orig_data - noise From ad87441c74d0159908f9a10d14909d7015771f29 Mon Sep 17 00:00:00 2001 From: David Law Date: Thu, 25 Jul 2024 11:32:11 -0400 Subject: [PATCH 193/687] Add change log entry --- CHANGES.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 612044094f..7c6dd57abc 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -14,6 +14,12 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] +emicorr +------- + +- Fixed a bug where MIRI EMI correction step would return NaNs when it was unable + to compute a correction. [#8675] + master_background ----------------- From 771c1c017252244a7f26813f0464b9fa339e3cfc Mon Sep 17 00:00:00 2001 From: David Law Date: Thu, 25 Jul 2024 13:42:34 -0400 Subject: [PATCH 194/687] Change test syntax --- jwst/emicorr/emicorr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/emicorr/emicorr.py b/jwst/emicorr/emicorr.py index 1f10c52085..099d5e908f 100644 --- a/jwst/emicorr/emicorr.py +++ b/jwst/emicorr/emicorr.py @@ -455,7 +455,7 @@ def apply_emicorr(input_model, emicorr_model, noise[:, :, :, noise_x + k] = dd_noise # Safety catch; anywhere the noise value is not finite, set it to zero - noise[np.isfinite(noise) == False] = 0 + noise[~np.isfinite(noise)] = 0 # Subtract EMI noise from the input data log.info('Subtracting EMI noise from data') From 859ac8ef69f01983308d8243b2143ae95895224b Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 25 Jul 2024 13:02:45 -0700 Subject: [PATCH 195/687] JP-3668: Move tweakreg into stcal (#8624) Co-authored-by: Brett Graham --- CHANGES.rst | 22 +- docs/jwst/tweakreg/README.rst | 3 + docs/jwst/tweakreg/astrometric_utils.rst | 12 - docs/jwst/tweakreg/index.rst | 1 - jwst/assign_wcs/assign_wcs_step.py | 4 +- jwst/assign_wcs/util.py | 204 +---------- jwst/resample/resample.py | 2 +- jwst/resample/resample_utils.py | 35 +- jwst/tweakreg/astrometric_utils.py | 207 ----------- jwst/tweakreg/tests/test_amutils.py | 75 ---- jwst/tweakreg/tests/test_multichip_jwst.py | 12 +- jwst/tweakreg/tests/test_tweakreg.py | 22 +- jwst/tweakreg/tests/test_utils.py | 3 +- jwst/tweakreg/tweakreg_step.py | 383 +++++---------------- jwst/tweakreg/utils.py | 43 +-- pyproject.toml | 2 +- 16 files changed, 158 insertions(+), 872 deletions(-) delete mode 100644 docs/jwst/tweakreg/astrometric_utils.rst delete mode 100644 jwst/tweakreg/astrometric_utils.py delete mode 100644 jwst/tweakreg/tests/test_amutils.py diff --git a/CHANGES.rst b/CHANGES.rst index 612044094f..16e0c470cb 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,12 +1,11 @@ 1.15.2 (unreleased) =================== +assign_wcs +---------- -scripts -------- - -- Removed many non-working and out-dated scripts. Including - many scripts that were replaced by ``strun``. [#8619] +- Moved `update_s_region_imaging`, `update_s_region_keyword`, and `wcs_from_footprints` + into stcal. [#8624] cube_build ---------- @@ -26,6 +25,12 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +scripts +------- + +- Removed many non-working and out-dated scripts. Including + many scripts that were replaced by ``strun``. [#8619] + stpipe ------ @@ -36,14 +41,15 @@ stpipe tweakreg -------- -- Removed direct setting of the ``self.skip`` attribute from within the step - itself. [#8600] - - Updated requirement for ``tweakwcs`` to version ``0.8.8`` which fixes a crash in the ``tweakreg`` step due to a ``MalformedPolygonError`` exception being raised by the ``spherical_geometry`` package for some data sets. [#8657, spacetelescope/tweakwcs#205] +- Moved all realignment methods to stcal. [#8624] + +- Removed direct setting of the ``self.skip`` attribute from within the step + itself. [#8600] 1.15.1 (2024-07-08) =================== diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index 8d5315e428..6c6811a86a 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -448,6 +448,9 @@ can be found at the following links: .. _SourceFinder: https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SourceFinder.html .. _SourceCatalog: https://photutils.readthedocs.io/en/stable/api/photutils.segmentation.SourceCatalog.html +The alignment and WCS correction portions of the step are handled by the `stcal` package. +Additional documentation may be found `here `_. + Reference Files =============== diff --git a/docs/jwst/tweakreg/astrometric_utils.rst b/docs/jwst/tweakreg/astrometric_utils.rst deleted file mode 100644 index faa6056653..0000000000 --- a/docs/jwst/tweakreg/astrometric_utils.rst +++ /dev/null @@ -1,12 +0,0 @@ -================= -astrometric_utils -================= - -The ``astrometric_utils`` module provides functions for generating -astrometric catalogs of sources for the field-of-view covered by a -set of images. - -.. currentmodule:: jwst.tweakreg.astrometric_utils - -.. automodapi:: jwst.tweakreg.astrometric_utils - :include-all-objects: diff --git a/docs/jwst/tweakreg/index.rst b/docs/jwst/tweakreg/index.rst index 56ddf9aa59..4c3ba35c0f 100644 --- a/docs/jwst/tweakreg/index.rst +++ b/docs/jwst/tweakreg/index.rst @@ -19,6 +19,5 @@ TweakReg tweakreg_catalog tweakreg_step utils - astrometric_utils .. automodapi:: jwst.tweakreg diff --git a/jwst/assign_wcs/assign_wcs_step.py b/jwst/assign_wcs/assign_wcs_step.py index 324bef826d..42b0818d03 100755 --- a/jwst/assign_wcs/assign_wcs_step.py +++ b/jwst/assign_wcs/assign_wcs_step.py @@ -5,8 +5,8 @@ from ..lib.exposure_types import IMAGING_TYPES import logging from .assign_wcs import load_wcs -from .util import MSAFileError, update_fits_wcsinfo -from .util import wfss_imaging_wcs, wcs_bbox_from_shape +from .util import (MSAFileError, wfss_imaging_wcs, + wcs_bbox_from_shape, update_fits_wcsinfo) from .nircam import imaging as nircam_imaging from .niriss import imaging as niriss_imaging diff --git a/jwst/assign_wcs/util.py b/jwst/assign_wcs/util.py index 2767182fe0..1b30ce2320 100644 --- a/jwst/assign_wcs/util.py +++ b/jwst/assign_wcs/util.py @@ -7,7 +7,6 @@ import numpy as np from astropy.coordinates import SkyCoord -from astropy.utils.misc import isiterable from astropy.io import fits from astropy.modeling import models as astmodels from astropy.table import QTable @@ -15,15 +14,14 @@ from typing import Union, List from gwcs import WCS -from gwcs.wcstools import wcs_from_fiducial, grid_from_bounding_box +from gwcs.wcstools import grid_from_bounding_box from gwcs import utils as gwutils from stpipe.exceptions import StpipeExitException +from stcal.alignment.util import compute_s_region_keyword, compute_s_region_imaging -from stdatamodels.jwst.datamodels import JwstDataModel from stdatamodels.jwst.datamodels import WavelengthrangeModel from stdatamodels.jwst.transforms.models import GrismObject -from . import pointing from ..lib.catalog_utils import SkyObject @@ -34,7 +32,7 @@ _MAX_SIP_DEGREE = 6 -__all__ = ["reproject", "wcs_from_footprints", "velocity_correction", +__all__ = ["reproject", "velocity_correction", "MSAFileError", "NoDataOnDetectorError", "compute_scale", "calc_rotation_matrix", "wrap_ra", "update_fits_wcsinfo"] @@ -197,164 +195,6 @@ def calc_rotation_matrix(roll_ref: float, v3i_yang: float, vparity: int = 1) -> return [pc1_1, pc1_2, pc2_1, pc2_2] -def wcs_from_footprints(dmodels, refmodel=None, transform=None, bounding_box=None, - pscale_ratio=None, pscale=None, rotation=None, - shape=None, crpix=None, crval=None, wcslist=None): - """ - Create a WCS from a list of input data models. - - A fiducial point in the output coordinate frame is created from the - footprints of all WCS objects. For a spatial frame this is the center - of the union of the footprints. For a spectral frame the fiducial is in - the beginning of the footprint range. - If ``refmodel`` is None, the first WCS object in the list is considered - a reference. The output coordinate frame and projection (for celestial frames) - is taken from ``refmodel``. - If ``transform`` is not supplied, a compound transform is created using - CDELTs and PC. - If ``bounding_box`` is not supplied, the bounding_box of the new WCS is computed - from bounding_box of all input WCSs. - - Parameters - ---------- - dmodels : list of `~jwst.datamodels.JwstDataModel` - A list of data models. - refmodel : `~jwst.datamodels.JwstDataModel`, optional - This model's WCS is used as a reference. - WCS. The output coordinate frame, the projection and a - scaling and rotation transform is created from it. If not supplied - the first model in the list is used as ``refmodel``. - transform : `~astropy.modeling.core.Model`, optional - A transform, passed to :meth:`~gwcs.wcstools.wcs_from_fiducial` - If not supplied Scaling | Rotation is computed from ``refmodel``. - bounding_box : tuple, optional - Bounding_box of the new WCS. - If not supplied it is computed from the bounding_box of all inputs. - pscale_ratio : float, None, optional - Ratio of input to output pixel scale. Ignored when either - ``transform`` or ``pscale`` are provided. - pscale : float, None, optional - Absolute pixel scale in degrees. When provided, overrides - ``pscale_ratio``. Ignored when ``transform`` is provided. - rotation : float, None, optional - Position angle of output image’s Y-axis relative to North. - A value of 0.0 would orient the final output image to be North up. - The default of `None` specifies that the images will not be rotated, - but will instead be resampled in the default orientation for the camera - with the x and y axes of the resampled image corresponding - approximately to the detector axes. Ignored when ``transform`` is - provided. - shape : tuple of int, None, optional - Shape of the image (data array) using ``numpy.ndarray`` convention - (``ny`` first and ``nx`` second). This value will be assigned to - ``pixel_shape`` and ``array_shape`` properties of the returned - WCS object. - crpix : tuple of float, None, optional - Position of the reference pixel in the image array. If ``crpix`` is not - specified, it will be set to the center of the bounding box of the - returned WCS object. - crval : tuple of float, None, optional - Right ascension and declination of the reference pixel. Automatically - computed if not provided. - - """ - bb = bounding_box - if wcslist is None: - wcslist = [im.meta.wcs for im in dmodels] - - if not isiterable(wcslist): - raise ValueError("Expected 'wcslist' to be an iterable of WCS objects.") - - if not all([isinstance(w, WCS) for w in wcslist]): - raise TypeError("All items in wcslist are to be instances of gwcs.WCS.") - - if refmodel is None: - refmodel = dmodels[0] - else: - if not isinstance(refmodel, JwstDataModel): - raise TypeError("Expected refmodel to be an instance of DataModel.") - - fiducial = compute_fiducial(wcslist, bb) - if crval is not None: - # overwrite spatial axes with user-provided CRVAL: - i = 0 - for k, axt in enumerate(wcslist[0].output_frame.axes_type): - if axt == 'SPATIAL': - fiducial[k] = crval[i] - i += 1 - - ref_fiducial = np.array([refmodel.meta.wcsinfo.ra_ref, refmodel.meta.wcsinfo.dec_ref]) - - prj = astmodels.Pix2Sky_TAN() - - if transform is None: - transform = [] - wcsinfo = pointing.wcsinfo_from_model(refmodel) - sky_axes, spec, other = gwutils.get_axes(wcsinfo) - - # Need to put the rotation matrix (List[float, float, float, float]) - # returned from calc_rotation_matrix into the correct shape for - # constructing the transformation - v3yangle = np.deg2rad(refmodel.meta.wcsinfo.v3yangle) - vparity = refmodel.meta.wcsinfo.vparity - if rotation is None: - roll_ref = np.deg2rad(refmodel.meta.wcsinfo.roll_ref) - else: - roll_ref = np.deg2rad(rotation) + (vparity * v3yangle) - - pc = np.reshape( - calc_rotation_matrix(roll_ref, v3yangle, vparity=vparity), - (2, 2) - ) - - rotation = astmodels.AffineTransformation2D(pc, name='pc_rotation_matrix') - transform.append(rotation) - - if sky_axes: - if not pscale: - pscale = compute_scale(refmodel.meta.wcs, ref_fiducial, - pscale_ratio=pscale_ratio) - transform.append(astmodels.Scale(pscale, name='cdelt1') & astmodels.Scale(pscale, name='cdelt2')) - - if transform: - transform = functools.reduce(lambda x, y: x | y, transform) - - out_frame = refmodel.meta.wcs.output_frame - input_frame = refmodel.meta.wcs.input_frame - wnew = wcs_from_fiducial(fiducial, coordinate_frame=out_frame, projection=prj, - transform=transform, input_frame=input_frame) - - footprints = [w.footprint().T for w in wcslist] - domain_bounds = np.hstack([wnew.backward_transform(*f) for f in footprints]) - axis_min_values = np.min(domain_bounds, axis=1) - domain_bounds = (domain_bounds.T - axis_min_values).T - - output_bounding_box = [] - for axis in out_frame.axes_order: - axis_min, axis_max = domain_bounds[axis].min(), domain_bounds[axis].max() - output_bounding_box.append((axis_min, axis_max)) - - output_bounding_box = tuple(output_bounding_box) - if crpix is None: - offset1, offset2 = wnew.backward_transform(*fiducial) - offset1 -= axis_min_values[0] - offset2 -= axis_min_values[1] - else: - offset1, offset2 = crpix - offsets = astmodels.Shift(-offset1, name='crpix1') & astmodels.Shift(-offset2, name='crpix2') - - wnew.insert_transform('detector', offsets, after=True) - wnew.bounding_box = output_bounding_box - - if shape is None: - shape = [int(axs[1] - axs[0] + 0.5) for axs in output_bounding_box[::-1]] - - wnew.pixel_shape = shape[::-1] - wnew.array_shape = shape - - return wnew - - def compute_fiducial(wcslist, bounding_box=None): """ For a celestial footprint this is the center. @@ -954,26 +794,9 @@ def update_s_region_imaging(model): """ Update the ``S_REGION`` keyword using ``WCS.footprint``. """ - - bbox = model.meta.wcs.bounding_box - - if bbox is None: - bbox = wcs_bbox_from_shape(model.data.shape) - model.meta.wcs.bounding_box = bbox - - # footprint is an array of shape (2, 4) as we - # are interested only in the footprint on the sky - footprint = model.meta.wcs.footprint(bbox, center=True, axis_type="spatial").T - # take only imaging footprint - footprint = footprint[:2, :] - - # Make sure RA values are all positive - negative_ind = footprint[0] < 0 - if negative_ind.any(): - footprint[0][negative_ind] = 360 + footprint[0][negative_ind] - - footprint = footprint.T - update_s_region_keyword(model, footprint) + s_region = compute_s_region_imaging(model.meta.wcs, shape=model.data.shape) + if s_region is not None: + model.meta.wcsinfo.s_region = s_region def compute_footprint_spectral(model): @@ -1052,18 +875,9 @@ def update_s_region_nrs_slit(slit): def update_s_region_keyword(model, footprint): """ Update the S_REGION keyword. """ - s_region = ( - "POLYGON ICRS " - " {0:.9f} {1:.9f}" - " {2:.9f} {3:.9f}" - " {4:.9f} {5:.9f}" - " {6:.9f} {7:.9f}".format(*footprint.flatten())) - if "nan" in s_region: - # do not update s_region if there are NaNs. - log.info("There are NaNs in s_region, S_REGION not updated.") - else: + s_region = compute_s_region_keyword(footprint) + if s_region is not None: model.meta.wcsinfo.s_region = s_region - log.info("Update S_REGION to {}".format(model.meta.wcsinfo.s_region)) def compute_footprint_nrs_ifu(dmodel, mod): @@ -1283,7 +1097,6 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, Parameters ---------- - datamodel : `ImageModel` The input data model for imaging or WFSS mode whose ``meta.wcsinfo`` field should be updated from GWCS. By default, ``datamodel.meta.wcs`` @@ -1386,7 +1199,6 @@ def update_fits_wcsinfo(datamodel, max_pix_error=0.01, degree=None, Notes ----- - Use of this requires a judicious choice of required accuracies. Attempts to use higher degrees (~7 or higher) will typically fail due to floating point problems that arise with high powers. diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 1a9ff734c3..641e6dc88f 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -14,7 +14,7 @@ from jwst.datamodels import ModelContainer from . import gwcs_drizzle -from . import resample_utils +from jwst.resample import resample_utils from ..model_blender import blendmeta log = logging.getLogger(__name__) diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index ed4447d8b5..0317216e58 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -9,7 +9,8 @@ from stdatamodels.dqflags import interpret_bit_flags from stdatamodels.jwst.datamodels.dqflags import pixel -from jwst.assign_wcs.util import wcs_from_footprints, wcs_bbox_from_shape +from jwst.assign_wcs.util import wcs_bbox_from_shape +from stcal.alignment import util log = logging.getLogger(__name__) @@ -22,21 +23,23 @@ def make_output_wcs(input_models, ref_wcs=None, pscale_ratio=None, pscale=None, rotation=None, shape=None, crpix=None, crval=None): - """ Generate output WCS here based on footprints of all input WCS objects + """Generate output WCS here based on footprints of all input WCS objects. + Parameters ---------- input_models : list of `~jwst.datamodel.JwstDataModel` Each datamodel must have a ~gwcs.WCS object. pscale_ratio : float, optional - Ratio of input to output pixel scale. Ignored when ``pscale`` is provided. + Ratio of input to output pixel scale. Ignored when ``pscale`` + is provided. pscale : float, None, optional Absolute pixel scale in degrees. When provided, overrides ``pscale_ratio``. rotation : float, None, optional - Position angle of output image’s Y-axis relative to North. + Position angle of output image Y-axis relative to North. A value of 0.0 would orient the final output image to be North up. The default of `None` specifies that the images will not be rotated, but will instead be resampled in the default orientation for the camera @@ -50,7 +53,7 @@ def make_output_wcs(input_models, ref_wcs=None, WCS object. crpix : tuple of float, None, optional - Position of the reference pixel in the image array. If ``crpix`` is not + Position of the reference pixel in the image array. If ``crpix`` is not specified, it will be set to the center of the bounding box of the returned WCS object. @@ -62,7 +65,6 @@ def make_output_wcs(input_models, ref_wcs=None, ------- output_wcs : object WCS object, with defined domain, covering entire set of input frames - """ if ref_wcs is None: wcslist = [i.meta.wcs for i in input_models] @@ -72,11 +74,14 @@ def make_output_wcs(input_models, ref_wcs=None, naxes = wcslist[0].output_frame.naxes if naxes != 2: - raise RuntimeError("Output WCS needs 2 spatial axes. " - f"{wcslist[0]} has {naxes}.") - - output_wcs = wcs_from_footprints( - input_models, + msg = ("Output WCS needs 2 spatial axes " + f"but the supplied WCS has {naxes} axes.") + raise RuntimeError(msg) + + output_wcs = util.wcs_from_footprints( + wcslist, + ref_wcs=wcslist[0], + ref_wcsinfo=input_models[0].meta.wcsinfo.instance, pscale_ratio=pscale_ratio, pscale=pscale, rotation=rotation, @@ -88,15 +93,17 @@ def make_output_wcs(input_models, ref_wcs=None, else: naxes = ref_wcs.output_frame.naxes if naxes != 2: - raise RuntimeError("Output WCS needs 2 spatial axes but the " - f"supplied WCS has {naxes} axes.") + msg = ("Output WCS needs 2 spatial axes " + f"but the supplied WCS has {naxes} axes.") + raise RuntimeError(msg) output_wcs = deepcopy(ref_wcs) if shape is not None: output_wcs.array_shape = shape # Check that the output data shape has no zero length dimensions if not np.prod(output_wcs.array_shape): - raise ValueError(f"Invalid output frame shape: {tuple(output_wcs.array_shape)}") + msg = f"Invalid output frame shape: {tuple(output_wcs.array_shape)}" + raise ValueError(msg) return output_wcs diff --git a/jwst/tweakreg/astrometric_utils.py b/jwst/tweakreg/astrometric_utils.py deleted file mode 100644 index a3034cb6c8..0000000000 --- a/jwst/tweakreg/astrometric_utils.py +++ /dev/null @@ -1,207 +0,0 @@ -import os -import requests - -from astropy import table -from astropy.time import Time -from astropy.table import Table -from astropy.coordinates import SkyCoord -from astropy import units as u - -from ..resample import resample_utils -from ..assign_wcs import util as wcsutil - -ASTROMETRIC_CAT_ENVVAR = "ASTROMETRIC_CATALOG_URL" -DEF_CAT_URL = 'http://gsss.stsci.edu/webservices' - -if ASTROMETRIC_CAT_ENVVAR in os.environ: - SERVICELOCATION = os.environ[ASTROMETRIC_CAT_ENVVAR] -else: - SERVICELOCATION = DEF_CAT_URL - -TIMEOUT = 30.0 # in seconds - -""" - -Primary function for creating an astrometric reference catalog. - -""" - - -__all__ = ["TIMEOUT", "compute_radius", "create_astrometric_catalog", "get_catalog"] - - -def create_astrometric_catalog(input_models, catalog="GAIADR3", output="ref_cat.ecsv", - gaia_only=False, table_format="ascii.ecsv", - existing_wcs=None, num_sources=None, epoch=None): - """Create an astrometric catalog that covers the inputs' field-of-view. - - Parameters - ---------- - input_models : list of `~jwst.datamodel.JwstDataModel` - Each datamodel must have a ~gwcs.WCS object. - - catalog : str, optional - Name of catalog to extract astrometric positions for sources in the - input images' field-of-view. Default: GAIADR3. Options available are - documented on the catalog web page. - - output : str, optional - Filename to give to the astrometric catalog read in from the master - catalog web service. If None, no file will be written out. - - gaia_only : bool, optional - Specify whether or not to only use sources from GAIA in output catalog - - existing_wcs : model - existing WCS object specified by the user as generated by - `resample.resample_utils.make_output_wcs` - - num_sources : int - Maximum number of brightest/faintest sources to return in catalog. - If `num_sources` is negative, return that number of the faintest - sources. By default, all sources are returned. - - epoch : float, optional - Reference epoch used to update the coordinates for proper motion - (in decimal year). If `None` then the epoch is obtained from - the metadata. - - Notes - ----- - This function will point to astrometric catalog web service defined - through the use of the ASTROMETRIC_CATALOG_URL environment variable. - - Returns - ------- - ref_table : `~astropy.table.Table` - Astropy Table object of the catalog - - """ - - # start by creating a composite field-of-view for all inputs - # This default output WCS will have the same plate-scale and orientation - # as the first member in the list. - # Fortunately, for alignment, this doesn't matter since no resampling of - # data will be performed. - if existing_wcs is not None: - outwcs = existing_wcs - else: - outwcs = resample_utils.make_output_wcs(input_models) - radius, fiducial = compute_radius(outwcs) - - # perform query for this field-of-view - epoch = ( - epoch - if epoch is not None - else Time(input_models[0].meta.observation.date).decimalyear - ) - ref_dict = get_catalog(fiducial[0], fiducial[1], epoch=epoch, sr=radius, catalog=catalog) - if len(ref_dict) == 0: - return ref_dict - - colnames = ('ra', 'dec', 'mag', 'objID', 'epoch') - ref_table = ref_dict[colnames] - - # Add catalog name as meta data - ref_table.meta['catalog'] = catalog - ref_table.meta['gaia_only'] = gaia_only - - # rename coordinate columns to be consistent with tweakwcs - ref_table.rename_column('ra', 'RA') - ref_table.rename_column('dec', 'DEC') - - # Append GAIA ID as a new column to the table... - gaia_sources = [] - for source in ref_dict: - if 'GAIAsourceID' in source: - g = source['GAIAsourceID'] - if gaia_only and g.strip() == '': - continue - else: - g = "-1" # indicator for no source ID extracted - gaia_sources.append(g) - - gaia_col = table.Column(data=gaia_sources, name='GaiaID', dtype='U25') - ref_table.add_column(gaia_col) - - # sort table by magnitude, fainter to brightest - ref_table.sort('mag', reverse=True) - - # If specified by the use through the 'num_sources' parameter, - # trim the returned catalog down to just the brightest 'num_sources' sources - # Should 'num_sources' be a negative value, it will return the faintest - # 'num_sources' sources. - if num_sources is not None: - indx = -1 * num_sources - ref_table = ref_table[:indx] if num_sources < 0 else ref_table[indx:] - - # Write out table to a file, if specified - if output is not None: - ref_table.write(output, format=table_format, overwrite=True) - - return ref_table - - -""" - -Utility functions for creating an astrometric reference catalog. - -""" - - -def compute_radius(wcs): - """Compute the radius from the center to the furthest edge of the WCS.""" - - fiducial = wcsutil.compute_fiducial([wcs], wcs.bounding_box) - img_center = SkyCoord(ra=fiducial[0] * u.degree, dec=fiducial[1] * u.degree) - wcs_foot = wcs.footprint() - img_corners = SkyCoord(ra=wcs_foot[:, 0] * u.degree, - dec=wcs_foot[:, 1] * u.degree) - radius = img_center.separation(img_corners).max().value - - return radius, fiducial - - -def get_catalog(ra, dec, epoch=2016.0, sr=0.1, catalog='GAIADR3'): - """ Extract catalog from VO web service. - - Parameters - ---------- - ra : float - Right Ascension (RA) of center of field-of-view (in decimal degrees) - - dec : float - Declination (Dec) of center of field-of-view (in decimal degrees) - - epoch : float, optional - Reference epoch used to update the coordinates for proper motion - (in decimal year). Default: 2016.0 - - sr : float, optional - Search radius (in decimal degrees) from field-of-view center to use - for sources from catalog. Default: 0.1 degrees - - catalog : str, optional - Name of catalog to query, as defined by web-service. Default: 'GAIADR3' - - Returns - ------- - csv : CSV object - CSV object of returned sources with all columns as provided by catalog - - """ - service_type = "vo/CatalogSearch.aspx" - spec_str = "RA={}&DEC={}&EPOCH={}&SR={}&FORMAT={}&CAT={}&MINDET=5" - headers = {"Content-Type": "text/csv"} - fmt = "CSV" - - spec = spec_str.format(ra, dec, epoch, sr, fmt, catalog) - service_url = f"{SERVICELOCATION}/{service_type}?{spec}" - rawcat = requests.get(service_url, headers=headers, timeout=TIMEOUT) - r_contents = rawcat.content.decode() # convert from bytes to a String - rstr = r_contents.split('\r\n') - # remove initial line describing the number of sources returned - # CRITICAL to proper interpretation of CSV data - del rstr[0] - - return Table.read(rstr, format='csv') diff --git a/jwst/tweakreg/tests/test_amutils.py b/jwst/tweakreg/tests/test_amutils.py deleted file mode 100644 index 3f2e40fcaf..0000000000 --- a/jwst/tweakreg/tests/test_amutils.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Test astrometric utility functions for alignment""" -import os - -import asdf -import numpy as np -import pytest - -from jwst.tweakreg import astrometric_utils as amutils - - -# Define input GWCS specification to be used for these tests -WCS_NAME = 'mosaic_long_i2d_gwcs.asdf' # Derived using B7.5 Level 3 product -EXPECTED_NUM_SOURCES = 2469 -EXPECTED_RADIUS = 0.02564497890604383 -TEST_CATALOG = 'GAIADR3' - - -@pytest.fixture(scope="module") -def wcsobj(): - path = os.path.join(os.path.dirname(__file__), WCS_NAME) - with asdf.open(path) as asdf_file: - wcs = asdf_file['wcs'] - - return wcs - - -def test_radius(wcsobj): - # compute radius - radius, fiducial = amutils.compute_radius(wcsobj) - - # check results - np.testing.assert_allclose(radius, EXPECTED_RADIUS, rtol=1e-6) - - -def test_get_catalog(wcsobj): - # Get radius and fiducial - radius, fiducial = amutils.compute_radius(wcsobj) - - # Get the catalog - cat = amutils.get_catalog(fiducial[0], fiducial[1], sr=radius, - catalog=TEST_CATALOG) - - assert len(cat) == EXPECTED_NUM_SOURCES - - -def test_create_catalog(wcsobj): - # Create catalog - gcat = amutils.create_astrometric_catalog( - None, - existing_wcs=wcsobj, - catalog=TEST_CATALOG, - output=None, - epoch='2016.0', - ) - # check that we got expected number of sources - assert len(gcat) == EXPECTED_NUM_SOURCES - - -def test_create_catalog_graceful_failure(wcsobj): - ''' - Ensure catalog retuns zero sources instead of failing outright - when the bounding box is too small to find any sources - ''' - wcsobj.bounding_box = ((0, 0.5), (0, 0.5)) - - # Create catalog - gcat = amutils.create_astrometric_catalog( - None, - existing_wcs=wcsobj, - catalog=TEST_CATALOG, - output=None, - epoch='2016.0', - ) - # check that we got expected number of sources - assert len(gcat) == 0 diff --git a/jwst/tweakreg/tests/test_multichip_jwst.py b/jwst/tweakreg/tests/test_multichip_jwst.py index 6daa383c33..952e61d81e 100644 --- a/jwst/tweakreg/tests/test_multichip_jwst.py +++ b/jwst/tweakreg/tests/test_multichip_jwst.py @@ -27,8 +27,6 @@ _REF_RMSE_RA = 3e-9 _REF_RMSE_DEC = 3e-10 -_RAD2ARCSEC = 3600.0 * np.rad2deg(1.0) -_ARCSEC2RAD = 1.0 / _RAD2ARCSEC data_path = os.path.split(os.path.abspath(data.__file__))[0] @@ -224,7 +222,7 @@ def test_multichip_jwst_alignment(monkeypatch): # 2. test_multichip_alignment_step() does not have access to 'fit_info' # in the meta data and so test_multichip_jwst_alignment() can test # the fit more extensively. - monkeypatch.setattr(tweakreg_step, 'align_wcs', _align_wcs) + monkeypatch.setattr(tweakreg_step.twk, 'align_wcs', _align_wcs) monkeypatch.setattr(tweakreg_step, 'make_tweakreg_catalog', _make_tweakreg_catalog) w1 = _make_gwcs_wcs(os.path.join(data_path, 'wfc3_uvis1.hdr')) @@ -294,7 +292,8 @@ def test_multichip_jwst_alignment(monkeypatch): def test_multichip_alignment_step(monkeypatch): - monkeypatch.setattr(tweakreg_step, 'align_wcs', _align_wcs) + + monkeypatch.setattr(tweakreg_step.twk, 'align_wcs', _align_wcs) monkeypatch.setattr(tweakreg_step, 'make_tweakreg_catalog', _make_tweakreg_catalog) # image 1 @@ -404,6 +403,8 @@ def test_multichip_alignment_step(monkeypatch): # step._is_wcs_correction_small = lambda x, y: True mr, m1, m2 = step.process(mc) + for im in [mr, m1, m2]: + assert im.meta.cal_step.tweakreg == 'COMPLETE' wc1 = m1.meta.wcs wc2 = m2.meta.wcs @@ -422,7 +423,7 @@ def test_multichip_alignment_step(monkeypatch): def test_multichip_alignment_step_abs(monkeypatch): - monkeypatch.setattr(tweakreg_step, 'align_wcs', _align_wcs) + monkeypatch.setattr(tweakreg_step.twk, 'align_wcs', _align_wcs) monkeypatch.setattr(tweakreg_step, 'make_tweakreg_catalog', _make_tweakreg_catalog) refcat_path = os.path.join(data_path, 'ref.ecsv') @@ -439,6 +440,7 @@ def test_multichip_alignment_step_abs(monkeypatch): mr.meta.observation.sequence_id = '0' mr.meta.observation.activity_id = '0' mr.meta.observation.exposure_number = '0' + mr.meta.observation.date = '2019-01-01' mr.meta.wcsinfo.v2_ref = 0 mr.meta.wcsinfo.v3_ref = 0 diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index da5e5bd894..f17b378def 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -14,12 +14,14 @@ from jwst.datamodels import ModelContainer from jwst.tweakreg import tweakreg_step from jwst.tweakreg import tweakreg_catalog -from jwst.tweakreg.utils import _wcsinfo_from_wcs_transform +from stcal.tweakreg.utils import _wcsinfo_from_wcs_transform +from stcal.tweakreg import tweakreg as twk BKG_LEVEL = 0.001 N_EXAMPLE_SOURCES = 21 N_CUSTOM_SOURCES = 15 +REFCAT = "GAIADR3" @pytest.fixture @@ -102,9 +104,9 @@ def __init__(self, wcs, original_skycoord): def meta(self): return {'original_skycoord': self._original_skycoord} - correctors = [FakeCorrector(twcs, tweakreg_step._wcs_to_skycoord(wcs))] + correctors = [FakeCorrector(twcs, twk._wcs_to_skycoord(wcs))] - assert step._is_wcs_correction_small(correctors) == is_good + assert twk._is_wcs_correction_small(correctors) == is_good def test_expected_failure_bad_starfinder(): @@ -146,6 +148,9 @@ def example_input(example_wcs): # add a wcs and wcsinfo m0.meta.wcs = example_wcs m0.meta.wcsinfo = _wcsinfo_from_wcs_transform(example_wcs) + m0.meta.wcsinfo.v3yangle = 0.0 + m0.meta.wcsinfo.vparity = -1 + m0.meta.observation.date = "2024-07-10T00:00:00.0" # and a few 'sources' m0.data[:] = BKG_LEVEL @@ -213,6 +218,7 @@ def test_src_confusion_pars(example_input, alignment_type): pars = { f"{alignment_type}separation": 1.0, f"{alignment_type}tolerance": 1.0, + "abs_refcat": REFCAT, } step = tweakreg_step.TweakRegStep(**pars) result = step(example_input) @@ -327,18 +333,18 @@ def test_custom_catalog(custom_catalog_path, example_input, catfile, asn, meta, step = tweakreg_step.TweakRegStep(**kwargs) # patch _construct_wcs_corrector to check the correct catalog was loaded - def patched_construct_wcs_corrector(model, catalog, _seen=[]): + def patched_construct_wcs_corrector(wcs, wcsinfo, catalog, group_id, _seen=[]): # we don't need to continue - if model.meta.group_id == 'a': + if group_id == 'a': assert len(catalog) == n_custom_sources - elif model.meta.group_id == 'b': + elif group_id == 'b': assert len(catalog) == N_EXAMPLE_SOURCES - _seen.append(model) + _seen.append(wcs) if len(_seen) == 2: raise ValueError("done testing") return None - monkeypatch.setattr(tweakreg_step, "_construct_wcs_corrector", patched_construct_wcs_corrector) + monkeypatch.setattr(twk, "construct_wcs_corrector", patched_construct_wcs_corrector) with pytest.raises(ValueError, match="done testing"): step(str(asn_path)) diff --git a/jwst/tweakreg/tests/test_utils.py b/jwst/tweakreg/tests/test_utils.py index e05fca7e98..05fb3969fc 100644 --- a/jwst/tweakreg/tests/test_utils.py +++ b/jwst/tweakreg/tests/test_utils.py @@ -14,9 +14,8 @@ from jwst.tweakreg.utils import ( adjust_wcs, transfer_wcs_correction, - _wcsinfo_from_wcs_transform ) - +from stcal.tweakreg.utils import _wcsinfo_from_wcs_transform data_path = path.split(path.abspath(data.__file__))[0] diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index f0d7d68600..0ae6a51eb0 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -5,29 +5,22 @@ """ from os import path -import math -from astropy import units as u -from astropy.coordinates import SkyCoord from astropy.table import Table from astropy.time import Time -from tweakwcs.imalign import align_wcs from tweakwcs.correctors import JWSTWCSCorrector -from tweakwcs.matchutils import XYXYMatch + +import stcal.tweakreg.tweakreg as twk from jwst.datamodels import ModelContainer from jwst.stpipe import record_step_status +from jwst.assign_wcs.util import update_fits_wcsinfo, update_s_region_imaging # LOCAL from ..stpipe import Step -from ..assign_wcs.util import update_fits_wcsinfo, update_s_region_imaging, wcs_from_footprints -from .astrometric_utils import create_astrometric_catalog from .tweakreg_catalog import make_tweakreg_catalog -_SQRT2 = math.sqrt(2.0) - - def _oxford_or_str_join(str_list): nelem = len(str_list) if not nelem: @@ -137,26 +130,6 @@ class TweakRegStep(Step): def process(self, input): images = ModelContainer(input) - if self.separation <= _SQRT2 * self.tolerance: - self.log.error( - "Parameter 'separation' must be larger than 'tolerance' by at " - "least a factor of sqrt(2) to avoid source confusion." - ) - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - self.log.warning("Skipping 'TweakRegStep' step.") - return input - - if self.abs_separation <= _SQRT2 * self.abs_tolerance: - self.log.error( - "Parameter 'abs_separation' must be larger than 'abs_tolerance' " - "by at least a factor of sqrt(2) to avoid source confusion." - ) - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - self.log.warning("Skipping 'TweakRegStep' step.") - return input - if len(images) == 0: raise ValueError("Input must contain at least one image model.") @@ -213,7 +186,7 @@ def process(self, input): # pre-allocate collectors (same length and order as images) correctors = [None] * len(images) - # Build the catalog and corrector for each input images + # Build the catalog for each input image for (model_index, image_model) in enumerate(images): # now that the model is open, check it's metadata for a custom catalog # only if it's not listed in the catdict @@ -242,7 +215,7 @@ def process(self, input): catalog = _rename_catalog_columns(catalog) # filter all sources outside the wcs bounding box - catalog = _filter_catalog_by_bounding_box( + catalog = twk.filter_catalog_by_bounding_box( catalog, image_model.meta.wcs.bounding_box) @@ -265,227 +238,93 @@ def process(self, input): image_model.meta.tweakreg_catalog = self._write_catalog(catalog, filename) # construct the corrector since the model is open (and already has a group_id) - correctors[model_index] = _construct_wcs_corrector(image_model, catalog) + correctors[model_index] = twk.construct_wcs_corrector(image_model.meta.wcs, + image_model.meta.wcsinfo.instance, + catalog, + image_model.meta.group_id,) self.log.info('') self.log.info("Number of image groups to be aligned: {:d}." .format(n_groups)) - # keep track of if 'local' alignment failed, even if this - # fails, absolute alignment might be run (if so configured) - local_align_failed = False - - # if we have >1 group of images, align them to each other + # wrapper to stcal tweakreg routines + # step skip conditions should throw TweakregError from stcal if n_groups > 1: - - # align images: - xyxymatch = XYXYMatch( - searchrad=self.searchrad, - separation=self.separation, - use2dhist=self.use2dhist, - tolerance=self.tolerance, - xoffset=self.xoffset, - yoffset=self.yoffset - ) - try: - align_wcs( - correctors, - refcat=None, - enforce_user_order=self.enforce_user_order, - expand_refcat=self.expand_refcat, - minobj=self.minobj, - match=xyxymatch, - fitgeom=self.fitgeometry, - nclip=self.nclip, - sigma=(self.sigma, 'rmse') - ) - - except ValueError as e: - msg = e.args[0] - if (msg == "Too few input images (or groups of images) with " - "non-empty catalogs."): - # we need at least two exposures to perform image alignment - self.log.warning(msg) - self.log.warning("At least two exposures are required for " - "image alignment.") - self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - record_step_status(images, "tweakreg", success=False) - if not align_to_abs_refcat: - return images - local_align_failed = True - else: - raise e - - except RuntimeError as e: - msg = e.args[0] - if msg.startswith("Number of output coordinates exceeded allocation"): - # we need at least two exposures to perform image alignment - self.log.error(msg) - self.log.error("Multiple sources within specified tolerance " - "matched to a single reference source. Try to " - "adjust 'tolerance' and/or 'separation' parameters.") - self.log.warning("Skipping 'TweakRegStep'...") - record_step_status(images, "tweakreg", success=False) - return images - else: - raise e - - if not local_align_failed and not self._is_wcs_correction_small(correctors): - if align_to_abs_refcat: - self.log.warning("Skipping relative alignment (stage 1)...") - else: - self.log.warning("Skipping 'TweakRegStep'...") - record_step_status(images, "tweakreg", success=False) - return images - - if align_to_abs_refcat: - # now, align things to the reference catalog - # this can occur after alignment between groups (only if >1 group) - - # Get catalog of GAIA sources for the field - # - # NOTE: If desired, the pipeline can write out the reference - # catalog as a separate product with a name based on - # whatever convention is determined by the JWST Cal Working - # Group. - - if self.save_abs_catalog: - if self.output_dir is None: - output_name = 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower()) - else: - output_name = path.join(self.output_dir, 'fit_{}_ref.ecsv'.format(self.abs_refcat.lower())) + # relative alignment of images to each other (if more than one group) + correctors = \ + twk.relative_align(correctors, + enforce_user_order=self.enforce_user_order, + expand_refcat=self.expand_refcat, + minobj=self.minobj, + fitgeometry=self.fitgeometry, + nclip=self.nclip, + sigma=self.sigma, + searchrad=self.searchrad, + use2dhist=self.use2dhist, + separation=self.separation, + tolerance=self.tolerance, + xoffset=self.xoffset, + yoffset=self.yoffset,) + except twk.TweakregError as e: + self.log.warning(str(e)) + local_align_failed = True else: - output_name = None - - # initial shift to be used with absolute astrometry - self.abs_xoffset = 0 - self.abs_yoffset = 0 - - self.abs_refcat = self.abs_refcat.strip() - gaia_cat_name = self.abs_refcat.upper() - - if gaia_cat_name in SINGLE_GROUP_REFCAT: - ref_model = images[0] - - epoch = Time(ref_model.meta.observation.date).decimalyear - - # combine all aligned wcs to compute a new footprint to - # filter the absolute catalog sources - combined_wcs = wcs_from_footprints( - None, - refmodel=ref_model, - wcslist=[corrector.wcs for corrector in correctors], - ) + local_align_failed = False + else: + local_align_failed = True - ref_cat = create_astrometric_catalog( - None, - gaia_cat_name, - existing_wcs=combined_wcs, - output=output_name, - epoch=epoch, - ) + # absolute alignment to the reference catalog + # can (and does) occur after alignment between groups + if align_to_abs_refcat: + try: + ref_image = images[0] + correctors = \ + twk.absolute_align(correctors, self.abs_refcat, + ref_wcs=ref_image.meta.wcs, + ref_wcsinfo=ref_image.meta.wcsinfo.instance, + epoch=Time(ref_image.meta.observation.date).decimalyear, + abs_minobj=self.abs_minobj, + abs_fitgeometry=self.abs_fitgeometry, + abs_nclip=self.abs_nclip, + abs_sigma=self.abs_sigma, + abs_searchrad=self.abs_searchrad, + abs_use2dhist=self.abs_use2dhist, + abs_separation=self.abs_separation, + abs_tolerance=self.abs_tolerance, + save_abs_catalog=self.save_abs_catalog, + abs_catalog_output_dir=self.output_dir, + ) + + except twk.TweakregError as e: + self.log.warning(str(e)) + for model in images: + model.meta.cal_step.tweakreg = "SKIPPED" + return images + + if local_align_failed and not align_to_abs_refcat: + for model in images: + record_step_status(model, "tweakreg", success=False) + return images - elif path.isfile(self.abs_refcat): - ref_cat = Table.read(self.abs_refcat) + # one final pass through all the models to update them based + # on the results of this step + self._apply_tweakreg_solution(images, correctors, + align_to_abs_refcat=align_to_abs_refcat) + return images - else: - raise ValueError("'abs_refcat' must be a path to an " - "existing file name or one of the supported " - f"reference catalogs: {_SINGLE_GROUP_REFCAT_STR}.") - - # Check that there are enough GAIA sources for a reliable/valid fit - num_ref = len(ref_cat) - if num_ref < self.abs_minobj: - self.log.warning( - f"Not enough sources ({num_ref}) in the reference catalog " - "for the single-group alignment step to perform a fit. " - f"Skipping alignment to the {self.abs_refcat} reference " - "catalog!" - ) - else: - # align images: - # Update to separation needed to prevent confusion of sources - # from overlapping images where centering is not consistent or - # for the possibility that errors still exist in relative overlap. - xyxymatch_gaia = XYXYMatch( - searchrad=self.abs_searchrad, - separation=self.abs_separation, - use2dhist=self.abs_use2dhist, - tolerance=self.abs_tolerance, - xoffset=self.abs_xoffset, - yoffset=self.abs_yoffset - ) - # Set group_id to same value so all get fit as one observation - # The assigned value, 987654, has been hard-coded to make it - # easy to recognize when alignment to GAIA was being performed - # as opposed to the group_id values used for relative alignment - # earlier in this step. - for corrector in correctors: - corrector.meta['group_id'] = 987654 - if ('fit_info' in corrector.meta and - 'REFERENCE' in corrector.meta['fit_info']['status']): - del corrector.meta['fit_info'] - - # Perform fit - try: - align_wcs( - correctors, - refcat=ref_cat, - enforce_user_order=True, - expand_refcat=False, - minobj=self.abs_minobj, - match=xyxymatch_gaia, - fitgeom=self.abs_fitgeometry, - nclip=self.abs_nclip, - sigma=(self.abs_sigma, 'rmse') - ) - except ValueError as e: - msg = e.args[0] - if (msg == "Too few input images (or groups of images) with " - "non-empty catalogs."): - # we need at least two exposures to perform image alignment - self.log.warning(msg) - self.log.warning( - "At least one exposure is required to align images " - "to an absolute reference catalog. Alignment to an " - "absolute reference catalog will not be performed." - ) - if local_align_failed or n_groups == 1: - self.log.warning("Nothing to do. Skipping 'TweakRegStep'...") - record_step_status(images, "tweakreg", success=False) - return images - else: - raise e - - except RuntimeError as e: - msg = e.args[0] - if msg.startswith("Number of output coordinates exceeded allocation"): - # we need at least two exposures to perform image alignment - self.log.error(msg) - self.log.error( - "Multiple sources within specified tolerance " - "matched to a single reference source. Try to " - "adjust 'tolerance' and/or 'separation' parameters." - "Alignment to an absolute reference catalog will " - "not be performed." - ) - if local_align_failed or n_groups == 1: - self.log.warning("Skipping 'TweakRegStep'...") - record_step_status(images, "tweakreg", success=False) - return images - else: - raise e + def _apply_tweakreg_solution(self, + images: ModelContainer, + correctors: list[JWSTWCSCorrector], + align_to_abs_refcat: bool = False, + ) -> ModelContainer: - # one final pass through all the models to update them based - # on the results of this step for (image_model, corrector) in zip(images, correctors): - image_model.meta.cal_step.tweakreg = 'COMPLETE' # retrieve fit status and update wcs if fit is successful: - if ('fit_info' in corrector.meta and - 'SUCCESS' in corrector.meta['fit_info']['status']): + if ("fit_info" in corrector.meta and + "SUCCESS" in corrector.meta["fit_info"]["status"]): # Update/create the WCS .name attribute with information # on this astrometric fit as the only record that it was @@ -498,7 +337,7 @@ def process(self, input): # translated to the FITS WCSNAME keyword # IF that is what gets recorded in the archive # for end-user searches. - corrector.wcs.name = "FIT-LVL3-{}".format(self.abs_refcat) + corrector.wcs.name = f"FIT-LVL3-{self.abs_refcat}" image_model.meta.wcs = corrector.wcs update_s_region_imaging(image_model) @@ -517,12 +356,13 @@ def process(self, input): crpix=None ) except (ValueError, RuntimeError) as e: - self.log.warning( - "Failed to update 'meta.wcsinfo' with FITS SIP " - f'approximation. Reported error is:\n"{e.args[0]}"' - ) + msg = f"Failed to update 'meta.wcsinfo' with FITS SIP \ + approximation. Reported error is: \n {e.args[0]}" + self.log.warning(msg) + record_step_status(image_model, "tweakreg", success=True) + + return image_model - return images def _write_catalog(self, catalog, filename): ''' @@ -597,24 +437,6 @@ def _find_sources(self, image_model): starfinder_kwargs=starfinder_kwargs, ) - def _is_wcs_correction_small(self, correctors): - # check for a small wcs correction, it should be small - if self.use2dhist: - max_corr = 2 * (self.searchrad + self.tolerance) * u.arcsec - else: - max_corr = 2 * (max(abs(self.xoffset), abs(self.yoffset)) + - self.tolerance) * u.arcsec - for corrector in correctors: - aligned_skycoord = _wcs_to_skycoord(corrector.wcs) - original_skycoord = corrector.meta['original_skycoord'] - separation = original_skycoord.separation(aligned_skycoord) - if not (separation < max_corr).all(): - # Large corrections are typically a result of source - # mis-matching or poorly-conditioned fit. Skip such models. - self.log.warning(f"WCS has been tweaked by more than {10 * self.tolerance} arcsec") - return False - return True - def _parse_catfile(catfile): """ @@ -672,41 +494,4 @@ def _rename_catalog_columns(catalog): "columns 'x' and 'y' or 'xcentroid' and " "'ycentroid'." ) - return catalog - - -def _filter_catalog_by_bounding_box(catalog, bounding_box): - if bounding_box is None: - return catalog - - # filter out sources outside the WCS bounding box - ((xmin, xmax), (ymin, ymax)) = bounding_box - x = catalog['x'] - y = catalog['y'] - mask = (x > xmin) & (x < xmax) & (y > ymin) & (y < ymax) - return catalog[mask] - - -def _wcs_to_skycoord(wcs): - ra, dec = wcs.footprint(axis_type="spatial").T - return SkyCoord(ra=ra, dec=dec, unit="deg") - - -def _construct_wcs_corrector(image_model, catalog): - # pre-compute skycoord here so we can later use it - # to check for a small wcs correction - wcs = image_model.meta.wcs - refang = image_model.meta.wcsinfo.instance - return JWSTWCSCorrector( - wcs=image_model.meta.wcs, - wcsinfo={'roll_ref': refang['roll_ref'], - 'v2_ref': refang['v2_ref'], - 'v3_ref': refang['v3_ref']}, - # catalog and group_id are required meta - meta={ - 'catalog': catalog, - 'name': catalog.meta.get('name'), - 'group_id': image_model.meta.group_id, - 'original_skycoord': _wcs_to_skycoord(wcs), - } - ) + return catalog \ No newline at end of file diff --git a/jwst/tweakreg/utils.py b/jwst/tweakreg/utils.py index 9b3fbaf6e4..6f4c97f8c3 100644 --- a/jwst/tweakreg/utils.py +++ b/jwst/tweakreg/utils.py @@ -1,6 +1,5 @@ from copy import deepcopy -from astropy.modeling.rotations import RotationSequence3D from astropy import units from gwcs.wcs import WCS import numpy as np @@ -8,8 +7,9 @@ from tweakwcs.linearfit import build_fit_matrix from stdatamodels.jwst.datamodels import ImageModel -from ..assign_wcs.util import update_fits_wcsinfo +from stcal.tweakreg.utils import _wcsinfo_from_wcs_transform from ..assign_wcs.pointing import _v23tosky +from ..assign_wcs.util import update_fits_wcsinfo _RAD2ARCSEC = 3600.0 * np.rad2deg(1.0) @@ -18,45 +18,6 @@ __all__ = ["adjust_wcs", "transfer_wcs_correction"] -def _wcsinfo_from_wcs_transform(wcs): - frames = wcs.available_frames - if 'v2v3' not in frames or 'world' not in frames or frames[-1] != 'world': - raise ValueError( - "Unsupported WCS structure." - ) - - # Initially get v2_ref, v3_ref, and roll_ref from - # the v2v3 to world transform. Also get ra_ref, dec_ref - t = wcs.get_transform(frames[-2], 'world') - for m in t: - if isinstance(m, RotationSequence3D) and m.parameters.size == 5: - v2_ref, nv3_ref, roll_ref, dec_ref, nra_ref = m.angles.value - break - else: - raise ValueError( - "Unsupported WCS structure." - ) - - # overwrite v2_ref, v3_ref, and roll_ref with - # values from the tangent plane when available: - if 'v2v3corr' in frames: - # get v2_ref, v3_ref, and roll_ref from - # the v2v3 to v2v3corr transform: - frm1 = 'v2v3vacorr' if 'v2v3vacorr' in frames else 'v2v3' - tpcorr = wcs.get_transform(frm1, 'v2v3corr') - v2_ref, nv3_ref, roll_ref = tpcorr['det_to_optic_axis'].angles.value - - wcsinfo = { - 'v2_ref': 3600 * v2_ref, - 'v3_ref': -3600 * nv3_ref, - 'roll_ref': roll_ref, - 'ra_ref': -nra_ref, - 'dec_ref': dec_ref - } - - return wcsinfo - - def adjust_wcs(wcs, delta_ra=0.0, delta_dec=0.0, delta_roll=0.0, scale_factor=1.0): """ diff --git a/pyproject.toml b/pyproject.toml index b422963bf4..06fa22799f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal>=1.7.3,<1.8.0", + "stcal@git+https://github.com/spacetelescope/stcal@main", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", "stsci.image>=2.3.5", From 2e3beb385d31692fadee684298bcb3b2832db3b7 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 25 Jun 2024 14:07:07 -0400 Subject: [PATCH 196/687] add notes about outlier detection step --- .../outlier_detection_step.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index cd633a42c2..c5315e48e1 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -50,6 +50,68 @@ class OutlierDetectionStep(Step): # The members of spec needs to be a super-set of all parameters needed # by the various versions of the outlier_detection algorithms, and each # version will pick and choose what they need while ignoring the rest. + # There are effectively N modes which use the following parameters: + # "-" = parameter is disabled + # " " = parameter is ignored + # "+" = parameter is used + # "!" = used only if another parameter is set + # "?" = idk? + # | Image | Coron | TSO | Spec(IFU) | Spec | + # -------------------------------------------------------------------- + # weight_type | ! | | + | | ! | + # pixfrac | ! | | | | ! | + # kernel | ! | | | | ! | + # fillval | ! | | | | ! | + # maskpt | | | + | | | + # snr | + | + | + | | | + # scale | + | + | + | | | + # backg | + | + | + | | | + # kernel_size | | | | + | | + # threshold_percent | | | | + | | + # rolling_window_width | | | + | | | + # ifu_second_check | | | | + | | + # save_intermediate_results | + | + | + | + | + | + # resample_data | + | - | - | | + | + # good_bits | + | + | + | + | + | + # search_output_file *2 | | | | | | + # allowed_memory | ! | | | | ! | + # in_memory *1 | + | + | + | + | + | + # + # *1 in_memory is used in the generic step when opening the input. But + # not all modes use the parameter internally. + # + # *2 This is used internally in stpipe. The table will not document this + # feature as it's difficult to know when it will and won't be used. + # + # For each mode the input is: + # Image : ModelContainer of ImageModel + # Coron : CubeModel (either a psf or target) + # TSO : CubeModel + # IFU : ModelContainer of IFUImageModel + # Spec : SourceModelContainer of SlitModel (created from MultiSlitModel) + # + # So the only mode that benefits from `in_memory` is Image. + # + # For Coron (which uses only the non-resampled version of Image) the fake + # ModelContainer made from the input CubeModel is all in memory so + # `_remove_file` is skipped (for the open models) for both the + # "drizzled_models" and "blot_models". + # + # For TSO `in_memory` isn't used (even for the calls to the parent + # `compute_weight_threshold` and `save_median`). + # + # For IFU `in_memory` isn't used (and the algorithm is very different using + # almost nothing from the parent class). + # + # For Spec(non-IFU) `in_memory` is used. However the input + # `SourceModelContainer` doesn't use the "save_open" "return_open" + # arguments when made from a `MultiSlitModel` (as in spec3). So the + # input is a list of "open" `SlitModel`s. The call to `ResampleSpec` does + # use `in_memory` and writes out the resampled `SlitModels` returning + # a `ModelContainer` (if `resample=True`). `create_median` and + # `blot_median` are used from the parent class and use `in_memory` in + # the same way as the Image mode. + spec = """ weight_type = option('ivm','exptime',default='ivm') pixfrac = float(default=1.0) From b416c8004a84f819c546b34f3e6da57425485f72 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 25 Jun 2024 19:55:35 -0400 Subject: [PATCH 197/687] WIP: more refactoring --- jwst/outlier_detection/outlier_detection.py | 409 +----------------- .../outlier_detection_spec.py | 27 +- .../outlier_detection_tso.py | 20 +- .../tests/test_outlier_detection.py | 12 +- jwst/outlier_detection/utils.py | 325 ++++++++++++++ 5 files changed, 353 insertions(+), 440 deletions(-) create mode 100644 jwst/outlier_detection/utils.py diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 92cbdc6db4..f8e9d68bae 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -6,27 +6,22 @@ import os import numpy as np -from astropy.stats import sigma_clip -from scipy import ndimage -from drizzle.cdrizzle import tblot from stdatamodels.jwst.datamodels.util import open as datamodel_open from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer from jwst.resample import resample -from jwst.resample.resample_utils import build_driz_weight, calc_gwcs_pixmap +from jwst.resample.resample_utils import build_driz_weight from jwst.stpipe import Step -from jwst.resample.resample import compute_image_pixel_area + +from .utils import _remove_file, create_median, detect_outliers log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE'] -OUTLIER = datamodels.dqflags.pixel['OUTLIER'] - -__all__ = ["OutlierDetection", "flag_cr", "abs_deriv"] +__all__ = ["OutlierDetection"] class OutlierDetection: @@ -100,7 +95,6 @@ def _convert_inputs(self): bits = self.outlierpars['good_bits'] if isinstance(self.inputs, ModelContainer): self.input_models = self.inputs - self.converted = False else: self.input_models = ModelContainer() num_inputs = self.inputs.data.shape[0] @@ -115,7 +109,6 @@ def _convert_inputs(self): weight_type=self.outlierpars['weight_type'], good_bits=bits) self.input_models.append(image) - self.converted = True def build_suffix(self, **pars): """Build suffix. @@ -165,7 +158,7 @@ def do_detection(self): median_model.meta.wcs = dm0.meta.wcs # Perform median combination on set of drizzled mosaics - median_model.data = self.create_median(drizzled_models) + median_model.data = create_median(drizzled_models, self.outlierpars['maskpt']) if self.outlierpars['save_intermediate_results']: self.save_median(median_model) @@ -176,80 +169,13 @@ def do_detection(self): for fn in drizzled_models._models: _remove_file(fn) - if pars['resample_data']: - # Blot the median image back to recreate each input image specified - # in the original input list/ASN/ModelContainer - blot_models = self.blot_median(median_model) - - else: - # Median image will serve as blot image - blot_models = ModelContainer(open_models=False) - for i in range(len(self.input_models)): - blot_models.append(median_model) - # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - self.detect_outliers(blot_models) + detect_outliers(self.input_models, median_model, **self.outlierpars) # clean-up (just to be explicit about being finished with # these results) - if not pars['save_intermediate_results'] and not pars['in_memory']: - for fn in blot_models._models: - _remove_file(fn) - del median_model, blot_models - - - def create_median(self, resampled_models): - """Create a median image from the singly resampled images. - - NOTES - ----- - This version is simplified from astrodrizzle's version in the - following ways: - - type of combination: fixed to 'median' - - 'minmed' not implemented as an option - """ - maskpt = self.outlierpars.get('maskpt', 0.7) - - log.info("Computing median") - - weight_thresholds = self.compute_weight_threshold_container(resampled_models, maskpt) - - # Now, set up buffered access to all input models - resampled_models.set_buffer(1.0) # Set buffer at 1Mb - resampled_sections = resampled_models.get_sections() - median_image = np.empty((resampled_models.imrows, resampled_models.imcols), - resampled_models.imtype) - median_image[:] = np.nan # initialize with NaNs - - for (resampled_sci, resampled_weight, (row1, row2)) in resampled_sections: - # Create a mask for each input image, masking out areas where there is - # no data or the data has very low weight - badmasks = [] - for weight, weight_threshold in zip(resampled_weight, weight_thresholds): - badmask = np.less(weight, weight_threshold) - log.debug("Percentage of pixels with low weight: {}".format( - np.sum(badmask) / len(weight.flat) * 100)) - badmasks.append(badmask) - - # Fill resampled_sci array with nan's where mask values are True - for f1, f2 in zip(resampled_sci, badmasks): - for elem1, elem2 in zip(f1, f2): - elem1[elem2] = np.nan - - del badmasks - - # For a stack of images with "bad" data replaced with Nan - # use np.nanmedian to compute the median. - with warnings.catch_warnings(): - warnings.filterwarnings(action="ignore", - message="All-NaN slice encountered", - category=RuntimeWarning) - median_image[row1:row2] = np.nanmedian(resampled_sci, axis=0) - del resampled_sci, resampled_weight - - return median_image - + del median_model def save_median(self, median_model): ''' @@ -260,7 +186,6 @@ def save_median(self, median_model): median_model : ~jwst.datamodels.ImageModel The median ImageModel or CubeModel to save ''' - if self.outlierpars.get('asn_id', None) is None: suffix_to_remove = self.resample_suffix else: @@ -270,323 +195,3 @@ def save_median(self, median_model): suffix='median') median_model.save(median_model_output_path) log.info(f"Saved model in {median_model_output_path}") - - - def compute_weight_threshold_container(self, resampled_models, maskpt): - ''' - Compute weight means without keeping datamodels for each input open - - Parameters - ---------- - resampled_models : ~jwst.datamodels.ModelContainer - The input data models. - - maskpt : float - The percentage of the mean weight to use as a threshold for masking. - - Returns - ------- - list - The weight thresholds for each integration. - ''' - - # Start by ensuring that the ModelContainer does NOT open and keep each datamodel - ropen_orig = resampled_models._return_open - resampled_models._return_open = True - # keep track of resulting computation for each input resampled datamodel - weight_thresholds = [] - # For each model, compute the bad-pixel threshold from the weight arrays - for resampled in resampled_models: - weight = resampled.wht - weight_threshold = self.compute_weight_threshold(weight, maskpt) - weight_thresholds.append(weight_threshold) - # close and delete the model, just to explicitly try to keep the memory as clean as possible - resampled.close() - del resampled - # Reset ModelContainer attribute to original value - resampled_models._return_open = ropen_orig - return weight_thresholds - - - def compute_weight_threshold(self, weight, maskpt): - ''' - Compute the weight threshold for a single image or cube. - - Parameters - ---------- - weight : numpy.ndarray - The weight array - - maskpt : float - The percentage of the mean weight to use as a threshold for masking. - - Returns - ------- - float - The weight threshold for this integration. - ''' - - # necessary in order to assure that mask gets applied correctly - if hasattr(weight, '_mask'): - del weight._mask - mask_zero_weight = np.equal(weight, 0.) - mask_nans = np.isnan(weight) - # Combine the masks - weight_masked = np.ma.array(weight, mask=np.logical_or( - mask_zero_weight, mask_nans)) - # Sigma-clip the unmasked data - weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) - mean_weight = np.mean(weight_masked) - # Mask pixels where weight falls below maskpt percent - weight_threshold = mean_weight * maskpt - return weight_threshold - - - def blot_median(self, median_model): - """Blot resampled median image back to the detector images.""" - interp = self.outlierpars.get('interp', 'linear') - sinscl = self.outlierpars.get('sinscl', 1.0) - - # Initialize container for output blot images - blot_models = ModelContainer(open_models=False) - - log.info("Blotting median") - for model in self.input_models: - blotted_median = model.copy() - - # clean out extra data not related to blot result - blotted_median.err *= 0.0 # None - blotted_median.dq *= 0 # None - - # apply blot to re-create model.data from median image - blotted_median.data = gwcs_blot(median_model, model, interp=interp, - sinscl=sinscl) - - model_path = self.make_output_path(basepath=model.meta.filename, suffix='blot') - blotted_median.save(model_path) - log.info(f"Saved model in {model_path}") - - # Append model name to the ModelContainer so it is not passed in memory - blot_models.append(model_path) - - return blot_models - - def detect_outliers(self, blot_models): - """Flag DQ array for cosmic rays in input images. - - The science frame in each ImageModel in input_models is compared to - the corresponding blotted median image in blot_models. The result is - an updated DQ array in each ImageModel in input_models. - - Parameters - ---------- - input_models: JWST ModelContainer object - data model container holding science ImageModels, modified in place - - blot_models : JWST ModelContainer object - data model container holding ImageModels of the median output frame - blotted back to the wcs and frame of the ImageModels in - input_models - - Returns - ------- - None - The dq array in each input model is modified in place - - """ - log.info("Flagging outliers") - for image, blot in zip(self.input_models, blot_models): - blot = datamodel_open(blot) - flag_cr(image, blot, **self.outlierpars) - blot.close() - del blot - - if self.converted: - # Make sure actual input gets updated with new results - for i in range(len(self.input_models)): - self.inputs.dq[i, :, :] = self.input_models[i].dq - - -def _remove_file(fn): - if isinstance(fn, str) and os.path.isfile(fn): - os.remove(fn) - log.info(f"Removing file {fn}") - - -def flag_cr(sci_image, blot_image, snr="5.0 4.0", scale="1.2 0.7", backg=0, - resample_data=True, **kwargs): - """Masks outliers in science image by updating DQ in-place - - Mask blemishes in dithered data by comparing a science image - with a model image and the derivative of the model image. - - Parameters - ---------- - sci_image : ~jwst.datamodels.ImageModel - the science data. Can also accept a CubeModel, but only if - resample_data is False - - blot_image : ~jwst.datamodels.ImageModel - the blotted median image of the dithered science frames. - Can also accept a CubeModel, but only if resample_data - is False - - snr : str - Signal-to-noise ratio - - scale : str - scaling factor applied to the derivative - - backg : float - Background value (scalar) to subtract - - resample_data : bool - Boolean to indicate whether blot_image is created from resampled, - dithered data or not - - Notes - ----- - Accepting a CubeModel for sci_image and blot_image with resample_data=True - appears to be a relatively simple extension, as the only thing that explicitly - relies on the dimensionality is the kernel, which could be generalized. - However, this is not currently needed, as CubeModels are only passed in for - TSO data, where resampling is always False. - """ - snr1, snr2 = [float(val) for val in snr.split()] - scale1, scale2 = [float(val) for val in scale.split()] - - # Get background level of science data if it has not been subtracted, so it - # can be added into the level of the blotted data, which has been - # background-subtracted - if (sci_image.meta.background.subtracted is False and - sci_image.meta.background.level is not None): - subtracted_background = sci_image.meta.background.level - log.debug(f"Adding background level {subtracted_background} to blotted image") - else: - # No subtracted background. Allow user-set value, which defaults to 0 - subtracted_background = backg - - sci_data = sci_image.data - blot_data = blot_image.data - blot_deriv = abs_deriv(blot_data) - err_data = np.nan_to_num(sci_image.err) - - # create the outlier mask - if resample_data: # dithered outlier detection - blot_data += subtracted_background - diff_noise = np.abs(sci_data - blot_data) - - # Create a boolean mask based on a scaled version of - # the derivative image (dealing with interpolating issues?) - # and the standard n*sigma above the noise - threshold1 = scale1 * blot_deriv + snr1 * err_data - mask1 = np.greater(diff_noise, threshold1) - - # Smooth the boolean mask with a 3x3 boxcar kernel - kernel = np.ones((3, 3), dtype=int) - mask1_smoothed = ndimage.convolve(mask1, kernel, mode='nearest') - - # Create a 2nd boolean mask based on the 2nd set of - # scale and threshold values - threshold2 = scale2 * blot_deriv + snr2 * err_data - mask2 = np.greater(diff_noise, threshold2) - - # Final boolean mask - cr_mask = mask1_smoothed & mask2 - - else: # stack outlier detection - diff_noise = np.abs(sci_data - blot_data) - - # straightforward detection of outliers for non-dithered data since - # err_data includes all noise sources (photon, read, and flat for baseline) - cr_mask = np.greater(diff_noise, snr1 * err_data) - - # Count existing DO_NOT_USE pixels - count_existing = np.count_nonzero(sci_image.dq & DO_NOT_USE) - - # Update the DQ array in the input image. - sci_image.dq = np.bitwise_or(sci_image.dq, cr_mask * (DO_NOT_USE | OUTLIER)) - - # Report number (and percent) of new DO_NOT_USE pixels found - count_outlier = np.count_nonzero(sci_image.dq & DO_NOT_USE) - count_added = count_outlier - count_existing - percent_cr = count_added / sci_image.dq.size * 100 - log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") - - -def abs_deriv(array): - """Take the absolute derivate of a numpy array.""" - tmp = np.zeros(array.shape, dtype=np.float64) - out = np.zeros(array.shape, dtype=np.float64) - - tmp[1:, :] = array[:-1, :] - tmp, out = _absolute_subtract(array, tmp, out) - tmp[:-1, :] = array[1:, :] - tmp, out = _absolute_subtract(array, tmp, out) - - tmp[:, 1:] = array[:, :-1] - tmp, out = _absolute_subtract(array, tmp, out) - tmp[:, :-1] = array[:, 1:] - tmp, out = _absolute_subtract(array, tmp, out) - - return out - - -def _absolute_subtract(array, tmp, out): - tmp = np.abs(array - tmp) - out = np.maximum(tmp, out) - tmp = tmp * 0. - return tmp, out - - -def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0): - """ - Resample the output/resampled image to recreate an input image based on - the input image's world coordinate system - - Parameters - ---------- - median_model : `~stdatamodels.jwst.datamodels.JwstDataModel` - - blot_img : datamodel - Datamodel containing header and WCS to define the 'blotted' image - - interp : str, optional - The type of interpolation used in the resampling. The - possible values are "nearest" (nearest neighbor interpolation), - "linear" (bilinear interpolation), "poly3" (cubic polynomial - interpolation), "poly5" (quintic polynomial interpolation), - "sinc" (sinc interpolation), "lan3" (3rd order Lanczos - interpolation), and "lan5" (5th order Lanczos interpolation). - - sincscl : float, optional - The scaling factor for sinc interpolation. - """ - blot_wcs = blot_img.meta.wcs - - # Compute the mapping between the input and output pixel coordinates - pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs, blot_img.data.shape) - log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) - log.debug("Sci shape: {}".format(blot_img.data.shape)) - - # Set array shape, needed to compute image pixel area - blot_img.meta.wcs.array_shape = blot_img.shape - if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: - input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians - input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) - pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) - else: - pix_ratio = 1.0 - log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape)) - - outsci = np.zeros(blot_img.shape, dtype=np.float32) - - # Currently tblot cannot handle nans in the pixmap, so we need to give some - # other value. -1 is not optimal and may have side effects. But this is - # what we've been doing up until now, so more investigation is needed - # before a change is made. Preferably, fix tblot in drizzle. - pixmap[np.isnan(pixmap)] = -1 - tblot(median_model.data, pixmap, outsci, scale=pix_ratio, kscale=1.0, - interp=interp, exptime=1.0, misval=0.0, sinscl=sinscl) - - return outsci diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index d4f4b799c5..ed2d3f8058 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -4,7 +4,8 @@ from jwst.datamodels import ModelContainer from ..resample import resample_spec, resample_utils -from .outlier_detection import OutlierDetection, _remove_file +from .outlier_detection import OutlierDetection +from .utils import _remove_file, create_median, detect_outliers import logging log = logging.getLogger(__name__) @@ -98,7 +99,7 @@ def do_detection(self): # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class - median_model.data = self.create_median(drizzled_models) + median_model.data = create_median(drizzled_models, self.outlierpars['maskpt']) if save_intermediate_results: log.info("Writing out MEDIAN image to: {}".format( @@ -112,24 +113,8 @@ def do_detection(self): _remove_file(fn) log.info(f"Removing file {fn}") - if pars['resample_data'] is True: - # Blot the median image back to recreate each input image specified - # in the original input list/ASN/ModelContainer - blot_models = self.blot_median(median_model) - else: - # Median image will serve as blot image - blot_models = ModelContainer() - for i in range(len(self.input_models)): - blot_models.append(median_model) - # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - self.detect_outliers(blot_models) - - # clean-up (just to be explicit about being finished - # with these results) - if not pars['save_intermediate_results']: - for fn in blot_models._models: - _remove_file(fn) - log.info(f"Removing file {fn}") - del median_model, blot_models + detect_outliers(self.input_models, median_model, **self.outlierpars) + + del median_model diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py index c283156866..443fce2794 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -1,8 +1,11 @@ import numpy as np -from .outlier_detection import OutlierDetection, flag_cr +from .outlier_detection import OutlierDetection from jwst.resample.resample_utils import build_mask from jwst import datamodels as dm + +from .utils import compute_weight_threshold, flag_cr + import logging log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -28,10 +31,8 @@ def __init__(self, input_model: dm.SlitModel | dm.CubeModel | dm.ModelContainer, super().__init__(input_model, **pars) if isinstance(self.inputs, dm.ModelContainer): raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") - - self.input_models = self.inputs - self.converted = False + self.input_models = self.inputs def do_detection(self): """Flag outlier pixels in DQ of input images.""" @@ -39,7 +40,7 @@ def do_detection(self): weighted_cube = self.weight_no_resample() maskpt = self.outlierpars.get('maskpt', 0.7) - weight_threshold = self.compute_weight_threshold(weighted_cube.wht, maskpt) + weight_threshold = compute_weight_threshold(weighted_cube.wht, maskpt) rolling_width = self.outlierpars.get("rolling_window_width", 25) if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): @@ -51,21 +52,19 @@ def do_detection(self): # for consistent shape with rolling median case medians = np.broadcast_to(medians, weighted_cube.shape) - # turn this into a cube model for saving and passing to flag_cr - median_model = dm.CubeModel(data=medians) - # Save median model if pars['save_intermediate_results'] is True # this will be a CubeModel with rolling median values. if self.outlierpars['save_intermediate_results']: + median_model = dm.CubeModel(data=medians) with dm.open(weighted_cube) as dm0: median_model.update(dm0) self.save_median(median_model) + del median_model # no need for blotting, resample is turned off for TSO # go straight to outlier detection log.info("Flagging outliers") - flag_cr(self.input_models, median_model, **self.outlierpars) - + flag_cr(self.input_models, medians, **self.outlierpars) def weight_no_resample(self): """ @@ -89,7 +88,6 @@ def weight_no_resample(self): weighted_cube.wht = dqmask.astype(np.float32) return weighted_cube - def compute_rolling_median(self, model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: ''' Set bad and low-weight data to NaN, then compute the rolling median over the time axis. diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index af139b5e33..f610311ef3 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -8,7 +8,7 @@ from jwst.datamodels import ModelContainer from jwst.outlier_detection import OutlierDetectionStep -from jwst.outlier_detection.outlier_detection import flag_cr +from jwst.outlier_detection.utils import flag_cr from jwst.outlier_detection.outlier_detection_step import ( IMAGE_MODES, TSO_SPEC_MODES, @@ -74,7 +74,7 @@ def test_flag_cr(sci_blot_image_pair): # run flag_cr() which updates in-place. Copy sci first. data_copy = sci.data.copy() - flag_cr(sci, blot) + flag_cr(sci, blot.data) # Make sure science data array is unchanged after flag_cr() np.testing.assert_allclose(sci.data, data_copy) @@ -193,10 +193,10 @@ def test_outlier_step(we_three_sci, tmp_cwd): OutlierDetectionStep.call(container) i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) - blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) + #blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) assert len(i2d_files) == 0 assert len(median_files) == 0 - assert len(blot_files) == 0 + #assert len(blot_files) == 0 result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True @@ -216,10 +216,10 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Verify that intermediary files are saved at the specified location i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) - blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) + #blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) assert len(i2d_files) != 0 assert len(median_files) != 0 - assert len(blot_files) != 0 + #assert len(blot_files) != 0 def test_outlier_step_on_disk(we_three_sci, tmp_cwd): diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py new file mode 100644 index 0000000000..f7a6f18557 --- /dev/null +++ b/jwst/outlier_detection/utils.py @@ -0,0 +1,325 @@ +""" +The ever-present utils sub-module. A home for all... +""" +import os +import warnings + +import numpy as np +from astropy.stats import sigma_clip +from drizzle.cdrizzle import tblot +from scipy import ndimage + +from jwst.resample.resample_utils import calc_gwcs_pixmap +from jwst.resample.resample import compute_image_pixel_area +from stdatamodels.jwst import datamodels + +import logging +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE'] +OUTLIER = datamodels.dqflags.pixel['OUTLIER'] + + +def _remove_file(fn): + if isinstance(fn, str) and os.path.isfile(fn): + os.remove(fn) + log.info(f"Removing file {fn}") + + +def compute_weight_threshold(weight, maskpt): + ''' + Compute the weight threshold for a single image or cube. + + Parameters + ---------- + weight : numpy.ndarray + The weight array + + maskpt : float + The percentage of the mean weight to use as a threshold for masking. + + Returns + ------- + float + The weight threshold for this integration. + ''' + + # necessary in order to assure that mask gets applied correctly + if hasattr(weight, '_mask'): + del weight._mask + mask_zero_weight = np.equal(weight, 0.) + mask_nans = np.isnan(weight) + # Combine the masks + weight_masked = np.ma.array(weight, mask=np.logical_or( + mask_zero_weight, mask_nans)) + # Sigma-clip the unmasked data + weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) + mean_weight = np.mean(weight_masked) + # Mask pixels where weight falls below maskpt percent + weight_threshold = mean_weight * maskpt + return weight_threshold + + +def compute_weight_threshold_container(resampled_models, maskpt): + ''' + Compute weight means without keeping datamodels for each input open + + Parameters + ---------- + resampled_models : ~jwst.datamodels.ModelContainer + The input data models. + + maskpt : float + The percentage of the mean weight to use as a threshold for masking. + + Returns + ------- + list + The weight thresholds for each integration. + ''' + + # Start by ensuring that the ModelContainer does NOT open and keep each datamodel + ropen_orig = resampled_models._return_open + resampled_models._return_open = True + # keep track of resulting computation for each input resampled datamodel + weight_thresholds = [] + # For each model, compute the bad-pixel threshold from the weight arrays + for resampled in resampled_models: + weight = resampled.wht + weight_threshold = compute_weight_threshold(weight, maskpt) + weight_thresholds.append(weight_threshold) + # close and delete the model, just to explicitly try to keep the memory as clean as possible + resampled.close() + del resampled + # Reset ModelContainer attribute to original value + resampled_models._return_open = ropen_orig + return weight_thresholds + + +def create_median(resampled_models, maskpt): + """Create a median image from the singly resampled images. + """ + log.info("Computing median") + + weight_thresholds = compute_weight_threshold_container(resampled_models, maskpt) + + # Now, set up buffered access to all input models + resampled_models.set_buffer(1.0) # Set buffer at 1Mb + resampled_sections = resampled_models.get_sections() + median_image = np.empty((resampled_models.imrows, resampled_models.imcols), + resampled_models.imtype) + median_image[:] = np.nan # initialize with NaNs + + for (resampled_sci, resampled_weight, (row1, row2)) in resampled_sections: + # Create a mask for each input image, masking out areas where there is + # no data or the data has very low weight + badmasks = [] + for weight, weight_threshold in zip(resampled_weight, weight_thresholds): + badmask = np.less(weight, weight_threshold) + log.debug("Percentage of pixels with low weight: {}".format( + np.sum(badmask) / len(weight.flat) * 100)) + badmasks.append(badmask) + + # Fill resampled_sci array with nan's where mask values are True + for f1, f2 in zip(resampled_sci, badmasks): + for elem1, elem2 in zip(f1, f2): + elem1[elem2] = np.nan + + del badmasks + + # For a stack of images with "bad" data replaced with Nan + # use np.nanmedian to compute the median. + with warnings.catch_warnings(): + warnings.filterwarnings(action="ignore", + message="All-NaN slice encountered", + category=RuntimeWarning) + median_image[row1:row2] = np.nanmedian(resampled_sci, axis=0) + del resampled_sci, resampled_weight + + return median_image + + +def _abs_deriv(array): + """Take the absolute derivate of a numpy array.""" + tmp = np.zeros(array.shape, dtype=np.float64) + out = np.zeros(array.shape, dtype=np.float64) + + tmp[1:, :] = array[:-1, :] + tmp, out = _absolute_subtract(array, tmp, out) + tmp[:-1, :] = array[1:, :] + tmp, out = _absolute_subtract(array, tmp, out) + + tmp[:, 1:] = array[:, :-1] + tmp, out = _absolute_subtract(array, tmp, out) + tmp[:, :-1] = array[:, 1:] + tmp, out = _absolute_subtract(array, tmp, out) + + return out + + +def _absolute_subtract(array, tmp, out): + tmp = np.abs(array - tmp) + out = np.maximum(tmp, out) + tmp = tmp * 0. + return tmp, out + + +def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, + resample_data=True, **kwargs): + """Masks outliers in science image by updating DQ in-place + + Mask blemishes in dithered data by comparing a science image + with a model image and the derivative of the model image. + + Parameters + ---------- + sci_image : ~jwst.datamodels.ImageModel + the science data. Can also accept a CubeModel, but only if + resample_data is False + + blot_array : np.ndarray + the blotted median image of the dithered science frames. + + snr : str + Signal-to-noise ratio + + scale : str + scaling factor applied to the derivative + + backg : float + Background value (scalar) to subtract + + resample_data : bool + Boolean to indicate whether blot_image is created from resampled, + dithered data or not + + Notes + ----- + Accepting a CubeModel for sci_image and blot_image with resample_data=True + appears to be a relatively simple extension, as the only thing that explicitly + relies on the dimensionality is the kernel, which could be generalized. + However, this is not currently needed, as CubeModels are only passed in for + TSO data, where resampling is always False. + """ + snr1, snr2 = [float(val) for val in snr.split()] + scale1, scale2 = [float(val) for val in scale.split()] + + # Get background level of science data if it has not been subtracted, so it + # can be added into the level of the blotted data, which has been + # background-subtracted + if (sci_image.meta.background.subtracted is False and + sci_image.meta.background.level is not None): + subtracted_background = sci_image.meta.background.level + log.debug(f"Adding background level {subtracted_background} to blotted image") + else: + # No subtracted background. Allow user-set value, which defaults to 0 + subtracted_background = backg + + sci_data = sci_image.data + err_data = np.nan_to_num(sci_image.err) + + # create the outlier mask + if resample_data: # dithered outlier detection + blot_deriv = _abs_deriv(blot_data) + blot_data += subtracted_background + diff_noise = np.abs(sci_data - blot_data) + + # Create a boolean mask based on a scaled version of + # the derivative image (dealing with interpolating issues?) + # and the standard n*sigma above the noise + threshold1 = scale1 * blot_deriv + snr1 * err_data + mask1 = np.greater(diff_noise, threshold1) + + # Smooth the boolean mask with a 3x3 boxcar kernel + kernel = np.ones((3, 3), dtype=int) + mask1_smoothed = ndimage.convolve(mask1, kernel, mode='nearest') + + # Create a 2nd boolean mask based on the 2nd set of + # scale and threshold values + threshold2 = scale2 * blot_deriv + snr2 * err_data + mask2 = np.greater(diff_noise, threshold2) + + # Final boolean mask + cr_mask = mask1_smoothed & mask2 + + else: # stack outlier detection + diff_noise = np.abs(sci_data - blot_data) + + # straightforward detection of outliers for non-dithered data since + # err_data includes all noise sources (photon, read, and flat for baseline) + cr_mask = np.greater(diff_noise, snr1 * err_data) + + # Count existing DO_NOT_USE pixels + count_existing = np.count_nonzero(sci_image.dq & DO_NOT_USE) + + # Update the DQ array in the input image. + # FIXME (or really "fixed") for a converted cube this used to overwrite + # the dq "view" of the cube with a new dq array. This broke the link + # between the converted ImageModel.dq and the original CubeModel.dq + # which required extra work at the end of outlier detection to update + # the cube. By modifying dq in-place we automatically update the cube. + # I put this as a FIXME because this comment can be removed when + # we don't convert cubes. + sci_image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) + + # Report number (and percent) of new DO_NOT_USE pixels found + count_outlier = np.count_nonzero(sci_image.dq & DO_NOT_USE) + count_added = count_outlier - count_existing + percent_cr = count_added / sci_image.dq.size * 100 + log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") + + +# FIXME (or fixed) interp and sinscl were "options" only when provided +# as part of the step spec (which becomes outlierpars). As neither was +# in the spec (and providing unknown arguments causes an error), these +# were never configurable and always defaulted to linear and 1.0 +def gwcs_blot(median_model, blot_img): + """ + Resample the output/resampled image to recreate an input image based on + the input image's world coordinate system + + Parameters + ---------- + median_model : `~stdatamodels.jwst.datamodels.JwstDataModel` + + blot_img : datamodel + Datamodel containing header and WCS to define the 'blotted' image + """ + blot_wcs = blot_img.meta.wcs + + # Compute the mapping between the input and output pixel coordinates + pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs, blot_img.data.shape) + log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) + log.debug("Sci shape: {}".format(blot_img.data.shape)) + + if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: + input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians + input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) + pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) + else: + pix_ratio = 1.0 + log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape)) + + outsci = np.zeros(blot_img.shape, dtype=np.float32) + + # Currently tblot cannot handle nans in the pixmap, so we need to give some + # other value. -1 is not optimal and may have side effects. But this is + # what we've been doing up until now, so more investigation is needed + # before a change is made. Preferably, fix tblot in drizzle. + pixmap[np.isnan(pixmap)] = -1 + tblot(median_model.data, pixmap, outsci, scale=pix_ratio, kscale=1.0, + interp='linear', exptime=1.0, misval=0.0, sinscl=1.0) + + return outsci + + +def detect_outliers(input_models, median_model, **kwargs): + for image in input_models: + if kwargs["resample_data"]: + blot = gwcs_blot(median_model, image) + else: + blot = median_model.data + # TODO save blot? are these actually useful? + flag_cr(image, blot, **kwargs) From 80c4cb9dd66c41077b9c160fb7446e2e3a46f766 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 25 Jun 2024 20:15:44 -0400 Subject: [PATCH 198/687] WIP: cleaning up kwargs --- jwst/outlier_detection/outlier_detection.py | 11 +++++++++-- jwst/outlier_detection/outlier_detection_spec.py | 9 ++++++++- jwst/outlier_detection/outlier_detection_tso.py | 9 ++++++++- jwst/outlier_detection/utils.py | 8 ++++---- 4 files changed, 29 insertions(+), 8 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index f8e9d68bae..2bb32ccb58 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -161,7 +161,7 @@ def do_detection(self): median_model.data = create_median(drizzled_models, self.outlierpars['maskpt']) if self.outlierpars['save_intermediate_results']: - self.save_median(median_model) + self.save_median(median_model) else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them @@ -171,7 +171,14 @@ def do_detection(self): # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - detect_outliers(self.input_models, median_model, **self.outlierpars) + detect_outliers( + self.input_models, + median_model, + self.outlierpars["snr"], + self.outlierpars["scale"], + self.outlierpars["backg"], + self.outlierpars["resample_data"], + ) # clean-up (just to be explicit about being finished with # these results) diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index ed2d3f8058..1ed6dcda24 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -115,6 +115,13 @@ def do_detection(self): # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - detect_outliers(self.input_models, median_model, **self.outlierpars) + detect_outliers( + self.input_models, + median_model, + self.outlierpars["snr"], + self.outlierpars["scale"], + self.outlierpars["backg"], + self.outlierpars["resample_data"], + ) del median_model diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py index 443fce2794..6c3b3d8867 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -64,7 +64,14 @@ def do_detection(self): # no need for blotting, resample is turned off for TSO # go straight to outlier detection log.info("Flagging outliers") - flag_cr(self.input_models, medians, **self.outlierpars) + flag_cr( + self.input_models, + medians, + self.outlierpars['snr'], + self.outlierpars['scale'], + self.outlierpars['backg'], + self.outlierpars['resample_data'], + ) def weight_no_resample(self): """ diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index f7a6f18557..74416467bc 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -167,7 +167,7 @@ def _absolute_subtract(array, tmp, out): def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, - resample_data=True, **kwargs): + resample_data=True): """Masks outliers in science image by updating DQ in-place Mask blemishes in dithered data by comparing a science image @@ -315,11 +315,11 @@ def gwcs_blot(median_model, blot_img): return outsci -def detect_outliers(input_models, median_model, **kwargs): +def detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data=True): for image in input_models: - if kwargs["resample_data"]: + if resample_data: blot = gwcs_blot(median_model, image) else: blot = median_model.data # TODO save blot? are these actually useful? - flag_cr(image, blot, **kwargs) + flag_cr(image, blot, snr, scale, backg, resample_data) From 3729aeb5d40dd47214dbb54ec0ca6c3fc9f7c71c Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 11:50:19 -0400 Subject: [PATCH 199/687] remove input reference in outlier detection step --- .../outlier_detection_step.py | 45 ++++++++++--------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index c5315e48e1..138397c667 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -89,6 +89,8 @@ class OutlierDetectionStep(Step): # TSO : CubeModel # IFU : ModelContainer of IFUImageModel # Spec : SourceModelContainer of SlitModel (created from MultiSlitModel) + # or ModelContainer of ImageModel (possible for MIR_LRS-FIXEDSLIT) + # or ModelContainer of SlitModel (for...?) # # So the only mode that benefits from `in_memory` is Image. # @@ -139,15 +141,14 @@ def process(self, input_data): """Perform outlier detection processing on input data.""" with datamodels.open(input_data, save_open=self.in_memory) as input_models: - self.input_models = input_models - if not isinstance(self.input_models, ModelContainer): + if not isinstance(input_models, ModelContainer): self.input_container = False else: self.input_container = True # Setup output path naming if associations are involved. asn_id = None try: - asn_id = self.input_models.meta.asn_table.asn_id + asn_id = input_models.meta.asn_table.asn_id except (AttributeError, KeyError): pass if asn_id is None: @@ -190,11 +191,11 @@ def process(self, input_data): # Select which version of OutlierDetection # needs to be used depending on the input data if self.input_container: - single_model = self.input_models[0] + single_model = input_models[0] else: - single_model = self.input_models + single_model = input_models exptype = single_model.meta.exposure.type - self.check_input() + self.check_input(input_models) if is_tso(single_model): # force resampling off and use rolling median @@ -221,38 +222,38 @@ def process(self, input_data): if not self.valid_input: if self.input_container: - for model in self.input_models: + for model in input_models: model.meta.cal_step.outlier_detection = "SKIPPED" else: - self.input_models.meta.cal_step.outlier_detection = "SKIPPED" - return self.input_models + input_models.meta.cal_step.outlier_detection = "SKIPPED" + return input_models self.log.debug(f"Using {detection_step.__name__} class for outlier_detection") # Set up outlier detection, then do detection - step = detection_step(self.input_models, asn_id=asn_id, **pars) + step = detection_step(input_models, asn_id=asn_id, **pars) step.do_detection() state = 'COMPLETE' if self.input_container: - for model in self.input_models: + for model in input_models: model.meta.cal_step.outlier_detection = state else: - self.input_models.meta.cal_step.outlier_detection = state - return self.input_models + input_models.meta.cal_step.outlier_detection = state + return input_models - def check_input(self): + def check_input(self, input_models): """Use this method to determine whether input is valid or not.""" if self.input_container: - self._check_input_container() + self._check_input_container(input_models) else: - self._check_input_cube() + self._check_input_cube(input_models) - def _check_input_container(self): + def _check_input_container(self, input_models): """Check to see whether input is the expected ModelContainer object.""" - ninputs = len(self.input_models) - if not isinstance(self.input_models, ModelContainer): + ninputs = len(input_models) + if not isinstance(input_models, ModelContainer): self.log.warning("Input is not a ModelContainer") self.log.warning("Outlier detection step will be skipped") self.valid_input = False @@ -264,10 +265,10 @@ def _check_input_container(self): self.valid_input = True self.log.info(f"Performing outlier detection on {ninputs} inputs") - def _check_input_cube(self): + def _check_input_cube(self, input_models): """Check to see whether input is the expected CubeModel object.""" - ninputs = self.input_models.shape[0] - if type(self.input_models) not in [datamodels.CubeModel, datamodels.SlitModel]: + ninputs = input_models.shape[0] + if type(input_models) not in [datamodels.CubeModel, datamodels.SlitModel]: self.log.warning("Input is not the expected CubeModel") self.log.warning("Outlier detection step will be skipped") self.valid_input = False From 2c90cefa654601b101ac4feeb01184de5c622e62 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 12:34:46 -0400 Subject: [PATCH 200/687] remove input reference in outlier detection sub-steps --- jwst/outlier_detection/outlier_detection.py | 57 ++++++++----------- .../outlier_detection_ifu.py | 41 ++++++------- .../outlier_detection_spec.py | 14 ++--- .../outlier_detection_step.py | 2 +- .../outlier_detection_tso.py | 23 ++++---- 5 files changed, 61 insertions(+), 76 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 2bb32ccb58..6d9856a831 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -65,12 +65,8 @@ def __init__(self, input_models, **pars): - resample_suffix """ - self.inputs = input_models - self.outlierpars = {} self.outlierpars.update(pars) - # Insure that self.input_models always refers to a ModelContainer - # representation of the inputs # Define how file names are created self.make_output_path = pars.get( @@ -78,12 +74,9 @@ def __init__(self, input_models, **pars): partial(Step._make_output_path, None) ) - def _convert_inputs(self): + def _convert_inputs(self, inputs): """Convert input into datamodel required for processing. - This method converts `self.inputs` into a version of - `self.input_models` suitable for processing by the class. - This base class works on imaging data, and relies on use of the ModelContainer class as the format needed for processing. However, the input may not always be a ModelContainer object, so this method @@ -93,22 +86,22 @@ def _convert_inputs(self): """ bits = self.outlierpars['good_bits'] - if isinstance(self.inputs, ModelContainer): - self.input_models = self.inputs - else: - self.input_models = ModelContainer() - num_inputs = self.inputs.data.shape[0] - log.debug("Converting CubeModel to ModelContainer with {} images". - format(num_inputs)) - for i in range(self.inputs.data.shape[0]): - image = datamodels.ImageModel(data=self.inputs.data[i], - err=self.inputs.err[i], - dq=self.inputs.dq[i]) - image.meta = self.inputs.meta - image.wht = build_driz_weight(image, - weight_type=self.outlierpars['weight_type'], - good_bits=bits) - self.input_models.append(image) + if isinstance(inputs, ModelContainer): + return inputs + input_models = ModelContainer() + num_inputs = inputs.data.shape[0] + log.debug("Converting CubeModel to ModelContainer with {} images". + format(num_inputs)) + for i in range(inputs.data.shape[0]): + image = datamodels.ImageModel(data=inputs.data[i], + err=inputs.err[i], + dq=inputs.dq[i]) + image.meta = inputs.meta + image.wht = build_driz_weight(image, + weight_type=self.outlierpars['weight_type'], + good_bits=bits) + input_models.append(image) + return input_models def build_suffix(self, **pars): """Build suffix. @@ -125,9 +118,9 @@ def build_suffix(self, **pars): log.debug("Defined output product suffix as: {}".format( self.resample_suffix)) - def do_detection(self): + def do_detection(self, inputs): """Flag outlier pixels in DQ of input images.""" - self._convert_inputs() + input_models = self._convert_inputs(inputs) self.build_suffix(**self.outlierpars) pars = self.outlierpars @@ -135,19 +128,19 @@ def do_detection(self): if pars['resample_data']: # Start by creating resampled/mosaic images for # each group of exposures - output_path = self.make_output_path(basepath=self.input_models[0].meta.filename, + output_path = self.make_output_path(basepath=input_models[0].meta.filename, suffix='') output_path = os.path.dirname(output_path) - resamp = resample.ResampleData(self.input_models, output=output_path, single=True, + resamp = resample.ResampleData(input_models, output=output_path, single=True, blendheaders=False, **pars) drizzled_models = resamp.do_drizzle() else: # for non-dithered data, the resampled image is just the original image - drizzled_models = self.input_models - for i in range(len(self.input_models)): + drizzled_models = input_models + for i in range(len(input_models)): drizzled_models[i].wht = build_driz_weight( - self.input_models[i], + input_models[i], weight_type=pars['weight_type'], good_bits=pars['good_bits']) @@ -172,7 +165,7 @@ def do_detection(self): # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image detect_outliers( - self.input_models, + input_models, median_model, self.outlierpars["snr"], self.outlierpars["scale"], diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index 2dc00336ad..55fafb21fa 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -93,24 +93,18 @@ def create_optional_results_model(self, opt_info): opt_model.meta.threshold_percent = threshold_percent return opt_model - def _find_detector_parameters(self): + def _find_detector_parameters(self, input_models): """Find the size of data and the axis to form the differences (perpendicular to disaxis) """ - if self.input_models[0].meta.instrument.name.upper() == 'MIRI': + if input_models[0].meta.instrument.name.upper() == 'MIRI': diffaxis = 1 - elif self.input_models[0].meta.instrument.name.upper() == 'NIRSPEC': + elif input_models[0].meta.instrument.name.upper() == 'NIRSPEC': diffaxis = 0 - ny, nx = self.inputs[0].data.shape + ny, nx = input_models[0].data.shape return (diffaxis, ny, nx) - def do_detection(self): + def do_detection(self, input_models): """Split data by detector to find outliers.""" - - # outlier_detection.py has the basic class that is used by all favors - # of outlier detection. This class sets up self.inputs. We need to fill - # in self.input_models with flagged outliers (this is what outlier_detection_step.py - # returns). - self.input_models = self.inputs self.build_suffix(**self.outlierpars) save_intermediate_results = \ self.outlierpars['save_intermediate_results'] @@ -133,14 +127,14 @@ def do_detection(self): log.info("New y kernel size is {}: ".format(kern_size[1])) threshold_percent = self.outlierpars['threshold_percent'] - (diffaxis, ny, nx) = self._find_detector_parameters() + (diffaxis, ny, nx) = self._find_detector_parameters(input_models) - nfiles = len(self.input_models) + nfiles = len(input_models) detector = np.empty(nfiles, dtype=' Date: Thu, 27 Jun 2024 12:46:33 -0400 Subject: [PATCH 201/687] remove input reference in resample sub-steps --- jwst/outlier_detection/outlier_detection.py | 2 +- .../outlier_detection_spec.py | 2 +- jwst/resample/resample.py | 43 +++++++++---------- jwst/resample/resample_spec.py | 38 ++++++++-------- jwst/resample/resample_spec_step.py | 4 +- jwst/resample/resample_step.py | 2 +- jwst/resample/tests/test_resample_step.py | 2 +- 7 files changed, 45 insertions(+), 48 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 6d9856a831..2c2a708a2b 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -133,7 +133,7 @@ def do_detection(self, inputs): output_path = os.path.dirname(output_path) resamp = resample.ResampleData(input_models, output=output_path, single=True, blendheaders=False, **pars) - drizzled_models = resamp.do_drizzle() + drizzled_models = resamp.do_drizzle(input_models) else: # for non-dithered data, the resampled image is just the original image diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 223ac0a62b..3a65ba5d52 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -72,7 +72,7 @@ def do_detection(self, input_models): # each group of exposures resamp = resample_spec.ResampleSpecData(input_models, single=True, blendheaders=False, **pars) - drizzled_models = resamp.do_drizzle() + drizzled_models = resamp.do_drizzle(input_models) if save_intermediate_results: for model in drizzled_models: model.meta.filename = self.make_output_path( diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 641e6dc88f..7f4a419eea 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -69,7 +69,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, deleted from memory. Default value is `True` to keep all products in memory. """ - self.input_models = input_models self.output_dir = None self.output_filename = output if output is not None and '.fits' not in str(output): @@ -125,7 +124,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, else: # Define output WCS based on all inputs, including a reference WCS: self.output_wcs = resample_utils.make_output_wcs( - self.input_models, + input_models, ref_wcs=output_wcs, pscale_ratio=self.pscale_ratio, pscale=pscale, @@ -190,15 +189,15 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, self.output_models = ModelContainer(open_models=False) - def do_drizzle(self): + def do_drizzle(self, input_models): """Pick the correct drizzling mode based on self.single """ if self.single: - return self.resample_many_to_many() + return self.resample_many_to_many(input_models) else: - return self.resample_many_to_one() + return self.resample_many_to_one(input_models) - def blend_output_metadata(self, output_model): + def blend_output_metadata(self, output_model, input_models): """Create new output metadata based on blending all input metadata.""" # Run fitsblender on output product output_file = output_model.meta.filename @@ -211,12 +210,12 @@ def blend_output_metadata(self, output_model): log.info('Blending metadata for {}'.format(output_file)) blendmeta.blendmodels( output_model, - inputs=self.input_models, + inputs=input_models, output=output_file, ignore=ignore_list ) - def resample_many_to_many(self): + def resample_many_to_many(self, input_models): """Resample many inputs to many outputs where outputs have a common frame. Coadd only different detectors of the same exposure, i.e. map NRCA5 and @@ -225,7 +224,7 @@ def resample_many_to_many(self): Used for outlier detection """ - for exposure in self.input_models.models_grouped: + for exposure in input_models.models_grouped: output_model = self.blank_output # Determine output file type from input exposure filenames # Use this for defining the output filename @@ -313,7 +312,7 @@ def resample_many_to_many(self): return self.output_models - def resample_many_to_one(self): + def resample_many_to_one(self, input_models): """Resample and coadd many inputs to a single output. Used for stage 3 resampling @@ -321,17 +320,17 @@ def resample_many_to_one(self): output_model = self.blank_output.copy() output_model.meta.filename = self.output_filename output_model.meta.resample.weight_type = self.weight_type - output_model.meta.resample.pointings = len(self.input_models.group_names) + output_model.meta.resample.pointings = len(input_models.group_names) if self.blendheaders: - self.blend_output_metadata(output_model) + self.blend_output_metadata(output_model, input_models) # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, kernel=self.kernel, fillval=self.fillval) log.info("Resampling science data") - for img in self.input_models: + for img in input_models: input_pixflux_area = img.meta.photometry.pixelarea_steradians if (input_pixflux_area and 'SPECTRAL' not in img.meta.wcs.output_frame.axes_type): @@ -381,8 +380,8 @@ def resample_many_to_one(self): ) del data, inwht - # Resample variance arrays in self.input_models to output_model - self.resample_variance_arrays(output_model) + # Resample variance arrays in input_models to output_model + self.resample_variance_arrays(output_model, input_models) var_components = [ output_model.var_rnoise, output_model.var_poisson, @@ -395,16 +394,16 @@ def resample_many_to_one(self): all_nan = np.all(np.isnan(var_components), axis=0) output_model.err[all_nan] = np.nan - self.update_exposure_times(output_model) + self.update_exposure_times(output_model, input_models) self.output_models.append(output_model) - for img in self.input_models: + for img in input_models: del img.meta.iscale return self.output_models - def resample_variance_arrays(self, output_model): - """Resample variance arrays from self.input_models to the output_model. + def resample_variance_arrays(self, output_model, input_models): + """Resample variance arrays from input_models to the output_model. Variance images from each input model are resampled individually and added to a weighted sum. If weight_type is 'ivm', the inverse of the @@ -420,7 +419,7 @@ def resample_variance_arrays(self, output_model): total_weight_rn_var = np.zeros_like(output_model.data) total_weight_pn_var = np.zeros_like(output_model.data) total_weight_flat_var = np.zeros_like(output_model.data) - for model in self.input_models: + for model in input_models: # Do the read noise variance first, so it can be # used for weights if needed rn_var = self._resample_one_variance_array( @@ -558,14 +557,14 @@ def _resample_one_variance_array(self, name, input_model, output_model): ) return resampled_error ** 2 - def update_exposure_times(self, output_model): + def update_exposure_times(self, output_model, input_models): """Modify exposure time metadata in-place""" total_exposure_time = 0. exposure_times = {'start': [], 'end': []} duration = 0.0 total_measurement_time = 0.0 measurement_time_failures = [] - for exposure in self.input_models.models_grouped: + for exposure in input_models.models_grouped: total_exposure_time += exposure[0].meta.exposure.exposure_time if not resample_utils.check_for_tmeasure(exposure[0]): measurement_time_failures.append(1) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index e462fb6f2e..f753b0da54 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -60,8 +60,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, kwargs : dict Other parameters """ - self.input_models = input_models - self.output_filename = output self.output_dir = None if output is not None and '.fits' not in str(output): @@ -89,21 +87,21 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, # Define output WCS based on all inputs, including a reference WCS if output_wcs is None: if resample_utils.is_sky_like( - self.input_models[0].meta.wcs.output_frame + input_models[0].meta.wcs.output_frame ): - if self.input_models[0].meta.instrument.name != "NIRSPEC": - self.output_wcs = self.build_interpolated_output_wcs() + if input_models[0].meta.instrument.name != "NIRSPEC": + self.output_wcs = self.build_interpolated_output_wcs(input_models) else: - self.output_wcs = self.build_nirspec_output_wcs() + self.output_wcs = self.build_nirspec_output_wcs(input_models) else: - self.output_wcs = self.build_nirspec_lamp_output_wcs() + self.output_wcs = self.build_nirspec_lamp_output_wcs(input_models) else: self.output_wcs = output_wcs if output_shape is not None: self.output_wcs.array_shape = output_shape[::-1] self.blank_output = datamodels.SlitModel(tuple(self.output_wcs.array_shape)) - self.blank_output.update(self.input_models[0]) + self.blank_output.update(input_models[0]) self.blank_output.meta.wcs = self.output_wcs self.output_models = ModelContainer() @@ -112,16 +110,16 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, log.info(f"Driz parameter fillval: {self.fillval}") log.info(f"Driz parameter weight_type: {self.weight_type}") - def build_nirspec_output_wcs(self, refmodel=None): + def build_nirspec_output_wcs(self, input_models, refmodel=None): """ Create a spatial/spectral WCS covering footprint of the input """ - all_wcs = [m.meta.wcs for m in self.input_models if m is not refmodel] + all_wcs = [m.meta.wcs for m in input_models if m is not refmodel] if refmodel: all_wcs.insert(0, refmodel.meta.wcs) else: # Use the first model with any good data as the reference model - for model in self.input_models: + for model in input_models: dq_mask = resample_utils.build_mask(model.dq, self.good_bits) good = np.isfinite(model.data) & (model.data != 0) & dq_mask if np.any(good) and refmodel is None: @@ -130,7 +128,7 @@ def build_nirspec_output_wcs(self, refmodel=None): # If no good data was found, use the first model. if refmodel is None: - refmodel = self.input_models[0] + refmodel = input_models[0] # make a copy of the data array for internal manipulation refmodel_data = refmodel.data.copy() @@ -335,7 +333,7 @@ def _max_virtual_slit_extent(self, wcs_list, target_ra, target_dec): return y_slit_min, y_slit_max - def build_interpolated_output_wcs(self, refmodel=None): + def build_interpolated_output_wcs(self, input_models, refmodel=None): """ Create a spatial/spectral WCS output frame using all the input models @@ -347,7 +345,7 @@ def build_interpolated_output_wcs(self, refmodel=None): ---------- refmodel : `~jwst.datamodels.JwstDataModel` The reference input image from which the fiducial WCS is created. - If not specified, the first image in self.input_models is used. + If not specified, the first image in input_models is used. Returns ------- @@ -368,7 +366,7 @@ def build_interpolated_output_wcs(self, refmodel=None): all_ra_slit = [] all_dec_slit = [] - for im, model in enumerate(self.input_models): + for im, model in enumerate(input_models): wcs = model.meta.wcs bbox = wcs.bounding_box grid = wcstools.grid_from_bounding_box(bbox) @@ -476,7 +474,7 @@ def build_interpolated_output_wcs(self, refmodel=None): # Check if the data is MIRI LRS FIXED Slit. If it is then # the wavelength array needs to be flipped so that the resampled # dispersion direction matches the dispersion direction on the detector. - if self.input_models[0].meta.exposure.type == 'MIR_LRS-FIXEDSLIT': + if input_models[0].meta.exposure.type == 'MIR_LRS-FIXEDSLIT': wavelength_array = np.flip(wavelength_array, axis=None) step = 1 / self.pscale_ratio @@ -531,7 +529,7 @@ def build_interpolated_output_wcs(self, refmodel=None): dec_center_final = (dec_max + dec_min) / 2.0 tan = Pix2Sky_TAN() - if len(self.input_models) == 1: # single model use ra_center_pt to be consistent + if len(input_models) == 1: # single model use ra_center_pt to be consistent # with how resample was done before ra_center_final = ra_center_pt dec_center_final = dec_center_pt @@ -556,7 +554,7 @@ def build_interpolated_output_wcs(self, refmodel=None): # single model use size of x_tan_array # to be consistent with method before - if len(self.input_models) == 1: + if len(input_models) == 1: x_size = len(x_tan_array) # define the output wcs @@ -587,7 +585,7 @@ def build_interpolated_output_wcs(self, refmodel=None): return output_wcs - def build_nirspec_lamp_output_wcs(self): + def build_nirspec_lamp_output_wcs(self, input_models): """ Create a spatial/spectral WCS output frame for NIRSpec lamp mode @@ -600,7 +598,7 @@ def build_nirspec_lamp_output_wcs(self): output_wcs : `~gwcs.WCS` object A gwcs WCS object defining the output frame WCS """ - model = self.input_models[0] + model = input_models[0] wcs = model.meta.wcs bbox = wcs.bounding_box grid = wcstools.grid_from_bounding_box(bbox) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index e215a7bb61..1ccef48f08 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -117,7 +117,7 @@ def _process_multislit(self, input_models): for container in containers.values(): resamp = resample_spec.ResampleSpecData(container, **self.drizpars) - drizzled_models = resamp.do_drizzle() + drizzled_models = resamp.do_drizzle(container) for model in drizzled_models: self.update_slit_metadata(model) @@ -150,7 +150,7 @@ def _process_slit(self, input_models): resamp = resample_spec.ResampleSpecData(input_models, **self.drizpars) - drizzled_models = resamp.do_drizzle() + drizzled_models = resamp.do_drizzle(input_models) result = drizzled_models[0] result.meta.cal_step.resample = "COMPLETE" diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 1f99d12a41..16046a48ea 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -89,7 +89,7 @@ def process(self, input): # Call the resampling routine resamp = resample.ResampleData(input_models, output=output, **kwargs) - result = resamp.do_drizzle() + result = resamp.do_drizzle(input_models) for model in result: model.meta.cal_step.resample = 'COMPLETE' diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index f9cfa56ef3..58c7f08fd6 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -427,7 +427,7 @@ def test_build_interpolated_output_wcs(miri_rate_pair): im1, im2 = miri_rate_pair driz = ResampleSpecData(ModelContainer([im1, im2])) - output_wcs = driz.build_interpolated_output_wcs() + output_wcs = driz.build_interpolated_output_wcs([im1, im2]) # Make sure that all RA, Dec values in the input image have a location in # the output frame From 0fa3600b6b49fa6fcc1f32cb2eec1ff558327107 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 12:59:45 -0400 Subject: [PATCH 202/687] WIP: refactoring and notes --- .../outlier_detection_ifu.py | 105 +++++++------ .../outlier_detection_spec.py | 20 --- .../outlier_detection_tso.py | 142 +++++++++--------- 3 files changed, 120 insertions(+), 147 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index 55fafb21fa..14da874b45 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -15,6 +15,7 @@ __all__ = ["OutlierDetectionIFU"] +# TODO move to utils? def medfilt(arr, kern_size): # scipy.signal.medfilt (and many other median filters) have undefined behavior # for nan inputs. See: https://github.com/scipy/scipy/issues/4800 @@ -52,57 +53,7 @@ class OutlierDetectionIFU(OutlierDetection): 7. Updates input ImageModel DQ arrays with mask of detected outliers. """ - - def __init__(self, input_models, **pars): - """Initialize class for IFU data processing. - - Parameters - ---------- - input_models : ~jwst.datamodels.ModelContainer, str - list of data models as ModelContainer or ASN file, - one data model for each input 2-D ImageModel - - """ - OutlierDetection.__init__(self, input_models, **pars) - - def create_optional_results_model(self, opt_info): - """ - Creates an OutlierOutputModel from the computed arrays from outlier detection on IFU data. - - Parameter - --------- - input_model: ~stdatamodels.jwst.datamodels.RampModel - - opt_info: tuple - The output arrays needed for the OultierOutputModel. - - Return - --------- - opt_model : OutlierIFUOutputModel - The optional OutlierIFUOutputModel to be returned from the outlier_detection_ifu step. - """ - (kernsize_x, kernsize_y, threshold_percent, - diffarr, minarr, normarr, minnorm) = opt_info - opt_model = datamodels.OutlierIFUOutputModel( - diffarr=diffarr, - minarr=minarr, - normarr=normarr, - minnorm=minnorm) - opt_model.meta.kernel_xsize = kernsize_x - opt_model.meta.kernel_ysize = kernsize_y - opt_model.meta.threshold_percent = threshold_percent - return opt_model - - def _find_detector_parameters(self, input_models): - """Find the size of data and the axis to form the differences (perpendicular to disaxis) """ - - if input_models[0].meta.instrument.name.upper() == 'MIRI': - diffaxis = 1 - elif input_models[0].meta.instrument.name.upper() == 'NIRSPEC': - diffaxis = 0 - ny, nx = input_models[0].data.shape - return (diffaxis, ny, nx) - + # TODO: break up this long function def do_detection(self, input_models): """Split data by detector to find outliers.""" self.build_suffix(**self.outlierpars) @@ -127,7 +78,7 @@ def do_detection(self, input_models): log.info("New y kernel size is {}: ".format(kern_size[1])) threshold_percent = self.outlierpars['threshold_percent'] - (diffaxis, ny, nx) = self._find_detector_parameters(input_models) + (diffaxis, ny, nx) = _find_detector_parameters(input_models) nfiles = len(input_models) detector = np.empty(nfiles, dtype=' 1) and (rolling_width < weighted_cube.shape[0]): - medians = self.compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) + medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) else: medians = np.nanmedian(weighted_cube.data, axis=0) @@ -70,67 +59,72 @@ def do_detection(self, input_model): self.outlierpars['resample_data'], ) - def weight_no_resample(self, input_model): - """ - give weights to model without resampling - - Notes - ----- - Prior to PR #8473, the `build_driz_weight` function was used to - create the weights for the input models for TSO data. However, that - function was simply returning a copy of the DQ array because the - var_noise was not being passed in by calwebb_tso3. As of PR #8473, - a cube model that includes the var_noise is passed into TSO - outlier detection, so `build_driz_weight` would weight the cube model - by the variance. Therefore `build_driz_weight` was removed in order to - preserve the original behavior. If it is determined later that exposure - time or inverse variance weighting should be used here, build_driz_weight - should be re-implemented. - """ - weighted_cube = input_model.copy() - dqmask = build_mask(input_model.dq, self.outlierpars['good_bits']) - weighted_cube.wht = dqmask.astype(np.float32) - return weighted_cube - - def compute_rolling_median(self, model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: - ''' - Set bad and low-weight data to NaN, then compute the rolling median over the time axis. - - Parameters - ---------- - model : ~jwst.datamodels.CubeModel - The input cube model - - weight_threshold : np.ndarray - The weight thresholds for each integration. - - w : int - The window size for the rolling median. - - Returns - ------- - np.ndarray - The rolling median of the input data. Same dimensions as input. - ''' - - sci = model.data - weight = model.wht - badmask = np.less(weight, weight_threshold) - log.debug("Percentage of pixels with low weight: {}".format( - np.sum(badmask) / weight.size * 100)) - - # Fill resampled_sci array with nan's where mask values are True - sci[badmask] = np.nan - del badmask - - if w > sci.shape[0]: - raise ValueError("Window size must be less than the number of integrations.") - meds = moving_median_over_zeroth_axis(sci, w) - - del sci - return meds + +# TODO move to utils? +def weight_no_resample(input_model, good_bits): + """ + give weights to model without resampling + + Notes + ----- + Prior to PR #8473, the `build_driz_weight` function was used to + create the weights for the input models for TSO data. However, that + function was simply returning a copy of the DQ array because the + var_noise was not being passed in by calwebb_tso3. As of PR #8473, + a cube model that includes the var_noise is passed into TSO + outlier detection, so `build_driz_weight` would weight the cube model + by the variance. Therefore `build_driz_weight` was removed in order to + preserve the original behavior. If it is determined later that exposure + time or inverse variance weighting should be used here, build_driz_weight + should be re-implemented. + """ + weighted_cube = input_model.copy() + dqmask = build_mask(input_model.dq, good_bits) + weighted_cube.wht = dqmask.astype(np.float32) + return weighted_cube + + +# TODO move to utils? +def compute_rolling_median(model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: + ''' + Set bad and low-weight data to NaN, then compute the rolling median over the time axis. + + Parameters + ---------- + model : ~jwst.datamodels.CubeModel + The input cube model + + weight_threshold : np.ndarray + The weight thresholds for each integration. + + w : int + The window size for the rolling median. + + Returns + ------- + np.ndarray + The rolling median of the input data. Same dimensions as input. + ''' + + sci = model.data + weight = model.wht + badmask = np.less(weight, weight_threshold) + log.debug("Percentage of pixels with low weight: {}".format( + np.sum(badmask) / weight.size * 100)) + + # Fill resampled_sci array with nan's where mask values are True + sci[badmask] = np.nan + del badmask + + if w > sci.shape[0]: + raise ValueError("Window size must be less than the number of integrations.") + meds = moving_median_over_zeroth_axis(sci, w) + + del sci + return meds +# TODO move to utils? def moving_median_over_zeroth_axis(x: np.ndarray, w: int) -> np.ndarray: """ Calculate the median of a moving window over the zeroth axis of an N-d array. From 9afe4c2661a10127eb8c8cfe1a57045cfc53c8f2 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 13:27:04 -0400 Subject: [PATCH 203/687] remove build_suffix default_suffix --- jwst/outlier_detection/outlier_detection.py | 29 ++++--------------- .../outlier_detection_ifu.py | 1 - .../outlier_detection_spec.py | 6 +--- .../outlier_detection_step.py | 2 -- .../outlier_detection_tso.py | 1 - 5 files changed, 6 insertions(+), 33 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 2c2a708a2b..5288d5492d 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -1,6 +1,5 @@ """Primary code for performing outlier detection on JWST observations.""" -from functools import partial import logging import warnings import os @@ -13,7 +12,6 @@ from jwst.datamodels import ModelContainer from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight -from jwst.stpipe import Step from .utils import _remove_file, create_median, detect_outliers @@ -47,8 +45,6 @@ class OutlierDetection: """ - default_suffix = 'i2d' - def __init__(self, input_models, **pars): """ Initialize the class with input ModelContainers. @@ -69,10 +65,8 @@ def __init__(self, input_models, **pars): self.outlierpars.update(pars) # Define how file names are created - self.make_output_path = pars.get( - 'make_output_path', - partial(Step._make_output_path, None) - ) + # TODO factor this out + self.make_output_path = pars["make_output_path"] def _convert_inputs(self, inputs): """Convert input into datamodel required for processing. @@ -103,25 +97,12 @@ def _convert_inputs(self, inputs): input_models.append(image) return input_models - def build_suffix(self, **pars): - """Build suffix. - - Class-specific method for defining the resample_suffix attribute - using a suffix specific to the sub-class. - - """ - # Parse any user-provided filename suffix for resampled products - self.resample_suffix = '_outlier_{}.fits'.format( - pars.get('resample_suffix', self.default_suffix)) - if 'resample_suffix' in pars: - del pars['resample_suffix'] - log.debug("Defined output product suffix as: {}".format( - self.resample_suffix)) - def do_detection(self, inputs): """Flag outlier pixels in DQ of input images.""" + + self.resample_suffix = "_outlier_i2d.fits" # TODO factor this out + input_models = self._convert_inputs(inputs) - self.build_suffix(**self.outlierpars) pars = self.outlierpars diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index 14da874b45..b2b25a0288 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -56,7 +56,6 @@ class OutlierDetectionIFU(OutlierDetection): # TODO: break up this long function def do_detection(self, input_models): """Split data by detector to find outliers.""" - self.build_suffix(**self.outlierpars) save_intermediate_results = \ self.outlierpars['save_intermediate_results'] diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index efb8fd6a06..9940efeb3d 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -37,13 +37,9 @@ class OutlierDetectionSpec(OutlierDetection): 6. Updates input data model DQ arrays with mask of detected outliers. """ - - default_suffix = 's2d' - def do_detection(self, input_models): """Flag outlier pixels in DQ of input images.""" self._convert_inputs() - self.build_suffix(**self.outlierpars) pars = self.outlierpars save_intermediate_results = pars['save_intermediate_results'] @@ -57,7 +53,7 @@ def do_detection(self, input_models): for model in drizzled_models: model.meta.filename = self.make_output_path( basepath=model.meta.filename, - suffix=self.resample_suffix + suffix="_outlier_s2d.fits", ) log.info("Writing out resampled spectra...") model.save(model.meta.filename) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index db7a34d98f..4de28fe1dc 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -208,10 +208,8 @@ def process(self, input_data): elif exptype in IMAGE_MODES: # imaging with resampling detection_step = outlier_registry['imaging'] - pars['resample_suffix'] = 'i2d' elif exptype in SLIT_SPEC_MODES: detection_step = outlier_registry['slitspec'] - pars['resample_suffix'] = 's2d' elif exptype in IFU_SPEC_MODES: # select algorithm for IFU data detection_step = outlier_registry['ifu'] diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py index d728f74448..40e77306ca 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -22,7 +22,6 @@ def do_detection(self, input_model): """Flag outlier pixels in DQ of input images.""" if isinstance(input_model, dm.ModelContainer): raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") - self.build_suffix(**self.outlierpars) weighted_cube = weight_no_resample(input_model, self.outlierpars['good_bits']) maskpt = self.outlierpars.get('maskpt', 0.7) From 36c2baafb3e4e943c4737f4b1b9e1a7135f992c9 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 13:30:52 -0400 Subject: [PATCH 204/687] WIP: cleanup make_output_path --- jwst/outlier_detection/outlier_detection.py | 8 ++------ jwst/outlier_detection/outlier_detection_ifu.py | 2 +- jwst/outlier_detection/outlier_detection_spec.py | 4 ++-- 3 files changed, 5 insertions(+), 9 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 5288d5492d..e66c41d017 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -64,10 +64,6 @@ def __init__(self, input_models, **pars): self.outlierpars = {} self.outlierpars.update(pars) - # Define how file names are created - # TODO factor this out - self.make_output_path = pars["make_output_path"] - def _convert_inputs(self, inputs): """Convert input into datamodel required for processing. @@ -109,7 +105,7 @@ def do_detection(self, inputs): if pars['resample_data']: # Start by creating resampled/mosaic images for # each group of exposures - output_path = self.make_output_path(basepath=input_models[0].meta.filename, + output_path = pars["make_output_path"](basepath=input_models[0].meta.filename, suffix='') output_path = os.path.dirname(output_path) resamp = resample.ResampleData(input_models, output=output_path, single=True, @@ -171,7 +167,7 @@ def save_median(self, median_model): suffix_to_remove = self.resample_suffix else: suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}" - median_model_output_path = self.make_output_path( + median_model_output_path = self.outlierpars["make_output_path"]( basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), suffix='median') median_model.save(median_model_output_path) diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index b2b25a0288..a3e96ef191 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -197,7 +197,7 @@ def flag_outliers(self, input_models, idet, uq_det, ndet_files, opt_info = (kern_size[0], kern_size[1], threshold_percent, diffarr, minarr, normarr, minarr_norm) opt_model = create_optional_results_model(opt_info) - opt_model.meta.filename = self.make_output_path( + opt_model.meta.filename = self.outlierpars["make_output_path"]( basepath=input_models.meta.asn_table.products[0].name, suffix=detector_name + '_outlier_output') log.info("Writing out intermediate outlier file {}".format(opt_model.meta.filename)) diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index 9940efeb3d..c654ac37e1 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -51,7 +51,7 @@ def do_detection(self, input_models): drizzled_models = resamp.do_drizzle(input_models) if save_intermediate_results: for model in drizzled_models: - model.meta.filename = self.make_output_path( + model.meta.filename = self.outlierpars["make_output_path"]( basepath=model.meta.filename, suffix="_outlier_s2d.fits", ) @@ -68,7 +68,7 @@ def do_detection(self, input_models): # Initialize intermediate products used in the outlier detection median_model = datamodels.ImageModel(drizzled_models[0].data.shape) median_model.meta = drizzled_models[0].meta - median_model.meta.filename = self.make_output_path( + median_model.meta.filename = self.outlierpars["make_output_path"]( basepath=input_models[0].meta.filename, suffix='median' ) From 649412c5a51fb0c01b212f247e969f6df36f436c Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 13:38:27 -0400 Subject: [PATCH 205/687] factor out outlierpars --- jwst/outlier_detection/outlier_detection.py | 65 +++++++------------ .../outlier_detection_ifu.py | 18 ++--- .../outlier_detection_spec.py | 32 ++++----- .../outlier_detection_step.py | 4 +- .../outlier_detection_tso.py | 20 +++--- 5 files changed, 60 insertions(+), 79 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index e66c41d017..bf23e22fbd 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -45,26 +45,7 @@ class OutlierDetection: """ - def __init__(self, input_models, **pars): - """ - Initialize the class with input ModelContainers. - - Parameters - ---------- - input_models : list of DataModels, str - list of data models as ModelContainer or ASN file, - one data model for each input image - - pars : dict, optional - Optional user-specified parameters to modify how outlier_detection - will operate. Valid parameters include: - - resample_suffix - - """ - self.outlierpars = {} - self.outlierpars.update(pars) - - def _convert_inputs(self, inputs): + def _convert_inputs(self, inputs, **kwargs): """Convert input into datamodel required for processing. This base class works on imaging data, and relies on use of the @@ -75,7 +56,7 @@ def _convert_inputs(self, inputs): whatever format the sub-class needs for processing. """ - bits = self.outlierpars['good_bits'] + bits = kwargs['good_bits'] if isinstance(inputs, ModelContainer): return inputs input_models = ModelContainer() @@ -88,28 +69,26 @@ def _convert_inputs(self, inputs): dq=inputs.dq[i]) image.meta = inputs.meta image.wht = build_driz_weight(image, - weight_type=self.outlierpars['weight_type'], + weight_type=kwargs['weight_type'], good_bits=bits) input_models.append(image) return input_models - def do_detection(self, inputs): + def do_detection(self, inputs, **kwargs): """Flag outlier pixels in DQ of input images.""" self.resample_suffix = "_outlier_i2d.fits" # TODO factor this out - input_models = self._convert_inputs(inputs) - - pars = self.outlierpars + input_models = self._convert_inputs(inputs, **kwargs) - if pars['resample_data']: + if kwargs['resample_data']: # Start by creating resampled/mosaic images for # each group of exposures - output_path = pars["make_output_path"](basepath=input_models[0].meta.filename, + output_path = kwargs["make_output_path"](basepath=input_models[0].meta.filename, suffix='') output_path = os.path.dirname(output_path) resamp = resample.ResampleData(input_models, output=output_path, single=True, - blendheaders=False, **pars) + blendheaders=False, **kwargs) drizzled_models = resamp.do_drizzle(input_models) else: @@ -118,8 +97,8 @@ def do_detection(self, inputs): for i in range(len(input_models)): drizzled_models[i].wht = build_driz_weight( input_models[i], - weight_type=pars['weight_type'], - good_bits=pars['good_bits']) + weight_type=kwargs['weight_type'], + good_bits=kwargs['good_bits']) # Initialize intermediate products used in the outlier detection with datamodel_open(drizzled_models[0]) as dm0: @@ -128,14 +107,14 @@ def do_detection(self, inputs): median_model.meta.wcs = dm0.meta.wcs # Perform median combination on set of drizzled mosaics - median_model.data = create_median(drizzled_models, self.outlierpars['maskpt']) + median_model.data = create_median(drizzled_models, kwargs['maskpt']) - if self.outlierpars['save_intermediate_results']: - self.save_median(median_model) + if kwargs['save_intermediate_results']: + self.save_median(median_model, **kwargs) else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them - if not self.outlierpars['in_memory']: + if not kwargs['in_memory']: for fn in drizzled_models._models: _remove_file(fn) @@ -144,17 +123,17 @@ def do_detection(self, inputs): detect_outliers( input_models, median_model, - self.outlierpars["snr"], - self.outlierpars["scale"], - self.outlierpars["backg"], - self.outlierpars["resample_data"], + kwargs["snr"], + kwargs["scale"], + kwargs["backg"], + kwargs["resample_data"], ) # clean-up (just to be explicit about being finished with # these results) del median_model - def save_median(self, median_model): + def save_median(self, median_model, **kwargs): ''' Save median if requested by user @@ -163,11 +142,11 @@ def save_median(self, median_model): median_model : ~jwst.datamodels.ImageModel The median ImageModel or CubeModel to save ''' - if self.outlierpars.get('asn_id', None) is None: + if kwargs.get('asn_id', None) is None: suffix_to_remove = self.resample_suffix else: - suffix_to_remove = f"_{self.outlierpars['asn_id']}{self.resample_suffix}" - median_model_output_path = self.outlierpars["make_output_path"]( + suffix_to_remove = f"_{kwargs['asn_id']}{self.resample_suffix}" + median_model_output_path = kwargs["make_output_path"]( basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), suffix='median') median_model.save(median_model_output_path) diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py index a3e96ef191..d1d8c6a03b 100644 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ b/jwst/outlier_detection/outlier_detection_ifu.py @@ -54,18 +54,18 @@ class OutlierDetectionIFU(OutlierDetection): """ # TODO: break up this long function - def do_detection(self, input_models): + def do_detection(self, input_models, **kwargs): """Split data by detector to find outliers.""" save_intermediate_results = \ - self.outlierpars['save_intermediate_results'] + kwargs['save_intermediate_results'] - kernel_size = self.outlierpars['kernel_size'] + kernel_size = kwargs['kernel_size'] sizex, sizey = [int(val) for val in kernel_size.split()] kern_size = np.zeros(2, dtype=int) kern_size[0] = sizex kern_size[1] = sizey - ifu_second_check = self.outlierpars['ifu_second_check'] + ifu_second_check = kwargs['ifu_second_check'] # check if kernel size is an odd value if kern_size[0] % 2 == 0: log.info("X kernel size is given as an even number. This value must be an odd number. Increasing number by 1") @@ -76,7 +76,7 @@ def do_detection(self, input_models): kern_size[1] = kern_size[1] + 1 log.info("New y kernel size is {}: ".format(kern_size[1])) - threshold_percent = self.outlierpars['threshold_percent'] + threshold_percent = kwargs['threshold_percent'] (diffaxis, ny, nx) = _find_detector_parameters(input_models) nfiles = len(input_models) @@ -98,7 +98,8 @@ def do_detection(self, input_models): diffaxis, nx, ny, kern_size, threshold_percent, save_intermediate_results, - ifu_second_check) + ifu_second_check, + kwargs["make_output_path"]) # TODO: break up this long function # TODO: the only reference to "self" is to make the output path @@ -109,7 +110,8 @@ def flag_outliers(self, input_models, idet, uq_det, ndet_files, diffaxis, nx, ny, kern_size, threshold_percent, save_intermediate_results, - ifu_second_check): + ifu_second_check, + make_output_path): """ Flag outlier pixels on IFU. In general we are searching for pixels that are a form of a bad pixel but not in bad pixel mask, because the bad pixels vary with @@ -197,7 +199,7 @@ def flag_outliers(self, input_models, idet, uq_det, ndet_files, opt_info = (kern_size[0], kern_size[1], threshold_percent, diffarr, minarr, normarr, minarr_norm) opt_model = create_optional_results_model(opt_info) - opt_model.meta.filename = self.outlierpars["make_output_path"]( + opt_model.meta.filename = make_output_path( basepath=input_models.meta.asn_table.products[0].name, suffix=detector_name + '_outlier_output') log.info("Writing out intermediate outlier file {}".format(opt_model.meta.filename)) diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py index c654ac37e1..cf9c38ac2e 100644 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ b/jwst/outlier_detection/outlier_detection_spec.py @@ -37,21 +37,21 @@ class OutlierDetectionSpec(OutlierDetection): 6. Updates input data model DQ arrays with mask of detected outliers. """ - def do_detection(self, input_models): + def do_detection(self, input_models, **kwargs): """Flag outlier pixels in DQ of input images.""" - self._convert_inputs() + # TODO factor this out + self._convert_inputs(**kwargs) - pars = self.outlierpars - save_intermediate_results = pars['save_intermediate_results'] - if pars['resample_data'] is True: + save_intermediate_results = kwargs['save_intermediate_results'] + if kwargs['resample_data'] is True: # Start by creating resampled/mosaic images for # each group of exposures resamp = resample_spec.ResampleSpecData(input_models, single=True, - blendheaders=False, **pars) + blendheaders=False, **kwargs) drizzled_models = resamp.do_drizzle(input_models) if save_intermediate_results: for model in drizzled_models: - model.meta.filename = self.outlierpars["make_output_path"]( + model.meta.filename = kwargs["make_output_path"]( basepath=model.meta.filename, suffix="_outlier_s2d.fits", ) @@ -62,20 +62,20 @@ def do_detection(self, input_models): for i in range(len(input_models)): drizzled_models[i].wht = resample_utils.build_driz_weight( input_models[i], - weight_type=pars['weight_type'], - good_bits=pars['good_bits']) + weight_type=kwargs['weight_type'], + good_bits=kwargs['good_bits']) # Initialize intermediate products used in the outlier detection median_model = datamodels.ImageModel(drizzled_models[0].data.shape) median_model.meta = drizzled_models[0].meta - median_model.meta.filename = self.outlierpars["make_output_path"]( + median_model.meta.filename = kwargs["make_output_path"]( basepath=input_models[0].meta.filename, suffix='median' ) # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class - median_model.data = create_median(drizzled_models, self.outlierpars['maskpt']) + median_model.data = create_median(drizzled_models, kwargs['maskpt']) if save_intermediate_results: log.info("Writing out MEDIAN image to: {}".format( @@ -84,7 +84,7 @@ def do_detection(self, input_models): else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them - if not pars['in_memory']: + if not kwargs['in_memory']: for fn in drizzled_models._models: _remove_file(fn) log.info(f"Removing file {fn}") @@ -94,10 +94,10 @@ def do_detection(self, input_models): detect_outliers( input_models, median_model, - self.outlierpars["snr"], - self.outlierpars["scale"], - self.outlierpars["backg"], - self.outlierpars["resample_data"], + kwargs["snr"], + kwargs["scale"], + kwargs["backg"], + kwargs["resample_data"], ) del median_model diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 4de28fe1dc..6626fac5b4 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -229,8 +229,8 @@ def process(self, input_data): self.log.debug(f"Using {detection_step.__name__} class for outlier_detection") # Set up outlier detection, then do detection - step = detection_step(input_models, asn_id=asn_id, **pars) - step.do_detection(input_models) + step = detection_step() + step.do_detection(input_models, asn_id=asn_id, **pars) state = 'COMPLETE' if self.input_container: diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/outlier_detection_tso.py index 40e77306ca..b13b87a691 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/outlier_detection_tso.py @@ -18,16 +18,16 @@ class OutlierDetectionTSO(OutlierDetection): """Class to flag outlier pixels in DQ of TSO data. Works similarly to imaging outlier detection, but does not resample and uses a rolling median.""" - def do_detection(self, input_model): + def do_detection(self, input_model, **kwargs): """Flag outlier pixels in DQ of input images.""" if isinstance(input_model, dm.ModelContainer): raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") - weighted_cube = weight_no_resample(input_model, self.outlierpars['good_bits']) + weighted_cube = weight_no_resample(input_model, kwargs['good_bits']) - maskpt = self.outlierpars.get('maskpt', 0.7) + maskpt = kwargs.get('maskpt', 0.7) weight_threshold = compute_weight_threshold(weighted_cube.wht, maskpt) - rolling_width = self.outlierpars.get("rolling_window_width", 25) + rolling_width = kwargs.get("rolling_window_width", 25) if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) @@ -39,11 +39,11 @@ def do_detection(self, input_model): # Save median model if pars['save_intermediate_results'] is True # this will be a CubeModel with rolling median values. - if self.outlierpars['save_intermediate_results']: + if kwargs['save_intermediate_results']: median_model = dm.CubeModel(data=medians) with dm.open(weighted_cube) as dm0: median_model.update(dm0) - self.save_median(median_model) + self.save_median(median_model, **kwargs) del median_model # no need for blotting, resample is turned off for TSO @@ -52,10 +52,10 @@ def do_detection(self, input_model): flag_cr( input_model, medians, - self.outlierpars['snr'], - self.outlierpars['scale'], - self.outlierpars['backg'], - self.outlierpars['resample_data'], + kwargs['snr'], + kwargs['scale'], + kwargs['backg'], + kwargs['resample_data'], ) From 8202642da85dcd7bcac2100a26e7577f62c8a6bd Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 14:41:56 -0400 Subject: [PATCH 206/687] convert classes to functions --- jwst/outlier_detection/ifu.py | 299 +++++++++++++++++ jwst/outlier_detection/imaging.py | 100 ++++++ jwst/outlier_detection/outlier_detection.py | 153 --------- .../outlier_detection_ifu.py | 306 ------------------ .../outlier_detection_spec.py | 103 ------ .../outlier_detection_step.py | 27 +- jwst/outlier_detection/spec.py | 99 ++++++ .../tests/test_algorithms.py | 4 +- .../{outlier_detection_tso.py => tso.py} | 91 +++--- jwst/outlier_detection/utils.py | 58 +++- 10 files changed, 607 insertions(+), 633 deletions(-) create mode 100644 jwst/outlier_detection/ifu.py create mode 100644 jwst/outlier_detection/imaging.py delete mode 100644 jwst/outlier_detection/outlier_detection.py delete mode 100644 jwst/outlier_detection/outlier_detection_ifu.py delete mode 100644 jwst/outlier_detection/outlier_detection_spec.py create mode 100644 jwst/outlier_detection/spec.py rename jwst/outlier_detection/{outlier_detection_tso.py => tso.py} (65%) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py new file mode 100644 index 0000000000..1669dd38ba --- /dev/null +++ b/jwst/outlier_detection/ifu.py @@ -0,0 +1,299 @@ +""" +Sub-class defined for performing outlier detection on IFU data. + +This is the controlling routine for the outlier detection process. +It loads and sets the various input data and parameters needed to flag +outliers. Pixel are flagged as outliers based on the MINIMUM difference +a pixel has with its neighbor across all the input cal files. + +Notes +----- +This routine performs the following operations:: + + 1. Extracts parameter settings from input ModelContainer and merges + them with any user-provided values + 2. Loop over cal files + a. read in science data + b. Store computed neighbor differences for all the pixels. + The neighbor pixel differences are defined by the dispersion axis. + For MIRI, with the dispersion axis along the y axis, the neighbors that are used to + to find the differences are to the left and right of each pixel being examined. + For NIRSpec, with the dispersion along the x axis, the neighbors that are used to + find the differences are above and below the pixel being examined. + 3. For each input file store the minimum of the pixel neighbor differences + 4. Comparing all the differences from all the input data find the minimum neighbor difference + 5. Normalize minimum difference to local median of difference array + 6. select outliers by flagging those normailzed minimum values > threshold_percent + 7. Updates input ImageModel DQ arrays with mask of detected outliers. +""" + +import logging + +import numpy as np +from skimage.util import view_as_windows + +from stdatamodels.jwst import datamodels +from stdatamodels.jwst.datamodels import dqflags + +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + +__all__ = ["detect_outliers"] + + +# TODO move to utils? +def medfilt(arr, kern_size): + # scipy.signal.medfilt (and many other median filters) have undefined behavior + # for nan inputs. See: https://github.com/scipy/scipy/issues/4800 + padded = np.pad(arr, [[k // 2] for k in kern_size]) + windows = view_as_windows(padded, kern_size, np.ones(len(kern_size), dtype='int')) + return np.nanmedian(windows, axis=np.arange(-len(kern_size), 0)) + + +# TODO: break up this long function +def detect_outliers(input_models, **kwargs): + """Split data by detector to find outliers.""" + save_intermediate_results = \ + kwargs['save_intermediate_results'] + + kernel_size = kwargs['kernel_size'] + sizex, sizey = [int(val) for val in kernel_size.split()] + kern_size = np.zeros(2, dtype=int) + kern_size[0] = sizex + kern_size[1] = sizey + + ifu_second_check = kwargs['ifu_second_check'] + # check if kernel size is an odd value + if kern_size[0] % 2 == 0: + log.info("X kernel size is given as an even number. This value must be an odd number. Increasing number by 1") + kern_size[0] = kern_size[0] + 1 + log.info("New x kernel size is {}: ".format(kern_size[0])) + if kern_size[1] % 2 == 0: + log.info("Y kernel size is given as an even number. This value must be an odd number. Increasing number by 1") + kern_size[1] = kern_size[1] + 1 + log.info("New y kernel size is {}: ".format(kern_size[1])) + + threshold_percent = kwargs['threshold_percent'] + (diffaxis, ny, nx) = _find_detector_parameters(input_models) + + nfiles = len(input_models) + detector = np.empty(nfiles, dtype=' pctmin + num_above = indx.sum() + + if save_intermediate_results: + detector_name = uq_det[idet] + opt_info = (kern_size[0], kern_size[1], threshold_percent, + diffarr, minarr, normarr, minarr_norm) + opt_model = create_optional_results_model(opt_info) + opt_model.meta.filename = make_output_path( + basepath=input_models.meta.asn_table.products[0].name, + suffix=detector_name + '_outlier_output') + log.info("Writing out intermediate outlier file {}".format(opt_model.meta.filename)) + opt_model.save(opt_model.meta.filename) + + del diffarr + + # store some information if the second flagging step is to be done. + if ifu_second_check: + # store where the minarr is nan (neighbor pixels have nan so differences produces a nan) + nanminarr = np.isnan(minarr) + nanindx = np.where(nanminarr) + + # Update DQ flag + for i in range(len(input_models)): + + detector = input_models[i].meta.instrument.detector.lower() + # only use data from the same detector + if detector == uq_det[idet]: + model = input_models[i] + sci = model.data + dq = model.dq + + # There could be a large number of pixels with a sci value of NaN + # but the dq flag of DO_NOT_USE has not been set. + # This can occur in Non-science regions of the detector. + check = np.where( + np.logical_and(~np.bitwise_and(dq, dqflags.pixel['DO_NOT_USE']).astype(bool), + np.isnan(sci))) + log.debug("Number of pixels DQ was not set to DO_NOT_USE and Sci array was Nan{} ". + format(len(check[0]))) + # set all pixels with dq = DO_NOT_USE to have sci values of Nan + bad = np.bitwise_and(dq, dqflags.pixel['DO_NOT_USE']).astype(bool) + sci[bad] = np.nan + + # Basic setting outliers: flagging those at are found in from Percentage cut + sci[indx] = np.nan + dq[indx] = np.bitwise_or(dq[indx], dqflags.pixel['DO_NOT_USE']) + dq[indx] = np.bitwise_or(dq[indx], dqflags.pixel['OUTLIER']) + + nadditional = 0 + # Second level of setting outliers: flagging pixels were minarr was a Nan + # This will also catch pixels that have a sci of Nan but the DQ flags did + # not have DO_NOT_USE set + if ifu_second_check: + # For counting purposes, count the number of science values that were valid (not Nan) + # after basic flagging in the nanminarr region that will now be flagged as a Nan. + nadditional = (~np.isnan(sci[nanindx])).sum() + sci[nanindx] = np.nan + dq[nanindx] = np.bitwise_or(dq[nanindx], dqflags.pixel['DO_NOT_USE']) + dq[nanindx] = np.bitwise_or(dq[nanindx], dqflags.pixel['OUTLIER']) + log.info("Number of outlier pixels flagged main ifu outlier flagging: {} on detector {} ".format( + len(indx[0]), uq_det[idet])) + log.info("Number of outlier pixels flagged in second check: {} on detector {} ".format( + nadditional, uq_det[idet])) + + total_bad = num_above + nadditional + percent_cr = total_bad / (model.data.shape[0] * model.data.shape[1]) * 100 + log.info(f"Total # pixels flagged as outliers: {total_bad} ({percent_cr:.2f}%)") + # update model + input_models[i] = model + + +# TODO move to utils? +def _find_detector_parameters(input_models): + """Find the size of data and the axis to form the differences (perpendicular to disaxis) """ + + if input_models[0].meta.instrument.name.upper() == 'MIRI': + diffaxis = 1 + elif input_models[0].meta.instrument.name.upper() == 'NIRSPEC': + diffaxis = 0 + ny, nx = input_models[0].data.shape + return (diffaxis, ny, nx) + + +# TODO move to utils? +def create_optional_results_model(opt_info): + """ + Creates an OutlierOutputModel from the computed arrays from outlier detection on IFU data. + + Parameter + --------- + input_model: ~stdatamodels.jwst.datamodels.RampModel + + opt_info: tuple + The output arrays needed for the OultierOutputModel. + + Return + --------- + opt_model : OutlierIFUOutputModel + The optional OutlierIFUOutputModel to be returned from the outlier_detection_ifu step. + """ + (kernsize_x, kernsize_y, threshold_percent, + diffarr, minarr, normarr, minnorm) = opt_info + opt_model = datamodels.OutlierIFUOutputModel( + diffarr=diffarr, + minarr=minarr, + normarr=normarr, + minnorm=minnorm) + opt_model.meta.kernel_xsize = kernsize_x + opt_model.meta.kernel_ysize = kernsize_y + opt_model.meta.threshold_percent = threshold_percent + return opt_model + diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py new file mode 100644 index 0000000000..aeaddb7d4c --- /dev/null +++ b/jwst/outlier_detection/imaging.py @@ -0,0 +1,100 @@ +""" +Main class for performing outlier detection. + +This is the controlling routine for the outlier detection process. +It loads and sets the various input data and parameters needed by +the various functions and then controls the operation of this process +through all the steps used for the detection. + +Notes +----- +This routine performs the following operations:: + + 1. Extracts parameter settings from input model and merges + them with any user-provided values + 2. Resamples all input images into grouped observation mosaics. + 3. Creates a median image from all grouped observation mosaics. + 4. Blot median image to match each original input image. + 5. Perform statistical comparison between blotted image and original + image to identify outliers. + 6. Updates input data model DQ arrays with mask of detected outliers. + +""" + + +import logging +import os + +import numpy as np + +from stdatamodels.jwst.datamodels.util import open as datamodel_open +from stdatamodels.jwst import datamodels + +from jwst.resample import resample +from jwst.resample.resample_utils import build_driz_weight + +from .utils import _convert_inputs, _detect_outliers, _remove_file, create_median, save_median + +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +__all__ = ["detect_outliers"] + + +def detect_outliers(inputs, **kwargs): + """Flag outlier pixels in DQ of input images.""" + + input_models = _convert_inputs(inputs, **kwargs) + + if kwargs['resample_data']: + # Start by creating resampled/mosaic images for + # each group of exposures + output_path = kwargs["make_output_path"](basepath=input_models[0].meta.filename, + suffix='') + output_path = os.path.dirname(output_path) + resamp = resample.ResampleData(input_models, output=output_path, single=True, + blendheaders=False, **kwargs) + drizzled_models = resamp.do_drizzle(input_models) + + else: + # for non-dithered data, the resampled image is just the original image + drizzled_models = input_models + for i in range(len(input_models)): + drizzled_models[i].wht = build_driz_weight( + input_models[i], + weight_type=kwargs['weight_type'], + good_bits=kwargs['good_bits']) + + # Initialize intermediate products used in the outlier detection + with datamodel_open(drizzled_models[0]) as dm0: + median_model = datamodels.ImageModel(dm0.data.shape) + median_model.update(dm0) + median_model.meta.wcs = dm0.meta.wcs + + # Perform median combination on set of drizzled mosaics + median_model.data = create_median(drizzled_models, kwargs['maskpt']) + + if kwargs['save_intermediate_results']: + save_median(median_model, **kwargs) + else: + # since we're not saving intermediate results if the drizzled models + # were written to disk, remove them + if not kwargs['in_memory']: + for fn in drizzled_models._models: + _remove_file(fn) + + # Perform outlier detection using statistical comparisons between + # each original input image and its blotted version of the median image + _detect_outliers( + input_models, + median_model, + kwargs["snr"], + kwargs["scale"], + kwargs["backg"], + kwargs["resample_data"], + ) + + # clean-up (just to be explicit about being finished with + # these results) + del median_model diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py deleted file mode 100644 index bf23e22fbd..0000000000 --- a/jwst/outlier_detection/outlier_detection.py +++ /dev/null @@ -1,153 +0,0 @@ -"""Primary code for performing outlier detection on JWST observations.""" - -import logging -import warnings -import os - -import numpy as np - -from stdatamodels.jwst.datamodels.util import open as datamodel_open -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer -from jwst.resample import resample -from jwst.resample.resample_utils import build_driz_weight - -from .utils import _remove_file, create_median, detect_outliers - -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -__all__ = ["OutlierDetection"] - - -class OutlierDetection: - """Main class for performing outlier detection. - - This is the controlling routine for the outlier detection process. - It loads and sets the various input data and parameters needed by - the various functions and then controls the operation of this process - through all the steps used for the detection. - - Notes - ----- - This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Resamples all input images into grouped observation mosaics. - 3. Creates a median image from all grouped observation mosaics. - 4. Blot median image to match each original input image. - 5. Perform statistical comparison between blotted image and original - image to identify outliers. - 6. Updates input data model DQ arrays with mask of detected outliers. - - """ - - def _convert_inputs(self, inputs, **kwargs): - """Convert input into datamodel required for processing. - - This base class works on imaging data, and relies on use of the - ModelContainer class as the format needed for processing. However, - the input may not always be a ModelContainer object, so this method - will convert the input to a ModelContainer object for processing. - Additionally, sub-classes may redefine this to set up the input as - whatever format the sub-class needs for processing. - - """ - bits = kwargs['good_bits'] - if isinstance(inputs, ModelContainer): - return inputs - input_models = ModelContainer() - num_inputs = inputs.data.shape[0] - log.debug("Converting CubeModel to ModelContainer with {} images". - format(num_inputs)) - for i in range(inputs.data.shape[0]): - image = datamodels.ImageModel(data=inputs.data[i], - err=inputs.err[i], - dq=inputs.dq[i]) - image.meta = inputs.meta - image.wht = build_driz_weight(image, - weight_type=kwargs['weight_type'], - good_bits=bits) - input_models.append(image) - return input_models - - def do_detection(self, inputs, **kwargs): - """Flag outlier pixels in DQ of input images.""" - - self.resample_suffix = "_outlier_i2d.fits" # TODO factor this out - - input_models = self._convert_inputs(inputs, **kwargs) - - if kwargs['resample_data']: - # Start by creating resampled/mosaic images for - # each group of exposures - output_path = kwargs["make_output_path"](basepath=input_models[0].meta.filename, - suffix='') - output_path = os.path.dirname(output_path) - resamp = resample.ResampleData(input_models, output=output_path, single=True, - blendheaders=False, **kwargs) - drizzled_models = resamp.do_drizzle(input_models) - - else: - # for non-dithered data, the resampled image is just the original image - drizzled_models = input_models - for i in range(len(input_models)): - drizzled_models[i].wht = build_driz_weight( - input_models[i], - weight_type=kwargs['weight_type'], - good_bits=kwargs['good_bits']) - - # Initialize intermediate products used in the outlier detection - with datamodel_open(drizzled_models[0]) as dm0: - median_model = datamodels.ImageModel(dm0.data.shape) - median_model.update(dm0) - median_model.meta.wcs = dm0.meta.wcs - - # Perform median combination on set of drizzled mosaics - median_model.data = create_median(drizzled_models, kwargs['maskpt']) - - if kwargs['save_intermediate_results']: - self.save_median(median_model, **kwargs) - else: - # since we're not saving intermediate results if the drizzled models - # were written to disk, remove them - if not kwargs['in_memory']: - for fn in drizzled_models._models: - _remove_file(fn) - - # Perform outlier detection using statistical comparisons between - # each original input image and its blotted version of the median image - detect_outliers( - input_models, - median_model, - kwargs["snr"], - kwargs["scale"], - kwargs["backg"], - kwargs["resample_data"], - ) - - # clean-up (just to be explicit about being finished with - # these results) - del median_model - - def save_median(self, median_model, **kwargs): - ''' - Save median if requested by user - - Parameters - ---------- - median_model : ~jwst.datamodels.ImageModel - The median ImageModel or CubeModel to save - ''' - if kwargs.get('asn_id', None) is None: - suffix_to_remove = self.resample_suffix - else: - suffix_to_remove = f"_{kwargs['asn_id']}{self.resample_suffix}" - median_model_output_path = kwargs["make_output_path"]( - basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), - suffix='median') - median_model.save(median_model_output_path) - log.info(f"Saved model in {median_model_output_path}") diff --git a/jwst/outlier_detection/outlier_detection_ifu.py b/jwst/outlier_detection/outlier_detection_ifu.py deleted file mode 100644 index d1d8c6a03b..0000000000 --- a/jwst/outlier_detection/outlier_detection_ifu.py +++ /dev/null @@ -1,306 +0,0 @@ -"""Class definition for performing outlier detection on IFU data.""" - -import logging - -import numpy as np -from skimage.util import view_as_windows - -from stdatamodels.jwst import datamodels -from stdatamodels.jwst.datamodels import dqflags -from .outlier_detection import OutlierDetection - -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - -__all__ = ["OutlierDetectionIFU"] - - -# TODO move to utils? -def medfilt(arr, kern_size): - # scipy.signal.medfilt (and many other median filters) have undefined behavior - # for nan inputs. See: https://github.com/scipy/scipy/issues/4800 - padded = np.pad(arr, [[k // 2] for k in kern_size]) - windows = view_as_windows(padded, kern_size, np.ones(len(kern_size), dtype='int')) - return np.nanmedian(windows, axis=np.arange(-len(kern_size), 0)) - - -class OutlierDetectionIFU(OutlierDetection): - """Sub-class defined for performing outlier detection on IFU data. - - This is the controlling routine for the outlier detection process. - It loads and sets the various input data and parameters needed to flag - outliers. Pixel are flagged as outliers based on the MINIMUM difference - a pixel has with its neighbor across all the input cal files. - - Notes - ----- - This routine performs the following operations:: - - 1. Extracts parameter settings from input ModelContainer and merges - them with any user-provided values - 2. Loop over cal files - a. read in science data - b. Store computed neighbor differences for all the pixels. - The neighbor pixel differences are defined by the dispersion axis. - For MIRI, with the dispersion axis along the y axis, the neighbors that are used to - to find the differences are to the left and right of each pixel being examined. - For NIRSpec, with the dispersion along the x axis, the neighbors that are used to - find the differences are above and below the pixel being examined. - 3. For each input file store the minimum of the pixel neighbor differences - 4. Comparing all the differences from all the input data find the minimum neighbor difference - 5. Normalize minimum difference to local median of difference array - 6. select outliers by flagging those normailzed minimum values > threshold_percent - 7. Updates input ImageModel DQ arrays with mask of detected outliers. - - """ - # TODO: break up this long function - def do_detection(self, input_models, **kwargs): - """Split data by detector to find outliers.""" - save_intermediate_results = \ - kwargs['save_intermediate_results'] - - kernel_size = kwargs['kernel_size'] - sizex, sizey = [int(val) for val in kernel_size.split()] - kern_size = np.zeros(2, dtype=int) - kern_size[0] = sizex - kern_size[1] = sizey - - ifu_second_check = kwargs['ifu_second_check'] - # check if kernel size is an odd value - if kern_size[0] % 2 == 0: - log.info("X kernel size is given as an even number. This value must be an odd number. Increasing number by 1") - kern_size[0] = kern_size[0] + 1 - log.info("New x kernel size is {}: ".format(kern_size[0])) - if kern_size[1] % 2 == 0: - log.info("Y kernel size is given as an even number. This value must be an odd number. Increasing number by 1") - kern_size[1] = kern_size[1] + 1 - log.info("New y kernel size is {}: ".format(kern_size[1])) - - threshold_percent = kwargs['threshold_percent'] - (diffaxis, ny, nx) = _find_detector_parameters(input_models) - - nfiles = len(input_models) - detector = np.empty(nfiles, dtype=' pctmin - num_above = indx.sum() - - if save_intermediate_results: - detector_name = uq_det[idet] - opt_info = (kern_size[0], kern_size[1], threshold_percent, - diffarr, minarr, normarr, minarr_norm) - opt_model = create_optional_results_model(opt_info) - opt_model.meta.filename = make_output_path( - basepath=input_models.meta.asn_table.products[0].name, - suffix=detector_name + '_outlier_output') - log.info("Writing out intermediate outlier file {}".format(opt_model.meta.filename)) - opt_model.save(opt_model.meta.filename) - - del diffarr - - # store some information if the second flagging step is to be done. - if ifu_second_check: - # store where the minarr is nan (neighbor pixels have nan so differences produces a nan) - nanminarr = np.isnan(minarr) - nanindx = np.where(nanminarr) - - # Update DQ flag - for i in range(len(input_models)): - - detector = input_models[i].meta.instrument.detector.lower() - # only use data from the same detector - if detector == uq_det[idet]: - model = input_models[i] - sci = model.data - dq = model.dq - - # There could be a large number of pixels with a sci value of NaN - # but the dq flag of DO_NOT_USE has not been set. - # This can occur in Non-science regions of the detector. - check = np.where( - np.logical_and(~np.bitwise_and(dq, dqflags.pixel['DO_NOT_USE']).astype(bool), - np.isnan(sci))) - log.debug("Number of pixels DQ was not set to DO_NOT_USE and Sci array was Nan{} ". - format(len(check[0]))) - # set all pixels with dq = DO_NOT_USE to have sci values of Nan - bad = np.bitwise_and(dq, dqflags.pixel['DO_NOT_USE']).astype(bool) - sci[bad] = np.nan - - # Basic setting outliers: flagging those at are found in from Percentage cut - sci[indx] = np.nan - dq[indx] = np.bitwise_or(dq[indx], dqflags.pixel['DO_NOT_USE']) - dq[indx] = np.bitwise_or(dq[indx], dqflags.pixel['OUTLIER']) - - nadditional = 0 - # Second level of setting outliers: flagging pixels were minarr was a Nan - # This will also catch pixels that have a sci of Nan but the DQ flags did - # not have DO_NOT_USE set - if ifu_second_check: - # For counting purposes, count the number of science values that were valid (not Nan) - # after basic flagging in the nanminarr region that will now be flagged as a Nan. - nadditional = (~np.isnan(sci[nanindx])).sum() - sci[nanindx] = np.nan - dq[nanindx] = np.bitwise_or(dq[nanindx], dqflags.pixel['DO_NOT_USE']) - dq[nanindx] = np.bitwise_or(dq[nanindx], dqflags.pixel['OUTLIER']) - log.info("Number of outlier pixels flagged main ifu outlier flagging: {} on detector {} ".format( - len(indx[0]), uq_det[idet])) - log.info("Number of outlier pixels flagged in second check: {} on detector {} ".format( - nadditional, uq_det[idet])) - - total_bad = num_above + nadditional - percent_cr = total_bad / (model.data.shape[0] * model.data.shape[1]) * 100 - log.info(f"Total # pixels flagged as outliers: {total_bad} ({percent_cr:.2f}%)") - # update model - input_models[i] = model - - -# TODO move to utils? -def _find_detector_parameters(input_models): - """Find the size of data and the axis to form the differences (perpendicular to disaxis) """ - - if input_models[0].meta.instrument.name.upper() == 'MIRI': - diffaxis = 1 - elif input_models[0].meta.instrument.name.upper() == 'NIRSPEC': - diffaxis = 0 - ny, nx = input_models[0].data.shape - return (diffaxis, ny, nx) - - -# TODO move to utils? -def create_optional_results_model(opt_info): - """ - Creates an OutlierOutputModel from the computed arrays from outlier detection on IFU data. - - Parameter - --------- - input_model: ~stdatamodels.jwst.datamodels.RampModel - - opt_info: tuple - The output arrays needed for the OultierOutputModel. - - Return - --------- - opt_model : OutlierIFUOutputModel - The optional OutlierIFUOutputModel to be returned from the outlier_detection_ifu step. - """ - (kernsize_x, kernsize_y, threshold_percent, - diffarr, minarr, normarr, minnorm) = opt_info - opt_model = datamodels.OutlierIFUOutputModel( - diffarr=diffarr, - minarr=minarr, - normarr=normarr, - minnorm=minnorm) - opt_model.meta.kernel_xsize = kernsize_x - opt_model.meta.kernel_ysize = kernsize_y - opt_model.meta.threshold_percent = threshold_percent - return opt_model - diff --git a/jwst/outlier_detection/outlier_detection_spec.py b/jwst/outlier_detection/outlier_detection_spec.py deleted file mode 100644 index cf9c38ac2e..0000000000 --- a/jwst/outlier_detection/outlier_detection_spec.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Class definition for performing outlier detection on spectra.""" -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer - -from ..resample import resample_spec, resample_utils -from .outlier_detection import OutlierDetection -from .utils import _remove_file, create_median, detect_outliers - -import logging -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -__all__ = ["OutlierDetectionSpec"] - - -class OutlierDetectionSpec(OutlierDetection): - """Class definition for performing outlier detection on spectra. - - This is the controlling routine for the outlier detection process. - It loads and sets the various input data and parameters needed by - the various functions and then controls the operation of this process - through all the steps used for the detection. - - Notes - ----- - This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Resamples all input images into grouped observation mosaics. - 3. Creates a median image from all grouped observation mosaics. - 4. Blot median image to match each original input image. - 5. Perform statistical comparison between blotted image and original - image to identify outliers. - 6. Updates input data model DQ arrays with mask of detected outliers. - - """ - def do_detection(self, input_models, **kwargs): - """Flag outlier pixels in DQ of input images.""" - # TODO factor this out - self._convert_inputs(**kwargs) - - save_intermediate_results = kwargs['save_intermediate_results'] - if kwargs['resample_data'] is True: - # Start by creating resampled/mosaic images for - # each group of exposures - resamp = resample_spec.ResampleSpecData(input_models, single=True, - blendheaders=False, **kwargs) - drizzled_models = resamp.do_drizzle(input_models) - if save_intermediate_results: - for model in drizzled_models: - model.meta.filename = kwargs["make_output_path"]( - basepath=model.meta.filename, - suffix="_outlier_s2d.fits", - ) - log.info("Writing out resampled spectra...") - model.save(model.meta.filename) - else: - drizzled_models = input_models - for i in range(len(input_models)): - drizzled_models[i].wht = resample_utils.build_driz_weight( - input_models[i], - weight_type=kwargs['weight_type'], - good_bits=kwargs['good_bits']) - - # Initialize intermediate products used in the outlier detection - median_model = datamodels.ImageModel(drizzled_models[0].data.shape) - median_model.meta = drizzled_models[0].meta - median_model.meta.filename = kwargs["make_output_path"]( - basepath=input_models[0].meta.filename, - suffix='median' - ) - - # Perform median combination on set of drizzled mosaics - # create_median should be called as a method from parent class - median_model.data = create_median(drizzled_models, kwargs['maskpt']) - - if save_intermediate_results: - log.info("Writing out MEDIAN image to: {}".format( - median_model.meta.filename)) - median_model.save(median_model.meta.filename) - else: - # since we're not saving intermediate results if the drizzled models - # were written to disk, remove them - if not kwargs['in_memory']: - for fn in drizzled_models._models: - _remove_file(fn) - log.info(f"Removing file {fn}") - - # Perform outlier detection using statistical comparisons between - # each original input image and its blotted version of the median image - detect_outliers( - input_models, - median_model, - kwargs["snr"], - kwargs["scale"], - kwargs["backg"], - kwargs["resample_data"], - ) - - del median_model diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 6626fac5b4..cf6634b848 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -7,17 +7,7 @@ from jwst.stpipe import Step from jwst.lib.pipe_utils import is_tso -from jwst.outlier_detection import outlier_detection -from jwst.outlier_detection import outlier_detection_ifu -from jwst.outlier_detection import outlier_detection_spec -from jwst.outlier_detection import outlier_detection_tso - -# Categorize all supported versions of outlier_detection -outlier_registry = {'imaging': outlier_detection.OutlierDetection, - 'ifu': outlier_detection_ifu.OutlierDetectionIFU, - 'slitspec': outlier_detection_spec.OutlierDetectionSpec, - 'tso': outlier_detection_tso.OutlierDetectionTSO - } +from . import ifu, imaging, tso, spec # Categorize all supported modes IMAGE_MODES = ['NRC_IMAGE', 'MIR_IMAGE', 'NRS_IMAGE', 'NIS_IMAGE', 'FGS_IMAGE'] @@ -200,19 +190,19 @@ def process(self, input_data): if is_tso(single_model): # force resampling off and use rolling median pars['resample_data'] = False - detection_step = outlier_registry['tso'] + mode = tso elif exptype in CORON_IMAGE_MODES: # force resampling off but use same workflow as imaging pars['resample_data'] = False - detection_step = outlier_registry['imaging'] + mode = imaging elif exptype in IMAGE_MODES: # imaging with resampling - detection_step = outlier_registry['imaging'] + mode = imaging elif exptype in SLIT_SPEC_MODES: - detection_step = outlier_registry['slitspec'] + mode = spec elif exptype in IFU_SPEC_MODES: # select algorithm for IFU data - detection_step = outlier_registry['ifu'] + mode = ifu else: self.log.error("Outlier detection failed for unknown/unsupported ", f"exposure type: {exptype}") @@ -226,11 +216,10 @@ def process(self, input_data): input_models.meta.cal_step.outlier_detection = "SKIPPED" return input_models - self.log.debug(f"Using {detection_step.__name__} class for outlier_detection") + self.log.debug(f"Using {mode.__name__} mode for outlier_detection") # Set up outlier detection, then do detection - step = detection_step() - step.do_detection(input_models, asn_id=asn_id, **pars) + mode.detect_outliers(input_models, asn_id=asn_id, **pars) state = 'COMPLETE' if self.input_container: diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py new file mode 100644 index 0000000000..f23e6630fa --- /dev/null +++ b/jwst/outlier_detection/spec.py @@ -0,0 +1,99 @@ +""" +Class definition for performing outlier detection on spectra. + +This is the controlling routine for the outlier detection process. +It loads and sets the various input data and parameters needed by +the various functions and then controls the operation of this process +through all the steps used for the detection. + +Notes +----- +This routine performs the following operations:: + + 1. Extracts parameter settings from input model and merges + them with any user-provided values + 2. Resamples all input images into grouped observation mosaics. + 3. Creates a median image from all grouped observation mosaics. + 4. Blot median image to match each original input image. + 5. Perform statistical comparison between blotted image and original + image to identify outliers. + 6. Updates input data model DQ arrays with mask of detected outliers. + +""" + +from stdatamodels.jwst import datamodels + +from ..resample import resample_spec, resample_utils +from .utils import _convert_inputs, _detect_outliers, _remove_file, create_median + +import logging +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +__all__ = ["detect_outliers"] + + +def do_detection(input_models, **kwargs): + """Flag outlier pixels in DQ of input images.""" + _convert_inputs(**kwargs) + + save_intermediate_results = kwargs['save_intermediate_results'] + if kwargs['resample_data'] is True: + # Start by creating resampled/mosaic images for + # each group of exposures + resamp = resample_spec.ResampleSpecData(input_models, single=True, + blendheaders=False, **kwargs) + drizzled_models = resamp.do_drizzle(input_models) + if save_intermediate_results: + for model in drizzled_models: + model.meta.filename = kwargs["make_output_path"]( + basepath=model.meta.filename, + suffix="_outlier_s2d.fits", + ) + log.info("Writing out resampled spectra...") + model.save(model.meta.filename) + else: + drizzled_models = input_models + for i in range(len(input_models)): + drizzled_models[i].wht = resample_utils.build_driz_weight( + input_models[i], + weight_type=kwargs['weight_type'], + good_bits=kwargs['good_bits']) + + # Initialize intermediate products used in the outlier detection + median_model = datamodels.ImageModel(drizzled_models[0].data.shape) + median_model.meta = drizzled_models[0].meta + median_model.meta.filename = kwargs["make_output_path"]( + basepath=input_models[0].meta.filename, + suffix='median' + ) + + # Perform median combination on set of drizzled mosaics + # create_median should be called as a method from parent class + median_model.data = create_median(drizzled_models, kwargs['maskpt']) + + if save_intermediate_results: + log.info("Writing out MEDIAN image to: {}".format( + median_model.meta.filename)) + median_model.save(median_model.meta.filename) + else: + # since we're not saving intermediate results if the drizzled models + # were written to disk, remove them + if not kwargs['in_memory']: + for fn in drizzled_models._models: + _remove_file(fn) + log.info(f"Removing file {fn}") + + # Perform outlier detection using statistical comparisons between + # each original input image and its blotted version of the median image + detect_outliers( + input_models, + median_model, + kwargs["snr"], + kwargs["scale"], + kwargs["backg"], + kwargs["resample_data"], + ) + + del median_model diff --git a/jwst/outlier_detection/tests/test_algorithms.py b/jwst/outlier_detection/tests/test_algorithms.py index 932b660266..f3dc79f415 100644 --- a/jwst/outlier_detection/tests/test_algorithms.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -4,8 +4,8 @@ import numpy as np import scipy.signal -from jwst.outlier_detection.outlier_detection_ifu import medfilt -from jwst.outlier_detection.outlier_detection_tso import moving_median_over_zeroth_axis +from jwst.outlier_detection.ifu import medfilt +from jwst.outlier_detection.tso import moving_median_over_zeroth_axis @pytest.mark.parametrize("shape,kern_size", [ diff --git a/jwst/outlier_detection/outlier_detection_tso.py b/jwst/outlier_detection/tso.py similarity index 65% rename from jwst/outlier_detection/outlier_detection_tso.py rename to jwst/outlier_detection/tso.py index b13b87a691..3e17b12b9d 100644 --- a/jwst/outlier_detection/outlier_detection_tso.py +++ b/jwst/outlier_detection/tso.py @@ -1,62 +1,57 @@ import numpy as np -from .outlier_detection import OutlierDetection from jwst.resample.resample_utils import build_mask from jwst import datamodels as dm -from .utils import compute_weight_threshold, flag_cr +from .utils import compute_weight_threshold, flag_cr, save_median import logging log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -__all__ = ["OutlierDetectionTSO"] - - -class OutlierDetectionTSO(OutlierDetection): - """Class to flag outlier pixels in DQ of TSO data. Works similarly to - imaging outlier detection, but does not resample and uses a rolling median.""" - - def do_detection(self, input_model, **kwargs): - """Flag outlier pixels in DQ of input images.""" - if isinstance(input_model, dm.ModelContainer): - raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") - weighted_cube = weight_no_resample(input_model, kwargs['good_bits']) - - maskpt = kwargs.get('maskpt', 0.7) - weight_threshold = compute_weight_threshold(weighted_cube.wht, maskpt) - - rolling_width = kwargs.get("rolling_window_width", 25) - if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): - medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) - - else: - medians = np.nanmedian(weighted_cube.data, axis=0) - # this is a 2-D array, need to repeat it into the time axis - # for consistent shape with rolling median case - medians = np.broadcast_to(medians, weighted_cube.shape) - - # Save median model if pars['save_intermediate_results'] is True - # this will be a CubeModel with rolling median values. - if kwargs['save_intermediate_results']: - median_model = dm.CubeModel(data=medians) - with dm.open(weighted_cube) as dm0: - median_model.update(dm0) - self.save_median(median_model, **kwargs) - del median_model - - # no need for blotting, resample is turned off for TSO - # go straight to outlier detection - log.info("Flagging outliers") - flag_cr( - input_model, - medians, - kwargs['snr'], - kwargs['scale'], - kwargs['backg'], - kwargs['resample_data'], - ) +__all__ = ["detect_outliers"] + + +def detect_outliers(input_model, **kwargs): + """Flag outlier pixels in DQ of input images.""" + if isinstance(input_model, dm.ModelContainer): + raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") + weighted_cube = weight_no_resample(input_model, kwargs['good_bits']) + + maskpt = kwargs.get('maskpt', 0.7) + weight_threshold = compute_weight_threshold(weighted_cube.wht, maskpt) + + rolling_width = kwargs.get("rolling_window_width", 25) + if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): + medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) + + else: + medians = np.nanmedian(weighted_cube.data, axis=0) + # this is a 2-D array, need to repeat it into the time axis + # for consistent shape with rolling median case + medians = np.broadcast_to(medians, weighted_cube.shape) + + # Save median model if pars['save_intermediate_results'] is True + # this will be a CubeModel with rolling median values. + if kwargs['save_intermediate_results']: + median_model = dm.CubeModel(data=medians) + with dm.open(weighted_cube) as dm0: + median_model.update(dm0) + save_median(median_model, **kwargs) + del median_model + + # no need for blotting, resample is turned off for TSO + # go straight to outlier detection + log.info("Flagging outliers") + flag_cr( + input_model, + medians, + kwargs['snr'], + kwargs['scale'], + kwargs['backg'], + kwargs['resample_data'], + ) # TODO move to utils? diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 74416467bc..89f918c32e 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -9,7 +9,8 @@ from drizzle.cdrizzle import tblot from scipy import ndimage -from jwst.resample.resample_utils import calc_gwcs_pixmap +from jwst.datamodels import ModelContainer +from jwst.resample.resample_utils import build_driz_weight, calc_gwcs_pixmap from jwst.resample.resample import compute_image_pixel_area from stdatamodels.jwst import datamodels @@ -315,7 +316,7 @@ def gwcs_blot(median_model, blot_img): return outsci -def detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data=True): +def _detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data=True): for image in input_models: if resample_data: blot = gwcs_blot(median_model, image) @@ -323,3 +324,56 @@ def detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", blot = median_model.data # TODO save blot? are these actually useful? flag_cr(image, blot, snr, scale, backg, resample_data) + + +# TODO remove kwargs +def save_median(median_model, **kwargs): + ''' + Save median if requested by user + + Parameters + ---------- + median_model : ~jwst.datamodels.ImageModel + The median ImageModel or CubeModel to save + ''' + default_suffix = "_outlier_i2d.fits" + if kwargs.get('asn_id', None) is None: + suffix_to_remove = default_suffix + else: + suffix_to_remove = f"_{kwargs['asn_id']}{default_suffix}" + median_model_output_path = kwargs["make_output_path"]( + basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), + suffix='median') + median_model.save(median_model_output_path) + log.info(f"Saved model in {median_model_output_path}") + + +def _convert_inputs(inputs, **kwargs): + """Convert input into datamodel required for processing. + + This base class works on imaging data, and relies on use of the + ModelContainer class as the format needed for processing. However, + the input may not always be a ModelContainer object, so this method + will convert the input to a ModelContainer object for processing. + Additionally, sub-classes may redefine this to set up the input as + whatever format the sub-class needs for processing. + + """ + bits = kwargs['good_bits'] + if isinstance(inputs, ModelContainer): + return inputs + input_models = ModelContainer() + num_inputs = inputs.data.shape[0] + log.debug("Converting CubeModel to ModelContainer with {} images". + format(num_inputs)) + for i in range(inputs.data.shape[0]): + image = datamodels.ImageModel(data=inputs.data[i], + err=inputs.err[i], + dq=inputs.dq[i]) + image.meta = inputs.meta + image.wht = build_driz_weight(image, + weight_type=kwargs['weight_type'], + good_bits=bits) + input_models.append(image) + return input_models + From a19d00780b07ad65e39d2099e42fa34946801cf2 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 15:19:36 -0400 Subject: [PATCH 207/687] WIP: kwargs removal --- jwst/badpix_selfcal/badpix_selfcal.py | 2 +- jwst/outlier_detection/imaging.py | 7 +++---- jwst/outlier_detection/spec.py | 2 +- jwst/outlier_detection/tso.py | 2 +- jwst/outlier_detection/utils.py | 16 +++++++--------- 5 files changed, 13 insertions(+), 16 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index f6724a8465..810964f94f 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -1,7 +1,7 @@ from __future__ import annotations import numpy as np import jwst.datamodels as dm -from jwst.outlier_detection.outlier_detection_ifu import medfilt +from jwst.outlier_detection.ifu import medfilt from stdatamodels.jwst.datamodels.dqflags import pixel import warnings diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index aeaddb7d4c..e37e543c57 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -42,10 +42,9 @@ __all__ = ["detect_outliers"] -def detect_outliers(inputs, **kwargs): +def detect_outliers(input_models, **kwargs): """Flag outlier pixels in DQ of input images.""" - - input_models = _convert_inputs(inputs, **kwargs) + input_models = _convert_inputs(input_models, kwargs["good_bits"], kwargs["weight_type"]) if kwargs['resample_data']: # Start by creating resampled/mosaic images for @@ -76,7 +75,7 @@ def detect_outliers(inputs, **kwargs): median_model.data = create_median(drizzled_models, kwargs['maskpt']) if kwargs['save_intermediate_results']: - save_median(median_model, **kwargs) + save_median(median_model, kwargs["make_output_path"], kwargs.get("asn_id", None)) else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index f23e6630fa..243b45d1f9 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -36,7 +36,7 @@ def do_detection(input_models, **kwargs): """Flag outlier pixels in DQ of input images.""" - _convert_inputs(**kwargs) + input_models = _convert_inputs(input_models, kwargs["good_bits"], kwargs["weight_type"]) save_intermediate_results = kwargs['save_intermediate_results'] if kwargs['resample_data'] is True: diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 3e17b12b9d..e39ed97ab6 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -38,7 +38,7 @@ def detect_outliers(input_model, **kwargs): median_model = dm.CubeModel(data=medians) with dm.open(weighted_cube) as dm0: median_model.update(dm0) - save_median(median_model, **kwargs) + save_median(median_model, kwargs["make_output_path"], kwargs.get("asn_id", None)) del median_model # no need for blotting, resample is turned off for TSO diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 89f918c32e..30fe2a9ca4 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -326,8 +326,7 @@ def _detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", flag_cr(image, blot, snr, scale, backg, resample_data) -# TODO remove kwargs -def save_median(median_model, **kwargs): +def save_median(median_model, make_output_path, asn_id=None): ''' Save median if requested by user @@ -337,18 +336,18 @@ def save_median(median_model, **kwargs): The median ImageModel or CubeModel to save ''' default_suffix = "_outlier_i2d.fits" - if kwargs.get('asn_id', None) is None: + if asn_id is None: suffix_to_remove = default_suffix else: - suffix_to_remove = f"_{kwargs['asn_id']}{default_suffix}" - median_model_output_path = kwargs["make_output_path"]( + suffix_to_remove = f"_{asn_id}{default_suffix}" + median_model_output_path = make_output_path( basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), suffix='median') median_model.save(median_model_output_path) log.info(f"Saved model in {median_model_output_path}") -def _convert_inputs(inputs, **kwargs): +def _convert_inputs(inputs, good_bits, weight_type): """Convert input into datamodel required for processing. This base class works on imaging data, and relies on use of the @@ -359,7 +358,6 @@ def _convert_inputs(inputs, **kwargs): whatever format the sub-class needs for processing. """ - bits = kwargs['good_bits'] if isinstance(inputs, ModelContainer): return inputs input_models = ModelContainer() @@ -372,8 +370,8 @@ def _convert_inputs(inputs, **kwargs): dq=inputs.dq[i]) image.meta = inputs.meta image.wht = build_driz_weight(image, - weight_type=kwargs['weight_type'], - good_bits=bits) + weight_type=weight_type, + good_bits=good_bits) input_models.append(image) return input_models From ac2346e2cbeba05cee1f71272252b4c41e9684b5 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 16:18:21 -0400 Subject: [PATCH 208/687] remove kwargs --- jwst/outlier_detection/ifu.py | 18 ++-- jwst/outlier_detection/imaging.py | 61 ++++++++---- .../outlier_detection_step.py | 94 +++++++++++++++---- jwst/outlier_detection/spec.py | 56 +++++++---- jwst/outlier_detection/tso.py | 33 ++++--- 5 files changed, 193 insertions(+), 69 deletions(-) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index 1669dd38ba..5a602113a8 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -41,7 +41,7 @@ __all__ = ["detect_outliers"] -# TODO move to utils? +# TODO move to utils? this IS used by badpix so maybe somewhere more central def medfilt(arr, kern_size): # scipy.signal.medfilt (and many other median filters) have undefined behavior # for nan inputs. See: https://github.com/scipy/scipy/issues/4800 @@ -51,18 +51,21 @@ def medfilt(arr, kern_size): # TODO: break up this long function -def detect_outliers(input_models, **kwargs): +def detect_outliers( + input_models, + save_intermediate_results, + kernel_size, + ifu_second_check, + threshold_percent, + make_output_path, +): """Split data by detector to find outliers.""" - save_intermediate_results = \ - kwargs['save_intermediate_results'] - kernel_size = kwargs['kernel_size'] sizex, sizey = [int(val) for val in kernel_size.split()] kern_size = np.zeros(2, dtype=int) kern_size[0] = sizex kern_size[1] = sizey - ifu_second_check = kwargs['ifu_second_check'] # check if kernel size is an odd value if kern_size[0] % 2 == 0: log.info("X kernel size is given as an even number. This value must be an odd number. Increasing number by 1") @@ -73,7 +76,6 @@ def detect_outliers(input_models, **kwargs): kern_size[1] = kern_size[1] + 1 log.info("New y kernel size is {}: ".format(kern_size[1])) - threshold_percent = kwargs['threshold_percent'] (diffaxis, ny, nx) = _find_detector_parameters(input_models) nfiles = len(input_models) @@ -96,7 +98,7 @@ def detect_outliers(input_models, **kwargs): kern_size, threshold_percent, save_intermediate_results, ifu_second_check, - kwargs["make_output_path"]) + make_output_path) # TODO: break up this long function def flag_outliers(input_models, idet, uq_det, ndet_files, diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index e37e543c57..738af9ab70 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -42,18 +42,47 @@ __all__ = ["detect_outliers"] -def detect_outliers(input_models, **kwargs): +def detect_outliers( + input_models, + save_intermediate_results, + good_bits, + maskpt, + snr, + scale, + backg, + resample_data, + weight_type, + pixfrac, + kernel, + fillval, + allowed_memory, + in_memory, + asn_id, + make_output_path, +): """Flag outlier pixels in DQ of input images.""" - input_models = _convert_inputs(input_models, kwargs["good_bits"], kwargs["weight_type"]) + input_models = _convert_inputs(input_models, good_bits, weight_type) - if kwargs['resample_data']: + if resample_data: # Start by creating resampled/mosaic images for # each group of exposures - output_path = kwargs["make_output_path"](basepath=input_models[0].meta.filename, + output_path = make_output_path(basepath=input_models[0].meta.filename, suffix='') output_path = os.path.dirname(output_path) - resamp = resample.ResampleData(input_models, output=output_path, single=True, - blendheaders=False, **kwargs) + resamp = resample.ResampleData( + input_models, + output=output_path, + single=True, + blendheaders=False, + wht_type=weight_type, + pixfrac=pixfrac, + kernel=kernel, + fillval=fillval, + good_bits=good_bits, + in_memory=in_memory, + asn_id=asn_id, + allowed_memory=allowed_memory, + ) drizzled_models = resamp.do_drizzle(input_models) else: @@ -62,8 +91,8 @@ def detect_outliers(input_models, **kwargs): for i in range(len(input_models)): drizzled_models[i].wht = build_driz_weight( input_models[i], - weight_type=kwargs['weight_type'], - good_bits=kwargs['good_bits']) + weight_type=weight_type, + good_bits=good_bits) # Initialize intermediate products used in the outlier detection with datamodel_open(drizzled_models[0]) as dm0: @@ -72,14 +101,14 @@ def detect_outliers(input_models, **kwargs): median_model.meta.wcs = dm0.meta.wcs # Perform median combination on set of drizzled mosaics - median_model.data = create_median(drizzled_models, kwargs['maskpt']) + median_model.data = create_median(drizzled_models, maskpt) - if kwargs['save_intermediate_results']: - save_median(median_model, kwargs["make_output_path"], kwargs.get("asn_id", None)) + if save_intermediate_results: + save_median(median_model, make_output_path, asn_id) else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them - if not kwargs['in_memory']: + if not in_memory: for fn in drizzled_models._models: _remove_file(fn) @@ -88,10 +117,10 @@ def detect_outliers(input_models, **kwargs): _detect_outliers( input_models, median_model, - kwargs["snr"], - kwargs["scale"], - kwargs["backg"], - kwargs["resample_data"], + snr, + scale, + backg, + resample_data, ) # clean-up (just to be explicit about being finished with diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index cf6634b848..fb30ac85f1 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -156,7 +156,6 @@ def process(self, input_data): # Setup outlier detection parameters pars = { 'weight_type': self.weight_type, # for calling the resample step - 'wht_type': self.weight_type, # for calling the resample class directly 'pixfrac': self.pixfrac, 'kernel': self.kernel, 'fillval': self.fillval, @@ -176,6 +175,7 @@ def process(self, input_data): 'resample_data': self.resample_data, 'good_bits': self.good_bits, 'make_output_path': self.make_output_path, + 'asn_id': asn_id, } # Select which version of OutlierDetection @@ -188,26 +188,91 @@ def process(self, input_data): self.check_input(input_models) if is_tso(single_model): - # force resampling off and use rolling median - pars['resample_data'] = False - mode = tso + tso.detect_outliers( + input_models, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + self.rolling_window_width, + self.snr, + self.scale, + self.backg, + asn_id, + self.make_output_path, + ) elif exptype in CORON_IMAGE_MODES: - # force resampling off but use same workflow as imaging - pars['resample_data'] = False - mode = imaging + imaging.detect_outliers( + input_models, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + self.snr, + self.scale, + self.backg, + False, # force resampling off but use the same workflow as imaging + self.weight_type, + self.pixfrac, + self.kernel, + self.fillval, + self.allowed_memory, + self.in_memory, + asn_id, + self.make_output_path, + ) elif exptype in IMAGE_MODES: - # imaging with resampling - mode = imaging + imaging.detect_outliers( + input_models, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + self.snr, + self.scale, + self.backg, + self.resample_data, + self.weight_type, + self.pixfrac, + self.kernel, + self.fillval, + self.allowed_memory, + self.in_memory, + asn_id, + self.make_output_path, + ) elif exptype in SLIT_SPEC_MODES: - mode = spec + spec.detect_outliers( + input_models, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + self.snr, + self.scale, + self.backg, + self.resample_data, + self.weight_type, + self.pixfrac, + self.kernel, + self.fillval, + self.in_memory, + asn_id, + self.make_output_path, + ) elif exptype in IFU_SPEC_MODES: - # select algorithm for IFU data - mode = ifu + ifu.detect_outliers( + input_models, + self.save_intermediate_results, + self.kernel_size, + self.ifu_second_check, + self.threshold_percent, + self.make_output_path, + ) else: self.log.error("Outlier detection failed for unknown/unsupported ", f"exposure type: {exptype}") + # FIXME: factor this out self.valid_input = False + # FIXME: self.valid_input can also be set to false during check_input + # and could skip before the mode selection above if not self.valid_input: if self.input_container: for model in input_models: @@ -216,11 +281,6 @@ def process(self, input_data): input_models.meta.cal_step.outlier_detection = "SKIPPED" return input_models - self.log.debug(f"Using {mode.__name__} mode for outlier_detection") - - # Set up outlier detection, then do detection - mode.detect_outliers(input_models, asn_id=asn_id, **pars) - state = 'COMPLETE' if self.input_container: for model in input_models: diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 243b45d1f9..8f70d15529 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -34,20 +34,44 @@ __all__ = ["detect_outliers"] -def do_detection(input_models, **kwargs): +def do_detection( + input_models, + save_intermediate_results, + good_bits, + maskpt, + snr, + scale, + backg, + resample_data, + weight_type, + pixfrac, + kernel, + fillval, + in_memory, + make_output_path, +): """Flag outlier pixels in DQ of input images.""" - input_models = _convert_inputs(input_models, kwargs["good_bits"], kwargs["weight_type"]) + input_models = _convert_inputs(input_models, good_bits, weight_type) - save_intermediate_results = kwargs['save_intermediate_results'] - if kwargs['resample_data'] is True: + if resample_data is True: # Start by creating resampled/mosaic images for # each group of exposures - resamp = resample_spec.ResampleSpecData(input_models, single=True, - blendheaders=False, **kwargs) + resamp = resample_spec.ResampleSpecData( + input_models, + single=True, + blendheaders=False, + wht_type=weight_type, + pixfrac=pixfrac, + kernel=kernel, + fillval=fillval, + good_bits=good_bits, + in_memory=in_memory, + asn_id=asn_id, + ) drizzled_models = resamp.do_drizzle(input_models) if save_intermediate_results: for model in drizzled_models: - model.meta.filename = kwargs["make_output_path"]( + model.meta.filename = make_output_path( basepath=model.meta.filename, suffix="_outlier_s2d.fits", ) @@ -58,20 +82,20 @@ def do_detection(input_models, **kwargs): for i in range(len(input_models)): drizzled_models[i].wht = resample_utils.build_driz_weight( input_models[i], - weight_type=kwargs['weight_type'], - good_bits=kwargs['good_bits']) + weight_type=weight_type, + good_bits=good_bits) # Initialize intermediate products used in the outlier detection median_model = datamodels.ImageModel(drizzled_models[0].data.shape) median_model.meta = drizzled_models[0].meta - median_model.meta.filename = kwargs["make_output_path"]( + median_model.meta.filename = make_output_path( basepath=input_models[0].meta.filename, suffix='median' ) # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class - median_model.data = create_median(drizzled_models, kwargs['maskpt']) + median_model.data = create_median(drizzled_models, maskpt) if save_intermediate_results: log.info("Writing out MEDIAN image to: {}".format( @@ -80,7 +104,7 @@ def do_detection(input_models, **kwargs): else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them - if not kwargs['in_memory']: + if not in_memory: for fn in drizzled_models._models: _remove_file(fn) log.info(f"Removing file {fn}") @@ -90,10 +114,10 @@ def do_detection(input_models, **kwargs): detect_outliers( input_models, median_model, - kwargs["snr"], - kwargs["scale"], - kwargs["backg"], - kwargs["resample_data"], + snr, + scale, + backg, + resample_data, ) del median_model diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index e39ed97ab6..7f632b5099 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -13,18 +13,27 @@ __all__ = ["detect_outliers"] -def detect_outliers(input_model, **kwargs): +def detect_outliers( + input_model, + save_intermediate_results, + good_bits, + maskpt, + rolling_window_width, + snr, + scale, + backg, + asn_id, + make_output_path, +): """Flag outlier pixels in DQ of input images.""" if isinstance(input_model, dm.ModelContainer): raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") - weighted_cube = weight_no_resample(input_model, kwargs['good_bits']) + weighted_cube = weight_no_resample(input_model, good_bits) - maskpt = kwargs.get('maskpt', 0.7) weight_threshold = compute_weight_threshold(weighted_cube.wht, maskpt) - rolling_width = kwargs.get("rolling_window_width", 25) - if (rolling_width > 1) and (rolling_width < weighted_cube.shape[0]): - medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_width) + if (rolling_window_width > 1) and (rolling_window_width < weighted_cube.shape[0]): + medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_window_width) else: medians = np.nanmedian(weighted_cube.data, axis=0) @@ -34,11 +43,11 @@ def detect_outliers(input_model, **kwargs): # Save median model if pars['save_intermediate_results'] is True # this will be a CubeModel with rolling median values. - if kwargs['save_intermediate_results']: + if save_intermediate_results: median_model = dm.CubeModel(data=medians) with dm.open(weighted_cube) as dm0: median_model.update(dm0) - save_median(median_model, kwargs["make_output_path"], kwargs.get("asn_id", None)) + save_median(median_model, make_output_path, asn_id) del median_model # no need for blotting, resample is turned off for TSO @@ -47,10 +56,10 @@ def detect_outliers(input_model, **kwargs): flag_cr( input_model, medians, - kwargs['snr'], - kwargs['scale'], - kwargs['backg'], - kwargs['resample_data'], + snr, + scale, + backg, + False, # force resampling off ) From 77ac9258b69537aa8d3811a71cf927b5cc69988f Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 27 Jun 2024 16:52:06 -0400 Subject: [PATCH 209/687] refactoring step input checking --- .../outlier_detection_step.py | 111 ++++-------------- 1 file changed, 26 insertions(+), 85 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index fb30ac85f1..c19bc1797e 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -131,10 +131,24 @@ def process(self, input_data): """Perform outlier detection processing on input data.""" with datamodels.open(input_data, save_open=self.in_memory) as input_models: - if not isinstance(input_models, ModelContainer): - self.input_container = False + if isinstance(input_models, ModelContainer): + ninputs = len(input_models) + if ninputs < 2: + self.log.warning(f"Input only contains {ninputs} exposure") + self.log.warning("Outlier detection step will be skipped") + return self._set_status(input_models, "SKIPPED") + elif isinstance(input_models, (datamodels.CubeModel, datamodels.SlitModel)): + ninputs = input_models.shape[0] + if ninputs < 2: + self.log.warning(f"Input only contains {ninputs} integration") + self.log.warning("Outlier detection step will be skipped") + return self._set_status(input_models, "SKIPPED") else: - self.input_container = True + self.log.warning("Input {input_models} is not supported") + self.log.warning("Outlier detection step will be skipped") + return self._set_status(input_models, "SKIPPED") + self.log.info(f"Performing outlier detection with {ninputs} inputs") + # Setup output path naming if associations are involved. asn_id = None try: @@ -153,39 +167,13 @@ def process(self, input_data): asn_id=asn_id ) - # Setup outlier detection parameters - pars = { - 'weight_type': self.weight_type, # for calling the resample step - 'pixfrac': self.pixfrac, - 'kernel': self.kernel, - 'fillval': self.fillval, - 'nlow': self.nlow, - 'nhigh': self.nhigh, - 'maskpt': self.maskpt, - 'snr': self.snr, - 'scale': self.scale, - 'backg': self.backg, - 'kernel_size': self.kernel_size, - 'threshold_percent': self.threshold_percent, - 'rolling_window_width': self.rolling_window_width, - 'ifu_second_check': self.ifu_second_check, - 'allowed_memory': self.allowed_memory, - 'in_memory': self.in_memory, - 'save_intermediate_results': self.save_intermediate_results, - 'resample_data': self.resample_data, - 'good_bits': self.good_bits, - 'make_output_path': self.make_output_path, - 'asn_id': asn_id, - } - # Select which version of OutlierDetection # needs to be used depending on the input data - if self.input_container: + if isinstance(input_models, ModelContainer): single_model = input_models[0] else: single_model = input_models exptype = single_model.meta.exposure.type - self.check_input(input_models) if is_tso(single_model): tso.detect_outliers( @@ -268,61 +256,14 @@ def process(self, input_data): else: self.log.error("Outlier detection failed for unknown/unsupported ", f"exposure type: {exptype}") - # FIXME: factor this out - self.valid_input = False - - # FIXME: self.valid_input can also be set to false during check_input - # and could skip before the mode selection above - if not self.valid_input: - if self.input_container: - for model in input_models: - model.meta.cal_step.outlier_detection = "SKIPPED" - else: - input_models.meta.cal_step.outlier_detection = "SKIPPED" - return input_models - - state = 'COMPLETE' - if self.input_container: - for model in input_models: - model.meta.cal_step.outlier_detection = state - else: - input_models.meta.cal_step.outlier_detection = state - return input_models + return self._set_status(input_models, "SKIPPED") + return self._set_status(input_models, "COMPLETE") - def check_input(self, input_models): - """Use this method to determine whether input is valid or not.""" - if self.input_container: - self._check_input_container(input_models) + def _set_status(self, input_models, status): + if isinstance(input_models, ModelContainer): + for model in input_models: + model.meta.cal_step.outlier_detection = status else: - self._check_input_cube(input_models) - - def _check_input_container(self, input_models): - """Check to see whether input is the expected ModelContainer object.""" - ninputs = len(input_models) - if not isinstance(input_models, ModelContainer): - self.log.warning("Input is not a ModelContainer") - self.log.warning("Outlier detection step will be skipped") - self.valid_input = False - elif ninputs < 2: - self.log.warning(f"Input only contains {ninputs} exposure") - self.log.warning("Outlier detection step will be skipped") - self.valid_input = False - else: - self.valid_input = True - self.log.info(f"Performing outlier detection on {ninputs} inputs") - - def _check_input_cube(self, input_models): - """Check to see whether input is the expected CubeModel object.""" - ninputs = input_models.shape[0] - if type(input_models) not in [datamodels.CubeModel, datamodels.SlitModel]: - self.log.warning("Input is not the expected CubeModel") - self.log.warning("Outlier detection step will be skipped") - self.valid_input = False - elif ninputs < 2: - self.log.warning(f"Input only contains {ninputs} integration") - self.log.warning("Outlier detection step will be skipped") - self.valid_input = False - else: - self.valid_input = True - self.log.info(f"Performing outlier detection with {ninputs} inputs") + input_models.meta.cal_step.outlier_detection = status + return input_models From 212c71c80c7868bb61c66a755733d46135524359 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 28 Jun 2024 07:05:19 -0400 Subject: [PATCH 210/687] fix typo --- jwst/outlier_detection/spec.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 8f70d15529..f266f04a6d 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -34,7 +34,7 @@ __all__ = ["detect_outliers"] -def do_detection( +def detect_outliers( input_models, save_intermediate_results, good_bits, From 15debccd9d475fa71e0d7880305e79c46ace0f36 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 28 Jun 2024 10:27:30 -0400 Subject: [PATCH 211/687] add missing argument --- jwst/outlier_detection/spec.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index f266f04a6d..f800c9430c 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -48,6 +48,7 @@ def detect_outliers( kernel, fillval, in_memory, + asn_id, make_output_path, ): """Flag outlier pixels in DQ of input images.""" @@ -111,7 +112,7 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - detect_outliers( + _detect_outliers( input_models, median_model, snr, From d52bb554f9a19333d040f74195d17b35f5864ec4 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 1 Jul 2024 16:13:14 -0400 Subject: [PATCH 212/687] no longer save blot models --- docs/jwst/outlier_detection/outlier_detection_imaging.rst | 1 - docs/jwst/outlier_detection/outlier_detection_spec.rst | 2 -- jwst/outlier_detection/utils.py | 5 ++++- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index d1b86b28f8..1294be65f1 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -77,7 +77,6 @@ Specifically, this routine performs the following operations: #. By default, the median image is blotted back (inverse of resampling) to match each original input image. - * Blotted images are written out to disk as `__blot.fits` by default. * **If resampling is turned off**, the median image is compared directly to each input image. diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index c1570c7257..9f8ce97d98 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -39,8 +39,6 @@ Specifically, this routine performs the following operations (modified from the parameter is set to `True` #. Blot median image to match each original input image - - Resampled/blotted images are written out to disk if the ``save_intermediate_results`` - parameter is set to `True` - **If resampling is turned off**, the median image is used for comparison with the original input models for detecting outliers #. Perform statistical comparison between blotted image and original image to identify outliers diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 30fe2a9ca4..f5100a4ec3 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -322,7 +322,10 @@ def _detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", blot = gwcs_blot(median_model, image) else: blot = median_model.data - # TODO save blot? are these actually useful? + # previous versions of the code generated all of the blot models and wrote + # them to disk. Now the code generates the blot model(s) only when needed + # and no longer needs to write them to disk. We could re-introduce saving + # of blot models here. flag_cr(image, blot, snr, scale, backg, resample_data) From 79e2a64b4779285af48ae612b6ab81902bc6fea3 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 2 Jul 2024 15:57:02 -0400 Subject: [PATCH 213/687] start organizing utils for moving to stcal --- jwst/outlier_detection/imaging.py | 13 +- .../outlier_detection_step.py | 27 ++- jwst/outlier_detection/spec.py | 12 +- .../tests/test_outlier_detection.py | 24 ++- jwst/outlier_detection/tso.py | 16 +- jwst/outlier_detection/utils.py | 164 +++++++++++------- 6 files changed, 168 insertions(+), 88 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 738af9ab70..e17ba0d81b 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -47,8 +47,10 @@ def detect_outliers( save_intermediate_results, good_bits, maskpt, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, resample_data, weight_type, @@ -95,6 +97,7 @@ def detect_outliers( good_bits=good_bits) # Initialize intermediate products used in the outlier detection + # TODO do we need to make this a model? with datamodel_open(drizzled_models[0]) as dm0: median_model = datamodels.ImageModel(dm0.data.shape) median_model.update(dm0) @@ -117,8 +120,10 @@ def detect_outliers( _detect_outliers( input_models, median_model, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, resample_data, ) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index c19bc1797e..4adb0b6956 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -175,6 +175,9 @@ def process(self, input_data): single_model = input_models exptype = single_model.meta.exposure.type + snr1, snr2 = [float(v) for v in self.snr.split()] + scale1, scale2 = [float(v) for v in self.scale.split()] + if is_tso(single_model): tso.detect_outliers( input_models, @@ -182,8 +185,10 @@ def process(self, input_data): self.good_bits, self.maskpt, self.rolling_window_width, - self.snr, - self.scale, + snr1, + snr2, + scale1, + scale2, self.backg, asn_id, self.make_output_path, @@ -194,8 +199,10 @@ def process(self, input_data): self.save_intermediate_results, self.good_bits, self.maskpt, - self.snr, - self.scale, + snr1, + snr2, + scale1, + scale2, # TODO turn off the things that aren't actually used self.backg, False, # force resampling off but use the same workflow as imaging self.weight_type, @@ -213,8 +220,10 @@ def process(self, input_data): self.save_intermediate_results, self.good_bits, self.maskpt, - self.snr, - self.scale, + snr1, + snr2, + scale1, + scale2, self.backg, self.resample_data, self.weight_type, @@ -232,8 +241,10 @@ def process(self, input_data): self.save_intermediate_results, self.good_bits, self.maskpt, - self.snr, - self.scale, + snr1, + snr2, + scale1, + scale2, self.backg, self.resample_data, self.weight_type, diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index f800c9430c..ca746efd0a 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -39,8 +39,10 @@ def detect_outliers( save_intermediate_results, good_bits, maskpt, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, resample_data, weight_type, @@ -115,8 +117,10 @@ def detect_outliers( _detect_outliers( input_models, median_model, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, resample_data, ) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index f610311ef3..ed003b6e28 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -8,7 +8,7 @@ from jwst.datamodels import ModelContainer from jwst.outlier_detection import OutlierDetectionStep -from jwst.outlier_detection.utils import flag_cr +from jwst.outlier_detection.utils import flag_cr_update_model from jwst.outlier_detection.outlier_detection_step import ( IMAGE_MODES, TSO_SPEC_MODES, @@ -42,7 +42,8 @@ def sci_blot_image_pair(): sci.meta.background.subtracted = False sci.meta.background.level = background - sci.data = np.random.normal(loc=background, size=shape, scale=sigma) + rng = np.random.default_rng(720) + sci.data = rng.normal(loc=background, size=shape, scale=sigma) sci.err = np.zeros(shape) + sigma sci.var_rnoise += 0 @@ -74,7 +75,16 @@ def test_flag_cr(sci_blot_image_pair): # run flag_cr() which updates in-place. Copy sci first. data_copy = sci.data.copy() - flag_cr(sci, blot.data) + flag_cr_update_model( + sci, + blot.data, + 5.0, + 4.0, + 1.2, + 0.7, + 0, + True, + ) # Make sure science data array is unchanged after flag_cr() np.testing.assert_allclose(sci.data, data_copy) @@ -133,7 +143,8 @@ def we_many_sci( # Replace the FITS-type WCS with an Identity WCS sci1.meta.wcs = create_fitswcs(sci1) - sci1.data = np.random.normal(loc=background, size=shape, scale=sigma) + rng = np.random.default_rng(720) + sci1.data = rng.normal(loc=background, size=shape, scale=sigma) sci1.err = np.zeros(shape) + sigma sci1.data[7, 7] += signal # update the noise for this source to include the photon/measurement noise @@ -149,7 +160,7 @@ def we_many_sci( all_sci = [sci1] for i in range(numsci - 1): tsci = sci1.copy() - tsci.data = np.random.normal(loc=background, size=shape, scale=sigma) + tsci.data = rng.normal(loc=background, size=shape, scale=sigma) # Add a source in the center tsci.data[7, 7] += signal tsci.meta.filename = f"foo{i + 2}_cal.fits" @@ -171,6 +182,9 @@ def test_outlier_step_no_outliers(we_three_sci, tmp_cwd): pristine = container.copy() result = OutlierDetectionStep.call(container) + # FIXME this test doesn't work as intended as there are no + # outliers so the input (even if it was modified, which it is) + # would not appear different from the "pristine" data # Make sure nothing changed in SCI and DQ arrays for image, uncorrected in zip(pristine, container): np.testing.assert_allclose(image.data, uncorrected.data) diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 7f632b5099..e2632cfdb3 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -3,7 +3,7 @@ from jwst import datamodels as dm -from .utils import compute_weight_threshold, flag_cr, save_median +from .utils import compute_weight_threshold, flag_cr_update_model, save_median import logging log = logging.getLogger(__name__) @@ -19,8 +19,10 @@ def detect_outliers( good_bits, maskpt, rolling_window_width, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, asn_id, make_output_path, @@ -53,11 +55,13 @@ def detect_outliers( # no need for blotting, resample is turned off for TSO # go straight to outlier detection log.info("Flagging outliers") - flag_cr( + flag_cr_update_model( input_model, medians, - snr, - scale, + snr1, + snr2, + scale1, + scale2, backg, False, # force resampling off ) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index f5100a4ec3..2439954fd1 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -23,12 +23,14 @@ OUTLIER = datamodels.dqflags.pixel['OUTLIER'] +# TODO fileio def _remove_file(fn): if isinstance(fn, str) and os.path.isfile(fn): os.remove(fn) log.info(f"Removing file {fn}") +# TODO stcal def compute_weight_threshold(weight, maskpt): ''' Compute the weight threshold for a single image or cube. @@ -99,6 +101,7 @@ def compute_weight_threshold_container(resampled_models, maskpt): return weight_thresholds +# TODO stcal as a "median computer" def create_median(resampled_models, maskpt): """Create a median image from the singly resampled images. """ @@ -142,6 +145,7 @@ def create_median(resampled_models, maskpt): return median_image +# TODO stcal utils def _abs_deriv(array): """Take the absolute derivate of a numpy array.""" tmp = np.zeros(array.shape, dtype=np.float64) @@ -160,6 +164,7 @@ def _abs_deriv(array): return out +# TODO stcal utils def _absolute_subtract(array, tmp, out): tmp = np.abs(array - tmp) out = np.maximum(tmp, out) @@ -167,8 +172,18 @@ def _absolute_subtract(array, tmp, out): return tmp, out -def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, - resample_data=True): +# TODO stcal utils +def flag_cr( + sci_data, + sci_err, + blot_data, + snr1, + snr2, + scale1, + scale2, + backg, + resample_data, +): """Masks outliers in science image by updating DQ in-place Mask blemishes in dithered data by comparing a science image @@ -176,6 +191,9 @@ def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, Parameters ---------- + + FIXME: update these + sci_image : ~jwst.datamodels.ImageModel the science data. Can also accept a CubeModel, but only if resample_data is False @@ -204,28 +222,12 @@ def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, However, this is not currently needed, as CubeModels are only passed in for TSO data, where resampling is always False. """ - snr1, snr2 = [float(val) for val in snr.split()] - scale1, scale2 = [float(val) for val in scale.split()] - - # Get background level of science data if it has not been subtracted, so it - # can be added into the level of the blotted data, which has been - # background-subtracted - if (sci_image.meta.background.subtracted is False and - sci_image.meta.background.level is not None): - subtracted_background = sci_image.meta.background.level - log.debug(f"Adding background level {subtracted_background} to blotted image") - else: - # No subtracted background. Allow user-set value, which defaults to 0 - subtracted_background = backg - - sci_data = sci_image.data - err_data = np.nan_to_num(sci_image.err) + err_data = np.nan_to_num(sci_err) # create the outlier mask if resample_data: # dithered outlier detection blot_deriv = _abs_deriv(blot_data) - blot_data += subtracted_background - diff_noise = np.abs(sci_data - blot_data) + diff_noise = np.abs(sci_data - blot_data - backg) # Create a boolean mask based on a scaled version of # the derivative image (dealing with interpolating issues?) @@ -252,31 +254,15 @@ def flag_cr(sci_image, blot_data, snr="5.0 4.0", scale="1.2 0.7", backg=0, # err_data includes all noise sources (photon, read, and flat for baseline) cr_mask = np.greater(diff_noise, snr1 * err_data) - # Count existing DO_NOT_USE pixels - count_existing = np.count_nonzero(sci_image.dq & DO_NOT_USE) - - # Update the DQ array in the input image. - # FIXME (or really "fixed") for a converted cube this used to overwrite - # the dq "view" of the cube with a new dq array. This broke the link - # between the converted ImageModel.dq and the original CubeModel.dq - # which required extra work at the end of outlier detection to update - # the cube. By modifying dq in-place we automatically update the cube. - # I put this as a FIXME because this comment can be removed when - # we don't convert cubes. - sci_image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) - - # Report number (and percent) of new DO_NOT_USE pixels found - count_outlier = np.count_nonzero(sci_image.dq & DO_NOT_USE) - count_added = count_outlier - count_existing - percent_cr = count_added / sci_image.dq.size * 100 - log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") + return cr_mask +# TODO can this be model-independent? # FIXME (or fixed) interp and sinscl were "options" only when provided # as part of the step spec (which becomes outlierpars). As neither was # in the spec (and providing unknown arguments causes an error), these # were never configurable and always defaulted to linear and 1.0 -def gwcs_blot(median_model, blot_img): +def gwcs_blot(median_data, median_wcs, blot_data, blot_wcs, pix_ratio): """ Resample the output/resampled image to recreate an input image based on the input image's world coordinate system @@ -288,47 +274,103 @@ def gwcs_blot(median_model, blot_img): blot_img : datamodel Datamodel containing header and WCS to define the 'blotted' image """ - blot_wcs = blot_img.meta.wcs - # Compute the mapping between the input and output pixel coordinates - pixmap = calc_gwcs_pixmap(blot_wcs, median_model.meta.wcs, blot_img.data.shape) + pixmap = calc_gwcs_pixmap(blot_wcs, median_wcs, blot_data.shape) log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) - log.debug("Sci shape: {}".format(blot_img.data.shape)) + log.debug("Sci shape: {}".format(blot_data.shape)) + log.info('Blotting {} <-- {}'.format(blot_data.shape, median_data.shape)) - if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: - input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians - input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) - pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) - else: - pix_ratio = 1.0 - log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape)) - - outsci = np.zeros(blot_img.shape, dtype=np.float32) + outsci = np.zeros(blot_data.shape, dtype=np.float32) # Currently tblot cannot handle nans in the pixmap, so we need to give some # other value. -1 is not optimal and may have side effects. But this is # what we've been doing up until now, so more investigation is needed # before a change is made. Preferably, fix tblot in drizzle. pixmap[np.isnan(pixmap)] = -1 - tblot(median_model.data, pixmap, outsci, scale=pix_ratio, kscale=1.0, + tblot(median_data, pixmap, outsci, scale=pix_ratio, kscale=1.0, interp='linear', exptime=1.0, misval=0.0, sinscl=1.0) return outsci -def _detect_outliers(input_models, median_model, snr="5.0 4.0", scale="1.2 0.7", backg=0, resample_data=True): +def _detect_outliers( + input_models, + median_model, + snr1, + snr2, + scale1, + scale2, + backg, + resample_data, +): for image in input_models: if resample_data: - blot = gwcs_blot(median_model, image) + if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: + input_pixflux_area = image.meta.photometry.pixelarea_steradians + input_pixel_area = compute_image_pixel_area(image.meta.wcs) + pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) + else: + pix_ratio = 1.0 + + blot = gwcs_blot(median_model.data, median_model.meta.wcs, image.data, image.meta.wcs, pix_ratio) else: blot = median_model.data - # previous versions of the code generated all of the blot models and wrote - # them to disk. Now the code generates the blot model(s) only when needed - # and no longer needs to write them to disk. We could re-introduce saving - # of blot models here. - flag_cr(image, blot, snr, scale, backg, resample_data) + # dq flags will be updated in-place + flag_cr_update_model(image, blot, snr1, snr2, scale1, scale2, backg, resample_data) + + +def flag_cr_update_model( + image, + blot, + snr1, + snr2, + scale1, + scale2, + backg, + resample_data, +): + # previous versions of the code generated all of the blot models and wrote + # them to disk. Now the code generates the blot model(s) only when needed + # and no longer needs to write them to disk. We could re-introduce saving + # of blot models here. + + # If the datamodel has a measured background that has not been subtracted + # use it instead of the user provided backg. + # Get background level of science data if it has not been subtracted, so it + # can be added into the level of the blotted data, which has been + # background-subtracted + if (image.meta.background.subtracted is False and + image.meta.background.level is not None): + backg = image.meta.background.level + log.debug(f"Adding background level {backg} to blotted image") + + cr_mask = flag_cr(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) + + # TODO is it necessary to do all this math for 1 possible log message? + # sould a count of flagged pixels in the mask be a suitable replacement? + # Count existing DO_NOT_USE pixels + count_existing = np.count_nonzero(image.dq & DO_NOT_USE) + # FIXME (or really "fixed") for a converted cube this used to overwrite + # the dq "view" of the cube with a new dq array. This broke the link + # between the converted ImageModel.dq and the original CubeModel.dq + # which required extra work at the end of outlier detection to update + # the cube. By modifying dq in-place we automatically update the cube. + # I put this as a FIXME because this comment can be removed when + # we don't convert cubes. + image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) + + # Report number (and percent) of new DO_NOT_USE pixels found + count_outlier = np.count_nonzero(image.dq & DO_NOT_USE) + count_added = count_outlier - count_existing + percent_cr = count_added / image.dq.size * 100 + log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") + + + + +# TODO fileio def save_median(median_model, make_output_path, asn_id=None): ''' Save median if requested by user @@ -350,6 +392,7 @@ def save_median(median_model, make_output_path, asn_id=None): log.info(f"Saved model in {median_model_output_path}") +# TODO fileio def _convert_inputs(inputs, good_bits, weight_type): """Convert input into datamodel required for processing. @@ -377,4 +420,3 @@ def _convert_inputs(inputs, good_bits, weight_type): good_bits=good_bits) input_models.append(image) return input_models - From e4ea5d9f98a56d1de8d516e031ab2b932df3a870 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 2 Jul 2024 16:21:59 -0400 Subject: [PATCH 214/687] only create median model if needed --- jwst/outlier_detection/ifu.py | 4 ---- jwst/outlier_detection/imaging.py | 29 ++++++++++++++++------------- jwst/outlier_detection/spec.py | 27 +++++++++++++++------------ jwst/outlier_detection/tso.py | 3 --- jwst/outlier_detection/utils.py | 8 +++++--- 5 files changed, 36 insertions(+), 35 deletions(-) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index 5a602113a8..dfec258c5c 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -50,7 +50,6 @@ def medfilt(arr, kern_size): return np.nanmedian(windows, axis=np.arange(-len(kern_size), 0)) -# TODO: break up this long function def detect_outliers( input_models, save_intermediate_results, @@ -100,7 +99,6 @@ def detect_outliers( ifu_second_check, make_output_path) -# TODO: break up this long function def flag_outliers(input_models, idet, uq_det, ndet_files, diffaxis, nx, ny, kern_size, threshold_percent, @@ -258,7 +256,6 @@ def flag_outliers(input_models, idet, uq_det, ndet_files, input_models[i] = model -# TODO move to utils? def _find_detector_parameters(input_models): """Find the size of data and the axis to form the differences (perpendicular to disaxis) """ @@ -270,7 +267,6 @@ def _find_detector_parameters(input_models): return (diffaxis, ny, nx) -# TODO move to utils? def create_optional_results_model(opt_info): """ Creates an OutlierOutputModel from the computed arrays from outlier detection on IFU data. diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index e17ba0d81b..62dedcbd71 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -96,19 +96,25 @@ def detect_outliers( weight_type=weight_type, good_bits=good_bits) - # Initialize intermediate products used in the outlier detection - # TODO do we need to make this a model? - with datamodel_open(drizzled_models[0]) as dm0: - median_model = datamodels.ImageModel(dm0.data.shape) - median_model.update(dm0) - median_model.meta.wcs = dm0.meta.wcs - # Perform median combination on set of drizzled mosaics - median_model.data = create_median(drizzled_models, maskpt) + median_data = create_median(drizzled_models, maskpt) if save_intermediate_results: + # make a median model + with datamodel_open(drizzled_models[0]) as dm0: + median_model = datamodels.ImageModel(median_data) + median_model.update(dm0) + median_wcs = dm0.meta.wcs + median_model.meta.wcs = median_wcs + median_model.data = median_data + save_median(median_model, make_output_path, asn_id) + del median_model else: + # read the median wcs from one of the drizzled models + with datamodel_open(drizzled_models[0]) as dm0: + median_wcs = dm0.meta.wcs + # since we're not saving intermediate results if the drizzled models # were written to disk, remove them if not in_memory: @@ -119,7 +125,8 @@ def detect_outliers( # each original input image and its blotted version of the median image _detect_outliers( input_models, - median_model, + median_data, + median_wcs, snr1, snr2, scale1, @@ -127,7 +134,3 @@ def detect_outliers( backg, resample_data, ) - - # clean-up (just to be explicit about being finished with - # these results) - del median_model diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index ca746efd0a..7e0a545cee 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -88,22 +88,26 @@ def detect_outliers( weight_type=weight_type, good_bits=good_bits) - # Initialize intermediate products used in the outlier detection - median_model = datamodels.ImageModel(drizzled_models[0].data.shape) - median_model.meta = drizzled_models[0].meta - median_model.meta.filename = make_output_path( - basepath=input_models[0].meta.filename, - suffix='median' - ) - # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class - median_model.data = create_median(drizzled_models, maskpt) + median_data = create_median(drizzled_models, maskpt) + median_wcs = drizzled_models[0].meta.wcs if save_intermediate_results: + # Initialize intermediate products used in the outlier detection + median_model = datamodels.ImageModel(median_data) + median_model.meta = drizzled_models[0].meta + median_model.meta.filename = make_output_path( + basepath=input_models[0].meta.filename, + suffix='median' + ) + median_model.data = median_data + log.info("Writing out MEDIAN image to: {}".format( median_model.meta.filename)) median_model.save(median_model.meta.filename) + + del median_model else: # since we're not saving intermediate results if the drizzled models # were written to disk, remove them @@ -116,7 +120,8 @@ def detect_outliers( # each original input image and its blotted version of the median image _detect_outliers( input_models, - median_model, + median_data, + median_wcs, snr1, snr2, scale1, @@ -124,5 +129,3 @@ def detect_outliers( backg, resample_data, ) - - del median_model diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index e2632cfdb3..e044b3ae39 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -67,7 +67,6 @@ def detect_outliers( ) -# TODO move to utils? def weight_no_resample(input_model, good_bits): """ give weights to model without resampling @@ -91,7 +90,6 @@ def weight_no_resample(input_model, good_bits): return weighted_cube -# TODO move to utils? def compute_rolling_median(model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: ''' Set bad and low-weight data to NaN, then compute the rolling median over the time axis. @@ -131,7 +129,6 @@ def compute_rolling_median(model: dm.CubeModel, weight_threshold: np.ndarray, w: return meds -# TODO move to utils? def moving_median_over_zeroth_axis(x: np.ndarray, w: int) -> np.ndarray: """ Calculate the median of a moving window over the zeroth axis of an N-d array. diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 2439954fd1..fa0813615c 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -19,6 +19,7 @@ log.setLevel(logging.DEBUG) +# flags used in flag_cr_update_model DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE'] OUTLIER = datamodels.dqflags.pixel['OUTLIER'] @@ -295,7 +296,8 @@ def gwcs_blot(median_data, median_wcs, blot_data, blot_wcs, pix_ratio): def _detect_outliers( input_models, - median_model, + median_data, + median_wcs, snr1, snr2, scale1, @@ -312,9 +314,9 @@ def _detect_outliers( else: pix_ratio = 1.0 - blot = gwcs_blot(median_model.data, median_model.meta.wcs, image.data, image.meta.wcs, pix_ratio) + blot = gwcs_blot(median_data, median_wcs, image.data, image.meta.wcs, pix_ratio) else: - blot = median_model.data + blot = median_data # dq flags will be updated in-place flag_cr_update_model(image, blot, snr1, snr2, scale1, scale2, backg, resample_data) From caca0f6cadfc04dc1ad17667e653dea2cb950ff7 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 2 Jul 2024 16:34:20 -0400 Subject: [PATCH 215/687] split out fileio utils --- jwst/outlier_detection/_fileio.py | 32 ++++++ jwst/outlier_detection/imaging.py | 5 +- .../outlier_detection_step.py | 2 +- jwst/outlier_detection/spec.py | 5 +- jwst/outlier_detection/tso.py | 3 +- jwst/outlier_detection/utils.py | 106 ++++++------------ 6 files changed, 78 insertions(+), 75 deletions(-) create mode 100644 jwst/outlier_detection/_fileio.py diff --git a/jwst/outlier_detection/_fileio.py b/jwst/outlier_detection/_fileio.py new file mode 100644 index 0000000000..6975ac1717 --- /dev/null +++ b/jwst/outlier_detection/_fileio.py @@ -0,0 +1,32 @@ +import os + +import logging +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +def remove_file(fn): + if isinstance(fn, str) and os.path.isfile(fn): + os.remove(fn) + log.info(f"Removing file {fn}") + + +def save_median(median_model, make_output_path, asn_id=None): + ''' + Save median if requested by user + + Parameters + ---------- + median_model : ~jwst.datamodels.ImageModel + The median ImageModel or CubeModel to save + ''' + default_suffix = "_outlier_i2d.fits" + if asn_id is None: + suffix_to_remove = default_suffix + else: + suffix_to_remove = f"_{asn_id}{default_suffix}" + median_model_output_path = make_output_path( + basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), + suffix='median') + median_model.save(median_model_output_path) + log.info(f"Saved model in {median_model_output_path}") diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 62dedcbd71..c06025ddc6 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -33,7 +33,8 @@ from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight -from .utils import _convert_inputs, _detect_outliers, _remove_file, create_median, save_median +from .utils import _convert_inputs, _detect_outliers, create_median +from ._fileio import remove_file, save_median log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -119,7 +120,7 @@ def detect_outliers( # were written to disk, remove them if not in_memory: for fn in drizzled_models._models: - _remove_file(fn) + remove_file(fn) # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 4adb0b6956..d4f0617115 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -202,7 +202,7 @@ def process(self, input_data): snr1, snr2, scale1, - scale2, # TODO turn off the things that aren't actually used + scale2, self.backg, False, # force resampling off but use the same workflow as imaging self.weight_type, diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 7e0a545cee..87aa2666ed 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -24,7 +24,8 @@ from stdatamodels.jwst import datamodels from ..resample import resample_spec, resample_utils -from .utils import _convert_inputs, _detect_outliers, _remove_file, create_median +from .utils import _convert_inputs, _detect_outliers, create_median +from ._fileio import remove_file import logging log = logging.getLogger(__name__) @@ -113,7 +114,7 @@ def detect_outliers( # were written to disk, remove them if not in_memory: for fn in drizzled_models._models: - _remove_file(fn) + remove_file(fn) log.info(f"Removing file {fn}") # Perform outlier detection using statistical comparisons between diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index e044b3ae39..4600e4d632 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -3,7 +3,8 @@ from jwst import datamodels as dm -from .utils import compute_weight_threshold, flag_cr_update_model, save_median +from .utils import compute_weight_threshold, flag_cr_update_model +from ._fileio import save_median import logging log = logging.getLogger(__name__) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index fa0813615c..c9996f4908 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -24,13 +24,6 @@ OUTLIER = datamodels.dqflags.pixel['OUTLIER'] -# TODO fileio -def _remove_file(fn): - if isinstance(fn, str) and os.path.isfile(fn): - os.remove(fn) - log.info(f"Removing file {fn}") - - # TODO stcal def compute_weight_threshold(weight, maskpt): ''' @@ -66,42 +59,6 @@ def compute_weight_threshold(weight, maskpt): return weight_threshold -def compute_weight_threshold_container(resampled_models, maskpt): - ''' - Compute weight means without keeping datamodels for each input open - - Parameters - ---------- - resampled_models : ~jwst.datamodels.ModelContainer - The input data models. - - maskpt : float - The percentage of the mean weight to use as a threshold for masking. - - Returns - ------- - list - The weight thresholds for each integration. - ''' - - # Start by ensuring that the ModelContainer does NOT open and keep each datamodel - ropen_orig = resampled_models._return_open - resampled_models._return_open = True - # keep track of resulting computation for each input resampled datamodel - weight_thresholds = [] - # For each model, compute the bad-pixel threshold from the weight arrays - for resampled in resampled_models: - weight = resampled.wht - weight_threshold = compute_weight_threshold(weight, maskpt) - weight_thresholds.append(weight_threshold) - # close and delete the model, just to explicitly try to keep the memory as clean as possible - resampled.close() - del resampled - # Reset ModelContainer attribute to original value - resampled_models._return_open = ropen_orig - return weight_thresholds - - # TODO stcal as a "median computer" def create_median(resampled_models, maskpt): """Create a median image from the singly resampled images. @@ -258,7 +215,7 @@ def flag_cr( return cr_mask -# TODO can this be model-independent? +# TODO stcal? (would require moving calc_gwcs_pixmap) # FIXME (or fixed) interp and sinscl were "options" only when provided # as part of the step spec (which becomes outlierpars). As neither was # in the spec (and providing unknown arguments causes an error), these @@ -294,6 +251,42 @@ def gwcs_blot(median_data, median_wcs, blot_data, blot_wcs, pix_ratio): return outsci +def compute_weight_threshold_container(resampled_models, maskpt): + ''' + Compute weight means without keeping datamodels for each input open + + Parameters + ---------- + resampled_models : ~jwst.datamodels.ModelContainer + The input data models. + + maskpt : float + The percentage of the mean weight to use as a threshold for masking. + + Returns + ------- + list + The weight thresholds for each integration. + ''' + + # Start by ensuring that the ModelContainer does NOT open and keep each datamodel + ropen_orig = resampled_models._return_open + resampled_models._return_open = True + # keep track of resulting computation for each input resampled datamodel + weight_thresholds = [] + # For each model, compute the bad-pixel threshold from the weight arrays + for resampled in resampled_models: + weight = resampled.wht + weight_threshold = compute_weight_threshold(weight, maskpt) + weight_thresholds.append(weight_threshold) + # close and delete the model, just to explicitly try to keep the memory as clean as possible + resampled.close() + del resampled + # Reset ModelContainer attribute to original value + resampled_models._return_open = ropen_orig + return weight_thresholds + + def _detect_outliers( input_models, median_data, @@ -370,31 +363,6 @@ def flag_cr_update_model( log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") - - -# TODO fileio -def save_median(median_model, make_output_path, asn_id=None): - ''' - Save median if requested by user - - Parameters - ---------- - median_model : ~jwst.datamodels.ImageModel - The median ImageModel or CubeModel to save - ''' - default_suffix = "_outlier_i2d.fits" - if asn_id is None: - suffix_to_remove = default_suffix - else: - suffix_to_remove = f"_{asn_id}{default_suffix}" - median_model_output_path = make_output_path( - basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), - suffix='median') - median_model.save(median_model_output_path) - log.info(f"Saved model in {median_model_output_path}") - - -# TODO fileio def _convert_inputs(inputs, good_bits, weight_type): """Convert input into datamodel required for processing. From cbd87c8b20795c975bea25efa265b635cecbd06b Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 10:20:17 -0400 Subject: [PATCH 216/687] move some outlier_detection utility functions to stcal --- jwst/outlier_detection/utils.py | 192 +------------------------------- pyproject.toml | 4 +- 2 files changed, 6 insertions(+), 190 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index c9996f4908..be977470b3 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -1,17 +1,14 @@ """ The ever-present utils sub-module. A home for all... """ -import os import warnings import numpy as np -from astropy.stats import sigma_clip -from drizzle.cdrizzle import tblot -from scipy import ndimage from jwst.datamodels import ModelContainer -from jwst.resample.resample_utils import build_driz_weight, calc_gwcs_pixmap +from jwst.resample.resample_utils import build_driz_weight from jwst.resample.resample import compute_image_pixel_area +from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_cr from stdatamodels.jwst import datamodels import logging @@ -24,42 +21,7 @@ OUTLIER = datamodels.dqflags.pixel['OUTLIER'] -# TODO stcal -def compute_weight_threshold(weight, maskpt): - ''' - Compute the weight threshold for a single image or cube. - - Parameters - ---------- - weight : numpy.ndarray - The weight array - - maskpt : float - The percentage of the mean weight to use as a threshold for masking. - - Returns - ------- - float - The weight threshold for this integration. - ''' - - # necessary in order to assure that mask gets applied correctly - if hasattr(weight, '_mask'): - del weight._mask - mask_zero_weight = np.equal(weight, 0.) - mask_nans = np.isnan(weight) - # Combine the masks - weight_masked = np.ma.array(weight, mask=np.logical_or( - mask_zero_weight, mask_nans)) - # Sigma-clip the unmasked data - weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5) - mean_weight = np.mean(weight_masked) - # Mask pixels where weight falls below maskpt percent - weight_threshold = mean_weight * maskpt - return weight_threshold - - -# TODO stcal as a "median computer" +# TODO figure out how to move to stcal def create_median(resampled_models, maskpt): """Create a median image from the singly resampled images. """ @@ -103,154 +65,6 @@ def create_median(resampled_models, maskpt): return median_image -# TODO stcal utils -def _abs_deriv(array): - """Take the absolute derivate of a numpy array.""" - tmp = np.zeros(array.shape, dtype=np.float64) - out = np.zeros(array.shape, dtype=np.float64) - - tmp[1:, :] = array[:-1, :] - tmp, out = _absolute_subtract(array, tmp, out) - tmp[:-1, :] = array[1:, :] - tmp, out = _absolute_subtract(array, tmp, out) - - tmp[:, 1:] = array[:, :-1] - tmp, out = _absolute_subtract(array, tmp, out) - tmp[:, :-1] = array[:, 1:] - tmp, out = _absolute_subtract(array, tmp, out) - - return out - - -# TODO stcal utils -def _absolute_subtract(array, tmp, out): - tmp = np.abs(array - tmp) - out = np.maximum(tmp, out) - tmp = tmp * 0. - return tmp, out - - -# TODO stcal utils -def flag_cr( - sci_data, - sci_err, - blot_data, - snr1, - snr2, - scale1, - scale2, - backg, - resample_data, -): - """Masks outliers in science image by updating DQ in-place - - Mask blemishes in dithered data by comparing a science image - with a model image and the derivative of the model image. - - Parameters - ---------- - - FIXME: update these - - sci_image : ~jwst.datamodels.ImageModel - the science data. Can also accept a CubeModel, but only if - resample_data is False - - blot_array : np.ndarray - the blotted median image of the dithered science frames. - - snr : str - Signal-to-noise ratio - - scale : str - scaling factor applied to the derivative - - backg : float - Background value (scalar) to subtract - - resample_data : bool - Boolean to indicate whether blot_image is created from resampled, - dithered data or not - - Notes - ----- - Accepting a CubeModel for sci_image and blot_image with resample_data=True - appears to be a relatively simple extension, as the only thing that explicitly - relies on the dimensionality is the kernel, which could be generalized. - However, this is not currently needed, as CubeModels are only passed in for - TSO data, where resampling is always False. - """ - err_data = np.nan_to_num(sci_err) - - # create the outlier mask - if resample_data: # dithered outlier detection - blot_deriv = _abs_deriv(blot_data) - diff_noise = np.abs(sci_data - blot_data - backg) - - # Create a boolean mask based on a scaled version of - # the derivative image (dealing with interpolating issues?) - # and the standard n*sigma above the noise - threshold1 = scale1 * blot_deriv + snr1 * err_data - mask1 = np.greater(diff_noise, threshold1) - - # Smooth the boolean mask with a 3x3 boxcar kernel - kernel = np.ones((3, 3), dtype=int) - mask1_smoothed = ndimage.convolve(mask1, kernel, mode='nearest') - - # Create a 2nd boolean mask based on the 2nd set of - # scale and threshold values - threshold2 = scale2 * blot_deriv + snr2 * err_data - mask2 = np.greater(diff_noise, threshold2) - - # Final boolean mask - cr_mask = mask1_smoothed & mask2 - - else: # stack outlier detection - diff_noise = np.abs(sci_data - blot_data) - - # straightforward detection of outliers for non-dithered data since - # err_data includes all noise sources (photon, read, and flat for baseline) - cr_mask = np.greater(diff_noise, snr1 * err_data) - - return cr_mask - - -# TODO stcal? (would require moving calc_gwcs_pixmap) -# FIXME (or fixed) interp and sinscl were "options" only when provided -# as part of the step spec (which becomes outlierpars). As neither was -# in the spec (and providing unknown arguments causes an error), these -# were never configurable and always defaulted to linear and 1.0 -def gwcs_blot(median_data, median_wcs, blot_data, blot_wcs, pix_ratio): - """ - Resample the output/resampled image to recreate an input image based on - the input image's world coordinate system - - Parameters - ---------- - median_model : `~stdatamodels.jwst.datamodels.JwstDataModel` - - blot_img : datamodel - Datamodel containing header and WCS to define the 'blotted' image - """ - # Compute the mapping between the input and output pixel coordinates - pixmap = calc_gwcs_pixmap(blot_wcs, median_wcs, blot_data.shape) - log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape)) - log.debug("Sci shape: {}".format(blot_data.shape)) - log.info('Blotting {} <-- {}'.format(blot_data.shape, median_data.shape)) - - outsci = np.zeros(blot_data.shape, dtype=np.float32) - - # Currently tblot cannot handle nans in the pixmap, so we need to give some - # other value. -1 is not optimal and may have side effects. But this is - # what we've been doing up until now, so more investigation is needed - # before a change is made. Preferably, fix tblot in drizzle. - pixmap[np.isnan(pixmap)] = -1 - tblot(median_data, pixmap, outsci, scale=pix_ratio, kscale=1.0, - interp='linear', exptime=1.0, misval=0.0, sinscl=1.0) - - return outsci - - def compute_weight_threshold_container(resampled_models, maskpt): ''' Compute weight means without keeping datamodels for each input open diff --git a/pyproject.toml b/pyproject.toml index 06fa22799f..ac91fda176 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,9 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal@git+https://github.com/spacetelescope/stcal@main", + # "stcal>=1.7.3,<1.8.0", + # FIXME: switch to main (and then released) when stcal is updated + "stcal @ git+https://github.com/braingram/stcal.git@outlier_detection", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", "stsci.image>=2.3.5", From e335b8a70beaef2d1860af7baebe2b6c0d2e50e2 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 13:07:29 -0400 Subject: [PATCH 217/687] split imaging and coron modes in outlier detection --- jwst/badpix_selfcal/badpix_selfcal.py | 6 +- jwst/outlier_detection/coron.py | 92 +++++++++++++++++++ jwst/outlier_detection/ifu.py | 11 +-- jwst/outlier_detection/imaging.py | 1 - .../outlier_detection_step.py | 10 +- jwst/outlier_detection/spec.py | 1 - .../tests/test_algorithms.py | 1 + .../tests/test_outlier_detection.py | 40 +++++--- jwst/outlier_detection/tso.py | 3 +- jwst/outlier_detection/utils.py | 14 ++- 10 files changed, 141 insertions(+), 38 deletions(-) create mode 100644 jwst/outlier_detection/coron.py diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 810964f94f..0691926552 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -1,9 +1,11 @@ from __future__ import annotations +import warnings + import numpy as np + import jwst.datamodels as dm -from jwst.outlier_detection.ifu import medfilt +from stcal.outlier_detection.utils import medfilt from stdatamodels.jwst.datamodels.dqflags import pixel -import warnings def badpix_selfcal(minimg: np.ndarray, diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py new file mode 100644 index 0000000000..aaaf106813 --- /dev/null +++ b/jwst/outlier_detection/coron.py @@ -0,0 +1,92 @@ +""" +Main class for performing outlier detection. + +This is the controlling routine for the outlier detection process. +It loads and sets the various input data and parameters needed by +the various functions and then controls the operation of this process +through all the steps used for the detection. + +Notes +----- +This routine performs the following operations:: + + 1. Extracts parameter settings from input model and merges + them with any user-provided values + 2. Resamples all input images into grouped observation mosaics. + 3. Creates a median image from all grouped observation mosaics. + 4. Blot median image to match each original input image. + 5. Perform statistical comparison between blotted image and original + image to identify outliers. + 6. Updates input data model DQ arrays with mask of detected outliers. + +""" + + +import logging +import os + +import numpy as np + +from stdatamodels.jwst.datamodels.util import open as datamodel_open +from stdatamodels.jwst import datamodels + +from jwst.resample import resample +from jwst.resample.resample_utils import build_driz_weight + +from .utils import _detect_outliers, create_cube_median +from ._fileio import remove_file, save_median + +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + + +__all__ = ["detect_outliers"] + + +def detect_outliers( + input_model, + save_intermediate_results, + good_bits, + maskpt, + snr1, + snr2, + scale1, + scale2, + backg, + weight_type, + asn_id, + make_output_path, +): + """Flag outlier pixels in DQ of input images.""" + # TODO only allow CubeModel + + # FIXME don't store this on the model + # FIXME does input have a var_rnoise? + input_model.wht = build_driz_weight(input_model, weight_type=weight_type, good_bits=good_bits) + + # Perform median combination on set of drizzled mosaics + median_data = create_cube_median(input_model, maskpt) + + if save_intermediate_results: + # make a median model + median_model = datamodels.ImageModel(median_data) + median_model.update(input_model) + median_model.meta.wcs = input_model.meta.wcs + + save_median(median_model, make_output_path, asn_id) + del median_model + + # Perform outlier detection using statistical comparisons between + # each original input image and its blotted version of the median image + # FIXME make this not expect a list/sequence + _detect_outliers( + [input_model], + median_data, + None, # no need for a wcs since this won't blot + snr1, + snr2, + scale1, + scale2, + backg, + False, + ) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index dfec258c5c..a8e4a5c701 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -30,10 +30,10 @@ import logging import numpy as np -from skimage.util import view_as_windows from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags +from stcal.outlier_detection.utils import medfilt log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -41,15 +41,6 @@ __all__ = ["detect_outliers"] -# TODO move to utils? this IS used by badpix so maybe somewhere more central -def medfilt(arr, kern_size): - # scipy.signal.medfilt (and many other median filters) have undefined behavior - # for nan inputs. See: https://github.com/scipy/scipy/issues/4800 - padded = np.pad(arr, [[k // 2] for k in kern_size]) - windows = view_as_windows(padded, kern_size, np.ones(len(kern_size), dtype='int')) - return np.nanmedian(windows, axis=np.arange(-len(kern_size), 0)) - - def detect_outliers( input_models, save_intermediate_results, diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index c06025ddc6..efb57b91be 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -107,7 +107,6 @@ def detect_outliers( median_model.update(dm0) median_wcs = dm0.meta.wcs median_model.meta.wcs = median_wcs - median_model.data = median_data save_median(median_model, make_output_path, asn_id) del median_model diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index d4f0617115..590ce7b16e 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -7,7 +7,7 @@ from jwst.stpipe import Step from jwst.lib.pipe_utils import is_tso -from . import ifu, imaging, tso, spec +from . import coron, ifu, imaging, tso, spec # Categorize all supported modes IMAGE_MODES = ['NRC_IMAGE', 'MIR_IMAGE', 'NRS_IMAGE', 'NIS_IMAGE', 'FGS_IMAGE'] @@ -194,7 +194,7 @@ def process(self, input_data): self.make_output_path, ) elif exptype in CORON_IMAGE_MODES: - imaging.detect_outliers( + coron.detect_outliers( input_models, self.save_intermediate_results, self.good_bits, @@ -204,13 +204,7 @@ def process(self, input_data): scale1, scale2, self.backg, - False, # force resampling off but use the same workflow as imaging self.weight_type, - self.pixfrac, - self.kernel, - self.fillval, - self.allowed_memory, - self.in_memory, asn_id, self.make_output_path, ) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 87aa2666ed..0692ad2f3e 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -102,7 +102,6 @@ def detect_outliers( basepath=input_models[0].meta.filename, suffix='median' ) - median_model.data = median_data log.info("Writing out MEDIAN image to: {}".format( median_model.meta.filename)) diff --git a/jwst/outlier_detection/tests/test_algorithms.py b/jwst/outlier_detection/tests/test_algorithms.py index f3dc79f415..6f4001da95 100644 --- a/jwst/outlier_detection/tests/test_algorithms.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -4,6 +4,7 @@ import numpy as np import scipy.signal +# TODO move this to stcal from jwst.outlier_detection.ifu import medfilt from jwst.outlier_detection.tso import moving_median_over_zeroth_axis diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index ed003b6e28..a6cb3b2c81 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -169,6 +169,22 @@ def we_many_sci( return all_sci +def container_to_cube(container): + """ + Utility function to convert the test container to a cube + for some tests + """ + cube_data = np.array([i.data for i in container]) + cube_err = np.array([i.err for i in container]) + cube_dq = np.array([i.dq for i in container]) + cube_var_noise = np.array([i.var_rnoise for i in container]) + cube = datamodels.CubeModel(data=cube_data, err=cube_err, dq=cube_dq, var_noise=cube_var_noise) + + # update metadata of cube to match the first image + cube.meta = container[0].meta + return cube + + @pytest.fixture def we_three_sci(): """Provide 3 science images with different noise but identical source @@ -333,18 +349,21 @@ def test_outlier_step_image_weak_CR_coron(exptype, tsovisit, tmp_cwd): # no noise so it should always be above the default threshold of 5 container[0].data[12, 12] = bkg + sig * 10 - result = OutlierDetectionStep.call(container) + # FIXME this test incorrectly provides ImageModels whereas + # coron3 will provide a CubeModel. This is fixed here. + cube = container_to_cube(container) + + result = OutlierDetectionStep.call(cube) # Make sure nothing changed in SCI array - for image, corrected in zip(container, result): - np.testing.assert_allclose(image.data, corrected.data) + for i, image in enumerate(container): + np.testing.assert_allclose(image.data, result.data[i]) # Verify source is not flagged - for r in result: - assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"] + assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) # Verify CR is flagged - assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE + assert np.all(result.dq[0, 12, 12] == OUTLIER_DO_NOT_USE) @pytest.mark.parametrize("exptype, tsovisit", exptypes_tso) @@ -372,14 +391,7 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): model.data[7, 7] += real_time_variability[i] model.err[7, 7] = np.sqrt(sig ** 2 + model.data[7, 7]) - cube_data = np.array([i.data for i in im]) - cube_err = np.array([i.err for i in im]) - cube_dq = np.array([i.dq for i in im]) - cube_var_noise = np.array([i.var_rnoise for i in im]) - cube = datamodels.CubeModel(data=cube_data, err=cube_err, dq=cube_dq, var_noise=cube_var_noise) - - # update metadata of cube to match the first image - cube.meta = im[0].meta + cube = container_to_cube(im) result = OutlierDetectionStep.call(cube, rolling_window_width=rolling_window_width) diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 4600e4d632..00266e52e0 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -3,7 +3,8 @@ from jwst import datamodels as dm -from .utils import compute_weight_threshold, flag_cr_update_model +from stcal.outlier_detection.utils import compute_weight_threshold +from .utils import flag_cr_update_model from ._fileio import save_median import logging diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index be977470b3..309cd6d661 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -21,6 +21,18 @@ OUTLIER = datamodels.dqflags.pixel['OUTLIER'] +def create_cube_median(cube_model, maskpt): + log.info("Computing median") + + weight_threshold = compute_weight_threshold(cube_model.wht, maskpt) + + median = np.nanmedian( + np.ma.masked_array(cube_model.data, np.less(cube_model.wht, weight_threshold), fill_value=np.nan), + axis=0, + ) + return median + + # TODO figure out how to move to stcal def create_median(resampled_models, maskpt): """Create a median image from the singly resampled images. @@ -157,7 +169,7 @@ def flag_cr_update_model( cr_mask = flag_cr(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) # TODO is it necessary to do all this math for 1 possible log message? - # sould a count of flagged pixels in the mask be a suitable replacement? + # would a count of flagged pixels in the mask be a suitable replacement? # Count existing DO_NOT_USE pixels count_existing = np.count_nonzero(image.dq & DO_NOT_USE) From ef2604d80c8d28873121088e3d41467a9d2de0b0 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 15:42:29 -0400 Subject: [PATCH 218/687] rename _detect_outliers --- jwst/outlier_detection/coron.py | 8 +++----- jwst/outlier_detection/imaging.py | 4 ++-- jwst/outlier_detection/spec.py | 4 ++-- jwst/outlier_detection/utils.py | 2 +- 4 files changed, 8 insertions(+), 10 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index aaaf106813..bec27d0887 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -33,7 +33,7 @@ from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight -from .utils import _detect_outliers, create_cube_median +from .utils import create_cube_median, flag_cr_update_model from ._fileio import remove_file, save_median log = logging.getLogger(__name__) @@ -78,11 +78,9 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - # FIXME make this not expect a list/sequence - _detect_outliers( - [input_model], + flag_cr_update_model( + input_model, median_data, - None, # no need for a wcs since this won't blot snr1, snr2, scale1, diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index efb57b91be..09e660340d 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -33,7 +33,7 @@ from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight -from .utils import _convert_inputs, _detect_outliers, create_median +from .utils import _convert_inputs, create_median, flag_crs_in_models from ._fileio import remove_file, save_median log = logging.getLogger(__name__) @@ -123,7 +123,7 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - _detect_outliers( + flag_crs_in_models( input_models, median_data, median_wcs, diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 0692ad2f3e..1be902009e 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -24,7 +24,7 @@ from stdatamodels.jwst import datamodels from ..resample import resample_spec, resample_utils -from .utils import _convert_inputs, _detect_outliers, create_median +from .utils import _convert_inputs, create_median, flag_crs_in_models from ._fileio import remove_file import logging @@ -118,7 +118,7 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - _detect_outliers( + flag_crs_in_models( input_models, median_data, median_wcs, diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 309cd6d661..55ae53ffef 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -113,7 +113,7 @@ def compute_weight_threshold_container(resampled_models, maskpt): return weight_thresholds -def _detect_outliers( +def flag_crs_in_models( input_models, median_data, median_wcs, From d96e2729f571a27a7b1bb86b441e8289a8aa25df Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 15:52:49 -0400 Subject: [PATCH 219/687] remove _convert_inputs --- jwst/outlier_detection/coron.py | 3 ++- jwst/outlier_detection/imaging.py | 6 ++++-- jwst/outlier_detection/spec.py | 7 +++++-- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index bec27d0887..52afcb0589 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -58,7 +58,8 @@ def detect_outliers( make_output_path, ): """Flag outlier pixels in DQ of input images.""" - # TODO only allow CubeModel + if not isinstance(input_model, datamodels.CubeModel): + raise Exception(f"Input must be a CubeModel: {input_models}") # FIXME don't store this on the model # FIXME does input have a var_rnoise? diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 09e660340d..7e4aada3e7 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -30,10 +30,11 @@ from stdatamodels.jwst.datamodels.util import open as datamodel_open from stdatamodels.jwst import datamodels +from jwst.datamodels import ModelContainer from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight -from .utils import _convert_inputs, create_median, flag_crs_in_models +from .utils import create_median, flag_crs_in_models from ._fileio import remove_file, save_median log = logging.getLogger(__name__) @@ -64,7 +65,8 @@ def detect_outliers( make_output_path, ): """Flag outlier pixels in DQ of input images.""" - input_models = _convert_inputs(input_models, good_bits, weight_type) + if not isinstance(input_models, ModelContainer): + raise Exception(f"Input must be a ModelContainer: {input_models}") if resample_data: # Start by creating resampled/mosaic images for diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 1be902009e..c0e0b8394e 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -23,8 +23,10 @@ from stdatamodels.jwst import datamodels +from jwst.datamodels import ModelContainer + from ..resample import resample_spec, resample_utils -from .utils import _convert_inputs, create_median, flag_crs_in_models +from .utils import create_median, flag_crs_in_models from ._fileio import remove_file import logging @@ -55,7 +57,8 @@ def detect_outliers( make_output_path, ): """Flag outlier pixels in DQ of input images.""" - input_models = _convert_inputs(input_models, good_bits, weight_type) + if not isinstance(input_models, ModelContainer): + raise Exception(f"Input must be a ModelContainer: {input_models}") if resample_data is True: # Start by creating resampled/mosaic images for From 824113bf696228a6c13f076fb6a4b3b997511f89 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 16:29:08 -0400 Subject: [PATCH 220/687] remove SlitModel as an allowed input --- jwst/outlier_detection/coron.py | 2 +- jwst/outlier_detection/outlier_detection_step.py | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 52afcb0589..e1dff50a6d 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -59,7 +59,7 @@ def detect_outliers( ): """Flag outlier pixels in DQ of input images.""" if not isinstance(input_model, datamodels.CubeModel): - raise Exception(f"Input must be a CubeModel: {input_models}") + raise Exception(f"Input must be a CubeModel: {input_model}") # FIXME don't store this on the model # FIXME does input have a var_rnoise? diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 590ce7b16e..31a23744de 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -137,12 +137,16 @@ def process(self, input_data): self.log.warning(f"Input only contains {ninputs} exposure") self.log.warning("Outlier detection step will be skipped") return self._set_status(input_models, "SKIPPED") - elif isinstance(input_models, (datamodels.CubeModel, datamodels.SlitModel)): + elif isinstance(input_models, datamodels.CubeModel): ninputs = input_models.shape[0] if ninputs < 2: self.log.warning(f"Input only contains {ninputs} integration") self.log.warning("Outlier detection step will be skipped") return self._set_status(input_models, "SKIPPED") + elif isinstance(input_models, datamodels.SlitModel): + # FIXME the CubeModel check above used to also check for + # SlitModel. I see no place where this is used + raise Exception() else: self.log.warning("Input {input_models} is not supported") self.log.warning("Outlier detection step will be skipped") From a3ae25a5799bbc5627c6405f17b1eb3629a9785b Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 3 Jul 2024 17:16:46 -0400 Subject: [PATCH 221/687] re-add SlitModel, add note --- jwst/outlier_detection/outlier_detection_step.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 31a23744de..bc0a6fab27 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -76,7 +76,7 @@ class OutlierDetectionStep(Step): # For each mode the input is: # Image : ModelContainer of ImageModel # Coron : CubeModel (either a psf or target) - # TSO : CubeModel + # TSO : CubeModel or 3D SlitModel (for nircam tsgrism) # IFU : ModelContainer of IFUImageModel # Spec : SourceModelContainer of SlitModel (created from MultiSlitModel) # or ModelContainer of ImageModel (possible for MIR_LRS-FIXEDSLIT) @@ -137,16 +137,14 @@ def process(self, input_data): self.log.warning(f"Input only contains {ninputs} exposure") self.log.warning("Outlier detection step will be skipped") return self._set_status(input_models, "SKIPPED") - elif isinstance(input_models, datamodels.CubeModel): + # TODO: tso data can be a CubeModel or SlitModel, coron is only a CubeModel? + # move this input checking into the modes since each is different + elif isinstance(input_models, (datamodels.CubeModel, datamodels.SlitModel)): ninputs = input_models.shape[0] if ninputs < 2: self.log.warning(f"Input only contains {ninputs} integration") self.log.warning("Outlier detection step will be skipped") return self._set_status(input_models, "SKIPPED") - elif isinstance(input_models, datamodels.SlitModel): - # FIXME the CubeModel check above used to also check for - # SlitModel. I see no place where this is used - raise Exception() else: self.log.warning("Input {input_models} is not supported") self.log.warning("Outlier detection step will be skipped") From 90cfb80821f199b35e3b04dab100bc7dcac0d434 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 4 Jul 2024 08:14:33 -0400 Subject: [PATCH 222/687] get wcs from resample --- jwst/outlier_detection/imaging.py | 10 ++++------ jwst/outlier_detection/spec.py | 4 +++- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 7e4aada3e7..c9de2e9705 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -88,8 +88,8 @@ def detect_outliers( asn_id=asn_id, allowed_memory=allowed_memory, ) + median_wcs = resample.output_wcs drizzled_models = resamp.do_drizzle(input_models) - else: # for non-dithered data, the resampled image is just the original image drizzled_models = input_models @@ -98,25 +98,23 @@ def detect_outliers( input_models[i], weight_type=weight_type, good_bits=good_bits) + # TODO copy for when saving median and input is a filename? + median_wcs = input_models[0].meta.wcs # Perform median combination on set of drizzled mosaics median_data = create_median(drizzled_models, maskpt) if save_intermediate_results: # make a median model + # TODO can i get the wcs from resample (or the input)? otherwise I need to copy it here with datamodel_open(drizzled_models[0]) as dm0: median_model = datamodels.ImageModel(median_data) median_model.update(dm0) - median_wcs = dm0.meta.wcs median_model.meta.wcs = median_wcs save_median(median_model, make_output_path, asn_id) del median_model else: - # read the median wcs from one of the drizzled models - with datamodel_open(drizzled_models[0]) as dm0: - median_wcs = dm0.meta.wcs - # since we're not saving intermediate results if the drizzled models # were written to disk, remove them if not in_memory: diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index c0e0b8394e..383e801ee4 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -75,6 +75,7 @@ def detect_outliers( in_memory=in_memory, asn_id=asn_id, ) + median_wcs = resamp.output_wcs drizzled_models = resamp.do_drizzle(input_models) if save_intermediate_results: for model in drizzled_models: @@ -91,11 +92,12 @@ def detect_outliers( input_models[i], weight_type=weight_type, good_bits=good_bits) + # TODO copy for when saving median and input is a filename? + median_wcs = input_models[0].meta.wcs # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class median_data = create_median(drizzled_models, maskpt) - median_wcs = drizzled_models[0].meta.wcs if save_intermediate_results: # Initialize intermediate products used in the outlier detection From e496d41ff72bc704f7fcdc17d4959a0c80ff3cf8 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 4 Jul 2024 08:14:43 -0400 Subject: [PATCH 223/687] 'fix' cube weighting --- jwst/outlier_detection/coron.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index e1dff50a6d..ea512e8f65 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -31,7 +31,7 @@ from stdatamodels.jwst import datamodels from jwst.resample import resample -from jwst.resample.resample_utils import build_driz_weight +from jwst.resample.resample_utils import build_driz_weight, build_mask from .utils import create_cube_median, flag_cr_update_model from ._fileio import remove_file, save_median @@ -62,8 +62,11 @@ def detect_outliers( raise Exception(f"Input must be a CubeModel: {input_model}") # FIXME don't store this on the model - # FIXME does input have a var_rnoise? - input_model.wht = build_driz_weight(input_model, weight_type=weight_type, good_bits=good_bits) + # FIXME weight_type could now be used here. Similar to tso data coron + # data was previously losing var_rnoise due to the conversion from a cube + # to a ModelContainer (which makes the default ivm weight ignore var_rnoise). + # Now that it's handled as a cube we could use the var_rnoise. + input_model.wht = build_mask(input_model.dq, good_bits).astype(np.float32) # Perform median combination on set of drizzled mosaics median_data = create_cube_median(input_model, maskpt) From b23aeafe32518359c4776c3d00f0fd253f0773cc Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 4 Jul 2024 11:44:06 -0400 Subject: [PATCH 224/687] typo --- jwst/outlier_detection/imaging.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index c9de2e9705..42172a63ec 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -88,7 +88,7 @@ def detect_outliers( asn_id=asn_id, allowed_memory=allowed_memory, ) - median_wcs = resample.output_wcs + median_wcs = resamp.output_wcs drizzled_models = resamp.do_drizzle(input_models) else: # for non-dithered data, the resampled image is just the original image From 23515992a49135402be10285042ddb305c5f9ed5 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 12:09:22 -0400 Subject: [PATCH 225/687] WIP: asn_id handling --- .../outlier_detection_step.py | 22 +++++++++++++++++++ jwst/pipeline/calwebb_coron3.py | 11 ++++++---- jwst/pipeline/calwebb_tso3.py | 3 +++ 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index bc0a6fab27..82271c3f84 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -1,5 +1,6 @@ """Public common step definition for OutlierDetection processing.""" from functools import partial +from pathlib import Path from stdatamodels.jwst import datamodels @@ -35,6 +36,13 @@ class OutlierDetectionStep(Step): """ + # TODO note that this step does not have a suffix, the image3, coron3 + # and spec3 pipelines set the suffix (tso3 does not). + # I think this means that running the step in isolation will produce + # a file with suffix "outlierdetectionstep" (which I think is a bug). + # FIXME coron3 also adds the asn_id to the suffix but didn't set + # the attribute (so I think intermediate files won't use the asn_id). + class_alias = "outlier_detection" # The members of spec needs to be a super-set of all parameters needed @@ -130,6 +138,14 @@ class OutlierDetectionStep(Step): def process(self, input_data): """Perform outlier detection processing on input data.""" + # The input only needs to be opened to: + # - determine the asn_id (if not set by the pipeline) + # - determine the "mode" (if not set by the pipeline) + + #if isinstance(input_data, (str, Path)): + # TODO allow pipelines to pre-select the mode? + + # FIXME in_memory only makes sense for some modes with datamodels.open(input_data, save_open=self.in_memory) as input_models: if isinstance(input_models, ModelContainer): ninputs = len(input_models) @@ -153,6 +169,7 @@ def process(self, input_data): # Setup output path naming if associations are involved. asn_id = None + # FIXME only makes sense for a ModelContainer input try: asn_id = input_models.meta.asn_table.asn_id except (AttributeError, KeyError): @@ -160,6 +177,8 @@ def process(self, input_data): if asn_id is None: asn_id = self.search_attr('asn_id') if asn_id is not None: + # FIXME why parent_first? this will (when run in a pipeline) + # get `_make_output_path` from the pipeline. _make_output_path = self.search_attr( '_make_output_path', parent_first=True ) @@ -267,6 +286,9 @@ def process(self, input_data): return self._set_status(input_models, "COMPLETE") + def _guess_mode(self, input_models): + pass + def _set_status(self, input_models, status): if isinstance(input_models, ModelContainer): for model in input_models: diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index c6f8cc60f8..9ad4474825 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -87,7 +87,10 @@ def process(self, user_input): # Create a DM object using the association table input_models = datamodels.open(user_input, asn_exptypes=asn_exptypes) - acid = input_models.meta.asn_table.asn_id + + # This asn_id assignment is important as it allows outlier detection + # to know the asn_id since that step receives the cube as input. + self.asn_id = input_models.meta.asn_table.asn_id # Store the output file for future use self.output_file = input_models.meta.asn_table.products[0].name @@ -100,7 +103,7 @@ def process(self, user_input): members_by_type[member['exptype'].lower()].append(member['expname']) # Set up required output products and formats - self.outlier_detection.suffix = f'{acid}_crfints' + self.outlier_detection.suffix = f'{self.asn_id}_crfints' self.outlier_detection.save_results = self.save_results self.resample.blendheaders = False @@ -173,7 +176,7 @@ def process(self, user_input): # Save the alignment results self.save_model( psf_aligned, output_file=target_file, - suffix='psfalign', acid=acid + suffix='psfalign', acid=self.asn_id ) # Call KLIP @@ -183,7 +186,7 @@ def process(self, user_input): # Save the psf subtraction results self.save_model( psf_sub, output_file=target_file, - suffix='psfsub', acid=acid + suffix='psfsub', acid=self.asn_id ) # Split out the integrations into separate models diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index 703d4a937d..bc0775b5a1 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -73,6 +73,9 @@ def process(self, input): if self.output_file is None: self.output_file = input_models.meta.asn_table.products[0].name + + # This asn_id assignment is important as it allows outlier detection + # to know the asn_id since that step receives the cube as input. self.asn_id = input_models.meta.asn_table.asn_id # Input may consist of multiple exposures, so loop over each of them From 738a8605450d5b7259cff95d16a3e4d8cca46b2c Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 13:32:50 -0400 Subject: [PATCH 226/687] add outlier detection mode and set the mode in the level 3 pipelines. If not set fall back to the old code where the input is inspected and checked against pre-defined modes. --- jwst/outlier_detection/coron.py | 5 +- jwst/outlier_detection/ifu.py | 10 + jwst/outlier_detection/imaging.py | 10 +- .../outlier_detection_step.py | 315 ++++++++++-------- jwst/outlier_detection/spec.py | 10 +- jwst/outlier_detection/tso.py | 3 + jwst/pipeline/calwebb_coron3.py | 1 + jwst/pipeline/calwebb_image3.py | 1 + jwst/pipeline/calwebb_spec3.py | 4 + jwst/pipeline/calwebb_tso3.py | 1 + 10 files changed, 209 insertions(+), 151 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index ea512e8f65..4e0f04cea6 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -27,7 +27,6 @@ import numpy as np -from stdatamodels.jwst.datamodels.util import open as datamodel_open from stdatamodels.jwst import datamodels from jwst.resample import resample @@ -58,6 +57,9 @@ def detect_outliers( make_output_path, ): """Flag outlier pixels in DQ of input images.""" + if not isinstance(input_model, datamodels.JwstDataModel): + input_model = datamodels.open(input_model) + if not isinstance(input_model, datamodels.CubeModel): raise Exception(f"Input must be a CubeModel: {input_model}") @@ -92,3 +94,4 @@ def detect_outliers( backg, False, ) + return input_model diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index a8e4a5c701..d82651e81a 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -50,6 +50,15 @@ def detect_outliers( make_output_path, ): """Split data by detector to find outliers.""" + if not isinstance(input_models, ModelContainer): + input_models = ModelContainer(input_models) + + if len(input_models) < 2: + log.warning(f"Input only contains {len(input_models)} exposures") + log.warning("Outlier detection will be skipped") + for model in input_models: + model.meta.cal_step.outlier_detection = "SKIPPED" + return input_models sizex, sizey = [int(val) for val in kernel_size.split()] kern_size = np.zeros(2, dtype=int) @@ -89,6 +98,7 @@ def detect_outliers( save_intermediate_results, ifu_second_check, make_output_path) + return input_models def flag_outliers(input_models, idet, uq_det, ndet_files, diffaxis, nx, ny, diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 42172a63ec..4e77173fe2 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -66,7 +66,14 @@ def detect_outliers( ): """Flag outlier pixels in DQ of input images.""" if not isinstance(input_models, ModelContainer): - raise Exception(f"Input must be a ModelContainer: {input_models}") + input_models = ModelContainer(input_models, save_open=in_memory) + + if len(input_models) < 2: + log.warning(f"Input only contains {len(input_models)} exposures") + log.warning("Outlier detection will be skipped") + for model in input_models: + model.meta.cal_step.outlier_detection = "SKIPPED" + return input_models if resample_data: # Start by creating resampled/mosaic images for @@ -134,3 +141,4 @@ def detect_outliers( backg, resample_data, ) + return input_models diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 82271c3f84..3f35dad406 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -20,6 +20,14 @@ TSO_IMAGE_MODES = ['NRC_TSIMAGE'] # missing MIR_IMAGE with TSOVIST=True, not really addable CORON_IMAGE_MODES = ['NRC_CORON', 'MIR_LYOT', 'MIR_4QPM'] +_STEP_MODES = { + 'coron': coron, + 'ifu': ifu, + 'imaging': imaging, + 'tso': tso, + 'spec': spec, +} + __all__ = ["OutlierDetectionStep"] @@ -138,158 +146,169 @@ class OutlierDetectionStep(Step): def process(self, input_data): """Perform outlier detection processing on input data.""" - # The input only needs to be opened to: - # - determine the asn_id (if not set by the pipeline) - # - determine the "mode" (if not set by the pipeline) - - #if isinstance(input_data, (str, Path)): - # TODO allow pipelines to pre-select the mode? - - # FIXME in_memory only makes sense for some modes - with datamodels.open(input_data, save_open=self.in_memory) as input_models: - if isinstance(input_models, ModelContainer): - ninputs = len(input_models) - if ninputs < 2: - self.log.warning(f"Input only contains {ninputs} exposure") - self.log.warning("Outlier detection step will be skipped") - return self._set_status(input_models, "SKIPPED") - # TODO: tso data can be a CubeModel or SlitModel, coron is only a CubeModel? - # move this input checking into the modes since each is different - elif isinstance(input_models, (datamodels.CubeModel, datamodels.SlitModel)): - ninputs = input_models.shape[0] - if ninputs < 2: - self.log.warning(f"Input only contains {ninputs} integration") - self.log.warning("Outlier detection step will be skipped") - return self._set_status(input_models, "SKIPPED") - else: - self.log.warning("Input {input_models} is not supported") - self.log.warning("Outlier detection step will be skipped") - return self._set_status(input_models, "SKIPPED") - self.log.info(f"Performing outlier detection with {ninputs} inputs") - - # Setup output path naming if associations are involved. - asn_id = None - # FIXME only makes sense for a ModelContainer input - try: - asn_id = input_models.meta.asn_table.asn_id - except (AttributeError, KeyError): - pass - if asn_id is None: - asn_id = self.search_attr('asn_id') - if asn_id is not None: - # FIXME why parent_first? this will (when run in a pipeline) - # get `_make_output_path` from the pipeline. - _make_output_path = self.search_attr( - '_make_output_path', parent_first=True - ) - - self._make_output_path = partial( - _make_output_path, - asn_id=asn_id - ) - - # Select which version of OutlierDetection - # needs to be used depending on the input data - if isinstance(input_models, ModelContainer): - single_model = input_models[0] - else: - single_model = input_models - exptype = single_model.meta.exposure.type - - snr1, snr2 = [float(v) for v in self.snr.split()] - scale1, scale2 = [float(v) for v in self.scale.split()] - - if is_tso(single_model): - tso.detect_outliers( - input_models, - self.save_intermediate_results, - self.good_bits, - self.maskpt, - self.rolling_window_width, - snr1, - snr2, - scale1, - scale2, - self.backg, - asn_id, - self.make_output_path, - ) - elif exptype in CORON_IMAGE_MODES: - coron.detect_outliers( - input_models, - self.save_intermediate_results, - self.good_bits, - self.maskpt, - snr1, - snr2, - scale1, - scale2, - self.backg, - self.weight_type, - asn_id, - self.make_output_path, - ) - elif exptype in IMAGE_MODES: - imaging.detect_outliers( - input_models, - self.save_intermediate_results, - self.good_bits, - self.maskpt, - snr1, - snr2, - scale1, - scale2, - self.backg, - self.resample_data, - self.weight_type, - self.pixfrac, - self.kernel, - self.fillval, - self.allowed_memory, - self.in_memory, - asn_id, - self.make_output_path, - ) - elif exptype in SLIT_SPEC_MODES: - spec.detect_outliers( - input_models, - self.save_intermediate_results, - self.good_bits, - self.maskpt, - snr1, - snr2, - scale1, - scale2, - self.backg, - self.resample_data, - self.weight_type, - self.pixfrac, - self.kernel, - self.fillval, - self.in_memory, - asn_id, - self.make_output_path, - ) - elif exptype in IFU_SPEC_MODES: - ifu.detect_outliers( - input_models, - self.save_intermediate_results, - self.kernel_size, - self.ifu_second_check, - self.threshold_percent, - self.make_output_path, - ) - else: - self.log.error("Outlier detection failed for unknown/unsupported ", - f"exposure type: {exptype}") - return self._set_status(input_models, "SKIPPED") - - return self._set_status(input_models, "COMPLETE") + # determine the "mode" (if not set by the pipeline) + mode = self._guess_mode(input_data) + if mode is None: + return self._set_status(input_data, "SKIPPED") + self.log.info(f"Outlier Detection mode: {mode}") + + # determine the asn_id (if not set by the pipeline) + asn_id = self._get_asn_id(input_data) + self.log.info(f"Outlier Detection asn_id: {asn_id}") + + snr1, snr2 = [float(v) for v in self.snr.split()] + scale1, scale2 = [float(v) for v in self.scale.split()] + + if mode == 'tso': + result_models = tso.detect_outliers( + input_data, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + self.rolling_window_width, + snr1, + snr2, + scale1, + scale2, + self.backg, + asn_id, + self.make_output_path, + ) + elif mode == 'coron': + result_models = coron.detect_outliers( + input_data, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + snr1, + snr2, + scale1, + scale2, + self.backg, + self.weight_type, + asn_id, + self.make_output_path, + ) + elif mode == 'imaging': + result_models = imaging.detect_outliers( + input_data, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + snr1, + snr2, + scale1, + scale2, + self.backg, + self.resample_data, + self.weight_type, + self.pixfrac, + self.kernel, + self.fillval, + self.allowed_memory, + self.in_memory, + asn_id, + self.make_output_path, + ) + elif mode == 'spec': + result_models = spec.detect_outliers( + input_data, + self.save_intermediate_results, + self.good_bits, + self.maskpt, + snr1, + snr2, + scale1, + scale2, + self.backg, + self.resample_data, + self.weight_type, + self.pixfrac, + self.kernel, + self.fillval, + self.in_memory, + asn_id, + self.make_output_path, + ) + elif mode == 'ifu': + result_models = ifu.detect_outliers( + input_data, + self.save_intermediate_results, + self.kernel_size, + self.ifu_second_check, + self.threshold_percent, + self.make_output_path, + ) + else: + self.log.error("Outlier detection failed for unknown/unsupported ", + f"mode: {mode}") + return self._set_status(input_data, "SKIPPED") + + return self._set_status(result_models, "COMPLETE") def _guess_mode(self, input_models): - pass + # The pipelines should set this mode or ideally these should + # be separate steps (but that would require new crds reference files). + if hasattr(self, "mode"): + return self.mode + + # guess mode from input type + if not isinstance(input_models, datamodels.JwstDataModel): + input_models = datamodels.open(input_models, asn_n_members=1) + + # Select which version of OutlierDetection + # needs to be used depending on the input data + if isinstance(input_models, ModelContainer): + single_model = input_models[0] + else: + single_model = input_models + + if is_tso(single_model): + return 'tso' + + exptype = single_model.meta.exposure.type + if exptype in CORON_IMAGE_MODES: + return 'coron' + if exptype in IMAGE_MODES: + return 'imaging' + if exptype in SLIT_SPEC_MODES: + return 'spec' + if exptype in IFU_SPEC_MODES: + return 'ifu' + + self.log.error("Outlier detection failed for unknown/unsupported ", + f"exposure type: {exptype}") + return None + + def _get_asn_id(self, input_models): + # handle if input_models isn't open + if not isinstance(input_models, datamodels.JwstDataModel): + input_models = datamodels.open(input_models, asn_n_members=1) + + # Setup output path naming if associations are involved. + asn_id = None + try: + asn_id = input_models.meta.asn_table.asn_id + except (AttributeError, KeyError): + pass + if asn_id is None: + asn_id = self.search_attr('asn_id') + if asn_id is not None: + _make_output_path = self.search_attr( + '_make_output_path', parent_first=True + ) + + self._make_output_path = partial( + _make_output_path, + asn_id=asn_id + ) + return asn_id def _set_status(self, input_models, status): + # this might be called with the input which might be a filename or path + if not isinstance(input_models, datamodels.JwstDataModel): + input_models = datamodels.open(input_models) + if isinstance(input_models, ModelContainer): for model in input_models: model.meta.cal_step.outlier_detection = status diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 383e801ee4..709f250aa6 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -58,7 +58,14 @@ def detect_outliers( ): """Flag outlier pixels in DQ of input images.""" if not isinstance(input_models, ModelContainer): - raise Exception(f"Input must be a ModelContainer: {input_models}") + input_models = ModelContainer(input_models, save_open=in_memory) + + if len(input_models) < 2: + log.warning(f"Input only contains {len(input_models)} exposures") + log.warning("Outlier detection will be skipped") + for model in input_models: + model.meta.cal_step.outlier_detection = "SKIPPED" + return input_models if resample_data is True: # Start by creating resampled/mosaic images for @@ -134,3 +141,4 @@ def detect_outliers( backg, resample_data, ) + return input_models diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 00266e52e0..b5d1ae19a2 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -30,6 +30,8 @@ def detect_outliers( make_output_path, ): """Flag outlier pixels in DQ of input images.""" + if not isinstance(input_model, dm.JwstDataModel): + input_model = dm.open(input_model) if isinstance(input_model, dm.ModelContainer): raise TypeError("OutlierDetectionTSO does not support ModelContainer input.") weighted_cube = weight_no_resample(input_model, good_bits) @@ -67,6 +69,7 @@ def detect_outliers( backg, False, # force resampling off ) + return input_model def weight_no_resample(input_model, good_bits): diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index 9ad4474825..ab7ff2ed41 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -104,6 +104,7 @@ def process(self, user_input): # Set up required output products and formats self.outlier_detection.suffix = f'{self.asn_id}_crfints' + self.outlier_detection.mode = 'coron' self.outlier_detection.save_results = self.save_results self.resample.blendheaders = False diff --git a/jwst/pipeline/calwebb_image3.py b/jwst/pipeline/calwebb_image3.py index d67a6171d5..9407d85913 100644 --- a/jwst/pipeline/calwebb_image3.py +++ b/jwst/pipeline/calwebb_image3.py @@ -61,6 +61,7 @@ def process(self, input_data): # Configure settings for saving results files self.outlier_detection.suffix = 'crf' + self.outlier_detection.mode = 'imaging' self.outlier_detection.save_results = self.save_results self.resample.suffix = 'i2d' diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 23b9902a7c..96a5b25d68 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -227,6 +227,10 @@ def process(self, input): # the _cal files are not saved they will not be updated for cal_array in result: cal_array.meta.asn.table_name = op.basename(input_models.asn_table_name) + if exptype in IFU_EXPTYPES: + self.outlier_detection.mode = 'ifu' + else: + self.outlier_detection.mode = 'spec' result = self.outlier_detection(result) # interpolate pixels that have a NaN value or are flagged diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index bc0775b5a1..2b9e14ae86 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -77,6 +77,7 @@ def process(self, input): # This asn_id assignment is important as it allows outlier detection # to know the asn_id since that step receives the cube as input. self.asn_id = input_models.meta.asn_table.asn_id + self.outlier_detection.mode = 'tso' # Input may consist of multiple exposures, so loop over each of them input_exptype = None From 4844dfddadd4323d99a391bec487ae5389339046 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 13:39:18 -0400 Subject: [PATCH 227/687] clean up fixmes/todos --- jwst/outlier_detection/coron.py | 1 - jwst/outlier_detection/imaging.py | 7 +++---- jwst/outlier_detection/outlier_detection_step.py | 4 +--- jwst/outlier_detection/spec.py | 5 +++-- 4 files changed, 7 insertions(+), 10 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 4e0f04cea6..c2b6ebf39b 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -63,7 +63,6 @@ def detect_outliers( if not isinstance(input_model, datamodels.CubeModel): raise Exception(f"Input must be a CubeModel: {input_model}") - # FIXME don't store this on the model # FIXME weight_type could now be used here. Similar to tso data coron # data was previously losing var_rnoise due to the conversion from a cube # to a ModelContainer (which makes the default ivm weight ignore var_rnoise). diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 4e77173fe2..0ae1b773d1 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -21,7 +21,7 @@ """ - +import copy import logging import os @@ -105,15 +105,14 @@ def detect_outliers( input_models[i], weight_type=weight_type, good_bits=good_bits) - # TODO copy for when saving median and input is a filename? - median_wcs = input_models[0].meta.wcs + # copy for when saving median and input is a filename? + median_wcs = copy.deepcopy(input_models[0].meta.wcs) # Perform median combination on set of drizzled mosaics median_data = create_median(drizzled_models, maskpt) if save_intermediate_results: # make a median model - # TODO can i get the wcs from resample (or the input)? otherwise I need to copy it here with datamodel_open(drizzled_models[0]) as dm0: median_model = datamodels.ImageModel(median_data) median_model.update(dm0) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 3f35dad406..fa187ec99d 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -44,12 +44,10 @@ class OutlierDetectionStep(Step): """ - # TODO note that this step does not have a suffix, the image3, coron3 + # FIXME note that this step does not have a suffix, the image3, coron3 # and spec3 pipelines set the suffix (tso3 does not). # I think this means that running the step in isolation will produce # a file with suffix "outlierdetectionstep" (which I think is a bug). - # FIXME coron3 also adds the asn_id to the suffix but didn't set - # the attribute (so I think intermediate files won't use the asn_id). class_alias = "outlier_detection" diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 709f250aa6..ccc78f5abd 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -20,6 +20,7 @@ 6. Updates input data model DQ arrays with mask of detected outliers. """ +import copy from stdatamodels.jwst import datamodels @@ -99,8 +100,8 @@ def detect_outliers( input_models[i], weight_type=weight_type, good_bits=good_bits) - # TODO copy for when saving median and input is a filename? - median_wcs = input_models[0].meta.wcs + # copy for when saving median and input is a filename? + median_wcs = copy.deepcopy(input_models[0].meta.wcs) # Perform median combination on set of drizzled mosaics # create_median should be called as a method from parent class From 3ed02c9766d0a29b81820ddcbb9a181f6f150b2f Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 13:43:49 -0400 Subject: [PATCH 228/687] fix style --- jwst/outlier_detection/coron.py | 6 ++---- jwst/outlier_detection/ifu.py | 1 + jwst/outlier_detection/imaging.py | 5 +---- jwst/outlier_detection/outlier_detection_step.py | 1 - 4 files changed, 4 insertions(+), 9 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index c2b6ebf39b..356e9dcb9b 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -23,17 +23,15 @@ import logging -import os import numpy as np from stdatamodels.jwst import datamodels -from jwst.resample import resample -from jwst.resample.resample_utils import build_driz_weight, build_mask +from jwst.resample.resample_utils import build_mask from .utils import create_cube_median, flag_cr_update_model -from ._fileio import remove_file, save_median +from ._fileio import save_median log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index d82651e81a..a60666c13b 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -31,6 +31,7 @@ import numpy as np +from jwst.datamodels import ModelContainer from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags from stcal.outlier_detection.utils import medfilt diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 0ae1b773d1..ebccb09bc3 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -25,9 +25,6 @@ import logging import os -import numpy as np - -from stdatamodels.jwst.datamodels.util import open as datamodel_open from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer @@ -113,7 +110,7 @@ def detect_outliers( if save_intermediate_results: # make a median model - with datamodel_open(drizzled_models[0]) as dm0: + with datamodels.open(drizzled_models[0]) as dm0: median_model = datamodels.ImageModel(median_data) median_model.update(dm0) median_model.meta.wcs = median_wcs diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index fa187ec99d..5b260b61c3 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -1,6 +1,5 @@ """Public common step definition for OutlierDetection processing.""" from functools import partial -from pathlib import Path from stdatamodels.jwst import datamodels From 688145ab8b3bae60c53a780db920137a5b111ef3 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 13:46:37 -0400 Subject: [PATCH 229/687] add changelog --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 16e0c470cb..06e5941de3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -25,6 +25,8 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +- Refactored to separate modes and move common code to stcal. [#8613] + scripts ------- @@ -51,6 +53,7 @@ tweakreg - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] + 1.15.1 (2024-07-08) =================== From 3737469a33ecad90def96be9852d6634408eb079 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 13:56:03 -0400 Subject: [PATCH 230/687] WIP: docs --- docs/jwst/outlier_detection/index.rst | 1 + .../outlier_detection_coron.rst | 59 +++++++++++++++++++ .../outlier_detection_ifu.rst | 2 +- .../outlier_detection_imaging.rst | 2 +- .../outlier_detection_spec.rst | 2 +- .../outlier_detection_step.rst | 12 ++-- .../outlier_detection_tso.rst | 2 +- 7 files changed, 70 insertions(+), 10 deletions(-) create mode 100644 docs/jwst/outlier_detection/outlier_detection_coron.rst diff --git a/docs/jwst/outlier_detection/index.rst b/docs/jwst/outlier_detection/index.rst index b2f76757a5..e78ff7c57f 100644 --- a/docs/jwst/outlier_detection/index.rst +++ b/docs/jwst/outlier_detection/index.rst @@ -11,6 +11,7 @@ Outlier Detection arguments.rst outlier_detection_step.rst outlier_detection_imaging.rst + outlier_detection_coron.rst outlier_detection_tso.rst outlier_detection_ifu.rst outlier_detection_spec.rst diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst new file mode 100644 index 0000000000..3efbd1dedb --- /dev/null +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -0,0 +1,59 @@ +.. _outlier-detection-coron: + +Coronography Outlier Detection Algorithm +======================================== + +This module serves as the interface for applying ``outlier_detection`` to coronographic +image observations. + +Specifically, this routine performs the following operations: + +#. Extract parameter settings from input model and merge them with any user-provided values. + See :ref:`outlier detection arguments ` for the full list + of parameters. + +#. Convert input data, as needed, to make sure it is in a format that can be processed. + A :py:class:`~jwst.datamodels.CubeModel` serves as the basic format for all processing + performed by this step. + +#. Create a median image preserving the spatial dimensions of the cube. + + * The ``maskpt`` parameter sets the percentage of the weight image values to + use, and any pixel with a weight below this value gets flagged as "bad" and + ignored when resampled. + +#. Perform statistical comparison between median image and original image to identify outliers. + + * This comparison uses the original input images, the + median image, and the derivative of the median image to + create a cosmic ray mask for each input image. + * The derivative of the median image gets created using the + median image to compute the absolute value of the difference between each pixel and + its four surrounding neighbors with the largest value being the recorded derivative. + * These derivative images are used to flag cosmic rays + and other blemishes, such as satellite trails. Where the difference is larger + than can be explained by noise statistics, the flattening effect of taking the + median, or an error in the shift (the latter two effects are estimated using + the image derivative), the suspect pixel is masked. + * The ``backg`` parameter specifies a user-provided value to be used as the + background estimate. This gets added to the background-subtracted + median image to attempt to match the original background levels of the + original input mosaic so that cosmic-rays (bad pixels) from the input + mosaic can be identified more easily as outliers compared to the median image. + * Cosmic rays are flagged using the following rule: + + .. math:: | image\_input - image\_median | > scale*image\_deriv + SNR*noise + + * The ``scale`` is defined as the multiplicative factor applied to the + derivative which is used to determine if the difference between the data + image and the median image is large enough to require masking. + * The ``noise`` is calculated using a combination of the detector read + noise and the poisson noise of the median image plus the sky background. + * The user must specify two cut-off signal-to-noise values using the + ``snr`` parameter for determining whether a pixel should be masked: + the first for detecting the primary cosmic ray, and the second for masking + lower-level bad pixels adjacent to those found in the first pass. Since + cosmic rays often extend across several pixels, the adjacent pixels make + use of a slightly lower SNR threshold. + +#. Update input data model DQ arrays with mask of detected outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_ifu.rst b/docs/jwst/outlier_detection/outlier_detection_ifu.rst index ba16718d51..f8a4c4d66e 100644 --- a/docs/jwst/outlier_detection/outlier_detection_ifu.rst +++ b/docs/jwst/outlier_detection/outlier_detection_ifu.rst @@ -31,4 +31,4 @@ is greater than the input threshold percentage. Pixels that are found to be out -.. automodapi:: jwst.outlier_detection.outlier_detection_ifu +.. automodapi:: jwst.outlier_detection.ifu diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 1294be65f1..56140af07a 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -198,4 +198,4 @@ Outlier Detection for Slit data See the :ref:`IFU outlier detection ` documentation for details. -.. automodapi:: jwst.outlier_detection.outlier_detection +.. automodapi:: jwst.outlier_detection.imaging diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index 9f8ce97d98..6d8cd13de5 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -45,4 +45,4 @@ Specifically, this routine performs the following operations (modified from the #. Update input data model DQ arrays with mask of detected outliers -.. automodapi:: jwst.outlier_detection.outlier_detection_spec +.. automodapi:: jwst.outlier_detection.spec diff --git a/docs/jwst/outlier_detection/outlier_detection_step.rst b/docs/jwst/outlier_detection/outlier_detection_step.rst index 3f8b613021..0713147a31 100644 --- a/docs/jwst/outlier_detection/outlier_detection_step.rst +++ b/docs/jwst/outlier_detection/outlier_detection_step.rst @@ -29,19 +29,19 @@ input data: #. Select outlier detection algorithm based on exposure type - **Images**: like those taken with NIRCam, will use - :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetection` as described + :py:mod:`~jwst.outlier_detection.outlier_detection.OutlierDetection` as described in :ref:`outlier-detection-imaging` - **Coronagraphic observations**: - use :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetection` with - resampling turned off as described in :ref:`outlier-detection-imaging` + use :py:mod:`~jwst.outlier_detection.coron` + as described in :ref:`outlier-detection-coron` - **Time-Series Observations(TSO)**: both imaging and spectroscopic modes, use - :py:class:`~jwst.outlier_detection.outlier_detection.OutlierDetectionTSO` + :py:mod:`~jwst.outlier_detection.tso` as described in :ref:`outlier-detection-tso` - **IFU observations**: use - :py:class:`~jwst.outlier_detection.outlier_detection_ifu.OutlierDetectionIFU` as + :py:mod:`~jwst.outlier_detection.ifu` as described in :ref:`outlier-detection-ifu` - **Long-slit spectroscopic observations**: use - :py:class:`~jwst.outlier_detection.outlier_detection_spec.OutlierDetectionSpec` as + :py:mod:`~jwst.outlier_detection.spec` as described in :ref:`outlier-detection-spec` #. Instantiate and run outlier detection class determined for the exposure type diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst index 0df852ea00..843ad95941 100644 --- a/docs/jwst/outlier_detection/outlier_detection_tso.rst +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -36,4 +36,4 @@ a few variations to accomodate the nature of these 3D data. #. The input data model DQ arrays are updated with the mask of detected outliers. -.. automodapi:: jwst.outlier_detection.outlier_detection_tso +.. automodapi:: jwst.outlier_detection.tso From 42a48cb2b6045b02181166c8038b7b042ce0f74d Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 8 Jul 2024 17:14:48 -0400 Subject: [PATCH 231/687] renaming and argument cleanup --- jwst/outlier_detection/coron.py | 18 ++------- .../outlier_detection_step.py | 9 ----- .../tests/test_algorithms.py | 40 ------------------- .../tests/test_outlier_detection.py | 4 +- jwst/outlier_detection/tso.py | 17 ++------ jwst/outlier_detection/utils.py | 27 ++++++------- 6 files changed, 22 insertions(+), 93 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 356e9dcb9b..8485d3e18c 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -30,7 +30,7 @@ from jwst.resample.resample_utils import build_mask -from .utils import create_cube_median, flag_cr_update_model +from .utils import create_cube_median, flag_model_crs from ._fileio import save_median log = logging.getLogger(__name__) @@ -45,12 +45,7 @@ def detect_outliers( save_intermediate_results, good_bits, maskpt, - snr1, - snr2, - scale1, - scale2, - backg, - weight_type, + snr, asn_id, make_output_path, ): @@ -81,14 +76,9 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - flag_cr_update_model( + flag_model_crs( input_model, median_data, - snr1, - snr2, - scale1, - scale2, - backg, - False, + snr, ) return input_model diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 5b260b61c3..1a33d8cf25 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -164,10 +164,6 @@ def process(self, input_data): self.maskpt, self.rolling_window_width, snr1, - snr2, - scale1, - scale2, - self.backg, asn_id, self.make_output_path, ) @@ -178,11 +174,6 @@ def process(self, input_data): self.good_bits, self.maskpt, snr1, - snr2, - scale1, - scale2, - self.backg, - self.weight_type, asn_id, self.make_output_path, ) diff --git a/jwst/outlier_detection/tests/test_algorithms.py b/jwst/outlier_detection/tests/test_algorithms.py index 6f4001da95..b3b9255bb4 100644 --- a/jwst/outlier_detection/tests/test_algorithms.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -1,48 +1,8 @@ -import warnings - -import pytest import numpy as np -import scipy.signal -# TODO move this to stcal -from jwst.outlier_detection.ifu import medfilt from jwst.outlier_detection.tso import moving_median_over_zeroth_axis -@pytest.mark.parametrize("shape,kern_size", [ - ([7, 7], [3, 3]), - ([7, 7], [3, 1]), - ([7, 7], [1, 3]), - ([7, 5], [3, 3]), - ([5, 7], [3, 3]), - ([42, 42], [7, 7]), - ([42, 42], [7, 5]), - ([42, 42], [5, 7]), - ([42, 7, 5], [3, 3, 3]), - ([5, 7, 42], [5, 5, 5]), -]) -def test_medfilt_against_scipy(shape, kern_size): - arr = np.arange(np.prod(shape), dtype='uint32').reshape(shape) - result = medfilt(arr, kern_size) - expected = scipy.signal.medfilt(arr, kern_size) - np.testing.assert_allclose(result, expected) - - -@pytest.mark.parametrize("arr,kern_size,expected", [ - ([2, np.nan, 0], [3], [1, 1, 0]), - ([np.nan, np.nan, np.nan], [3], [0, np.nan, 0]), -]) -def test_medfilt_nan(arr, kern_size, expected): - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", - message="All-NaN slice", - category=RuntimeWarning - ) - result = medfilt(arr, kern_size) - np.testing.assert_allclose(result, expected) - - def test_rolling_median(): time_axis = np.arange(10) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index a6cb3b2c81..13b77fa26f 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -8,7 +8,7 @@ from jwst.datamodels import ModelContainer from jwst.outlier_detection import OutlierDetectionStep -from jwst.outlier_detection.utils import flag_cr_update_model +from jwst.outlier_detection.utils import flag_resampled_model_crs from jwst.outlier_detection.outlier_detection_step import ( IMAGE_MODES, TSO_SPEC_MODES, @@ -75,7 +75,7 @@ def test_flag_cr(sci_blot_image_pair): # run flag_cr() which updates in-place. Copy sci first. data_copy = sci.data.copy() - flag_cr_update_model( + flag_resampled_model_crs( sci, blot.data, 5.0, diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index b5d1ae19a2..f43e6c2c34 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -4,7 +4,7 @@ from jwst import datamodels as dm from stcal.outlier_detection.utils import compute_weight_threshold -from .utils import flag_cr_update_model +from .utils import flag_model_crs from ._fileio import save_median import logging @@ -21,11 +21,7 @@ def detect_outliers( good_bits, maskpt, rolling_window_width, - snr1, - snr2, - scale1, - scale2, - backg, + snr, asn_id, make_output_path, ): @@ -59,15 +55,10 @@ def detect_outliers( # no need for blotting, resample is turned off for TSO # go straight to outlier detection log.info("Flagging outliers") - flag_cr_update_model( + flag_model_crs( input_model, medians, - snr1, - snr2, - scale1, - scale2, - backg, - False, # force resampling off + snr, ) return input_model diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 55ae53ffef..8b42158770 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -8,7 +8,7 @@ from jwst.datamodels import ModelContainer from jwst.resample.resample_utils import build_driz_weight from jwst.resample.resample import compute_image_pixel_area -from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_cr +from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_crs, flag_resampled_crs from stdatamodels.jwst import datamodels import logging @@ -16,7 +16,6 @@ log.setLevel(logging.DEBUG) -# flags used in flag_cr_update_model DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE'] OUTLIER = datamodels.dqflags.pixel['OUTLIER'] @@ -138,10 +137,10 @@ def flag_crs_in_models( blot = median_data # dq flags will be updated in-place - flag_cr_update_model(image, blot, snr1, snr2, scale1, scale2, backg, resample_data) + flag_resampled_model_crs(image, blot, snr1, snr2, scale1, scale2, backg, resample_data) -def flag_cr_update_model( +def flag_resampled_model_crs( image, blot, snr1, @@ -166,12 +165,7 @@ def flag_cr_update_model( backg = image.meta.background.level log.debug(f"Adding background level {backg} to blotted image") - cr_mask = flag_cr(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) - - # TODO is it necessary to do all this math for 1 possible log message? - # would a count of flagged pixels in the mask be a suitable replacement? - # Count existing DO_NOT_USE pixels - count_existing = np.count_nonzero(image.dq & DO_NOT_USE) + cr_mask = flag_resampled_crs(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) # FIXME (or really "fixed") for a converted cube this used to overwrite # the dq "view" of the cube with a new dq array. This broke the link @@ -182,11 +176,14 @@ def flag_cr_update_model( # we don't convert cubes. image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) - # Report number (and percent) of new DO_NOT_USE pixels found - count_outlier = np.count_nonzero(image.dq & DO_NOT_USE) - count_added = count_outlier - count_existing - percent_cr = count_added / image.dq.size * 100 - log.info(f"New pixels flagged as outliers: {count_added} ({percent_cr:.2f}%)") + log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") + + +def flag_model_crs(image, blot, snr): + cr_mask = flag_crs(image.data, image.err, blot, snr) + # update dq array in-place + image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) + log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") def _convert_inputs(inputs, good_bits, weight_type): From 2dd8b9880be12a266292b068ba5841b14a656b97 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 9 Jul 2024 07:38:55 -0400 Subject: [PATCH 232/687] fix coron3 filenames --- jwst/pipeline/calwebb_coron3.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index ab7ff2ed41..a309fcc14d 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -103,7 +103,7 @@ def process(self, user_input): members_by_type[member['exptype'].lower()].append(member['expname']) # Set up required output products and formats - self.outlier_detection.suffix = f'{self.asn_id}_crfints' + self.outlier_detection.suffix = 'crfints' self.outlier_detection.mode = 'coron' self.outlier_detection.save_results = self.save_results self.resample.blendheaders = False From 26e115bef82ec961aaa84822d0af7b70052fe263 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 9 Jul 2024 20:38:26 -0400 Subject: [PATCH 233/687] fix gwcs_blot arguments --- jwst/outlier_detection/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 8b42158770..a2100d1064 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -132,7 +132,7 @@ def flag_crs_in_models( else: pix_ratio = 1.0 - blot = gwcs_blot(median_data, median_wcs, image.data, image.meta.wcs, pix_ratio) + blot = gwcs_blot(median_data, median_wcs, image.data.shape, image.meta.wcs, pix_ratio) else: blot = median_data From 74d2f6f2dd684eff6b58f31ca9c6f5017e6f6a34 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 9 Jul 2024 20:55:18 -0400 Subject: [PATCH 234/687] remove todo --- jwst/outlier_detection/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index a2100d1064..7ae673b7bd 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -32,7 +32,6 @@ def create_cube_median(cube_model, maskpt): return median -# TODO figure out how to move to stcal def create_median(resampled_models, maskpt): """Create a median image from the singly resampled images. """ From 67e7175fb9e2089925668ca228fb8aff078fc1e8 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 11:22:40 -0400 Subject: [PATCH 235/687] assign wcs array_shape if it's None --- .../outlier_detection_step.py | 67 ------------------- jwst/outlier_detection/utils.py | 2 + 2 files changed, 2 insertions(+), 67 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 1a33d8cf25..ae56a4e5e8 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -50,73 +50,6 @@ class OutlierDetectionStep(Step): class_alias = "outlier_detection" - # The members of spec needs to be a super-set of all parameters needed - # by the various versions of the outlier_detection algorithms, and each - # version will pick and choose what they need while ignoring the rest. - # There are effectively N modes which use the following parameters: - # "-" = parameter is disabled - # " " = parameter is ignored - # "+" = parameter is used - # "!" = used only if another parameter is set - # "?" = idk? - # | Image | Coron | TSO | Spec(IFU) | Spec | - # -------------------------------------------------------------------- - # weight_type | ! | | + | | ! | - # pixfrac | ! | | | | ! | - # kernel | ! | | | | ! | - # fillval | ! | | | | ! | - # maskpt | | | + | | | - # snr | + | + | + | | | - # scale | + | + | + | | | - # backg | + | + | + | | | - # kernel_size | | | | + | | - # threshold_percent | | | | + | | - # rolling_window_width | | | + | | | - # ifu_second_check | | | | + | | - # save_intermediate_results | + | + | + | + | + | - # resample_data | + | - | - | | + | - # good_bits | + | + | + | + | + | - # search_output_file *2 | | | | | | - # allowed_memory | ! | | | | ! | - # in_memory *1 | + | + | + | + | + | - # - # *1 in_memory is used in the generic step when opening the input. But - # not all modes use the parameter internally. - # - # *2 This is used internally in stpipe. The table will not document this - # feature as it's difficult to know when it will and won't be used. - # - # For each mode the input is: - # Image : ModelContainer of ImageModel - # Coron : CubeModel (either a psf or target) - # TSO : CubeModel or 3D SlitModel (for nircam tsgrism) - # IFU : ModelContainer of IFUImageModel - # Spec : SourceModelContainer of SlitModel (created from MultiSlitModel) - # or ModelContainer of ImageModel (possible for MIR_LRS-FIXEDSLIT) - # or ModelContainer of SlitModel (for...?) - # - # So the only mode that benefits from `in_memory` is Image. - # - # For Coron (which uses only the non-resampled version of Image) the fake - # ModelContainer made from the input CubeModel is all in memory so - # `_remove_file` is skipped (for the open models) for both the - # "drizzled_models" and "blot_models". - # - # For TSO `in_memory` isn't used (even for the calls to the parent - # `compute_weight_threshold` and `save_median`). - # - # For IFU `in_memory` isn't used (and the algorithm is very different using - # almost nothing from the parent class). - # - # For Spec(non-IFU) `in_memory` is used. However the input - # `SourceModelContainer` doesn't use the "save_open" "return_open" - # arguments when made from a `MultiSlitModel` (as in spec3). So the - # input is a list of "open" `SlitModel`s. The call to `ResampleSpec` does - # use `in_memory` and writes out the resampled `SlitModels` returning - # a `ModelContainer` (if `resample=True`). `create_median` and - # `blot_median` are used from the parent class and use `in_memory` in - # the same way as the Image mode. - spec = """ weight_type = option('ivm','exptime',default='ivm') pixfrac = float(default=1.0) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 7ae673b7bd..f1c853c3c3 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -126,6 +126,8 @@ def flag_crs_in_models( if resample_data: if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: input_pixflux_area = image.meta.photometry.pixelarea_steradians + if image.meta.wcs.array_shape is None: + image.meta.wcs.array_shape = image.data.shape input_pixel_area = compute_image_pixel_area(image.meta.wcs) pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) else: From 2996d3f415b2e343c5f477b57f1d653403ef390c Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 11:52:43 -0400 Subject: [PATCH 236/687] clean up a few comments --- jwst/outlier_detection/tests/test_outlier_detection.py | 7 +------ jwst/outlier_detection/utils.py | 8 +------- 2 files changed, 2 insertions(+), 13 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 13b77fa26f..0ec374922a 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -223,10 +223,8 @@ def test_outlier_step(we_three_sci, tmp_cwd): OutlierDetectionStep.call(container) i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) - #blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) assert len(i2d_files) == 0 assert len(median_files) == 0 - #assert len(blot_files) == 0 result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True @@ -246,10 +244,8 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Verify that intermediary files are saved at the specified location i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) - #blot_files = glob(os.path.join(tmp_cwd, '*blot.fits')) assert len(i2d_files) != 0 assert len(median_files) != 0 - #assert len(blot_files) != 0 def test_outlier_step_on_disk(we_three_sci, tmp_cwd): @@ -349,8 +345,7 @@ def test_outlier_step_image_weak_CR_coron(exptype, tsovisit, tmp_cwd): # no noise so it should always be above the default threshold of 5 container[0].data[12, 12] = bkg + sig * 10 - # FIXME this test incorrectly provides ImageModels whereas - # coron3 will provide a CubeModel. This is fixed here. + # coron3 will provide a CubeModel so convert the container to a cube cube = container_to_cube(container) result = OutlierDetectionStep.call(cube) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index f1c853c3c3..e3378c096a 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -168,13 +168,7 @@ def flag_resampled_model_crs( cr_mask = flag_resampled_crs(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) - # FIXME (or really "fixed") for a converted cube this used to overwrite - # the dq "view" of the cube with a new dq array. This broke the link - # between the converted ImageModel.dq and the original CubeModel.dq - # which required extra work at the end of outlier detection to update - # the cube. By modifying dq in-place we automatically update the cube. - # I put this as a FIXME because this comment can be removed when - # we don't convert cubes. + # update the dq flags in-place image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") From 321ace5c334b345145fce74f61ae305e74cfed0f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 11:59:35 -0400 Subject: [PATCH 237/687] fix test --- jwst/outlier_detection/tests/test_outlier_detection.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 0ec374922a..9fa2ffe8bd 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -195,22 +195,14 @@ def we_three_sci(): def test_outlier_step_no_outliers(we_three_sci, tmp_cwd): """Test whole step, no outliers""" container = ModelContainer(list(we_three_sci)) - pristine = container.copy() + pristine = ModelContainer([m.copy() for m in container]) result = OutlierDetectionStep.call(container) - # FIXME this test doesn't work as intended as there are no - # outliers so the input (even if it was modified, which it is) - # would not appear different from the "pristine" data # Make sure nothing changed in SCI and DQ arrays for image, uncorrected in zip(pristine, container): np.testing.assert_allclose(image.data, uncorrected.data) np.testing.assert_allclose(image.dq, uncorrected.dq) - # Make sure nothing changed in SCI and DQ arrays - for image, corrected in zip(container, result): - np.testing.assert_allclose(image.data, corrected.data) - np.testing.assert_allclose(image.dq, corrected.dq) - def test_outlier_step(we_three_sci, tmp_cwd): """Test whole step with an outlier including saving intermediate and results files""" From 1c2516b3922f7bbd24530f98e3dbb697f4133dc6 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 12:17:14 -0400 Subject: [PATCH 238/687] undo array_shape fix --- jwst/outlier_detection/utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index e3378c096a..c056f9cda5 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -126,8 +126,6 @@ def flag_crs_in_models( if resample_data: if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: input_pixflux_area = image.meta.photometry.pixelarea_steradians - if image.meta.wcs.array_shape is None: - image.meta.wcs.array_shape = image.data.shape input_pixel_area = compute_image_pixel_area(image.meta.wcs) pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) else: From 2cef06e177487c0aaa14391d3272aff2192408c0 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 13:14:35 -0400 Subject: [PATCH 239/687] fix style --- jwst/outlier_detection/tests/test_outlier_detection.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 9fa2ffe8bd..5683021516 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -196,7 +196,7 @@ def test_outlier_step_no_outliers(we_three_sci, tmp_cwd): """Test whole step, no outliers""" container = ModelContainer(list(we_three_sci)) pristine = ModelContainer([m.copy() for m in container]) - result = OutlierDetectionStep.call(container) + OutlierDetectionStep.call(container) # Make sure nothing changed in SCI and DQ arrays for image, uncorrected in zip(pristine, container): From 53257a28fe83857c4ea5854ffeb43cc3950033d0 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 14:43:55 -0400 Subject: [PATCH 240/687] clean up docs based on review --- CHANGES.rst | 3 +- .../outlier_detection_coron.rst | 38 +++---------------- .../outlier_detection_imaging.rst | 8 +++- jwst/outlier_detection/coron.py | 21 +--------- jwst/outlier_detection/ifu.py | 2 +- jwst/outlier_detection/imaging.py | 21 +--------- jwst/outlier_detection/spec.py | 21 +--------- 7 files changed, 18 insertions(+), 96 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 06e5941de3..eae0155253 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -25,7 +25,8 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] -- Refactored to separate modes and move common code to stcal. [#8613] +- Refactored separate modes into submodules instead of inheriting from a base class. + Moved non-JWST-specific code to stcal. [#8613] scripts ------- diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 3efbd1dedb..9db44f4993 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -1,7 +1,7 @@ .. _outlier-detection-coron: -Coronography Outlier Detection Algorithm -======================================== +Outlier Detection for Coronography Data +======================================= This module serves as the interface for applying ``outlier_detection`` to coronographic image observations. @@ -24,36 +24,8 @@ Specifically, this routine performs the following operations: #. Perform statistical comparison between median image and original image to identify outliers. - * This comparison uses the original input images, the - median image, and the derivative of the median image to - create a cosmic ray mask for each input image. - * The derivative of the median image gets created using the - median image to compute the absolute value of the difference between each pixel and - its four surrounding neighbors with the largest value being the recorded derivative. - * These derivative images are used to flag cosmic rays - and other blemishes, such as satellite trails. Where the difference is larger - than can be explained by noise statistics, the flattening effect of taking the - median, or an error in the shift (the latter two effects are estimated using - the image derivative), the suspect pixel is masked. - * The ``backg`` parameter specifies a user-provided value to be used as the - background estimate. This gets added to the background-subtracted - median image to attempt to match the original background levels of the - original input mosaic so that cosmic-rays (bad pixels) from the input - mosaic can be identified more easily as outliers compared to the median image. - * Cosmic rays are flagged using the following rule: - - .. math:: | image\_input - image\_median | > scale*image\_deriv + SNR*noise - - * The ``scale`` is defined as the multiplicative factor applied to the - derivative which is used to determine if the difference between the data - image and the median image is large enough to require masking. - * The ``noise`` is calculated using a combination of the detector read - noise and the poisson noise of the median image plus the sky background. - * The user must specify two cut-off signal-to-noise values using the - ``snr`` parameter for determining whether a pixel should be masked: - the first for detecting the primary cosmic ray, and the second for masking - lower-level bad pixels adjacent to those found in the first pass. Since - cosmic rays often extend across several pixels, the adjacent pixels make - use of a slightly lower SNR threshold. + The core detection algorithm uses the following to generate an outlier mask + + .. math:: | image\_input - image\_median | > SNR*input_\err #. Update input data model DQ arrays with mask of detected outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 56140af07a..6b800c132f 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -82,7 +82,13 @@ Specifically, this routine performs the following operations: #. Perform statistical comparison between blotted image and original image to identify outliers. - * This comparison uses the original input images, the blotted + * If resampling is disabled (``resample_data == False``) a large number of parameters + will be ignored and instead the outlier mask will be computed using the following + formula: + + .. math:: | image\_input - image\_median | > SNR*input_err + + * When resampling is enabled, the comparison uses the original input images, the blotted median image, and the derivative of the blotted image to create a cosmic ray mask for each input image. * The derivative of the blotted image gets created using the blotted diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 8485d3e18c..f675eb2405 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -1,24 +1,5 @@ """ -Main class for performing outlier detection. - -This is the controlling routine for the outlier detection process. -It loads and sets the various input data and parameters needed by -the various functions and then controls the operation of this process -through all the steps used for the detection. - -Notes ------ -This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Resamples all input images into grouped observation mosaics. - 3. Creates a median image from all grouped observation mosaics. - 4. Blot median image to match each original input image. - 5. Perform statistical comparison between blotted image and original - image to identify outliers. - 6. Updates input data model DQ arrays with mask of detected outliers. - +Submodule for performing outlier detection on coronography data. """ diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index a60666c13b..4a882bef0a 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -1,5 +1,5 @@ """ -Sub-class defined for performing outlier detection on IFU data. +Submodule defined for performing outlier detection on IFU data. This is the controlling routine for the outlier detection process. It loads and sets the various input data and parameters needed to flag diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index ebccb09bc3..93daf6b9de 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -1,24 +1,5 @@ """ -Main class for performing outlier detection. - -This is the controlling routine for the outlier detection process. -It loads and sets the various input data and parameters needed by -the various functions and then controls the operation of this process -through all the steps used for the detection. - -Notes ------ -This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Resamples all input images into grouped observation mosaics. - 3. Creates a median image from all grouped observation mosaics. - 4. Blot median image to match each original input image. - 5. Perform statistical comparison between blotted image and original - image to identify outliers. - 6. Updates input data model DQ arrays with mask of detected outliers. - +Submodule for performing outlier detection on imaging data. """ import copy diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index ccc78f5abd..bd0e686cb9 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -1,24 +1,5 @@ """ -Class definition for performing outlier detection on spectra. - -This is the controlling routine for the outlier detection process. -It loads and sets the various input data and parameters needed by -the various functions and then controls the operation of this process -through all the steps used for the detection. - -Notes ------ -This routine performs the following operations:: - - 1. Extracts parameter settings from input model and merges - them with any user-provided values - 2. Resamples all input images into grouped observation mosaics. - 3. Creates a median image from all grouped observation mosaics. - 4. Blot median image to match each original input image. - 5. Perform statistical comparison between blotted image and original - image to identify outliers. - 6. Updates input data model DQ arrays with mask of detected outliers. - +Submodule for performing outlier detection on spectra. """ import copy From e1ba112e0b9d0b5fb9f80257697e5367a5dbb388 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 10 Jul 2024 14:44:29 -0400 Subject: [PATCH 241/687] Update jwst/outlier_detection/tso.py Co-authored-by: Ned Molter --- jwst/outlier_detection/tso.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index f43e6c2c34..c4fd5f503f 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -149,7 +149,6 @@ def moving_median_over_zeroth_axis(x: np.ndarray, w: int) -> np.ndarray: Notes ----- - Returns NaN for the first and last w//2 elements. """ if w <= 1: raise ValueError("Rolling median window size must be greater than 1.") From f13127d7342b3721982f8edfe72efe749a87624c Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 16:33:17 -0400 Subject: [PATCH 242/687] remove comment, add simple docstring --- jwst/outlier_detection/utils.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index c056f9cda5..656e0d8a34 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -149,11 +149,9 @@ def flag_resampled_model_crs( backg, resample_data, ): - # previous versions of the code generated all of the blot models and wrote - # them to disk. Now the code generates the blot model(s) only when needed - # and no longer needs to write them to disk. We could re-introduce saving - # of blot models here. - + """ + Flag crs in image based on a resampled (and blotted) data (blot). + """ # If the datamodel has a measured background that has not been subtracted # use it instead of the user provided backg. # Get background level of science data if it has not been subtracted, so it From d14974f928e4d09ee19f52d8a9b2719db5e7f5cd Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 16:45:09 -0400 Subject: [PATCH 243/687] use set_step_status --- jwst/outlier_detection/ifu.py | 4 ++-- jwst/outlier_detection/imaging.py | 4 ++-- jwst/outlier_detection/outlier_detection_step.py | 13 +++++-------- jwst/outlier_detection/spec.py | 4 ++-- 4 files changed, 11 insertions(+), 14 deletions(-) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index 4a882bef0a..83a6fb2157 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -32,6 +32,7 @@ import numpy as np from jwst.datamodels import ModelContainer +from jwst.stpipe.utilities import record_step_status from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags from stcal.outlier_detection.utils import medfilt @@ -57,8 +58,7 @@ def detect_outliers( if len(input_models) < 2: log.warning(f"Input only contains {len(input_models)} exposures") log.warning("Outlier detection will be skipped") - for model in input_models: - model.meta.cal_step.outlier_detection = "SKIPPED" + record_step_status(input_models, "outlier_detection", False) return input_models sizex, sizey = [int(val) for val in kernel_size.split()] diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 93daf6b9de..867f624bdc 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -11,6 +11,7 @@ from jwst.datamodels import ModelContainer from jwst.resample import resample from jwst.resample.resample_utils import build_driz_weight +from jwst.stpipe.utilities import record_step_status from .utils import create_median, flag_crs_in_models from ._fileio import remove_file, save_median @@ -49,8 +50,7 @@ def detect_outliers( if len(input_models) < 2: log.warning(f"Input only contains {len(input_models)} exposures") log.warning("Outlier detection will be skipped") - for model in input_models: - model.meta.cal_step.outlier_detection = "SKIPPED" + record_step_status(input_models, "outlier_detection", False) return input_models if resample_data: diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index ae56a4e5e8..901dd8e792 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -5,6 +5,7 @@ from jwst.datamodels import ModelContainer from jwst.stpipe import Step +from jwst.stpipe.utilities import record_step_status from jwst.lib.pipe_utils import is_tso from . import coron, ifu, imaging, tso, spec @@ -79,7 +80,7 @@ def process(self, input_data): # determine the "mode" (if not set by the pipeline) mode = self._guess_mode(input_data) if mode is None: - return self._set_status(input_data, "SKIPPED") + return self._set_status(input_data, False) self.log.info(f"Outlier Detection mode: {mode}") # determine the asn_id (if not set by the pipeline) @@ -163,9 +164,9 @@ def process(self, input_data): else: self.log.error("Outlier detection failed for unknown/unsupported ", f"mode: {mode}") - return self._set_status(input_data, "SKIPPED") + return self._set_status(input_data, False) - return self._set_status(result_models, "COMPLETE") + return self._set_status(result_models, True) def _guess_mode(self, input_models): # The pipelines should set this mode or ideally these should @@ -230,9 +231,5 @@ def _set_status(self, input_models, status): if not isinstance(input_models, datamodels.JwstDataModel): input_models = datamodels.open(input_models) - if isinstance(input_models, ModelContainer): - for model in input_models: - model.meta.cal_step.outlier_detection = status - else: - input_models.meta.cal_step.outlier_detection = status + record_step_status(input_models, "outlier_detection", status) return input_models diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index bd0e686cb9..0954746d9c 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -6,6 +6,7 @@ from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer +from jwst.stpipe.utilities import record_step_status from ..resample import resample_spec, resample_utils from .utils import create_median, flag_crs_in_models @@ -45,8 +46,7 @@ def detect_outliers( if len(input_models) < 2: log.warning(f"Input only contains {len(input_models)} exposures") log.warning("Outlier detection will be skipped") - for model in input_models: - model.meta.cal_step.outlier_detection = "SKIPPED" + record_step_status(input_models, "outlier_detection", False) return input_models if resample_data is True: From 947375cbbd802841314359f04dd1c11f7b912ee7 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 10 Jul 2024 16:49:26 -0400 Subject: [PATCH 244/687] add stub docstring for do_detection modes --- jwst/outlier_detection/coron.py | 6 +++++- jwst/outlier_detection/ifu.py | 6 +++++- jwst/outlier_detection/imaging.py | 6 +++++- jwst/outlier_detection/spec.py | 6 +++++- jwst/outlier_detection/tso.py | 6 +++++- 5 files changed, 25 insertions(+), 5 deletions(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index f675eb2405..6037066300 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -30,7 +30,11 @@ def detect_outliers( asn_id, make_output_path, ): - """Flag outlier pixels in DQ of input images.""" + """ + Flag outliers in coronography data. + + See `OutlierDetectionStep.spec` for documentation of these arguments. + """ if not isinstance(input_model, datamodels.JwstDataModel): input_model = datamodels.open(input_model) diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index 83a6fb2157..dfd7c7f6ed 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -51,7 +51,11 @@ def detect_outliers( threshold_percent, make_output_path, ): - """Split data by detector to find outliers.""" + """ + Flag outliers in ifu data. + + See `OutlierDetectionStep.spec` for documentation of these arguments. + """ if not isinstance(input_models, ModelContainer): input_models = ModelContainer(input_models) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 867f624bdc..f75685eb65 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -43,7 +43,11 @@ def detect_outliers( asn_id, make_output_path, ): - """Flag outlier pixels in DQ of input images.""" + """ + Flag outliers in imaging data. + + See `OutlierDetectionStep.spec` for documentation of these arguments. + """ if not isinstance(input_models, ModelContainer): input_models = ModelContainer(input_models, save_open=in_memory) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 0954746d9c..3962304ff3 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -39,7 +39,11 @@ def detect_outliers( asn_id, make_output_path, ): - """Flag outlier pixels in DQ of input images.""" + """ + Flag outliers in spec data. + + See `OutlierDetectionStep.spec` for documentation of these arguments. + """ if not isinstance(input_models, ModelContainer): input_models = ModelContainer(input_models, save_open=in_memory) diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index c4fd5f503f..58aedd19b0 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -25,7 +25,11 @@ def detect_outliers( asn_id, make_output_path, ): - """Flag outlier pixels in DQ of input images.""" + """ + Flag outliers in tso data. + + See `OutlierDetectionStep.spec` for documentation of these arguments. + """ if not isinstance(input_model, dm.JwstDataModel): input_model = dm.open(input_model) if isinstance(input_model, dm.ModelContainer): From ead55306a3d228ec6f54eb940c33d05100d7664a Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 18 Jul 2024 12:26:38 -0400 Subject: [PATCH 245/687] apply array shape fix from #8645 --- jwst/outlier_detection/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 656e0d8a34..86879340ec 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -126,6 +126,8 @@ def flag_crs_in_models( if resample_data: if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: input_pixflux_area = image.meta.photometry.pixelarea_steradians + # Set array shape, needed to compute image pixel area + image.meta.wcs.array_shape = image.shape input_pixel_area = compute_image_pixel_area(image.meta.wcs) pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) else: From 828684aa702213296dcc2376c8bf25f650043564 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 26 Jul 2024 08:00:16 -0400 Subject: [PATCH 246/687] Update docs/jwst/outlier_detection/outlier_detection_coron.rst Co-authored-by: Ned Molter --- docs/jwst/outlier_detection/outlier_detection_coron.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 9db44f4993..0f7fc342a0 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -1,6 +1,6 @@ .. _outlier-detection-coron: -Outlier Detection for Coronography Data +Outlier Detection for Coronagraphic Data ======================================= This module serves as the interface for applying ``outlier_detection`` to coronographic From 50fd4e6c5c3a907abf0a4fefaa2386945b0623b0 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 26 Jul 2024 08:00:30 -0400 Subject: [PATCH 247/687] Update docs/jwst/outlier_detection/outlier_detection_coron.rst Co-authored-by: Ned Molter --- docs/jwst/outlier_detection/outlier_detection_coron.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 0f7fc342a0..7319bad870 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -3,7 +3,7 @@ Outlier Detection for Coronagraphic Data ======================================= -This module serves as the interface for applying ``outlier_detection`` to coronographic +This module serves as the interface for applying ``outlier_detection`` to coronagraphic image observations. Specifically, this routine performs the following operations: From 6bbf1e6d5f270f54d9ca997a53c42469055d73f2 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 26 Jul 2024 08:00:51 -0400 Subject: [PATCH 248/687] Update jwst/outlier_detection/coron.py Co-authored-by: Ned Molter --- jwst/outlier_detection/coron.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 6037066300..4a2e9746ed 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -39,7 +39,7 @@ def detect_outliers( input_model = datamodels.open(input_model) if not isinstance(input_model, datamodels.CubeModel): - raise Exception(f"Input must be a CubeModel: {input_model}") + raise TypeError(f"Input must be a CubeModel: {input_model}") # FIXME weight_type could now be used here. Similar to tso data coron # data was previously losing var_rnoise due to the conversion from a cube From 741e2c702642cfcb3afe25a26ad25046f8bdbaf0 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 26 Jul 2024 08:01:11 -0400 Subject: [PATCH 249/687] Update jwst/outlier_detection/coron.py Co-authored-by: Ned Molter --- jwst/outlier_detection/coron.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 4a2e9746ed..500ad49602 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -1,5 +1,5 @@ """ -Submodule for performing outlier detection on coronography data. +Submodule for performing outlier detection on coronagraphy data. """ From 9a73deb5e39e9cbb0ce731902dddafa84996943c Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:03:35 -0400 Subject: [PATCH 250/687] fix outlier detection imaging title --- docs/jwst/outlier_detection/outlier_detection_imaging.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 6b800c132f..6cf235c3f2 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -1,7 +1,7 @@ .. _outlier-detection-imaging: -Default Outlier Detection Algorithm -=================================== +Outlier Detection Algorithm for Imaging Data +============================================ This module serves as the interface for applying ``outlier_detection`` to direct image observations, like those taken with MIRI, NIRCam and NIRISS. The code implements the From 0c9cb67af81456d073e06efc103cd0d69a861f14 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:28:20 -0400 Subject: [PATCH 251/687] fix docs heading underline --- docs/jwst/outlier_detection/outlier_detection_coron.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 7319bad870..7ff039e44e 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -1,7 +1,7 @@ .. _outlier-detection-coron: Outlier Detection for Coronagraphic Data -======================================= +======================================== This module serves as the interface for applying ``outlier_detection`` to coronagraphic image observations. From 0a5caa3600ee51867e2f815918aecb879580aad0 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:29:51 -0400 Subject: [PATCH 252/687] add automodapi entry for outlier_detection coron page --- docs/jwst/outlier_detection/outlier_detection_coron.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 7ff039e44e..1a808861ef 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -29,3 +29,5 @@ Specifically, this routine performs the following operations: .. math:: | image\_input - image\_median | > SNR*input_\err #. Update input data model DQ arrays with mask of detected outliers. + +.. automodapi:: jwst.outlier_detection.coron From d26cc5a374f3f692a24380727c0761f45734c2d8 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:35:06 -0400 Subject: [PATCH 253/687] remove FIXME --- jwst/outlier_detection/outlier_detection_step.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 901dd8e792..936ec3767b 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -44,11 +44,6 @@ class OutlierDetectionStep(Step): """ - # FIXME note that this step does not have a suffix, the image3, coron3 - # and spec3 pipelines set the suffix (tso3 does not). - # I think this means that running the step in isolation will produce - # a file with suffix "outlierdetectionstep" (which I think is a bug). - class_alias = "outlier_detection" spec = """ From 7d97170f080771ef1f7a6276052f250089adf3bb Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 26 Jul 2024 08:35:58 -0400 Subject: [PATCH 254/687] JP-3547: Flux conservation for spectral resampling (#8596) Co-authored-by: Ned Molter Co-authored-by: Tyler Pauly --- CHANGES.rst | 17 + docs/jwst/resample/arguments.rst | 46 +- docs/jwst/resample/resample.rst | 5 +- docs/jwst/resample/resample_step.rst | 5 +- jwst/outlier_detection/outlier_detection.py | 6 + jwst/regtest/test_nirspec_fs_spec3.py | 27 +- .../test_nirspec_fs_spec3_moving_target.py | 57 ++ jwst/regtest/test_nirspec_mos_spec3.py | 25 +- jwst/resample/resample.py | 102 +-- jwst/resample/resample_spec.py | 720 +++++++++-------- jwst/resample/resample_spec_step.py | 15 +- jwst/resample/resample_utils.py | 21 + jwst/resample/tests/test_resample_step.py | 730 +++++++++++++++--- jwst/resample/tests/test_utils.py | 9 + 14 files changed, 1227 insertions(+), 558 deletions(-) create mode 100644 jwst/regtest/test_nirspec_fs_spec3_moving_target.py diff --git a/CHANGES.rst b/CHANGES.rst index 16e0c470cb..1ade3e9c42 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -25,6 +25,23 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +resample_spec +------------- + +- Modified the output NIRSpec spectral WCS to sample the input data linearly in sky + coordinates, rather than slit coordinates, in order to conserve spectral + flux in default reductions. [#8596] + +- Updated handling for the ``pixel_scale_ratio`` parameter to apply only to the + spatial dimension, to match the sense of the parameter application to the + documented intent, and to conserve spectral flux when applied. [#8596] + +- Implemented handling for the ``pixel_scale`` parameter, which was previously + ignored for spectral resampling. [#8596] + +- Fixed a bug resulting in incorrect output slit coordinates for NIRSpec moving + targets in the ``calwebb_spec3`` pipeline. [#8596] + scripts ------- diff --git a/docs/jwst/resample/arguments.rst b/docs/jwst/resample/arguments.rst index c21f711b48..123e5d4ac4 100644 --- a/docs/jwst/resample/arguments.rst +++ b/docs/jwst/resample/arguments.rst @@ -15,15 +15,52 @@ image. image. Available kernels are `square`, `gaussian`, `point`, `turbo`, `lanczos2`, and `lanczos3`. + For spectral data, only the `square` and `point` kernels should be used. + The other kernels do not conserve spectral flux. + ``--pixel_scale_ratio`` (float, default=1.0) - Ratio of input to output pixel scale. A value of 0.5 means the output + Ratio of input to output pixel scale. + + For imaging data, a value of 0.5 means the output image would have 4 pixels sampling each input pixel. + + For spectral data, values greater than 1 indicate that the input + pixels have a larger spatial scale, so more output pixels will + sample the same input pixel. For example, a value of 2.0 + means the output image would have 2 pixels sampling each input + spatial pixel. If the input data has units of flux density (MJy/pixel), + the output flux per pixel will be half the input flux per pixel. + If the input data has units of surface brightness (MJy/sr), the output + flux per pixel is not scaled. + + Note that this parameter is only applied in the cross-dispersion + direction for spectral data: sampling wavelengths are not affected. + Ignored when ``pixel_scale`` or ``output_wcs`` are provided. + .. note:: + If this parameter is modified for spectral data, the extraction + aperture for the :ref:`extract_1d ` step must + also be modified, since it is specified in pixels. + ``--pixel_scale`` (float, default=None) Absolute pixel scale in ``arcsec``. When provided, overrides ``pixel_scale_ratio``. Ignored when ``output_wcs`` is provided. + For spectral data, if the input data has units of flux density + (MJy/pixel), the output flux per pixel will be scaled by the ratio + of the selected output pixel scale to an average input pixel scale. + If the input data has units of surface brightness (MJy/sr), + the output flux per pixel is not scaled. + + Note that this parameter is only applied in the cross-dispersion + direction for spectral data: sampling wavelengths are not affected. + + .. note:: + If this parameter is modified for spectral data, the extraction + aperture for the :ref:`extract_1d ` step must + also be modified, since it is specified in pixels. + ``--rotation`` (float, default=None) Position angle of output image’s Y-axis relative to North. A value of 0.0 would orient the final output image to be North up. @@ -31,19 +68,20 @@ image. but will instead be resampled in the default orientation for the camera with the x and y axes of the resampled image corresponding approximately to the detector axes. Ignored when ``pixel_scale`` - or ``output_wcs`` are provided. + or ``output_wcs`` are provided. Also ignored for all spectral data. ``--crpix`` (tuple of float, default=None) Position of the reference pixel in the image array in the ``x, y`` order. If ``crpix`` is not specified, it will be set to the center of the bounding box of the returned WCS object. When supplied from command line, it should be a comma-separated list of floats. Ignored when ``output_wcs`` - is provided. + is provided. Also ignored for all spectral data. ``--crval`` (tuple of float, default=None) Right ascension and declination of the reference pixel. Automatically computed if not provided. When supplied from command line, it should be a comma-separated list of floats. Ignored when ``output_wcs`` is provided. + Also ignored for all spectral data. ``--output_shape`` (tuple of int, default=None) Shape of the image (data array) using "standard" ``nx`` first and ``ny`` @@ -117,6 +155,8 @@ image. For example, if set to ``0.5``, only resampled images that use less than half the available memory can be created. + This parameter is ignored for spectral data. + ``--in_memory`` (boolean, default=True) Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when diff --git a/docs/jwst/resample/resample.rst b/docs/jwst/resample/resample.rst index 3e264d726e..0a2bba77e2 100644 --- a/docs/jwst/resample/resample.rst +++ b/docs/jwst/resample/resample.rst @@ -1,6 +1,7 @@ .. resample_: -Python Interface to Drizzle: ResampleData() -=========================================== +Python Interface to Drizzle: ResampleData() and ResampleSpecData() +================================================================== .. automodapi:: jwst.resample.resample +.. automodapi:: jwst.resample.resample_spec diff --git a/docs/jwst/resample/resample_step.rst b/docs/jwst/resample/resample_step.rst index d0a87a5b75..afa5d95df4 100644 --- a/docs/jwst/resample/resample_step.rst +++ b/docs/jwst/resample/resample_step.rst @@ -1,6 +1,7 @@ .. resample_step_: -Python Step Interface: ResampleStep() -===================================== +Python Step Interface: ResampleStep() and ResampleSpecStep() +============================================================ .. automodapi:: jwst.resample.resample_step +.. automodapi:: jwst.resample.resample_spec_step diff --git a/jwst/outlier_detection/outlier_detection.py b/jwst/outlier_detection/outlier_detection.py index 92cbdc6db4..829a5ae622 100644 --- a/jwst/outlier_detection/outlier_detection.py +++ b/jwst/outlier_detection/outlier_detection.py @@ -572,10 +572,16 @@ def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0): # Set array shape, needed to compute image pixel area blot_img.meta.wcs.array_shape = blot_img.shape if 'SPECTRAL' not in blot_img.meta.wcs.output_frame.axes_type: + # Account for intensity scaling, needed if there is a difference + # between nominal pixel area and average pixel area, + # computed from the WCS input_pixflux_area = blot_img.meta.photometry.pixelarea_steradians input_pixel_area = compute_image_pixel_area(blot_img.meta.wcs) pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) else: + # Note: spectral scaling is only needed if the pixel ratio + # is not set to 1.0, which is not supported for + # outlier detection. pix_ratio = 1.0 log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape)) diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index fd6bb4e783..4f3bca7edd 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -46,24 +46,13 @@ def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - if output == "s2d": - # Compare the calculated wavelengths + # Check output wavelength array against its own wcs + if suffix == "s2d": tolerance = 1e-03 - dmt = datamodels.open(rtdata.truth) dmr = datamodels.open(rtdata.output) - if isinstance(dmt, datamodels.MultiSlitModel): - names = [s.name for s in dmt.slits] - for name in names: - st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] - w = dmt.slits[st_idx].meta.wcs - x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) - _, _, wave = w(x, y) - sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] - wlr = dmr.slits[sr_idx].wavelength - assert np.all(np.isclose(wave, wlr, atol=tolerance)) - else: - w = dmt.meta.wcs - x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) - _, _, wave = w(x, y) - wlr = dmr.wavelength - assert np.all(np.isclose(wave, wlr, atol=tolerance)) + + w = dmr.meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) diff --git a/jwst/regtest/test_nirspec_fs_spec3_moving_target.py b/jwst/regtest/test_nirspec_fs_spec3_moving_target.py new file mode 100644 index 0000000000..8845cd4201 --- /dev/null +++ b/jwst/regtest/test_nirspec_fs_spec3_moving_target.py @@ -0,0 +1,57 @@ +from astropy.io.fits.diff import FITSDiff +import pytest +import numpy as np +from gwcs import wcstools + +from jwst.stpipe import Step +from stdatamodels.jwst import datamodels + + +@pytest.fixture(scope="module") +def run_pipeline(rtdata_module): + """ + Run the calwebb_spec3 pipeline on a NIRSpec FS moving target. + """ + rtdata = rtdata_module + + # Get the ASN file and input exposures + rtdata.get_asn('nirspec/fs/jw01245-o002_20240701t053319_spec3_00001_asn.json') + + # Run the calwebb_spec3 pipeline; save results from intermediate steps + args = ["calwebb_spec3", rtdata.input, + "--steps.outlier_detection.save_results=true", + "--steps.resample_spec.save_results=true", + "--steps.extract_1d.save_results=true"] + Step.from_cmdline(args) + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) +def test_nirspec_fs_spec3_moving_target( + run_pipeline, rtdata_module, fitsdiff_default_kwargs, suffix): + """Test spec3 pipeline on a NIRSpec FS moving target.""" + rtdata = rtdata_module + + output = f"jw01245-o002_s000000001_nirspec_clear-prism-s200a1-subs200a1_{suffix}.fits" + rtdata.output = output + rtdata.get_truth(f"truth/test_nirspec_fs_spec3_moving_target/{output}") + + # Adjust tolerance for machine precision with float32 drizzle code + if suffix == "s2d": + fitsdiff_default_kwargs["rtol"] = 1e-2 + fitsdiff_default_kwargs["atol"] = 2e-4 + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + # Check output wavelength array against its own wcs + if suffix == "s2d": + tolerance = 1e-03 + dmr = datamodels.open(rtdata.output) + + w = dmr.meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) diff --git a/jwst/regtest/test_nirspec_mos_spec3.py b/jwst/regtest/test_nirspec_mos_spec3.py index 4e54e30a11..c7f33128a7 100644 --- a/jwst/regtest/test_nirspec_mos_spec3.py +++ b/jwst/regtest/test_nirspec_mos_spec3.py @@ -42,24 +42,13 @@ def test_nirspec_mos_spec3(run_pipeline, suffix, source_id, fitsdiff_default_kwa diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + # Check output wavelength array against its own wcs if suffix == "s2d": - # Compare the calculated wavelengths tolerance = 1e-03 - dmt = datamodels.open(rtdata.truth) dmr = datamodels.open(rtdata.output) - if isinstance(dmt, datamodels.MultiSlitModel): - names = [s.name for s in dmt.slits] - for name in names: - st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] - w = dmt.slits[st_idx].meta.wcs - x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) - _, _, wave = w(x, y) - sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] - wlr = dmr.slits[sr_idx].wavelength - assert np.all(np.isclose(wave, wlr, atol=tolerance)) - else: - w = dmt.meta.wcs - x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) - _, _, wave = w(x, y) - wlr = dmr.wavelength - assert np.all(np.isclose(wave, wlr, atol=tolerance)) + + w = dmr.meta.wcs + x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) + _, _, wave = w(x, y) + wlr = dmr.wavelength + assert np.all(np.isclose(wave, wlr, atol=tolerance)) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 641e6dc88f..95eaaef3e9 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -3,9 +3,8 @@ import warnings import numpy as np -from drizzle import util -from drizzle import cdrizzle import psutil +from drizzle import cdrizzle, util from spherical_geometry.polygon import SphericalPolygon from stdatamodels.jwst import datamodels @@ -216,6 +215,57 @@ def blend_output_metadata(self, output_model): ignore=ignore_list ) + def _get_intensity_scale(self, img): + """ + Compute an intensity scale from the input and output pixel area. + + For imaging data, the scaling is used to account for differences + between the nominal pixel area and the average pixel area for + the input data. + + For spectral data, the scaling is used to account for flux + conservation with non-unity pixel scale ratios, when the + data units are flux density. + + Parameters + ---------- + img : DataModel + The input data model. + + Returns + ------- + iscale : float + The scale to apply to the input data before drizzling. + """ + input_pixflux_area = img.meta.photometry.pixelarea_steradians + if input_pixflux_area: + if 'SPECTRAL' in img.meta.wcs.output_frame.axes_type: + # Use the nominal area as is + input_pixel_area = input_pixflux_area + + # If input image is in flux density units, correct the + # flux for the user-specified change to the spatial dimension + if resample_utils.is_flux_density(img.meta.bunit_data): + input_pixel_area *= self.pscale_ratio + else: + img.meta.wcs.array_shape = img.data.shape + input_pixel_area = compute_image_pixel_area(img.meta.wcs) + if input_pixel_area is None: + raise ValueError( + "Unable to compute input pixel area from WCS of input " + f"image {repr(img.meta.filename)}." + ) + if self.input_pixscale0 is None: + self.input_pixscale0 = np.rad2deg( + np.sqrt(input_pixel_area) + ) + if self._recalc_pscale_ratio: + self.pscale_ratio = self.pscale / self.input_pixscale0 + iscale = np.sqrt(input_pixflux_area / input_pixel_area) + else: + iscale = 1.0 + return iscale + def resample_many_to_many(self): """Resample many inputs to many outputs where outputs have a common frame. @@ -245,28 +295,9 @@ def resample_many_to_many(self): log.info(f"{len(exposure)} exposures to drizzle together") for img in exposure: img = datamodels.open(img) + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') - input_pixflux_area = img.meta.photometry.pixelarea_steradians - if (input_pixflux_area and - 'SPECTRAL' not in img.meta.wcs.output_frame.axes_type): - img.meta.wcs.array_shape = img.data.shape - input_pixel_area = compute_image_pixel_area(img.meta.wcs) - if input_pixel_area is None: - raise ValueError( - "Unable to compute input pixel area from WCS of input " - f"image {repr(img.meta.filename)}." - ) - if self.input_pixscale0 is None: - self.input_pixscale0 = np.rad2deg( - np.sqrt(input_pixel_area) - ) - if self._recalc_pscale_ratio: - self.pscale_ratio = self.pscale / self.input_pixscale0 - iscale = np.sqrt(input_pixflux_area / input_pixel_area) - else: - iscale = 1.0 - - # TODO: should weight_type=None here? inwht = resample_utils.build_driz_weight( img, weight_type=self.weight_type, @@ -332,26 +363,8 @@ def resample_many_to_one(self): log.info("Resampling science data") for img in self.input_models: - input_pixflux_area = img.meta.photometry.pixelarea_steradians - if (input_pixflux_area and - 'SPECTRAL' not in img.meta.wcs.output_frame.axes_type): - img.meta.wcs.array_shape = img.data.shape - input_pixel_area = compute_image_pixel_area(img.meta.wcs) - if input_pixel_area is None: - raise ValueError( - "Unable to compute input pixel area from WCS of input " - f"image {repr(img.meta.filename)}." - ) - if self.input_pixscale0 is None: - self.input_pixscale0 = np.rad2deg( - np.sqrt(input_pixel_area) - ) - if self._recalc_pscale_ratio: - self.pscale_ratio = self.pscale / self.input_pixscale0 - iscale = np.sqrt(input_pixflux_area / input_pixel_area) - else: - iscale = 1.0 - + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') img.meta.iscale = iscale inwht = resample_utils.build_driz_weight(img, @@ -829,7 +842,7 @@ def compute_image_pixel_area(wcs): k = 0 dxy = [1, -1, -1, 1] - + ra, dec, center = np.nan, np.nan, (np.nan, np.nan) while xmin < xmax and ymin < ymax: try: x, y, image_area, center, b, r, t, l = _get_boundary_points( @@ -854,7 +867,6 @@ def compute_image_pixel_area(wcs): if not (np.all(np.isfinite(ra[sl])) and np.all(np.isfinite(dec[sl]))): limits[k] += dxy[k] - ymin, xmax, ymax, xmin = limits k = (k + 1) % 4 break k = (k + 1) % 4 diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index e462fb6f2e..43333df674 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -2,24 +2,23 @@ import warnings import numpy as np -from scipy.optimize import minimize_scalar from astropy import coordinates as coord from astropy import units as u from astropy.modeling.models import ( - Mapping, Tabular1D, Linear1D, Pix2Sky_TAN, RotateNative2Celestial, Const1D + Const1D, Linear1D, Mapping, Pix2Sky_TAN, RotateNative2Celestial, Tabular1D ) from astropy.modeling.fitting import LinearLSQFitter +from astropy.stats import sigma_clip +from astropy.utils.exceptions import AstropyUserWarning from gwcs import wcstools, WCS from gwcs import coordinate_frames as cf from gwcs.geometry import SphericalToCartesian - from stdatamodels.jwst import datamodels +from jwst.assign_wcs.util import compute_scale, wrap_ra from jwst.datamodels import ModelContainer - -from ..assign_wcs.util import wrap_ra -from . import resample_utils -from .resample import ResampleData +from jwst.resample import resample_utils +from jwst.resample.resample import ResampleData log = logging.getLogger(__name__) @@ -27,10 +26,12 @@ _S2C = SphericalToCartesian() +__all__ = ["ResampleSpecData"] + class ResampleSpecData(ResampleData): """ - This is the controlling routine for the resampling process. + This is the controlling routine for the resampling process for spectral data. Notes ----- @@ -61,14 +62,13 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, Other parameters """ self.input_models = input_models - - self.output_filename = output self.output_dir = None + self.output_filename = output if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None + self.pscale_ratio = pscale_ratio - self.pscale = pscale self.single = single self.blendheaders = blendheaders self.pixfrac = pixfrac @@ -77,54 +77,169 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.weight_type = wht_type self.good_bits = good_bits self.in_memory = kwargs.get('in_memory', True) + self._recalc_pscale_ratio = False + + log.info(f"Driz parameter kernal: {self.kernel}") + log.info(f"Driz parameter pixfrac: {self.pixfrac}") + log.info(f"Driz parameter fillval: {self.fillval}") + log.info(f"Driz parameter weight_type: {self.weight_type}") output_wcs = kwargs.get('output_wcs', None) output_shape = kwargs.get('output_shape', None) - - self.input_pixscale0 = None # computed pixel scale of the first image (deg) - self._recalc_pscale_ratio = pscale is not None - self.asn_id = kwargs.get('asn_id', None) - # Define output WCS based on all inputs, including a reference WCS - if output_wcs is None: - if resample_utils.is_sky_like( - self.input_models[0].meta.wcs.output_frame - ): + # Get an average input pixel scale for parameter calculations + disp_axis = self.input_models[0].meta.wcsinfo.dispersion_direction + self.input_pixscale0 = compute_spectral_pixel_scale( + self.input_models[0].meta.wcs, disp_axis=disp_axis) + if np.isnan(self.input_pixscale0): + log.warning('Input pixel scale could not be determined.') + if pscale is not None: + log.warning('Output pixel scale setting is not supported ' + 'without an input pixel scale. Setting pscale=None.') + pscale = None + + nominal_area = self.input_models[0].meta.photometry.pixelarea_steradians + if nominal_area is None: + log.warning('Nominal pixel area not set in input data.') + if pscale is not None: + log.warning('Output pixel scale setting is not supported ' + 'without a nominal pixel scale. Setting pscale=None.') + pscale = None + + if output_wcs: + # Use user-supplied reference WCS for the resampled image: + self.output_wcs = output_wcs + if output_wcs.pixel_area is None: + if nominal_area is not None: + # Compare input and output spatial scale to update nominal area + output_pscale = compute_spectral_pixel_scale( + output_wcs, disp_axis=disp_axis) + if np.isnan(output_pscale) or np.isnan(self.input_pixscale0): + log.warning('Output pixel scale could not be determined.') + output_pix_area = None + else: + log.debug(f'Setting output pixel area from the approximate ' + f'output spatial scale: {output_pscale}') + output_pix_area = (output_pscale * nominal_area + / self.input_pixscale0) + else: + log.warning("Unable to compute output pixel area " + "from 'output_wcs'.") + output_pix_area = None + else: # pragma: no cover + # This clause is not reachable under usual circumstances: + # gwcs WCS discards the pixel_area attribute when saved as ASDF + log.debug(f'Setting output pixel area from wcs.pixel_area: ' + f'{output_wcs.pixel_area}') + output_pix_area = output_wcs.pixel_area + + # Set the pscale ratio for scaling reasons + if output_pix_area is not None: + self.pscale_ratio = nominal_area / output_pix_area + else: + self.pscale_ratio = 1.0 + + # Set the output shape if specified + if output_shape is not None: + self.output_wcs.array_shape = output_shape[::-1] + else: + if pscale is not None and nominal_area is not None: + log.info(f'Specified output pixel scale: {pscale} arcsec.') + pscale /= 3600.0 + + # Set the pscale ratio from the input pixel scale + # (pscale is input / output) + if self.pscale_ratio != 1.0: + log.warning('Ignoring input pixel_scale_ratio in favor ' + 'of explicit pixel_scale.') + self.pscale_ratio = self.input_pixscale0 / pscale + log.info(f'Computed output pixel scale ratio: {self.pscale_ratio:.5g}') + + # Define output WCS based on all inputs, including a reference WCS. + # These functions internally use self.pscale_ratio to accommodate + # user settings. + # Any other customizations (crpix, crval, rotation) are ignored. + if resample_utils.is_sky_like(self.input_models[0].meta.wcs.output_frame): if self.input_models[0].meta.instrument.name != "NIRSPEC": self.output_wcs = self.build_interpolated_output_wcs() else: self.output_wcs = self.build_nirspec_output_wcs() else: self.output_wcs = self.build_nirspec_lamp_output_wcs() - else: - self.output_wcs = output_wcs - if output_shape is not None: - self.output_wcs.array_shape = output_shape[::-1] + # Use the nominal output pixel area in sr if available, + # scaling for user-set pixel_scale ratio if needed. + if nominal_area is not None: + # Note that there is only one spatial dimension so the + # pscale_ratio is not squared. + output_pix_area = nominal_area / self.pscale_ratio + else: + output_pix_area = None + + if pscale is None: + log.info(f'Specified output pixel scale ratio: {self.pscale_ratio}.') + pscale = compute_spectral_pixel_scale( + self.output_wcs, disp_axis=disp_axis) + log.info(f'Computed output pixel scale: {3600.0 * pscale:.5g} arcsec.') + + # Output model self.blank_output = datamodels.SlitModel(tuple(self.output_wcs.array_shape)) - self.blank_output.update(self.input_models[0]) + + # update meta data and wcs + self.blank_output.update(input_models[0]) self.blank_output.meta.wcs = self.output_wcs - self.output_models = ModelContainer() + if output_pix_area is not None: + self.blank_output.meta.photometry.pixelarea_steradians = output_pix_area + self.blank_output.meta.photometry.pixelarea_arcsecsq = ( + output_pix_area * np.rad2deg(3600)**2) - log.info(f"Driz parameter kernal: {self.kernel}") - log.info(f"Driz parameter pixfrac: {self.pixfrac}") - log.info(f"Driz parameter fillval: {self.fillval}") - log.info(f"Driz parameter weight_type: {self.weight_type}") + self.output_models = ModelContainer() def build_nirspec_output_wcs(self, refmodel=None): """ - Create a spatial/spectral WCS covering footprint of the input + Create a spatial/spectral WCS covering the footprint of the input. + + Creates the output frame by linearly fitting RA, Dec along the slit + and producing a lookup table to interpolate wavelengths in the + dispersion direction. + + For NIRSpec, the output WCS must also provide slit coordinates + to support source location in the spectral extraction step. To do so, + this step creates a lookup table for virtual slit coordinates, + corresponding to the slit y-position at the center of the array + in the input reference model. Slit x-position is set to zero for + all pixels. + + Frames available in the output WCS are: + + - `detector`: image x, y + - `slit_frame`: slit x, slit y, wavelength + - `world`: RA, Dec, wavelength + + Parameters + ---------- + refmodel : `~jwst.datamodels.JwstDataModel`, optional + The reference input image from which the fiducial WCS is created. + If not specified, the first image in self.input_models. If the + first model is empty (all-NaN or all-zero), the first non-empty + model is used. + + Returns + ------- + output_wcs : `~gwcs.WCS` + A gwcs WCS object defining the output frame WCS. """ all_wcs = [m.meta.wcs for m in self.input_models if m is not refmodel] if refmodel: all_wcs.insert(0, refmodel.meta.wcs) else: - # Use the first model with any good data as the reference model + # Use the first model with a reasonable amount of good data + # as the reference model for model in self.input_models: dq_mask = resample_utils.build_mask(model.dq, self.good_bits) good = np.isfinite(model.data) & (model.data != 0) & dq_mask - if np.any(good) and refmodel is None: + if np.sum(good) > 100 and refmodel is None: refmodel = model break @@ -132,137 +247,189 @@ def build_nirspec_output_wcs(self, refmodel=None): if refmodel is None: refmodel = self.input_models[0] - # make a copy of the data array for internal manipulation + # Make a copy of the data array for internal manipulation refmodel_data = refmodel.data.copy() - # renormalize to the minimum value, for best results when + # Renormalize to the minimum value, for best results when # computing the weighted mean below with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") refmodel_data -= np.nanmin(refmodel_data) - # save the wcs of the reference model + # Save the wcs of the reference model refwcs = refmodel.meta.wcs - # setup the transforms that are needed + # Set up the transforms that are needed s2d = refwcs.get_transform('slit_frame', 'detector') d2s = refwcs.get_transform('detector', 'slit_frame') - s2w = refwcs.get_transform('slit_frame', 'world') + if 'moving_target' in refwcs.available_frames: + s2w = refwcs.get_transform('slit_frame', 'moving_target') + w2s = refwcs.get_transform('moving_target', 'slit_frame') + else: + s2w = refwcs.get_transform('slit_frame', 'world') + w2s = refwcs.get_transform('world', 'slit_frame') - # estimate position of the target without relying on the meta.target: + # Estimate position of the target without relying on the meta.target: # compute the mean spatial and wavelength coords weighted # by the spectral intensity bbox = refwcs.bounding_box grid = wcstools.grid_from_bounding_box(bbox) _, s, lam = np.array(d2s(*grid)) - sd = s * refmodel_data - ld = lam * refmodel_data - good_s = np.isfinite(sd) - total = np.sum(refmodel_data[good_s]) - if np.any(good_s) and total != 0: - wmean_s = np.sum(sd[good_s]) / total - wmean_l = np.sum(ld[good_s]) / total + + # Find invalid values + good = np.isfinite(s) & np.isfinite(lam) & np.isfinite(refmodel_data) + refmodel_data[~good] = np.nan + + # Reject the worst outliers in the data + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=AstropyUserWarning, + message=".*automatically clipped.*") + weights = sigma_clip(refmodel_data, masked=True, sigma=100.0) + weights = np.ma.filled(weights, fill_value=0.0) + if not np.all(weights == 0.0): + wmean_s = np.average(s[good], weights=weights[good]) else: - wmean_s = 0.5 * (refmodel.slit_ymax - refmodel.slit_ymin) - wmean_l = d2s(*np.mean(bbox, axis=1))[2] + wmean_s = np.nanmean(s) + wmean_l = np.nanmean(lam) - # transform the weighted means into target RA/Dec + # Transform the weighted means into target RA/Dec + # (at the center of the slit in x) targ_ra, targ_dec, _ = s2w(0, wmean_s, wmean_l) + sx, sy = s2d(0, wmean_s, wmean_l) + log.debug(f'Fiducial RA, Dec, wavelength: ' + f'{targ_ra}, {targ_dec}, {wmean_l}') + log.debug(f'Index at fiducial center: x={sx}, y={sy}') + + # Estimate spatial sampling from the reference model + # at the center of the array + lam_center_idx = int(np.mean(bbox, axis=1)[0]) + log.debug(f'Center of dispersion axis: {lam_center_idx}') + grid_center = grid[0][:, lam_center_idx], grid[1][:, lam_center_idx] + ra_ref, dec_ref, _ = np.array(refwcs(*grid_center)) + + # Convert ra and dec to tangent projection + tan = Pix2Sky_TAN() + native2celestial = RotateNative2Celestial(targ_ra, targ_dec, 180) + undist2sky = tan | native2celestial + x_tan, y_tan = undist2sky.inverse(ra_ref, dec_ref) + is_nan = np.isnan(x_tan) | np.isnan(y_tan) + x_tan = x_tan[~is_nan] + y_tan = y_tan[~is_nan] + + # Estimate the spatial sampling from the tangent projection + # offset from center + fitter = LinearLSQFitter() + fit_model = Linear1D() + + xstop = x_tan.shape[0] * self.pscale_ratio + x_idx = np.linspace(0, xstop, x_tan.shape[0], endpoint=False) + ystop = y_tan.shape[0] * self.pscale_ratio + y_idx = np.linspace(0, ystop, y_tan.shape[0], endpoint=False) + pix_to_xtan = fitter(fit_model, x_idx, x_tan) + pix_to_ytan = fitter(fit_model, y_idx, y_tan) - ref_lam = _find_nirspec_output_sampling_wavelengths( - all_wcs, - targ_ra, targ_dec - ) - ref_lam = np.array(ref_lam) - n_lam = ref_lam.size + # Check whether sampling is more along RA or along Dec + swap_xy = abs(pix_to_xtan.slope) < abs(pix_to_ytan.slope) + log.debug(f'Swap xy: {swap_xy}') + + # Get output wavelengths from all data + ref_lam = _find_nirspec_output_sampling_wavelengths(all_wcs) + n_lam = len(ref_lam) if not n_lam: raise ValueError("Not enough data to construct output WCS.") - x_slit = np.zeros(n_lam) - lam = 1e-6 * ref_lam - - # Find the spatial pixel scale: - y_slit_min, y_slit_max = self._max_virtual_slit_extent(all_wcs, targ_ra, targ_dec) - - nsampl = 50 - xy_min = s2d( - nsampl * [0], - nsampl * [y_slit_min], - lam[(tuple((i * n_lam) // nsampl for i in range(nsampl)), )] - ) - xy_max = s2d( - nsampl * [0], - nsampl * [y_slit_max], - lam[(tuple((i * n_lam) // nsampl for i in range(nsampl)), )] - ) - - good = np.logical_and(np.isfinite(xy_min), np.isfinite(xy_max)) - if not np.any(good): - raise ValueError("Error estimating output WCS pixel scale.") - - xy1 = s2d(x_slit, np.full(n_lam, refmodel.slit_ymin), lam) - xy2 = s2d(x_slit, np.full(n_lam, refmodel.slit_ymax), lam) - xylen = np.nanmax(np.linalg.norm(np.array(xy1) - np.array(xy2), axis=0)) + 1 - pscale = (refmodel.slit_ymax - refmodel.slit_ymin) / xylen - - # compute image span along Y-axis (length of the slit in the detector plane) - # det_slit_span = np.linalg.norm(np.subtract(xy_max, xy_min)) - det_slit_span = np.nanmax(np.linalg.norm(np.subtract(xy_max, xy_min), axis=0)) - ny = int(np.ceil(det_slit_span * self.pscale_ratio + 0.5)) + 1 - - border = 0.5 * (ny - det_slit_span * self.pscale_ratio) - 0.5 - - if xy_min[1][1] < xy_max[1][1]: - y_slit_model = Linear1D( - slope=pscale / self.pscale_ratio, - intercept=y_slit_min - border * pscale * self.pscale_ratio - ) + # Find the spatial extent in x/y tangent + min_tan, max_tan = self._max_spatial_extent(all_wcs, undist2sky.inverse, swap_xy) + diff = np.abs(max_tan - min_tan) + if swap_xy: + pix_to_tan_slope = np.abs(pix_to_ytan.slope) + slope_sign = np.sign(pix_to_ytan.slope) + else: + pix_to_tan_slope = np.abs(pix_to_xtan.slope) + slope_sign = np.sign(pix_to_xtan.slope) + + # Image size in spatial dimension from the maximum slope + # and tangent offset span, plus one pixel to make sure + # we catch all the data + ny = int(np.ceil(diff / pix_to_tan_slope)) + 1 + + # Correct the intercept for the new minimum value. + # Also account for integer pixel size to make sure the + # data is centered in the array. + offset = ny/2 * pix_to_tan_slope - diff/2 + if slope_sign > 0: + zero_value = min_tan else: - y_slit_model = Linear1D( - slope=-pscale / self.pscale_ratio, - intercept=y_slit_max + border * pscale * self.pscale_ratio - ) + zero_value = max_tan + if swap_xy: + pix_to_ytan.intercept = zero_value - slope_sign * offset + else: + pix_to_xtan.intercept = zero_value - slope_sign * offset - # extrapolate 1/2 pixel at the edges and make tabular model w/inverse: - lam = lam.tolist() - pixel_coord = list(range(n_lam)) + # Now set up the final transforms + # For wavelengths, extrapolate 1/2 pixel at the edges and + # make tabular model w/inverse + pixel_coord = list(range(n_lam)) if len(pixel_coord) > 1: # left: - slope = (lam[1] - lam[0]) / pixel_coord[1] - lam.insert(0, -0.5 * slope + lam[0]) + slope = (ref_lam[1] - ref_lam[0]) / pixel_coord[1] + ref_lam.insert(0, -0.5 * slope + ref_lam[0]) pixel_coord.insert(0, -0.5) + # right: - slope = (lam[-1] - lam[-2]) / (pixel_coord[-1] - pixel_coord[-2]) - lam.append(slope * (pixel_coord[-1] + 0.5) + lam[-2]) + slope = (ref_lam[-1] - ref_lam[-2]) / (pixel_coord[-1] - pixel_coord[-2]) + ref_lam.append(slope * (pixel_coord[-1] + 0.5) + ref_lam[-2]) pixel_coord.append(pixel_coord[-1] + 0.5) - else: - lam = 3 * lam + ref_lam = 3 * ref_lam pixel_coord = [-0.5, 0, 0.5] - wavelength_transform = Tabular1D(points=pixel_coord, - lookup_table=lam, + lookup_table=ref_lam, bounds_error=False, fill_value=np.nan) - wavelength_transform.inverse = Tabular1D(points=lam, - lookup_table=pixel_coord, - bounds_error=False, - fill_value=np.nan) - self.data_size = (ny, len(ref_lam)) - - # Construct the final transform. - # First coordinate is set to 0 to represent the "horizontal" center - # of the slit (if we imagine slit to be vertical in the usual X-Y 2D - # cartesian frame): + + # For spatial coordinates, map detector pixels to tangent offset, + # then to world coordinates (RA, Dec, wavelength in um). + # Make sure the inverse returns the axis with the larger slope, + # in case the smaller one is close to zero + mapping = Mapping((1, 1, 0)) + if swap_xy: + mapping.inverse = Mapping((2, 1)) + else: + mapping.inverse = Mapping((2, 0)) + pix2world = mapping | (pix_to_xtan & pix_to_ytan | undist2sky) & wavelength_transform + + # For NIRSpec, slit coordinates are still needed to locate the + # planned source position. Since the slit is now rectified, + # return the central slit coords for all x, converting from pixels + # to world coordinates, then back to slit units. + slit_center = w2s(*pix2world(np.full(ny, n_lam // 2), np.arange(ny)))[1] + + # Make a 1D lookup table for all ny. + # Allow linear extrapolation at the edges. + slit_transform = Tabular1D( + points=np.arange(ny), lookup_table=slit_center, + bounds_error=False, fill_value=None) + + # In the transform, the first slit coordinate is always set to 0 + # to represent the "horizontal" center of the slit + # (if we imagine the slit to be vertical in the usual + # X-Y 2D cartesian frame). mapping = Mapping((0, 1, 0)) - inv_mapping = Mapping((2, 1)) + inv_mapping = Mapping((2, 1), n_inputs=3) inv_mapping.inverse = mapping mapping.inverse = inv_mapping zero_model = Const1D(0) zero_model.inverse = zero_model - det2slit = mapping | zero_model & y_slit_model & wavelength_transform - # Create coordinate frames + # Final detector to slit transform (x, y -> slit_x, slit_y, wavelength) + det2slit = mapping | zero_model & slit_transform & wavelength_transform + + # The slit to world coordinates is just the inverse of the slit transform, + # piped back into the pixel to world transform + slit2world = det2slit.inverse | pix2world + + # Create coordinate frames: detector, slit_frame, and world det = cf.Frame2D(name='detector', axes_order=(0, 1)) slit_spatial = cf.Frame2D(name='slit_spatial', axes_order=(0, 1), unit=("", ""), axes_names=('x_slit', 'y_slit')) @@ -273,81 +440,59 @@ def build_nirspec_output_wcs(self, refmodel=None): reference_frame=coord.ICRS()) world = cf.CompositeFrame([sky, spec], name='world') - pipeline = [(det, det2slit), (slit_frame, s2w), (world, None)] + pipeline = [(det, det2slit), (slit_frame, slit2world), (world, None)] output_wcs = WCS(pipeline) - # Compute bounding box and output array shape. Add one to the y (slit) - # height to account for the half pixel at top and bottom due to pixel - # coordinates being centers of pixels + # Compute bounding box and output array shape. + self.data_size = (ny, n_lam) bounding_box = resample_utils.wcs_bbox_from_shape(self.data_size) output_wcs.bounding_box = bounding_box output_wcs.array_shape = self.data_size return output_wcs - def _max_virtual_slit_extent(self, wcs_list, target_ra, target_dec): + def _max_spatial_extent(self, wcs_list, transform, swap_xy): """ - Compute min & max slit coordinates for all nods in the "virtual" + Compute min & max spatial coordinates for all nods in the "virtual" slit frame. - - NOTE: this code, potentially, might have troubles dealing - with large dithers such that ``target_ra`` and ``target_dec`` - may not be converted to slit frame (i.e., result in ``NaN``). - - A more sophisticated algorithm may be needed to "stitch" large - dithers. But then distortions may come into play. """ - y_slit_min = np.inf - y_slit_max = -np.inf - - t0 = 0 - + min_tan_all, max_tan_all = np.inf, -np.inf for wcs in wcs_list: - d2s = wcs.get_transform('detector', 'slit_frame') - w2s = wcs.get_transform('world', 'slit_frame') - x, y = wcstools.grid_from_bounding_box(wcs.bounding_box) - ra, dec, lam = wcs(x, y) + ra, dec, _ = wcs(x, y) good = np.logical_and(np.isfinite(ra), np.isfinite(dec)) - x = x[good] - y = y[good] - lm = lam[good] + ra = ra[good] + dec = dec[good] - _, yslit, _ = d2s(x, y) - - # position of the target in the slit relative to its position - # for the refence image: - ts = w2s(target_ra, target_dec, np.mean(lm))[1] - t0 - - if wcs is wcs_list[0]: - t0 = ts - ts = 0 - - y_slit_min_i = np.min(yslit) - ts - y_slit_max_i = np.max(yslit) - ts + xtan, ytan = transform(ra, dec) + if swap_xy: + tan_all = ytan + else: + tan_all = xtan - if y_slit_min_i < y_slit_min: - y_slit_min = y_slit_min_i + min_tan = np.min(tan_all) + max_tan = np.max(tan_all) - if y_slit_max_i > y_slit_max: - y_slit_max = y_slit_max_i + if min_tan < min_tan_all: + min_tan_all = min_tan + if max_tan > max_tan_all: + max_tan_all = max_tan - return y_slit_min, y_slit_max + return min_tan_all, max_tan_all - def build_interpolated_output_wcs(self, refmodel=None): + def build_interpolated_output_wcs(self): """ - Create a spatial/spectral WCS output frame using all the input models + Create a spatial/spectral WCS output frame using all the input models. Creates output frame by linearly fitting RA, Dec along the slit and producing a lookup table to interpolate wavelengths in the dispersion direction. - Parameters - ---------- - refmodel : `~jwst.datamodels.JwstDataModel` - The reference input image from which the fiducial WCS is created. - If not specified, the first image in self.input_models is used. + Frames available in the output WCS are: + + - `detector`: image x, y + - `world`: RA, Dec, wavelength Returns ------- @@ -386,7 +531,7 @@ def build_interpolated_output_wcs(self, refmodel=None): wavelength_array = wavelength_array[~np.isnan(wavelength_array)] # We need to estimate the spatial sampling to use for the output WCS. - # Tt is assumed the spatial sampling is the same for all the input + # It is assumed the spatial sampling is the same for all the input # models. So we can use the first input model to set the spatial # sampling. @@ -405,12 +550,14 @@ def build_interpolated_output_wcs(self, refmodel=None): # find the center ra and dec for this slit at central wavelength lam_center_index = int((bbox[spectral_axis][1] - bbox[spectral_axis][0]) / 2) - if spatial_axis == 0: # MIRI LRS, the WCS x axis is spatial + if spatial_axis == 0: + # MIRI LRS, the WCS x axis is spatial ra_slice = ra[lam_center_index, :] dec_slice = dec[lam_center_index, :] else: ra_slice = ra[:, lam_center_index] dec_slice = dec[:, lam_center_index] + # wrap RA if near zero ra_center_pt = np.nanmean(wrap_ra(ra_slice)) dec_center_pt = np.nanmean(dec_slice) @@ -419,6 +566,7 @@ def build_interpolated_output_wcs(self, refmodel=None): tan = Pix2Sky_TAN() native2celestial = RotateNative2Celestial(ra_center_pt, dec_center_pt, 180) undist2sky1 = tan | native2celestial + # Filter out RuntimeWarnings due to computed NaNs in the WCS with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) # was ignore. need to make more specific @@ -439,12 +587,13 @@ def build_interpolated_output_wcs(self, refmodel=None): # estimate the spatial sampling fitter = LinearLSQFitter() fit_model = Linear1D() - xstop = x_tan_array.shape[0] / self.pscale_ratio - xstep = 1 / self.pscale_ratio - ystop = y_tan_array.shape[0] / self.pscale_ratio - ystep = 1 / self.pscale_ratio - pix_to_xtan = fitter(fit_model, np.arange(0, xstop, xstep), x_tan_array) - pix_to_ytan = fitter(fit_model, np.arange(0, ystop, ystep), y_tan_array) + + xstop = x_tan_array.shape[0] * self.pscale_ratio + x_idx = np.linspace(0, xstop, x_tan_array.shape[0], endpoint=False) + ystop = y_tan_array.shape[0] * self.pscale_ratio + y_idx = np.linspace(0, ystop, y_tan_array.shape[0], endpoint=False) + pix_to_xtan = fitter(fit_model, x_idx, x_tan_array) + pix_to_ytan = fitter(fit_model, y_idx, y_tan_array) # append all ra and dec values to use later to find min and max # ra and dec @@ -479,8 +628,8 @@ def build_interpolated_output_wcs(self, refmodel=None): if self.input_models[0].meta.exposure.type == 'MIR_LRS-FIXEDSLIT': wavelength_array = np.flip(wavelength_array, axis=None) - step = 1 / self.pscale_ratio - stop = wavelength_array.shape[0] / self.pscale_ratio + step = 1 + stop = wavelength_array.shape[0] points = np.arange(0, stop, step) pix_to_wavelength = Tabular1D(points=points, lookup_table=wavelength_array, @@ -576,8 +725,8 @@ def build_interpolated_output_wcs(self, refmodel=None): # compute the output array size in WCS axes order, i.e. (x, y) output_array_size = [0, 0] - output_array_size[spectral_axis] = int(np.ceil(len(wavelength_array) / self.pscale_ratio)) - output_array_size[spatial_axis] = int(np.ceil(x_size / self.pscale_ratio)) + output_array_size[spectral_axis] = int(np.ceil(len(wavelength_array))) + output_array_size[spatial_axis] = int(np.ceil(x_size * self.pscale_ratio)) # turn the size into a numpy shape in (y, x) order output_wcs.array_shape = output_array_size[::-1] @@ -595,10 +744,15 @@ def build_nirspec_lamp_output_wcs(self): producing a lookup table to interpolate wavelengths in the dispersion direction. + Frames available in the output WCS are: + + - `detector`: image x, y + - `world`: MSA x, MSA y, wavelength + Returns ------- output_wcs : `~gwcs.WCS` object - A gwcs WCS object defining the output frame WCS + A gwcs WCS object defining the output frame WCS. """ model = self.input_models[0] wcs = model.meta.wcs @@ -628,15 +782,15 @@ def build_nirspec_lamp_output_wcs(self): # Estimate and fit the spatial sampling fitter = LinearLSQFitter() fit_model = Linear1D() - xstop = x_msa_array.shape[0] / self.pscale_ratio - xstep = 1 / self.pscale_ratio - ystop = y_msa_array.shape[0] / self.pscale_ratio - ystep = 1 / self.pscale_ratio - pix_to_x_msa = fitter(fit_model, np.arange(0, xstop, xstep), x_msa_array) - pix_to_y_msa = fitter(fit_model, np.arange(0, ystop, ystep), y_msa_array) - - step = 1 / self.pscale_ratio - stop = wavelength_array.shape[0] / self.pscale_ratio + xstop = x_msa_array.shape[0] * self.pscale_ratio + x_idx = np.linspace(0, xstop, x_msa_array.shape[0], endpoint=False) + ystop = y_msa_array.shape[0] * self.pscale_ratio + y_idx = np.linspace(0, ystop, y_msa_array.shape[0], endpoint=False) + pix_to_x_msa = fitter(fit_model, x_idx, x_msa_array) + pix_to_y_msa = fitter(fit_model, y_idx, y_msa_array) + + step = 1 + stop = wavelength_array.shape[0] points = np.arange(0, stop, step) pix_to_wavelength = Tabular1D(points=points, lookup_table=wavelength_array, @@ -679,9 +833,10 @@ def build_nirspec_lamp_output_wcs(self): # Compute the output array size and bounding box output_array_size = [0, 0] - output_array_size[spectral_axis] = int(np.ceil(len(wavelength_array) / self.pscale_ratio)) + output_array_size[spectral_axis] = len(wavelength_array) x_size = len(x_msa_array) - output_array_size[spatial_axis] = int(np.ceil(x_size / self.pscale_ratio)) + output_array_size[spatial_axis] = int(np.ceil(x_size * self.pscale_ratio)) + # turn the size into a numpy shape in (y, x) order output_wcs.array_shape = output_array_size[::-1] output_wcs.pixel_shape = output_array_size @@ -700,33 +855,14 @@ def find_dispersion_axis(refmodel): return dispaxis - 1 -def _spherical_sep(j, k, wcs, xyz_ref): - """ - Objective function that computes the angle between two points - on the sphere for small separations. - """ - ra, dec, _ = wcs(k, j, with_bounding_box=False) - return 1 - np.dot(_S2C(ra, dec), xyz_ref) - - -def _find_nirspec_output_sampling_wavelengths(wcs_list, targ_ra, targ_dec, mode='median'): - assert mode in ['median', 'fast', 'accurate'] +def _find_nirspec_output_sampling_wavelengths(wcs_list): refwcs = wcs_list[0] bbox = refwcs.bounding_box grid = wcstools.grid_from_bounding_box(bbox) ra, dec, lambdas = refwcs(*grid) - if mode == 'median': - ref_lam = sorted(np.nanmedian(lambdas[:, np.any(np.isfinite(lambdas), axis=0)], axis=0)) - else: - ref_lam, _, _ = _find_nirspec_sampling_wavelengths( - refwcs, - targ_ra, targ_dec, - ra, dec, - fast=mode == 'fast' - ) - + ref_lam = sorted(np.nanmedian(lambdas[:, np.any(np.isfinite(lambdas), axis=0)], axis=0)) lam1 = ref_lam[0] lam2 = ref_lam[-1] @@ -737,15 +873,7 @@ def _find_nirspec_output_sampling_wavelengths(wcs_list, targ_ra, targ_dec, mode= bbox = w.bounding_box grid = wcstools.grid_from_bounding_box(bbox) ra, dec, lambdas = w(*grid) - if mode == 'median': - lam = sorted(np.nanmedian(lambdas[:, np.any(np.isfinite(lambdas), axis=0)], axis=0)) - else: - lam, _, _ = _find_nirspec_sampling_wavelengths( - w, - targ_ra, targ_dec, - ra, dec, - fast=mode == 'fast' - ) + lam = sorted(np.nanmedian(lambdas[:, np.any(np.isfinite(lambdas), axis=0)], axis=0)) image_lam.append((lam, np.min(lam), np.max(lam))) min_delta = min(min_delta, np.fabs(np.ediff1d(ref_lam).min())) @@ -794,121 +922,29 @@ def _find_nirspec_output_sampling_wavelengths(wcs_list, targ_ra, targ_dec, mode= return ref_lam -def _find_nirspec_sampling_wavelengths(wcs, ra0, dec0, ra, dec, fast=True): - xdet = [] - lms = [] - ys = [] - skipped = [] - - eps = 10 * np.finfo(float).eps - - xyz_ref = _S2C(ra0, dec0) - ymax, xmax = ra.shape - good = np.logical_and(np.isfinite(ra), np.isfinite(dec)) - - j0 = 0 - - for k in range(xmax): - if not any(good[:, k]): - if xdet: - skipped.append(k) - continue - - idx = np.flatnonzero(good[:, k]).tolist() - if j0 in idx: - i = idx.index(j0) - else: - i = 0 - j0 = idx[0] - - dmin = _spherical_sep(j0, k, wcs, xyz_ref) - - for j in idx[i + 1:]: - d = _spherical_sep(j, k, wcs, xyz_ref) - if d < dmin: - dmin = d - j0 = j - elif d > dmin: - break - - for j in idx[max(i - 1, 0):None if i else 0:-1]: - d = _spherical_sep(j, k, wcs, xyz_ref) - if d < dmin: - dmin = d - j0 = j - elif d > dmin: - break - - if j0 == 0 or not good[j0 - 1, k]: - j1 = j0 - 0.49999 - else: - j1 = j0 - 0.99999 - - if j0 == ymax - 1 or not good[j0 + 1, k]: - j2 = j0 + 0.49999 - else: - j2 = j0 + 0.99999 - - if fast: - # parabolic minimization: - f0 = dmin - f1 = _spherical_sep(j1, k, wcs, xyz_ref) - if not np.isfinite(f1): - # give another try with 1/2 step: - j1 = 0.5 * (j1 + j0) - f1 = _spherical_sep(j1, k, wcs, xyz_ref) - - f2 = _spherical_sep(j2, k, wcs, xyz_ref) - if not np.isfinite(f2): - # give another try with 1/2 step: - j2 = 0.5 * (j2 + j0) - f2 = _spherical_sep(j2, k, wcs, xyz_ref) - - if np.isfinite(f1) and np.isfinite(f2): - dn = (j0 - j1) * (f0 - f2) - (j0 - j2) * (f0 - f1) - if np.abs(dn) < eps: - jmin = j0 - else: - jmin = j0 - 0.5 * ((j0 - j1)**2 * (f0 - f2) - - (j0 - j2)**2 * (f0 - f1)) / dn - jmin = max(min(jmin, j2), j1) - else: - jmin = j0 +def compute_spectral_pixel_scale(wcs, fiducial=None, disp_axis=1): + """Compute an approximate spatial pixel scale for spectral data. + + Parameters + ---------- + wcs : gwcs.WCS + Spatial/spectral WCS. + fiducial : tuple of float, optional + (RA, Dec, wavelength) taken as the fiducial reference. If + not specified, the center of the array is used. + disp_axis : int + Dispersion axis for the data. Assumes the same convention + as `wcsinfo.dispersion_direction` (1 for NIRSpec, 2 for MIRI). + + Returns + ------- + pixel_scale : float + The spatial scale in degrees. + """ + # Get the coordinates for the center of the array + if fiducial is None: + center_x, center_y = np.mean(wcs.bounding_box, axis=1) + fiducial = wcs(center_x, center_y) - else: - r = minimize_scalar( - _spherical_sep, - method='golden', - bracket=(j1, j2), - args=(k, wcs, xyz_ref), - tol=None, - options={'maxiter': 10, 'xtol': 1e-2 / (j0 + 1)} - ) - jmin = r['x'] - if np.isfinite(jmin): - jmin = max(min(jmin, j2), j1) - else: - jmin = j0 - - targ_lam = wcs(k, jmin)[-1] - if not np.isfinite(targ_lam): - targ_lam = wcs(k, j0)[-1] - - if not np.isfinite(targ_lam): - if xdet: - skipped.append(k) - continue - - lms.append(targ_lam) - ys.append(jmin) - xdet.append(k) - - skipped = [s for s in skipped if s <= xdet[-1]] - if skipped and skipped[0] < xdet[-1]: - # there are columns with all pixels having invalid world, - # coordinates. Fill the gaps using linear interpolation. - raise NotImplementedError( - "Support for discontinuous sampling was not implemented." - ) - - return lms, xdet, ys + pixel_scale = compute_scale(wcs, fiducial, disp_axis=disp_axis) + return float(pixel_scale) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index e215a7bb61..b0630ed590 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -114,6 +114,7 @@ def _process_multislit(self, input_models): result.update(input_models[0]) + pscale_ratio = None for container in containers.values(): resamp = resample_spec.ResampleSpecData(container, **self.drizpars) @@ -124,10 +125,17 @@ def _process_multislit(self, input_models): update_s_region_spectral(model) result.slits.append(model) + # Keep the first computed pixel scale ratio for storage + if self.pixel_scale is not None and pscale_ratio is None: + pscale_ratio = resamp.pscale_ratio + result.meta.cal_step.resample = "COMPLETE" result.meta.asn.pool_name = input_models.asn_pool_name result.meta.asn.table_name = input_models.asn_table_name - result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio + if self.pixel_scale is None or pscale_ratio is None: + result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio + else: + result.meta.resample.pixel_scale_ratio = pscale_ratio result.meta.resample.pixfrac = self.pixfrac return result @@ -157,7 +165,10 @@ def _process_slit(self, input_models): result.meta.asn.pool_name = input_models.asn_pool_name result.meta.asn.table_name = input_models.asn_table_name result.meta.bunit_data = drizzled_models[0].meta.bunit_data - result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio + if self.pixel_scale is None: + result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio + else: + result.meta.resample.pixel_scale_ratio = resamp.pscale_ratio result.meta.resample.pixfrac = self.pixfrac self.update_slit_metadata(result) update_s_region_spectral(result) diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index 0317216e58..0ba20c62c9 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -219,6 +219,27 @@ def is_sky_like(frame): return u.Unit("deg") in frame.unit or u.Unit("arcsec") in frame.unit +def is_flux_density(bunit): + """ + Differentiate between surface brightness and flux density data units. + + Parameters + ---------- + bunit : str or `~astropy.units.Unit` + Data units, e.g. 'MJy' (is flux density) or 'MJy/sr' (is not). + + Returns + ------- + bool + True if the units are equivalent to flux density units. + """ + try: + flux_density = u.Unit(bunit).is_equivalent(u.Jy) + except (ValueError, TypeError): + flux_density = False + return flux_density + + def decode_context(context, x, y): """ Get 0-based indices of input images that contributed to (resampled) output pixel with coordinates ``x`` and ``y``. diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index f9cfa56ef3..62af3e496f 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -10,10 +10,11 @@ from jwst.datamodels import ModelContainer from jwst.assign_wcs import AssignWcsStep from jwst.assign_wcs.util import compute_fiducial, compute_scale +from jwst.exp_to_source import multislit_to_container from jwst.extract_2d import Extract2dStep from jwst.resample import ResampleSpecStep, ResampleStep from jwst.resample.resample import compute_image_pixel_area -from jwst.resample.resample_spec import ResampleSpecData +from jwst.resample.resample_spec import ResampleSpecData, compute_spectral_pixel_scale def _set_photom_kwd(im): @@ -37,42 +38,36 @@ def _set_photom_kwd(im): @pytest.fixture -def nirspec_rate(): - ysize = 2048 - xsize = 2048 +def miri_rate(): + xsize = 72 + ysize = 416 shape = (ysize, xsize) im = ImageModel(shape) + im.data += 5 im.var_rnoise += 1 - im.meta.target = {'ra': 100.1237, 'dec': 39.86} im.meta.wcsinfo = { 'dec_ref': 40, 'ra_ref': 100, - 'roll_ref': 0, + 'roll_ref': 0.0, 'v2_ref': -453.5134, 'v3_ref': -373.4826, 'v3yangle': 0.0, 'vparity': -1} im.meta.instrument = { - 'detector': 'NRS1', - 'filter': 'CLEAR', - 'grating': 'PRISM', - 'name': 'NIRSPEC', - 'gwa_tilt': 37.0610, - 'gwa_xtilt': 0.0001, - 'gwa_ytilt': 0.0001, - 'fixed_slit': 'S200A1'} + 'detector': 'MIRIMAGE', + 'filter': 'P750L', + 'name': 'MIRI'} + im.meta.observation = { + 'date': '2019-01-01', + 'time': '17:00:00'} im.meta.subarray = { 'fastaxis': 1, - 'name': 'SUBS200A1', + 'name': 'SLITLESSPRISM', 'slowaxis': 2, - 'xsize': 72, + 'xsize': xsize, 'xstart': 1, - 'ysize': 416, + 'ysize': ysize, 'ystart': 529} - im.meta.observation = { - 'program_number': '1234', - 'date': '2016-09-05', - 'time': '8:59:37'} im.meta.exposure = { 'duration': 11.805952, 'end_time': 58119.85416, @@ -87,29 +82,40 @@ def nirspec_rate(): 'nints': 1, 'nresets_between_ints': 0, 'nsamples': 1, - 'readpatt': 'NRSRAPID', + 'readpatt': 'FAST', 'sample_time': 10.0, 'start_time': 58119.8333, - 'type': 'NRS_FIXEDSLIT', + 'type': 'MIR_LRS-SLITLESS', 'zero_frame': False} + yield im + im.close() + + +@pytest.fixture +def miri_cal(miri_rate): + im = AssignWcsStep.call(miri_rate) + _set_photom_kwd(im) + + # Add non-zero values to check flux conservation + im.data += 1.0 - return im + yield im + im.close() @pytest.fixture -def miri_rate(): - xsize = 72 - ysize = 416 +def miri_rate_zero_crossing(): + xsize = 1032 + ysize = 1024 shape = (ysize, xsize) im = ImageModel(shape) - im.data += 5 - im.var_rnoise += 1 + im.var_rnoise = np.random.random(shape) im.meta.wcsinfo = { - 'dec_ref': 40, - 'ra_ref': 100, + 'dec_ref': 2.16444343946559e-05, + 'ra_ref': -0.00026031780056776, 'roll_ref': 0.0, - 'v2_ref': -453.5134, - 'v3_ref': -373.4826, + 'v2_ref': -415.0690466121227, + 'v3_ref': -400.575920398547, 'v3yangle': 0.0, 'vparity': -1} im.meta.instrument = { @@ -121,17 +127,16 @@ def miri_rate(): 'time': '17:00:00'} im.meta.subarray = { 'fastaxis': 1, - 'name': 'SLITLESSPRISM', + 'name': 'FULL', 'slowaxis': 2, 'xsize': xsize, 'xstart': 1, 'ysize': ysize, - 'ystart': 529} + 'ystart': 1} im.meta.exposure = { 'duration': 11.805952, 'end_time': 58119.85416, 'exposure_time': 11.776, - 'measurement_time': 11.65824, 'frame_time': 0.11776, 'group_time': 0.11776, 'groupgap': 0, @@ -144,10 +149,26 @@ def miri_rate(): 'readpatt': 'FAST', 'sample_time': 10.0, 'start_time': 58119.8333, - 'type': 'MIR_LRS-SLITLESS', + 'type': 'MIR_LRS-FIXEDSLIT', 'zero_frame': False} - return im + yield im + im.close() + + +@pytest.fixture +def miri_rate_pair(miri_rate_zero_crossing): + im1 = miri_rate_zero_crossing + # Create a nodded version + im2 = im1.copy() + im2.meta.wcsinfo.ra_ref = 0.00026308279776455 + im2.meta.wcsinfo.dec_ref = -2.1860888891293e-05 + im1 = AssignWcsStep.call(im1) + im2 = AssignWcsStep.call(im2) + + yield im1, im2 + im1.close() + im2.close() @pytest.fixture @@ -220,22 +241,133 @@ def nircam_rate(): 'pixelarea_steradians': 1e-13, 'pixelarea_arcsecsq': 4e-3, } + yield im + im.close() - return im +@pytest.fixture +def nirspec_rate(): + ysize = 2048 + xsize = 2048 + shape = (ysize, xsize) + im = ImageModel(shape) + im.var_rnoise += 1 + im.meta.target = {'ra': 100.1237, 'dec': 39.86} + im.meta.wcsinfo = { + 'dec_ref': 40, + 'ra_ref': 100, + 'roll_ref': 0, + 'v2_ref': -453.5134, + 'v3_ref': -373.4826, + 'v3yangle': 0.0, + 'vparity': -1} + im.meta.instrument = { + 'detector': 'NRS1', + 'filter': 'CLEAR', + 'grating': 'PRISM', + 'name': 'NIRSPEC', + 'gwa_tilt': 37.0610, + 'gwa_xtilt': 0.0001, + 'gwa_ytilt': 0.0001, + 'fixed_slit': 'S200A1'} + im.meta.subarray = { + 'fastaxis': 1, + 'name': 'SUBS200A1', + 'slowaxis': 2, + 'xsize': 72, + 'xstart': 1, + 'ysize': 416, + 'ystart': 529} + im.meta.observation = { + 'program_number': '1234', + 'date': '2016-09-05', + 'time': '8:59:37'} + im.meta.exposure = { + 'duration': 11.805952, + 'end_time': 58119.85416, + 'exposure_time': 11.776, + 'measurement_time': 11.65824, + 'frame_time': 0.11776, + 'group_time': 0.11776, + 'groupgap': 0, + 'integration_time': 11.776, + 'nframes': 1, + 'ngroups': 100, + 'nints': 1, + 'nresets_between_ints': 0, + 'nsamples': 1, + 'readpatt': 'NRSRAPID', + 'sample_time': 10.0, + 'start_time': 58119.8333, + 'type': 'NRS_FIXEDSLIT', + 'zero_frame': False} + + yield im + im.close() -def test_nirspec_wcs_roundtrip(nirspec_rate): + +@pytest.fixture +def nirspec_cal(nirspec_rate): im = AssignWcsStep.call(nirspec_rate) # Since the ra_targ, and dec_targ are flux-weighted, we need non-zero - # flux values. Add random values. - rng = np.random.default_rng(1234) - im.data += rng.random(im.data.shape) + # flux values. + im.data += 1.0 im = Extract2dStep.call(im) for slit in im.slits: _set_photom_kwd(slit) - im = ResampleSpecStep.call(im) + yield im + im.close() + + +@pytest.fixture +def nirspec_cal_pair(nirspec_rate): + # copy the rate model to make files with different filters + rate1 = nirspec_rate + rate1.meta.instrument.grating = 'G140H' + rate1.meta.instrument.filter = 'F070LP' + rate2 = nirspec_rate.copy() + rate2.meta.instrument.grating = 'G140H' + rate2.meta.instrument.filter = 'F100LP' + + im1 = AssignWcsStep.call(nirspec_rate) + im2 = AssignWcsStep.call(rate2) + + # Since the ra_targ, and dec_targ are flux-weighted, we need non-zero + # flux values. + im1.data += 1.0 + im2.data += 1.0 + + im1 = Extract2dStep.call(im1) + im2 = Extract2dStep.call(im2) + for slit in im1.slits: + _set_photom_kwd(slit) + for slit in im2.slits: + _set_photom_kwd(slit) + yield im1, im2 + im1.close() + im2.close() + + +@pytest.fixture +def nirspec_lamp(nirspec_rate): + nirspec_rate.meta.exposure.type = 'NRS_LAMP' + nirspec_rate.meta.instrument.lamp_mode = 'FIXEDSLIT' + nirspec_rate.meta.instrument.lamp_state = 'FLAT' + im = AssignWcsStep.call(nirspec_rate) + im.data += 1.0 + + im = Extract2dStep.call(im) + for slit in im.slits: + _set_photom_kwd(slit) + + yield im + im.close() + + +def test_nirspec_wcs_roundtrip(nirspec_cal): + im = ResampleSpecStep.call(nirspec_cal) for slit in im.slits: x, y = grid_from_bounding_box(slit.meta.wcs.bounding_box) @@ -244,29 +376,31 @@ def test_nirspec_wcs_roundtrip(nirspec_rate): assert_allclose(x, xp, rtol=0, atol=1e-8) assert_allclose(y, yp, rtol=0, atol=3e-4) + im.close() -def test_miri_wcs_roundtrip(miri_rate): - im = AssignWcsStep.call(miri_rate) - _set_photom_kwd(im) - im = ResampleSpecStep.call(im) +def test_nirspec_lamp_wcs_roundtrip(nirspec_lamp): + im = ResampleSpecStep.call(nirspec_lamp) + + for slit in im.slits: + x, y = grid_from_bounding_box(slit.meta.wcs.bounding_box) + ra, dec, lam = slit.meta.wcs(x, y) + xp, yp = slit.meta.wcs.invert(ra, dec, lam) + + assert_allclose(x, xp, rtol=0, atol=1e-8) + assert_allclose(y, yp, rtol=0, atol=3e-4) + im.close() + +def test_miri_wcs_roundtrip(miri_cal): + im = ResampleSpecStep.call(miri_cal) x, y = grid_from_bounding_box(im.meta.wcs.bounding_box) ra, dec, lam = im.meta.wcs(x, y) xp, yp = im.meta.wcs.invert(ra, dec, lam) assert_allclose(x, xp, atol=1e-8) assert_allclose(y, yp, atol=1e-8) - - -@pytest.mark.parametrize("ratio", [0.5, 0.7, 1.0]) -def test_pixel_scale_ratio_spec(miri_rate, ratio): - im = AssignWcsStep.call(miri_rate, sip_approx=False) - _set_photom_kwd(im) - result1 = ResampleSpecStep.call(im) - result2 = ResampleSpecStep.call(im, pixel_scale_ratio=ratio) - - assert_allclose(np.array(result1.data.shape), np.array(result2.data.shape) * ratio, rtol=1, atol=1) + im.close() @pytest.mark.parametrize("ratio", [0.5, 0.7, 1.0]) @@ -293,6 +427,129 @@ def test_pixel_scale_ratio_imaging(nircam_rate, ratio): assert result1.meta.resample.pixel_scale_ratio == 1.0 assert result2.meta.resample.pixel_scale_ratio == ratio + im.close() + result1.close() + result2.close() + + +@pytest.mark.parametrize("units", ["MJy", "MJy/sr"]) +@pytest.mark.parametrize("ratio", [0.7, 1.0, 1.3]) +def test_pixel_scale_ratio_spec_miri(miri_cal, ratio, units): + miri_cal.meta.bunit_data = units + + # Make an input pixel scale equivalent to the specified ratio + input_scale = compute_spectral_pixel_scale(miri_cal.meta.wcs, disp_axis=2) + pscale = 3600.0 * input_scale / ratio + + result1 = ResampleSpecStep.call(miri_cal) + result2 = ResampleSpecStep.call(miri_cal, pixel_scale_ratio=ratio) + result3 = ResampleSpecStep.call(miri_cal, pixel_scale=pscale) + + # pixel_scale and pixel_scale_ratio should be equivalent + nn = np.isnan(result2.data) | np.isnan(result3.data) + assert np.allclose(result2.data[~nn], result3.data[~nn]) + + # Check result2 for expected results + + # wavelength size does not change + assert result1.data.shape[0] == result2.data.shape[0] + + # spatial dimension is scaled + assert np.isclose(result1.data.shape[1], result2.data.shape[1] / ratio, atol=1) + + # data is non-trivial + assert np.nansum(result1.data) > 0.0 + assert np.nansum(result2.data) > 0.0 + + # flux is conserved + if 'sr' not in units: + # flux density conservation: sum over pixels in each row + # needs to be about the same, other than the edges + # Check the maximum sums, to avoid edges. + assert np.allclose(np.max(np.nansum(result1.data, axis=1)), + np.max(np.nansum(result1.data, axis=1)), rtol=0.05) + else: + # surface brightness conservation: mean values are the same + assert np.allclose(np.nanmean(result1.data, axis=1), + np.nanmean(result2.data, axis=1), rtol=0.05, + equal_nan=True) + + # output area is updated either way + area1 = result1.meta.photometry.pixelarea_steradians + area2 = result2.meta.photometry.pixelarea_steradians + area3 = result2.meta.photometry.pixelarea_steradians + assert np.isclose(area1 / area2, ratio) + assert np.isclose(area1 / area3, ratio) + + assert result1.meta.resample.pixel_scale_ratio == 1.0 + assert result2.meta.resample.pixel_scale_ratio == ratio + assert np.isclose(result3.meta.resample.pixel_scale_ratio, ratio) + + result1.close() + result2.close() + result3.close() + + +@pytest.mark.parametrize("units", ["MJy", "MJy/sr"]) +@pytest.mark.parametrize("ratio", [0.7, 1.0, 1.3]) +def test_pixel_scale_ratio_spec_nirspec(nirspec_cal, ratio, units): + for slit in nirspec_cal.slits: + slit.meta.bunit_data = units + + # Make an input pixel scale equivalent to the specified ratio + input_scale = compute_spectral_pixel_scale( + nirspec_cal.slits[0].meta.wcs, disp_axis=1) + pscale = 3600.0 * input_scale / ratio + + result1 = ResampleSpecStep.call(nirspec_cal) + result2 = ResampleSpecStep.call(nirspec_cal, pixel_scale_ratio=ratio) + result3 = ResampleSpecStep.call(nirspec_cal, pixel_scale=pscale) + + for slit1, slit2, slit3 in zip(result1.slits, result2.slits, result3.slits): + # pixel_scale and pixel_scale_ratio should be equivalent + nn = np.isnan(slit2.data) | np.isnan(slit3.data) + assert np.allclose(slit2.data[~nn], slit3.data[~nn]) + + # Check result2 for expected results + + # wavelength size does not change + assert slit1.data.shape[1] == slit2.data.shape[1] + + # spatial dimension is scaled + assert np.isclose(slit1.data.shape[0], slit2.data.shape[0] / ratio, atol=1) + + # data is non-trivial + assert np.nansum(slit1.data) > 0.0 + assert np.nansum(slit2.data) > 0.0 + + # flux is conserved + if 'sr' not in units: + # flux density conservation: sum over pixels in each column + # needs to be about the same, other than edge effects. + # Check the maximum sums, to avoid edges. + assert np.allclose(np.max(np.nansum(slit1.data, axis=0)), + np.max(np.nansum(slit2.data, axis=0)), rtol=0.05) + else: + # surface brightness conservation: mean values are the same + assert np.allclose(np.nanmean(slit1.data, axis=0), + np.nanmean(slit2.data, axis=0), rtol=0.05, + equal_nan=True) + + # output area is updated either way + area1 = slit1.meta.photometry.pixelarea_steradians + area2 = slit2.meta.photometry.pixelarea_steradians + area3 = slit3.meta.photometry.pixelarea_steradians + assert np.isclose(area1 / area2, ratio) + assert np.isclose(area1 / area3, ratio) + + assert result1.meta.resample.pixel_scale_ratio == 1.0 + assert result2.meta.resample.pixel_scale_ratio == ratio + assert np.isclose(result3.meta.resample.pixel_scale_ratio, ratio) + + result1.close() + result2.close() + result3.close() + def test_weight_type(nircam_rate, tmp_cwd): """Check that weight_type of exptime and ivm work""" @@ -336,6 +593,13 @@ def test_weight_type(nircam_rate, tmp_cwd): assert_allclose(result3.data[100:105, 100:105], 6.667, rtol=1e-2) assert_allclose(result3.wht[100:105, 100:105], expectation_value * expected_ratio, rtol=1e-2) + im1.close() + im2.close() + im3.close() + result1.close() + result2.close() + result3.close() + def test_sip_coeffs_do_not_propagate(nircam_rate): im = AssignWcsStep.call(nircam_rate, sip_degree=2) @@ -357,70 +621,8 @@ def test_sip_coeffs_do_not_propagate(nircam_rate): # Make sure we have a PC matrix assert result.meta.wcsinfo.pc1_1 is not None - -@pytest.fixture -def miri_rate_zero_crossing(): - xsize = 1032 - ysize = 1024 - shape = (ysize, xsize) - im = ImageModel(shape) - im.var_rnoise = np.random.random(shape) - im.meta.wcsinfo = { - 'dec_ref': 2.16444343946559e-05, - 'ra_ref': -0.00026031780056776, - 'roll_ref': 0.0, - 'v2_ref': -415.0690466121227, - 'v3_ref': -400.575920398547, - 'v3yangle': 0.0, - 'vparity': -1} - im.meta.instrument = { - 'detector': 'MIRIMAGE', - 'filter': 'P750L', - 'name': 'MIRI'} - im.meta.observation = { - 'date': '2019-01-01', - 'time': '17:00:00'} - im.meta.subarray = { - 'fastaxis': 1, - 'name': 'FULL', - 'slowaxis': 2, - 'xsize': xsize, - 'xstart': 1, - 'ysize': ysize, - 'ystart': 1} - im.meta.exposure = { - 'duration': 11.805952, - 'end_time': 58119.85416, - 'exposure_time': 11.776, - 'frame_time': 0.11776, - 'group_time': 0.11776, - 'groupgap': 0, - 'integration_time': 11.776, - 'nframes': 1, - 'ngroups': 100, - 'nints': 1, - 'nresets_between_ints': 0, - 'nsamples': 1, - 'readpatt': 'FAST', - 'sample_time': 10.0, - 'start_time': 58119.8333, - 'type': 'MIR_LRS-FIXEDSLIT', - 'zero_frame': False} - - return im - - -@pytest.fixture -def miri_rate_pair(miri_rate_zero_crossing): - im1 = miri_rate_zero_crossing - # Create a nodded version - im2 = im1.copy() - im2.meta.wcsinfo.ra_ref = 0.00026308279776455 - im2.meta.wcsinfo.dec_ref = -2.1860888891293e-05 - im1 = AssignWcsStep.call(im1) - im2 = AssignWcsStep.call(im2) - - return im1, im2 + im.close() + result.close() def test_build_interpolated_output_wcs(miri_rate_pair): @@ -434,15 +636,58 @@ def test_build_interpolated_output_wcs(miri_rate_pair): grid = grid_from_bounding_box(im2.meta.wcs.bounding_box) ra, dec, lam = im2.meta.wcs(*grid) x, y = output_wcs.invert(ra, dec, lam) - - # This currently fails, as we see a slight offset - # assert (x > 0).all() + nn = ~(np.isnan(x) | np.isnan(y)) + assert np.sum(nn) > 0 + assert np.all(x[nn] > -1) # Make sure the output slit size is larger than the input slit size # for this nodded data assert output_wcs.array_shape[1] > ra.shape[1] +def test_build_nirspec_output_wcs(nirspec_cal_pair): + im1, im2 = nirspec_cal_pair + containers = multislit_to_container([im1, im2]) + driz = ResampleSpecData(containers['1']) + output_wcs = driz.build_nirspec_output_wcs() + + # Make sure that all slit values in the input images have a + # location in the output frame, in both RA/Dec and slit units + output_s2d = output_wcs.get_transform('slit_frame', 'detector') + for im in [im1, im2]: + grid = grid_from_bounding_box(im.slits[0].meta.wcs.bounding_box) + + # check slit values + input_d2s = im.slits[0].meta.wcs.get_transform('detector', 'slit_frame') + sx, sy, lam = input_d2s(*grid) + x, y = output_s2d(np.full_like(sy, 0), sy, lam * 1e6) + nn = ~(np.isnan(x) | np.isnan(y)) + assert np.sum(nn) > 0 + assert np.all(y[nn] > -1) + + # check RA, Dec, lam + ra, dec, lam = im.slits[0].meta.wcs(*grid) + x, y = output_wcs.invert(ra, dec, lam) + nn = ~(np.isnan(x) | np.isnan(y)) + assert np.sum(nn) > 0 + assert np.all(y[nn] > -1) + + # Make a WCS for each input individually + containers = multislit_to_container([im1]) + driz = ResampleSpecData(containers['1']) + compare_wcs_1 = driz.build_nirspec_output_wcs() + + containers = multislit_to_container([im2]) + driz = ResampleSpecData(containers['1']) + compare_wcs_2 = driz.build_nirspec_output_wcs() + + # The output shape should be the larger of the two + assert output_wcs.array_shape[0] == max( + compare_wcs_1.array_shape[0], compare_wcs_2.array_shape[0]) + assert output_wcs.array_shape[1] == max( + compare_wcs_1.array_shape[1], compare_wcs_2.array_shape[1]) + + def test_wcs_keywords(nircam_rate): """Make sure certain wcs keywords are removed after resample """ @@ -457,6 +702,9 @@ def test_wcs_keywords(nircam_rate): assert result.meta.wcsinfo.v3yangle is None assert result.meta.wcsinfo.vparity is None + im.close() + result.close() + @pytest.mark.parametrize("n_images,weight_type", [(1, 'ivm'), (2, 'ivm'), (3, 'ivm'), (9, 'ivm'), @@ -484,6 +732,9 @@ def test_resample_variance(nircam_rate, n_images, weight_type): assert_allclose(result.var_rnoise[5:-5, 5:-5].mean(), var_rnoise / n_images, atol=1e-7) assert_allclose(result.var_poisson[5:-5, 5:-5].mean(), var_poisson / n_images, atol=1e-7) + im.close() + result.close() + @pytest.mark.parametrize("shape", [(0, ), (10, 1)]) def test_resample_undefined_variance(nircam_rate, shape): @@ -504,6 +755,9 @@ def test_resample_undefined_variance(nircam_rate, shape): assert_allclose(result.var_poisson, np.nan) assert_allclose(result.var_flat, np.nan) + im.close() + result.close() + @pytest.mark.parametrize('ratio', [0.7, 1.2]) @pytest.mark.parametrize('rotation', [0, 15, 135]) @@ -541,6 +795,9 @@ def test_custom_wcs_resample_imaging(nircam_rate, ratio, rotation, crpix, crval, # test output image shape assert result.data.shape == shape[::-1] + im.close() + result.close() + @pytest.mark.parametrize( 'output_shape2, match', @@ -604,6 +861,105 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, assert np.isclose(input_mean * iscale**2, output_mean_1, atol=1e-4) assert np.isclose(input_mean * iscale**2, output_mean_2, atol=1e-4) + im.close() + result.close() + + +@pytest.mark.parametrize('ratio', [0.7, 1.0, 1.3]) +def test_custom_refwcs_resample_miri(miri_cal, tmp_path, ratio): + im = miri_cal + miri_cal.meta.bunit_data = "MJy" + + # mock a spectrum by giving the first slit some random + # values at the center + rng = np.random.default_rng(seed=77) + new_values = rng.random(im.data.shape) + + center = im.data.shape[1] // 2 + im.data[:] = 0.0 + im.data[:, center - 2:center + 2] = new_values[:, center - 2:center + 2] + + # first pass: create a reference output WCS with a custom pixel scale + result = ResampleSpecStep.call(im, pixel_scale_ratio=ratio) + + # make sure results are nontrivial + data1 = result.data + assert not np.all(np.isnan(data1)) + + # save the wcs from the output + refwcs = str(tmp_path / "resample_refwcs.asdf") + asdf.AsdfFile({"wcs": result.meta.wcs}).write_to(refwcs) + + # run again, this time using the created WCS as input + result = ResampleSpecStep.call(im, output_wcs=refwcs) + data2 = result.data + assert not np.all(np.isnan(data2)) + + # check output data against first pass + assert data1.shape == data2.shape + assert np.allclose(data1, data2, equal_nan=True, rtol=1e-4) + + # make sure flux is conserved: sum over spatial dimension + # should be same in input and output + # (assuming inputs are in flux density units) + input_sum = np.nanmean(np.nansum(im.data, axis=1)) + output_sum_1 = np.nanmean(np.nansum(data1, axis=1)) + output_sum_2 = np.nanmean(np.nansum(data2, axis=1)) + assert np.allclose(input_sum, output_sum_1, rtol=0.005) + assert np.allclose(input_sum, output_sum_2, rtol=0.005) + + im.close() + result.close() + + +@pytest.mark.parametrize('ratio', [0.7, 1.0, 1.3]) +def test_custom_refwcs_resample_nirspec(nirspec_cal, tmp_path, ratio): + im = nirspec_cal + for slit in im.slits: + slit.meta.bunit_data = "MJy" + + # mock a spectrum by giving the first slit some random + # values at the center + rng = np.random.default_rng(seed=77) + new_values = rng.random(im.slits[0].data.shape) + + center = im.slits[0].data.shape[0] // 2 + im.slits[0].data[:] = 0.0 + im.slits[0].data[center - 2:center + 2, :] = new_values[center - 2:center + 2, :] + + # first pass: create a reference output WCS with a custom pixel scale + result = ResampleSpecStep.call(im, pixel_scale_ratio=ratio) + + # make sure results are nontrivial + data1 = result.slits[0].data + assert not np.all(np.isnan(data1)) + + # save the wcs from the output + refwcs = str(tmp_path / "resample_refwcs.asdf") + asdf.AsdfFile({"wcs": result.slits[0].meta.wcs}).write_to(tmp_path / refwcs) + + # run again, this time using the created WCS as input + result = ResampleSpecStep.call(im, output_wcs=refwcs) + + data2 = result.slits[0].data + assert not np.all(np.isnan(data2)) + + # check output data against first pass + assert data1.shape == data2.shape + assert np.allclose(data1, data2, equal_nan=True, rtol=1e-4) + + # make sure flux is conserved: sum over spatial dimension + # should be same in input and output + # (assuming inputs are in flux density units) + input_sum = np.nanmean(np.nansum(im.slits[0].data, axis=0)) + output_sum_1 = np.nanmean(np.nansum(data1, axis=0)) + output_sum_2 = np.nanmean(np.nansum(data2, axis=0)) + assert np.allclose(input_sum, output_sum_1, rtol=0.005) + assert np.allclose(input_sum, output_sum_2, rtol=0.005) + + im.close() + result.close() + @pytest.mark.parametrize('ratio', [1.3, 1]) def test_custom_wcs_pscale_resample_imaging(nircam_rate, ratio): @@ -622,6 +978,46 @@ def test_custom_wcs_pscale_resample_imaging(nircam_rate, ratio): # test scales are close assert np.allclose(output_scale, input_scale * 0.75) + im.close() + result.close() + + +@pytest.mark.parametrize('ratio', [1.3, 1]) +def test_custom_wcs_pscale_resample_miri(miri_cal, ratio): + im = miri_cal + + # pass both ratio and direct scale: ratio is ignored in favor of scale + input_scale = compute_spectral_pixel_scale(im.meta.wcs, disp_axis=2) + result = ResampleSpecStep.call( + im, + pixel_scale_ratio=ratio, + pixel_scale=3600 * input_scale * 0.75 + ) + output_scale = compute_spectral_pixel_scale(result.meta.wcs, disp_axis=2) + + # test scales are close to scale specified, regardless of ratio + assert np.allclose(output_scale, input_scale * 0.75) + + result.close() + +@pytest.mark.parametrize('ratio', [1.3, 1]) +def test_custom_wcs_pscale_resample_nirspec(nirspec_cal, ratio): + im = nirspec_cal.slits[0] + + # pass both ratio and direct scale: ratio is ignored in favor of scale + input_scale = compute_spectral_pixel_scale(im.meta.wcs, disp_axis=1) + result = ResampleSpecStep.call( + nirspec_cal, + pixel_scale_ratio=ratio, + pixel_scale=3600 * input_scale * 0.75 + ) + output_scale = compute_spectral_pixel_scale(result.slits[0].meta.wcs, disp_axis=1) + + # test scales are close to scale specified, regardless of ratio + assert np.allclose(output_scale, input_scale * 0.75) + + result.close() + def test_pixscale(nircam_rate): @@ -643,6 +1039,9 @@ def test_pixscale(nircam_rate): res = ResampleStep.call(im, pixel_scale_ratio=0.7) assert res.meta.resample.pixel_scale_ratio == 0.7 + im.close() + res.close() + def test_phot_keywords(nircam_rate): # test that resample keywords agree with photometry keywords after step is run @@ -675,3 +1074,84 @@ def test_phot_keywords(nircam_rate): atol=0, rtol=1e-12 ) + + im.close() + res.close() + + +def test_missing_nominal_area(miri_cal, tmp_path): + # remove nominal pixel area + miri_cal.meta.photometry.pixelarea_steradians = None + miri_cal.meta.photometry.pixelarea_arcsecsq = None + + # result should still process okay + result = ResampleSpecStep.call(miri_cal) + + # no area keywords in output + assert result.meta.photometry.pixelarea_steradians is None + assert result.meta.photometry.pixelarea_arcsecsq is None + + # direct pixel scale setting is not supported + result2 = ResampleSpecStep.call(miri_cal, pixel_scale=0.5) + assert np.allclose(result2.data, result.data, equal_nan=True) + assert result2.meta.resample.pixel_scale_ratio == 1.0 + + # setting pixel_scale_ratio is still allowed, + # output area is still None + result3 = ResampleSpecStep.call(miri_cal, pixel_scale_ratio=0.5) + assert result3.data.shape[0] == result.data.shape[0] + assert result3.data.shape[1] < result.data.shape[1] + assert result3.meta.resample.pixel_scale_ratio == 0.5 + assert result3.meta.photometry.pixelarea_steradians is None + assert result3.meta.photometry.pixelarea_arcsecsq is None + + # specifying a custom WCS without nominal area sets output pixel + # scale ratio to 1, since direct scale cannot be computed + # save the wcs from the output + refwcs = str(tmp_path / "resample_refwcs.asdf") + asdf.AsdfFile({"wcs": result3.meta.wcs}).write_to(refwcs) + result4 = ResampleSpecStep.call(miri_cal, output_wcs=refwcs) + assert result4.data.shape == result3.data.shape + assert result4.meta.resample.pixel_scale_ratio == 1.0 + assert result4.meta.photometry.pixelarea_steradians is None + assert result4.meta.photometry.pixelarea_arcsecsq is None + + result.close() + result2.close() + result3.close() + result4.close() + + +def test_nirspec_lamp_pixscale(nirspec_lamp, tmp_path): + result = ResampleSpecStep.call(nirspec_lamp) + + # output data should have the same wavelength size, + # spatial size is close + assert np.isclose(result.slits[0].data.shape[0], + nirspec_lamp.slits[0].data.shape[0], atol=5) + assert (result.slits[0].data.shape[1] + == nirspec_lamp.slits[0].data.shape[1]) + + # test pixel scale setting: will not work without sky-based WCS + result2 = ResampleSpecStep.call(nirspec_lamp, pixel_scale=0.5) + assert np.allclose(result2.slits[0].data, result.slits[0].data, equal_nan=True) + assert result2.meta.resample.pixel_scale_ratio == 1.0 + + # setting pixel_scale_ratio is still allowed + result3 = ResampleSpecStep.call(nirspec_lamp, pixel_scale_ratio=0.5) + assert result3.slits[0].data.shape[0] < result.slits[0].data.shape[0] + assert result3.slits[0].data.shape[1] == result.slits[0].data.shape[1] + assert result3.meta.resample.pixel_scale_ratio == 0.5 + + # specifying a custom WCS should work, but output ratio is 1.0, + # since output scale cannot be determined + refwcs = str(tmp_path / "resample_refwcs.asdf") + asdf.AsdfFile({"wcs": result3.slits[0].meta.wcs}).write_to(refwcs) + result4 = ResampleSpecStep.call(nirspec_lamp, output_wcs=refwcs) + assert result4.slits[0].data.shape == result3.slits[0].data.shape + assert result4.meta.resample.pixel_scale_ratio == 1.0 + + result.close() + result2.close() + result3.close() + result4.close() diff --git a/jwst/resample/tests/test_utils.py b/jwst/resample/tests/test_utils.py index af41f9730b..7e7deb3070 100644 --- a/jwst/resample/tests/test_utils.py +++ b/jwst/resample/tests/test_utils.py @@ -15,6 +15,7 @@ build_mask, build_driz_weight, decode_context, + is_flux_density, reproject ) @@ -202,3 +203,11 @@ def test_reproject(wcs1, wcs2, offset, request): def test_reproject_with_garbage_input(): with pytest.raises(TypeError): reproject("foo", "bar") + + +@pytest.mark.parametrize('unit,result', + [('Jy', True), ('MJy', True), + ('MJy/sr', False), ('DN/s', False), + ('bad_unit', False), (None, False)]) +def test_is_flux_density(unit, result): + assert is_flux_density(unit) is result From d5e129159951a4b4eadb3f81f274b4f90d2c11d1 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:38:12 -0400 Subject: [PATCH 255/687] remove unused _convert_inputs --- jwst/outlier_detection/utils.py | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 86879340ec..24313aa20e 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -5,8 +5,6 @@ import numpy as np -from jwst.datamodels import ModelContainer -from jwst.resample.resample_utils import build_driz_weight from jwst.resample.resample import compute_image_pixel_area from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_crs, flag_resampled_crs from stdatamodels.jwst import datamodels @@ -177,32 +175,3 @@ def flag_model_crs(image, blot, snr): # update dq array in-place image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") - - -def _convert_inputs(inputs, good_bits, weight_type): - """Convert input into datamodel required for processing. - - This base class works on imaging data, and relies on use of the - ModelContainer class as the format needed for processing. However, - the input may not always be a ModelContainer object, so this method - will convert the input to a ModelContainer object for processing. - Additionally, sub-classes may redefine this to set up the input as - whatever format the sub-class needs for processing. - - """ - if isinstance(inputs, ModelContainer): - return inputs - input_models = ModelContainer() - num_inputs = inputs.data.shape[0] - log.debug("Converting CubeModel to ModelContainer with {} images". - format(num_inputs)) - for i in range(inputs.data.shape[0]): - image = datamodels.ImageModel(data=inputs.data[i], - err=inputs.err[i], - dq=inputs.dq[i]) - image.meta = inputs.meta - image.wht = build_driz_weight(image, - weight_type=weight_type, - good_bits=good_bits) - input_models.append(image) - return input_models From 6f0ea390c524664042c0b3470c1862021b5bf977 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 08:54:08 -0400 Subject: [PATCH 256/687] remove resample_data boolean argument --- jwst/outlier_detection/imaging.py | 26 +++++++------- jwst/outlier_detection/spec.py | 26 +++++++------- .../tests/test_outlier_detection.py | 1 - jwst/outlier_detection/utils.py | 36 ++++++++++--------- 4 files changed, 48 insertions(+), 41 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index f75685eb65..c6ba99da13 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -13,7 +13,7 @@ from jwst.resample.resample_utils import build_driz_weight from jwst.stpipe.utilities import record_step_status -from .utils import create_median, flag_crs_in_models +from .utils import create_median, flag_crs_in_models, flag_crs_in_models_with_resampling from ._fileio import remove_file, save_median log = logging.getLogger(__name__) @@ -111,15 +111,17 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - flag_crs_in_models( - input_models, - median_data, - median_wcs, - snr1, - snr2, - scale1, - scale2, - backg, - resample_data, - ) + if resample_data: + flag_crs_in_models_with_resampling( + input_models, + median_data, + median_wcs, + snr1, + snr2, + scale1, + scale2, + backg, + ) + else: + flag_crs_in_models(input_models, median_data, snr1) return input_models diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 3962304ff3..7f35733aa6 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -9,7 +9,7 @@ from jwst.stpipe.utilities import record_step_status from ..resample import resample_spec, resample_utils -from .utils import create_median, flag_crs_in_models +from .utils import create_median, flag_crs_in_models, flag_crs_in_models_with_resampling from ._fileio import remove_file import logging @@ -116,15 +116,17 @@ def detect_outliers( # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image - flag_crs_in_models( - input_models, - median_data, - median_wcs, - snr1, - snr2, - scale1, - scale2, - backg, - resample_data, - ) + if resample_data: + flag_crs_in_models_with_resampling( + input_models, + median_data, + median_wcs, + snr1, + snr2, + scale1, + scale2, + backg, + ) + else: + flag_crs_in_models(input_models, median_data, snr1) return input_models diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 5683021516..622fc791ff 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -83,7 +83,6 @@ def test_flag_cr(sci_blot_image_pair): 1.2, 0.7, 0, - True, ) # Make sure science data array is unchanged after flag_cr() diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 24313aa20e..f8c6cc1333 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -110,6 +110,16 @@ def compute_weight_threshold_container(resampled_models, maskpt): def flag_crs_in_models( + input_models, + median_data, + snr1, +): + for image in input_models: + # dq flags will be updated in-place + flag_model_crs(image, median_data, snr1) + + +def flag_crs_in_models_with_resampling( input_models, median_data, median_wcs, @@ -118,25 +128,20 @@ def flag_crs_in_models( scale1, scale2, backg, - resample_data, ): for image in input_models: - if resample_data: - if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: - input_pixflux_area = image.meta.photometry.pixelarea_steradians - # Set array shape, needed to compute image pixel area - image.meta.wcs.array_shape = image.shape - input_pixel_area = compute_image_pixel_area(image.meta.wcs) - pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) - else: - pix_ratio = 1.0 - - blot = gwcs_blot(median_data, median_wcs, image.data.shape, image.meta.wcs, pix_ratio) + if 'SPECTRAL' not in image.meta.wcs.output_frame.axes_type: + input_pixflux_area = image.meta.photometry.pixelarea_steradians + # Set array shape, needed to compute image pixel area + image.meta.wcs.array_shape = image.shape + input_pixel_area = compute_image_pixel_area(image.meta.wcs) + pix_ratio = np.sqrt(input_pixflux_area / input_pixel_area) else: - blot = median_data + pix_ratio = 1.0 + blot = gwcs_blot(median_data, median_wcs, image.data.shape, image.meta.wcs, pix_ratio) # dq flags will be updated in-place - flag_resampled_model_crs(image, blot, snr1, snr2, scale1, scale2, backg, resample_data) + flag_resampled_model_crs(image, blot, snr1, snr2, scale1, scale2, backg) def flag_resampled_model_crs( @@ -147,7 +152,6 @@ def flag_resampled_model_crs( scale1, scale2, backg, - resample_data, ): """ Flag crs in image based on a resampled (and blotted) data (blot). @@ -162,7 +166,7 @@ def flag_resampled_model_crs( backg = image.meta.background.level log.debug(f"Adding background level {backg} to blotted image") - cr_mask = flag_resampled_crs(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg, resample_data) + cr_mask = flag_resampled_crs(image.data, image.err, blot, snr1, snr2, scale1, scale2, backg) # update the dq flags in-place image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) From 7198d892ddece590371b41d0a83b12656560325b Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 10:35:27 -0400 Subject: [PATCH 257/687] fix errors introduced with merge --- jwst/resample/resample_spec.py | 6 +++--- jwst/resample/tests/test_resample_step.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 4453127efc..f3f6414011 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -161,11 +161,11 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, # Any other customizations (crpix, crval, rotation) are ignored. if resample_utils.is_sky_like(input_models[0].meta.wcs.output_frame): if input_models[0].meta.instrument.name != "NIRSPEC": - self.output_wcs = self.build_interpolated_output_wcs() + self.output_wcs = self.build_interpolated_output_wcs(input_models) else: self.output_wcs = self.build_nirspec_output_wcs(input_models) else: - self.output_wcs = self.build_nirspec_lamp_output_wcs() + self.output_wcs = self.build_nirspec_lamp_output_wcs(input_models) # Use the nominal output pixel area in sr if available, # scaling for user-set pixel_scale ratio if needed. @@ -480,7 +480,7 @@ def _max_spatial_extent(self, wcs_list, transform, swap_xy): return min_tan_all, max_tan_all - def build_interpolated_output_wcs(self): + def build_interpolated_output_wcs(self, input_models): """ Create a spatial/spectral WCS output frame using all the input models. diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index ddaf721741..7f8c479f99 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -649,7 +649,7 @@ def test_build_nirspec_output_wcs(nirspec_cal_pair): im1, im2 = nirspec_cal_pair containers = multislit_to_container([im1, im2]) driz = ResampleSpecData(containers['1']) - output_wcs = driz.build_nirspec_output_wcs() + output_wcs = driz.build_nirspec_output_wcs(containers['1']) # Make sure that all slit values in the input images have a # location in the output frame, in both RA/Dec and slit units @@ -675,11 +675,11 @@ def test_build_nirspec_output_wcs(nirspec_cal_pair): # Make a WCS for each input individually containers = multislit_to_container([im1]) driz = ResampleSpecData(containers['1']) - compare_wcs_1 = driz.build_nirspec_output_wcs() + compare_wcs_1 = driz.build_nirspec_output_wcs(containers['1']) containers = multislit_to_container([im2]) driz = ResampleSpecData(containers['1']) - compare_wcs_2 = driz.build_nirspec_output_wcs() + compare_wcs_2 = driz.build_nirspec_output_wcs(containers['1']) # The output shape should be the larger of the two assert output_wcs.array_shape[0] == max( From f97dc68227f99bbd1b46d8a0e548a88396ba863f Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Fri, 26 Jul 2024 15:58:51 -0400 Subject: [PATCH 258/687] [SCSB-155] build with Numpy 2.0 (#8527) --- CHANGES.rst | 2 ++ pyproject.toml | 8 +++++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 1ade3e9c42..e56a5e119e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -271,6 +271,8 @@ general - Increase minimum required stdatamodels and stpipe. [#8592] +- build with Numpy 2.0 release candidate to address ABI changes [#8527] + lib --- diff --git a/pyproject.toml b/pyproject.toml index 06fa22799f..aa74812f77 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -123,7 +123,7 @@ requires = [ "setuptools>=61.2", "setuptools_scm[toml]>=3.4", "wheel", - "oldest-supported-numpy", + "numpy>=2.0", ] build-backend = "setuptools.build_meta" @@ -208,7 +208,8 @@ results_root = "jwst-pipeline-results" text_file_format = "rst" doctest_plus = "enabled" doctest_rst = "enabled" -addopts = ["-p no:legacypath", +addopts = [ + "-p no:legacypath", "--show-capture=no", "--report-crds-context", ] @@ -260,11 +261,12 @@ exclude = [ ".eggs", "build", ] +line-length = 130 + lint.ignore = [ "E741", # ambiguous variable name (O/0, l/I, etc.) "E722", # Do not use bare `except` ] -line-length = 130 [tool.ruff.lint.per-file-ignores] "jwst/**/__init__.py" = [ From 9134db066805a80dcba7e462dd8c9e931de538e1 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 12:12:32 -0400 Subject: [PATCH 259/687] fix bug in AmiAverageStep spec --- jwst/ami/ami_average_step.py | 2 +- jwst/ami/tests/test_ami_average.py | 10 ++++++++++ 2 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 jwst/ami/tests/test_ami_average.py diff --git a/jwst/ami/ami_average_step.py b/jwst/ami/ami_average_step.py index 231c9453a3..60fc003ac8 100755 --- a/jwst/ami/ami_average_step.py +++ b/jwst/ami/ami_average_step.py @@ -12,7 +12,7 @@ class AmiAverageStep(Step): class_alias = "ami_average" spec = """ - skip = True # Do not run this step + skip = boolean(default=True) # Do not run this step """ def flatten_input(self, input_items): diff --git a/jwst/ami/tests/test_ami_average.py b/jwst/ami/tests/test_ami_average.py new file mode 100644 index 0000000000..5fc9eb47d9 --- /dev/null +++ b/jwst/ami/tests/test_ami_average.py @@ -0,0 +1,10 @@ +from jwst.ami.ami_average_step import AmiAverageStep + + +def test_step_init(): + """ + Just a simple smoke test to make sure the step + can be created with the default spec. + """ + step = AmiAverageStep() + assert step From cf917245051c35d81fb704fc62b89c4da7174c7c Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 26 Jul 2024 12:16:24 -0400 Subject: [PATCH 260/687] add changelog --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index e56a5e119e..4625345faf 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ 1.15.2 (unreleased) =================== +ami_average +----------- + +- Fix error in step spec that prevents step creation. [#8677] + assign_wcs ---------- From 351157e43fe56eaf5ae10c27672c1160fc3a3868 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 30 Jul 2024 12:56:45 -0400 Subject: [PATCH 261/687] set stcal version to main --- pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 460dcd6f15..b9e7a1b224 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,8 +35,7 @@ dependencies = [ "scipy>=1.9.3", "spherical-geometry>=1.2.22", # "stcal>=1.7.3,<1.8.0", - # FIXME: switch to main (and then released) when stcal is updated - "stcal @ git+https://github.com/braingram/stcal.git@outlier_detection", + "stcal @ git+https://github.com/spacetelescope/stcal.git", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", "stsci.image>=2.3.5", From 9891e10368fd9c938aa50c19a1e8c0c6be01f0be Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 2 Aug 2024 11:41:40 -0400 Subject: [PATCH 262/687] Reset 'seen' flag in log watcher after check --- jwst/tests/helpers.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jwst/tests/helpers.py b/jwst/tests/helpers.py index 6cef376f6b..3536f310de 100644 --- a/jwst/tests/helpers.py +++ b/jwst/tests/helpers.py @@ -59,3 +59,6 @@ def __call__(self, *args, **kwargs): def assert_seen(self): assert self.seen, f"{self.message} not in logs" + + # reset flag after check + self.seen = False From 4c504268216145a7a3181437d7314e2260b8f2b0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 2 Aug 2024 12:37:53 -0400 Subject: [PATCH 263/687] Add to docs for LogWatcher --- jwst/tests/helpers.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/jwst/tests/helpers.py b/jwst/tests/helpers.py index 3536f310de..19e2461d8f 100644 --- a/jwst/tests/helpers.py +++ b/jwst/tests/helpers.py @@ -43,9 +43,17 @@ class LogWatcher: """ The pytest caplog fixture only works for the root logger. We use all sorts of loggers which can lead - to random test failures with caplog. This class - can be monkeypatched onto a logger method to - check for a specific message. + to random test failures with caplog. + + This class can be monkeypatched onto a logger method to + check for a specific message. When the logger method is + called, a `seen` attribute is set in the watcher, + if the calling message matches the expected value. + + The `seen` flag can be checked via `assert_seen`. When + called, this function will reset the `seen` attribute + to False. This allows the same watcher to be reused for + multiple function calls. """ def __init__(self, message): self.seen = False @@ -58,6 +66,10 @@ def __call__(self, *args, **kwargs): self.seen = True def assert_seen(self): + """Check if message has been seen. + + After calling, the `seen` attribute is reset to False. + """ assert self.seen, f"{self.message} not in logs" # reset flag after check From e34f4d844c6fa4d798498cfc17f384f834f43333 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Fri, 2 Aug 2024 16:05:29 -0400 Subject: [PATCH 264/687] replace usage of `copy_arrays=True` with `memmap=False` (#8660) --- CHANGES.rst | 5 +++++ jwst/lib/set_telescope_pointing.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 215b6e447a..5ea557e270 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -40,6 +40,11 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +set_telescope_pointing +---------------------- + +- replace usage of ``copy_arrays=True`` with ``memmap=False`` [#8660] + - Refactored separate modes into submodules instead of inheriting from a base class. Moved non-JWST-specific code to stcal. [#8613] diff --git a/jwst/lib/set_telescope_pointing.py b/jwst/lib/set_telescope_pointing.py index 06dfd587a3..111b415ce3 100644 --- a/jwst/lib/set_telescope_pointing.py +++ b/jwst/lib/set_telescope_pointing.py @@ -357,7 +357,7 @@ def from_asdf(cls, asdf_file): if isinstance(asdf_file, asdf.AsdfFile): transforms = asdf_file.tree['transforms'] else: - with asdf.open(asdf_file, copy_arrays=True, lazy_load=False) as af: + with asdf.open(asdf_file, memmap=False, lazy_load=False) as af: transforms = af.tree['transforms'] return cls(**transforms) From bea07eb7b9b90319f343118eae26888d0aec9cb4 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 2 Jul 2024 10:38:55 -0400 Subject: [PATCH 265/687] Simplify MIRI LRS spec3 regtest --- jwst/regtest/test_miri_lrs_slit_spec3.py | 61 ++---------------------- 1 file changed, 5 insertions(+), 56 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index 04b9912e14..a1b53b3a4a 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -11,68 +11,21 @@ from jwst import datamodels -@pytest.fixture( - scope="module", - params=["default_wcs", "user_wcs", "user_wcs+shape", "user_wcs+shape1"] -) +@pytest.fixture(scope='module') def run_pipeline(rtdata_module, request): """ Run the calwebb_spec3 pipeline on an ASN of nodded MIRI LRS - fixed-slit exposures using different options for the WCS and output - image shape for the resample step. - - The first iteration ("default_wcs") creates an output WCS for the combined - product based on the built-in WCS's in the inputs. - - The second iteration ("user_wcs") creates a user-supplied WCS input file - using the WCS from the default product, just to prove that you get the - identical result when using a user-supplied WCS. - - The third iteration ("user_wcs+shape") uses the same user-supplied WCS and also - specifies the output 2D file shape, using a shape that is identical to the - default. Hence all of the first 3 iterations should produce identical results - and therefore are compared to a single set of truth files. - - The fourth iteration ("user_wcs+shape1") uses the same user-specified WCS and - specifies an output 2D file shape that is 1 pixel larger than the default in - both axes. Hence the resulting s2d product needs a separate (larger) truth file. - Meanwhile, the x1d product from this iteration should still be identical to - the first 3, because the extra row and column of the 2D data file are ignored - during extraction. + fixed-slit exposures. """ rtdata = rtdata_module # Get the spec3 ASN and its members rtdata.get_asn("miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json") - root_file = "jw01530-o005_t004_miri_p750l_" - args = [ "calwebb_spec3", rtdata.input ] - rtdata.custom_wcs_mode = request.param - - if request.param != "default_wcs": - # Get the s2d product that was just created using "default_wcs" - default_s2d = root_file + "s2d.fits" - dm = datamodels.open(default_s2d) - # Create a user-supplied WCS file that is identical to the default WCS - af = asdf.AsdfFile({"wcs": dm.meta.wcs}) - wcs_file = default_s2d[:-8] + 'wcs.asdf' - af.write_to(wcs_file) - args.append(f"--steps.resample_spec.output_wcs={wcs_file}") - - if request.param == "user_wcs+shape": - output_shape = ','.join(map(str, dm.data.shape[::-1])) - args.append(f"--steps.resample_spec.output_shape={output_shape}") - - elif request.param == "user_wcs+shape1": - output_shape = ','.join(map(str, (d + 1 for d in dm.data.shape[::-1]))) - args.append(f"--steps.resample_spec.output_shape={output_shape}") - output_file = root_file + 'shape1.fits' - args.append(f"--steps.resample_spec.output_file={output_file}") - # Run the calwebb_spec3 pipeline; save results from intermediate steps Step.from_cmdline(args) @@ -87,10 +40,7 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg # Run the pipeline and retrieve outputs rtdata = rtdata_module - if rtdata.custom_wcs_mode == 'user_wcs+shape1' and suffix == "s2d": - output = f"jw01530-o005_t004_miri_p750l_shape1_{suffix}.fits" - else: - output = f"jw01530-o005_t004_miri_p750l_{suffix}.fits" + output = f"jw01530-o005_t004_miri_p750l_{suffix}.fits" rtdata.output = output # Get the truth files @@ -108,11 +58,11 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg if isinstance(dmt, datamodels.MultiSlitModel): names = [s.name for s in dmt.slits] for name in names: - st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name == name] w = dmt.slits[st_idx].meta.wcs x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) _, _, wave = w(x, y) - sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name == name] wlr = dmr.slits[sr_idx].wavelength assert np.all(np.isclose(wave, wlr, atol=tolerance)) else: @@ -121,4 +71,3 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg _, _, wave = w(x, y) wlr = dmr.wavelength assert np.all(np.isclose(wave, wlr, atol=tolerance)) - From 66d6b3f0546426e0488eafaa665fd55b5ec9078b Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 2 Jul 2024 12:14:12 -0400 Subject: [PATCH 266/687] Separate ResampleSpecStep from ResampleStep --- jwst/resample/resample_spec_step.py | 47 ++++++++++++++++++++++++-- jwst/resample/resample_step.py | 40 +++------------------- jwst/resample/resample_utils.py | 52 ++++++++++++++++++++++++++++- 3 files changed, 99 insertions(+), 40 deletions(-) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index 1b436c7f2f..2e10b5c218 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -3,10 +3,11 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import MultiSlitModel, ImageModel -from jwst.datamodels import ModelContainer -from . import resample_spec, ResampleStep +from . import resample_spec, resample_utils from ..exp_to_source import multislit_to_container from ..assign_wcs.util import update_s_region_spectral +from ..stpipe import Step +from jwst.datamodels import ModelContainer from jwst.lib.wcs_utils import get_wavelengths @@ -14,7 +15,7 @@ GOOD_BITS = '~DO_NOT_USE+NON_SCIENCE' -class ResampleSpecStep(ResampleStep): +class ResampleSpecStep(Step): """ ResampleSpecStep: Resample input data onto a regular grid using the drizzle algorithm. @@ -27,6 +28,19 @@ class ResampleSpecStep(ResampleStep): class_alias = "resample_spec" + spec = """ + pixfrac = float(min=0.0, max=1.0, default=1.0) # Pixel shrinkage factor + kernel = option('square','gaussian','point','turbo','lanczos2','lanczos3',default='square') # Flux distribution kernel + fillval = string(default='NAN') # Output value for pixels with no weight or flux + weight_type = option('ivm', 'exptime', None, default='ivm') # Input image weighting type + pixel_scale_ratio = float(default=1.0) # Ratio of input to output spatial pixel scale + pixel_scale = float(default=None) # Spatial pixel scale in arcsec + output_wcs = string(default='') # Custom output WCS + single = boolean(default=False) # Resample each input to its own output grid + blendheaders = boolean(default=True) # Blend metadata from inputs into output + in_memory = boolean(default=True) # Keep images in memory + """ + def process(self, input): self.wht_type = self.weight_type input_new = datamodels.open(input) @@ -140,6 +154,33 @@ def _process_multislit(self, input_models): return result + def get_drizpars(self): + """ + Load all drizzle-related parameter values into kwargs list. + """ + # Define the keys pulled from step parameters + kwargs = dict( + pixfrac=self.pixfrac, + kernel=self.kernel, + fillval=self.fillval, + wht_type=self.weight_type, + good_bits=GOOD_BITS, + single=self.single, + blendheaders=self.blendheaders, + in_memory=self.in_memory + ) + + # Custom output WCS parameters + kwargs['output_wcs'] = resample_utils.load_custom_wcs(self.output_wcs) + kwargs['pscale'] = self.pixel_scale + kwargs['pscale_ratio'] = self.pixel_scale_ratio + + # Report values to processing log + for k, v in kwargs.items(): + self.log.debug(' {}={}'.format(k, v)) + + return kwargs + def _process_slit(self, input_models): """ Resample Slit data diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 16046a48ea..f200049aaa 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -8,7 +8,7 @@ from jwst.datamodels import ModelContainer -from . import resample +from . import resample, resample_utils from ..stpipe import Step from ..assign_wcs import util @@ -129,38 +129,6 @@ def _check_list_pars(vals, name, min_vals=None): else: raise ValueError(f"Both '{name}' values must be either None or not None.") - @staticmethod - def _load_custom_wcs(asdf_wcs_file, output_shape): - if not asdf_wcs_file: - return None - - with asdf.open(asdf_wcs_file) as af: - wcs = deepcopy(af.tree["wcs"]) - wcs.pixel_area = af.tree.get("pixel_area", None) - wcs.array_shape = af.tree.get("pixel_shape", None) - wcs.array_shape = af.tree.get("array_shape", None) - - if output_shape is not None: - wcs.array_shape = output_shape[::-1] - wcs.pixel_shape = output_shape - elif wcs.pixel_shape is not None: - wcs.array_shape = wcs.pixel_shape[::-1] - elif wcs.array_shape is not None: - wcs.pixel_shape = wcs.array_shape[::-1] - elif wcs.bounding_box is not None: - wcs.array_shape = tuple( - int(axs[1] + 0.5) - for axs in wcs.bounding_box.bounding_box(order="C") - ) - else: - raise ValueError( - "Step argument 'output_shape' is required when custom WCS " - "does not have neither of 'array_shape', 'pixel_shape', or " - "'bounding_box' attributes set." - ) - - return wcs - def get_drizpars(self): """ Load all drizzle-related parameter values into kwargs list. @@ -174,8 +142,8 @@ def get_drizpars(self): good_bits=GOOD_BITS, single=self.single, blendheaders=self.blendheaders, - allowed_memory = self.allowed_memory, - in_memory = self.in_memory + allowed_memory=self.allowed_memory, + in_memory=self.in_memory ) # Custom output WCS parameters. @@ -184,7 +152,7 @@ def get_drizpars(self): 'output_shape', min_vals=[1, 1] ) - kwargs['output_wcs'] = self._load_custom_wcs( + kwargs['output_wcs'] = resample_utils.load_custom_wcs( self.output_wcs, kwargs['output_shape'] ) diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index 0ba20c62c9..028ae2845b 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -3,8 +3,9 @@ import warnings import numpy as np -from astropy import units as u +import asdf import gwcs +from astropy import units as u from stdatamodels.dqflags import interpret_bit_flags from stdatamodels.jwst.datamodels.dqflags import pixel @@ -352,3 +353,52 @@ def check_for_tmeasure(model): return 0 except AttributeError: return 0 + + +def load_custom_wcs(asdf_wcs_file, output_shape=None): + """ + Load a custom output WCS from an ASDF file. + + Parameters + ---------- + asdf_wcs_file : str + Path to an ASDF file containing a GWCS structure. + output_shape : tuple of int, optional + Array shape for the output data. If not provided, + the custom WCS must specify one of: pixel_shape, + array_shape, or bounding_box. + + Returns + ------- + wcs : WCS + The output WCS to resample into. + """ + if not asdf_wcs_file: + return None + + with asdf.open(asdf_wcs_file) as af: + wcs = deepcopy(af.tree["wcs"]) + wcs.pixel_area = af.tree.get("pixel_area", None) + wcs.array_shape = af.tree.get("pixel_shape", None) + wcs.array_shape = af.tree.get("array_shape", None) + + if output_shape is not None: + wcs.array_shape = output_shape[::-1] + wcs.pixel_shape = output_shape + elif wcs.pixel_shape is not None: + wcs.array_shape = wcs.pixel_shape[::-1] + elif wcs.array_shape is not None: + wcs.pixel_shape = wcs.array_shape[::-1] + elif wcs.bounding_box is not None: + wcs.array_shape = tuple( + int(axs[1] + 0.5) + for axs in wcs.bounding_box.bounding_box(order="C") + ) + else: + raise ValueError( + "Step argument 'output_shape' is required when custom WCS " + "does not have neither of 'array_shape', 'pixel_shape', or " + "'bounding_box' attributes set." + ) + + return wcs From ddf309b22fdaf1bf4d59c52d6edf86a2385d396e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 2 Jul 2024 12:21:39 -0400 Subject: [PATCH 267/687] Remove drizzle kernel options that assume 2D imaging data --- jwst/resample/resample_spec_step.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index 2e10b5c218..7c932c1409 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -30,7 +30,7 @@ class ResampleSpecStep(Step): spec = """ pixfrac = float(min=0.0, max=1.0, default=1.0) # Pixel shrinkage factor - kernel = option('square','gaussian','point','turbo','lanczos2','lanczos3',default='square') # Flux distribution kernel + kernel = option('square', 'point', default='square') # Flux distribution kernel fillval = string(default='NAN') # Output value for pixels with no weight or flux weight_type = option('ivm', 'exptime', None, default='ivm') # Input image weighting type pixel_scale_ratio = float(default=1.0) # Ratio of input to output spatial pixel scale From 5717adb6f46db161cfcb7c40ec12910409ee6d76 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 3 Jul 2024 12:13:27 -0400 Subject: [PATCH 268/687] Update change notes --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 5ea557e270..b0903f553e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -5,6 +5,7 @@ align_refs ---------- - Compute alignment shifts from the first integration of the science exposure only. [#8643] + ami_average ----------- @@ -65,6 +66,10 @@ resample_spec - Fixed a bug resulting in incorrect output slit coordinates for NIRSpec moving targets in the ``calwebb_spec3`` pipeline. [#8596] +- Separate ``resample_spec`` step parameters from ``resample`` step parameters + so that the spectral resampling step only exposes parameters that are appropriate + for spectral data. [#8622] + scripts ------- From ef21550e44d38cddaffd8b46905d23b783462280 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 3 Jul 2024 12:16:55 -0400 Subject: [PATCH 269/687] Code style fixes --- jwst/regtest/test_miri_lrs_slit_spec3.py | 1 - jwst/resample/resample_step.py | 3 --- 2 files changed, 4 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index a1b53b3a4a..0155ac7758 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -4,7 +4,6 @@ import numpy as np from gwcs import wcstools -import asdf from astropy.io.fits.diff import FITSDiff from jwst.stpipe import Step diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index f200049aaa..fd43bfd5b8 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -1,8 +1,5 @@ import logging import re -from copy import deepcopy - -import asdf from stdatamodels.jwst import datamodels From 9f1993209a812f73d6b85bdae460ab783beee42f Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 3 Jul 2024 13:18:37 -0400 Subject: [PATCH 270/687] Restore output_shape handling for resample_spec --- jwst/regtest/test_miri_lrs_slit_spec3.py | 62 +++++++++++++++-- jwst/resample/resample_spec_step.py | 11 ++- jwst/resample/resample_step.py | 87 ++++++++++++++++++++++-- jwst/resample/resample_utils.py | 52 +------------- 4 files changed, 148 insertions(+), 64 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_slit_spec3.py b/jwst/regtest/test_miri_lrs_slit_spec3.py index 0155ac7758..04b9912e14 100644 --- a/jwst/regtest/test_miri_lrs_slit_spec3.py +++ b/jwst/regtest/test_miri_lrs_slit_spec3.py @@ -4,27 +4,75 @@ import numpy as np from gwcs import wcstools +import asdf from astropy.io.fits.diff import FITSDiff from jwst.stpipe import Step from jwst import datamodels -@pytest.fixture(scope='module') +@pytest.fixture( + scope="module", + params=["default_wcs", "user_wcs", "user_wcs+shape", "user_wcs+shape1"] +) def run_pipeline(rtdata_module, request): """ Run the calwebb_spec3 pipeline on an ASN of nodded MIRI LRS - fixed-slit exposures. + fixed-slit exposures using different options for the WCS and output + image shape for the resample step. + + The first iteration ("default_wcs") creates an output WCS for the combined + product based on the built-in WCS's in the inputs. + + The second iteration ("user_wcs") creates a user-supplied WCS input file + using the WCS from the default product, just to prove that you get the + identical result when using a user-supplied WCS. + + The third iteration ("user_wcs+shape") uses the same user-supplied WCS and also + specifies the output 2D file shape, using a shape that is identical to the + default. Hence all of the first 3 iterations should produce identical results + and therefore are compared to a single set of truth files. + + The fourth iteration ("user_wcs+shape1") uses the same user-specified WCS and + specifies an output 2D file shape that is 1 pixel larger than the default in + both axes. Hence the resulting s2d product needs a separate (larger) truth file. + Meanwhile, the x1d product from this iteration should still be identical to + the first 3, because the extra row and column of the 2D data file are ignored + during extraction. """ rtdata = rtdata_module # Get the spec3 ASN and its members rtdata.get_asn("miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json") + root_file = "jw01530-o005_t004_miri_p750l_" + args = [ "calwebb_spec3", rtdata.input ] + rtdata.custom_wcs_mode = request.param + + if request.param != "default_wcs": + # Get the s2d product that was just created using "default_wcs" + default_s2d = root_file + "s2d.fits" + dm = datamodels.open(default_s2d) + # Create a user-supplied WCS file that is identical to the default WCS + af = asdf.AsdfFile({"wcs": dm.meta.wcs}) + wcs_file = default_s2d[:-8] + 'wcs.asdf' + af.write_to(wcs_file) + args.append(f"--steps.resample_spec.output_wcs={wcs_file}") + + if request.param == "user_wcs+shape": + output_shape = ','.join(map(str, dm.data.shape[::-1])) + args.append(f"--steps.resample_spec.output_shape={output_shape}") + + elif request.param == "user_wcs+shape1": + output_shape = ','.join(map(str, (d + 1 for d in dm.data.shape[::-1]))) + args.append(f"--steps.resample_spec.output_shape={output_shape}") + output_file = root_file + 'shape1.fits' + args.append(f"--steps.resample_spec.output_file={output_file}") + # Run the calwebb_spec3 pipeline; save results from intermediate steps Step.from_cmdline(args) @@ -39,7 +87,10 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg # Run the pipeline and retrieve outputs rtdata = rtdata_module - output = f"jw01530-o005_t004_miri_p750l_{suffix}.fits" + if rtdata.custom_wcs_mode == 'user_wcs+shape1' and suffix == "s2d": + output = f"jw01530-o005_t004_miri_p750l_shape1_{suffix}.fits" + else: + output = f"jw01530-o005_t004_miri_p750l_{suffix}.fits" rtdata.output = output # Get the truth files @@ -57,11 +108,11 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg if isinstance(dmt, datamodels.MultiSlitModel): names = [s.name for s in dmt.slits] for name in names: - st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name == name] + st_idx = [(s.wcs, s.wavelength) for s in dmt.slits if s.name==name] w = dmt.slits[st_idx].meta.wcs x, y = wcstools.grid_from_bounding_box(w.bounding_box, step=(1, 1), center=True) _, _, wave = w(x, y) - sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name == name] + sr_idx = [(s.wcs, s.wavelength) for s in dmr.slits if s.name==name] wlr = dmr.slits[sr_idx].wavelength assert np.all(np.isclose(wave, wlr, atol=tolerance)) else: @@ -70,3 +121,4 @@ def test_miri_lrs_slit_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwarg _, _, wave = w(x, y) wlr = dmr.wavelength assert np.all(np.isclose(wave, wlr, atol=tolerance)) + diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index 7c932c1409..b529f5690b 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -3,7 +3,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import MultiSlitModel, ImageModel -from . import resample_spec, resample_utils +from . import resample_spec, ResampleStep from ..exp_to_source import multislit_to_container from ..assign_wcs.util import update_s_region_spectral from ..stpipe import Step @@ -33,6 +33,7 @@ class ResampleSpecStep(Step): kernel = option('square', 'point', default='square') # Flux distribution kernel fillval = string(default='NAN') # Output value for pixels with no weight or flux weight_type = option('ivm', 'exptime', None, default='ivm') # Input image weighting type + output_shape = int_list(min=2, max=2, default=None) # [x, y] order pixel_scale_ratio = float(default=1.0) # Ratio of input to output spatial pixel scale pixel_scale = float(default=None) # Spatial pixel scale in arcsec output_wcs = string(default='') # Custom output WCS @@ -171,7 +172,13 @@ def get_drizpars(self): ) # Custom output WCS parameters - kwargs['output_wcs'] = resample_utils.load_custom_wcs(self.output_wcs) + kwargs['output_shape'] = ResampleStep.check_list_pars( + self.output_shape, + 'output_shape', + min_vals=[1, 1] + ) + kwargs['output_wcs'] = ResampleStep.load_custom_wcs( + self.output_wcs, kwargs['output_shape']) kwargs['pscale'] = self.pixel_scale kwargs['pscale_ratio'] = self.pixel_scale_ratio diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index fd43bfd5b8..444a2a9050 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -1,11 +1,14 @@ import logging import re +from copy import deepcopy + +import asdf from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer -from . import resample, resample_utils +from . import resample from ..stpipe import Step from ..assign_wcs import util @@ -111,7 +114,30 @@ def process(self, input): return result @staticmethod - def _check_list_pars(vals, name, min_vals=None): + def check_list_pars(vals, name, min_vals=None): + """ + Validate step parameters that may take a 2-element list. + + Parameters + ---------- + vals : list or None + Values to validate. + name : str + Parameter name. + min_vals : list, optional + Minimum allowed values for the parameter. Must + have 2 values. + + Returns + ------- + values : list + The validated list of values. + + Raises + ------ + ValueError + If the values do not have expected values. + """ if vals is None: return None if len(vals) != 2: @@ -126,6 +152,55 @@ def _check_list_pars(vals, name, min_vals=None): else: raise ValueError(f"Both '{name}' values must be either None or not None.") + @staticmethod + def load_custom_wcs(asdf_wcs_file, output_shape=None): + """ + Load a custom output WCS from an ASDF file. + + Parameters + ---------- + asdf_wcs_file : str + Path to an ASDF file containing a GWCS structure. + output_shape : tuple of int, optional + Array shape for the output data. If not provided, + the custom WCS must specify one of: pixel_shape, + array_shape, or bounding_box. + + Returns + ------- + wcs : WCS + The output WCS to resample into. + """ + if not asdf_wcs_file: + return None + + with asdf.open(asdf_wcs_file) as af: + wcs = deepcopy(af.tree["wcs"]) + wcs.pixel_area = af.tree.get("pixel_area", None) + wcs.array_shape = af.tree.get("pixel_shape", None) + wcs.array_shape = af.tree.get("array_shape", None) + + if output_shape is not None: + wcs.array_shape = output_shape[::-1] + wcs.pixel_shape = output_shape + elif wcs.pixel_shape is not None: + wcs.array_shape = wcs.pixel_shape[::-1] + elif wcs.array_shape is not None: + wcs.pixel_shape = wcs.array_shape[::-1] + elif wcs.bounding_box is not None: + wcs.array_shape = tuple( + int(axs[1] + 0.5) + for axs in wcs.bounding_box.bounding_box(order="C") + ) + else: + raise ValueError( + "Step argument 'output_shape' is required when custom WCS " + "does not have neither of 'array_shape', 'pixel_shape', or " + "'bounding_box' attributes set." + ) + + return wcs + def get_drizpars(self): """ Load all drizzle-related parameter values into kwargs list. @@ -144,17 +219,17 @@ def get_drizpars(self): ) # Custom output WCS parameters. - kwargs['output_shape'] = self._check_list_pars( + kwargs['output_shape'] = self.check_list_pars( self.output_shape, 'output_shape', min_vals=[1, 1] ) - kwargs['output_wcs'] = resample_utils.load_custom_wcs( + kwargs['output_wcs'] = self.load_custom_wcs( self.output_wcs, kwargs['output_shape'] ) - kwargs['crpix'] = self._check_list_pars(self.crpix, 'crpix') - kwargs['crval'] = self._check_list_pars(self.crval, 'crval') + kwargs['crpix'] = self.check_list_pars(self.crpix, 'crpix') + kwargs['crval'] = self.check_list_pars(self.crval, 'crval') kwargs['rotation'] = self.rotation kwargs['pscale'] = self.pixel_scale kwargs['pscale_ratio'] = self.pixel_scale_ratio diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index 028ae2845b..0ba20c62c9 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -3,9 +3,8 @@ import warnings import numpy as np -import asdf -import gwcs from astropy import units as u +import gwcs from stdatamodels.dqflags import interpret_bit_flags from stdatamodels.jwst.datamodels.dqflags import pixel @@ -353,52 +352,3 @@ def check_for_tmeasure(model): return 0 except AttributeError: return 0 - - -def load_custom_wcs(asdf_wcs_file, output_shape=None): - """ - Load a custom output WCS from an ASDF file. - - Parameters - ---------- - asdf_wcs_file : str - Path to an ASDF file containing a GWCS structure. - output_shape : tuple of int, optional - Array shape for the output data. If not provided, - the custom WCS must specify one of: pixel_shape, - array_shape, or bounding_box. - - Returns - ------- - wcs : WCS - The output WCS to resample into. - """ - if not asdf_wcs_file: - return None - - with asdf.open(asdf_wcs_file) as af: - wcs = deepcopy(af.tree["wcs"]) - wcs.pixel_area = af.tree.get("pixel_area", None) - wcs.array_shape = af.tree.get("pixel_shape", None) - wcs.array_shape = af.tree.get("array_shape", None) - - if output_shape is not None: - wcs.array_shape = output_shape[::-1] - wcs.pixel_shape = output_shape - elif wcs.pixel_shape is not None: - wcs.array_shape = wcs.pixel_shape[::-1] - elif wcs.array_shape is not None: - wcs.pixel_shape = wcs.array_shape[::-1] - elif wcs.bounding_box is not None: - wcs.array_shape = tuple( - int(axs[1] + 0.5) - for axs in wcs.bounding_box.bounding_box(order="C") - ) - else: - raise ValueError( - "Step argument 'output_shape' is required when custom WCS " - "does not have neither of 'array_shape', 'pixel_shape', or " - "'bounding_box' attributes set." - ) - - return wcs From 0eb8a3d1e02a65d2cd28ce96a3166553ca572425 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 3 Jul 2024 13:18:52 -0400 Subject: [PATCH 271/687] Separate docs for resample_spec --- docs/jwst/package_index.rst | 1 + docs/jwst/resample/index.rst | 6 +- docs/jwst/resample/main.rst | 14 +-- docs/jwst/resample_spec/arguments.rst | 95 +++++++++++++++++++ docs/jwst/resample_spec/index.rst | 17 ++++ docs/jwst/resample_spec/main.rst | 32 +++++++ docs/jwst/resample_spec/reference_files.rst | 3 + docs/jwst/resample_spec/resample_spec.rst | 6 ++ .../jwst/resample_spec/resample_spec_step.rst | 6 ++ 9 files changed, 165 insertions(+), 15 deletions(-) create mode 100644 docs/jwst/resample_spec/arguments.rst create mode 100644 docs/jwst/resample_spec/index.rst create mode 100644 docs/jwst/resample_spec/main.rst create mode 100644 docs/jwst/resample_spec/reference_files.rst create mode 100644 docs/jwst/resample_spec/resample_spec.rst create mode 100644 docs/jwst/resample_spec/resample_spec_step.rst diff --git a/docs/jwst/package_index.rst b/docs/jwst/package_index.rst index c5d2096ae8..da12f11e51 100644 --- a/docs/jwst/package_index.rst +++ b/docs/jwst/package_index.rst @@ -54,6 +54,7 @@ Package Index references_general/index.rst refpix/index.rst resample/index.rst + resample_spec/index.rst reset/index.rst residual_fringe/index.rst rscd/index.rst diff --git a/docs/jwst/resample/index.rst b/docs/jwst/resample/index.rst index a1a8f7a8b9..79bcaed6cc 100644 --- a/docs/jwst/resample/index.rst +++ b/docs/jwst/resample/index.rst @@ -1,8 +1,8 @@ .. _resample_step: -========== -Resampling -========== +======================= +Resampling Imaging Data +======================= .. toctree:: :maxdepth: 2 diff --git a/docs/jwst/resample/main.rst b/docs/jwst/resample/main.rst index 9291519018..613d3e167a 100644 --- a/docs/jwst/resample/main.rst +++ b/docs/jwst/resample/main.rst @@ -1,8 +1,8 @@ Description =========== -:Classes: `jwst.resample.ResampleStep`, `jwst.resample.ResampleSpecStep` -:Alias: resample, resample_spec +:Classes: `jwst.resample.ResampleStep` +:Alias: resample This routine will resample each input 2D image based on the WCS and distortion information, and will combine multiple resampled images @@ -146,16 +146,6 @@ For convenience, this functionality was implemented in the :py:func:`~jwst.resample.resample_utils.decode_context` function. -Spectroscopic Data ------------------- - -Use the ``resample_spec`` step for spectroscopic data. The dispersion -direction is needed for this case, and this is obtained from the -DISPAXIS keyword. For the NIRSpec Fixed Slit mode, the ``resample_spec`` -step will be skipped if the input is a rateints product, as 3D input for -the mode is not supported. - - References ---------- diff --git a/docs/jwst/resample_spec/arguments.rst b/docs/jwst/resample_spec/arguments.rst new file mode 100644 index 0000000000..5488149ce2 --- /dev/null +++ b/docs/jwst/resample_spec/arguments.rst @@ -0,0 +1,95 @@ +.. _resample_spec_step_args: + +Step Arguments +============== +The `resample_spec` step has the following optional arguments that control +the behavior of the processing and the characteristics of the resampled +image. + +``--pixfrac`` (float, default=1.0) + The fraction by which input pixels are "shrunk" before being drizzled + onto the output image grid, given as a real number between 0 and 1. + +``--kernel`` (str, default='square') + The form of the kernel function used to distribute flux onto the output + image. Available kernels are `square` and `point`. + +``--pixel_scale_ratio`` (float, default=1.0) + Ratio of input to output spatial pixel scale. Values greater than 1 + indicate that the input pixels have a larger spatial scale, so more output + pixels will sample the same input pixel. For example, a value of 2.0 + means the output image would have 4 pixels sampling each input pixel. + This parameter is only applied in the cross-dispersion + direction: sampling wavelengths are not affected. + Ignored when ``pixel_scale`` or ``output_wcs`` are provided. + +``--pixel_scale`` (float, default=None) + Absolute pixel scale in ``arcsec``. This parameter is only applied in + the cross-dispersion direction. When provided, overrides + ``pixel_scale_ratio``. Ignored when ``output_wcs`` is provided. + +``--output_shape`` (tuple of int, default=None) + Shape of the image (data array) using "standard" ``nx`` first and ``ny`` + second (as opposite to the ``numpy.ndarray`` convention - ``ny`` first and + ``nx`` second). This value will be assigned to + ``pixel_shape`` and ``array_shape`` properties of the returned + WCS object. When supplied from command line, it should be a comma-separated + list of integers ``nx, ny``. + + .. note:: + Specifying ``output_shape`` *is required* when the WCS in + ``output_wcs`` does not have ``bounding_box`` property set. + +``--output_wcs`` (str, default='') + File name of a ``ASDF`` file with a GWCS stored under the ``"wcs"`` key + under the root of the file. The output image size is determined from the + bounding box of the WCS (if any). Argument ``output_shape`` overrides + computed image size and it is required when output WCS does not have + ``bounding_box`` property set or if ``pixel_shape`` or ``array_shape`` keys + (see below) are not provided. + + Additional information may be stored under + other keys under the root of the file. Currently, the following keys are + recognized: + + - ``pixel_area``: Indicates average pixel area of the output WCS in + units of steradians. When provided, this value will be used for updating + photometric quantities ``PIXAR_SR`` and ``PIXAR_A2`` of the output image. + If ``pixel_area`` is not provided, the code will attempt to estimate + this value from the WCS. + + - ``pixel_shape``: dimensions of the output image in the order (nx, ny). + Overrides the value of ``array_shape`` if provided. + + - ``array_shape``: shape of the output image in ``numpy`` order: (ny, nx). + + .. note:: + When ``output_wcs`` is specified, WCS-related arguments + ``pixel_scale_ratio`` and ``pixel_scale`` will be ignored. + +``--fillval`` (str, default='NAN') + The value to assign to output pixels that have zero weight or do not + receive any flux from any input pixels during drizzling. + +``--weight_type`` (str, default='ivm') + The weighting type for each input image. + If `weight_type=ivm` (the default), the scaling value + will be determined per-pixel using the inverse of the read noise + (VAR_RNOISE) array stored in each input image. If the VAR_RNOISE array does + not exist, the variance is set to 1 for all pixels (equal weighting). + If `weight_type=exptime`, the scaling value will be set equal to the + measurement time (TMEASURE) found in the image header if available; + if unavailable, the scaling will be set equal to the exposure time (EFFEXPTM). + +``--single`` (bool, default=False) + If set to `True`, resample each input image into a separate output. If + `False` (the default), each input is resampled additively (with weights) to + a common output + +``--blendheaders`` (bool, default=True) + Blend metadata from all input images into the resampled output image. + +``--in_memory`` (boolean, default=True) + Specifies whether or not to load and create all images that are used during + processing into memory. If ``False``, input files are loaded from disk when + needed and all intermediate files are stored on disk, rather than in memory. diff --git a/docs/jwst/resample_spec/index.rst b/docs/jwst/resample_spec/index.rst new file mode 100644 index 0000000000..afecbd1ce0 --- /dev/null +++ b/docs/jwst/resample_spec/index.rst @@ -0,0 +1,17 @@ +.. _resample_spec_step: + +======================== +Resampling Spectral Data +======================== + +.. toctree:: + :maxdepth: 2 + + main.rst + arguments.rst + reference_files.rst + resample_spec_step.rst + resample_spec.rst + +See the :ref:`resample step ` for full API documentation +for the the `resample` module. diff --git a/docs/jwst/resample_spec/main.rst b/docs/jwst/resample_spec/main.rst new file mode 100644 index 0000000000..f2fa46149f --- /dev/null +++ b/docs/jwst/resample_spec/main.rst @@ -0,0 +1,32 @@ +Description +=========== + +:Classes: `jwst.resample.ResampleSpecStep` +:Alias: resample_spec + +This routine will resample each input 2D spectral image based on the WCS and +distortion information, and will combine multiple resampled images +into a single rectified product. The distortion information should have +been incorporated into the image using the +:ref:`assign_wcs ` step. + +The ``resample`` step can take as input either: + +#. a single 2D input image +#. an association table (in json format) + +The underlying resampling algorithm is the same as is used in the +:ref:`resample step`: 2D spectral images are resampled into +a rectified spatial/spectral output WCS via the drizzle algorithm. See +the documentation for that step for more information on error propagation +and output extensions, as well as further references for the drizzle +algorithm. + +The output WCS is created from a combination of the WCS information of +all input images. The first input image is taken as the reference +for the output WCS and expanded to include the spatial and spectral +range of all inputs. + +The parameters for the drizzle operation are provided via ``resample_spec`` +step parameters, which may be overridden by a step parameter reference +file from CRDS. diff --git a/docs/jwst/resample_spec/reference_files.rst b/docs/jwst/resample_spec/reference_files.rst new file mode 100644 index 0000000000..f327035738 --- /dev/null +++ b/docs/jwst/resample_spec/reference_files.rst @@ -0,0 +1,3 @@ +Reference File +============== +The ``resample_spec`` step does not use any reference files. diff --git a/docs/jwst/resample_spec/resample_spec.rst b/docs/jwst/resample_spec/resample_spec.rst new file mode 100644 index 0000000000..bd249b6a9c --- /dev/null +++ b/docs/jwst/resample_spec/resample_spec.rst @@ -0,0 +1,6 @@ +.. resample_spec_: + +Python Interface to Drizzle: ResampleSpecData() +=============================================== + +.. automodapi:: jwst.resample.resample_spec diff --git a/docs/jwst/resample_spec/resample_spec_step.rst b/docs/jwst/resample_spec/resample_spec_step.rst new file mode 100644 index 0000000000..feeb180b3b --- /dev/null +++ b/docs/jwst/resample_spec/resample_spec_step.rst @@ -0,0 +1,6 @@ +.. resample_spec_step_: + +Python Step Interface: ResampleSpecStep() +========================================= + +.. automodapi:: jwst.resample.resample_spec_step From b217ba2694cbb80bd27588d32b04e7b133f10ed0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 30 Jul 2024 12:43:26 -0400 Subject: [PATCH 272/687] Update docs from changes in #8596 --- docs/jwst/resample/arguments.rst | 45 ++------------------------ docs/jwst/resample/resample.rst | 5 ++- docs/jwst/resample/resample_step.rst | 5 ++- docs/jwst/resample_spec/arguments.rst | 46 +++++++++++++++++++++------ 4 files changed, 44 insertions(+), 57 deletions(-) diff --git a/docs/jwst/resample/arguments.rst b/docs/jwst/resample/arguments.rst index 123e5d4ac4..e2cc2e78f5 100644 --- a/docs/jwst/resample/arguments.rst +++ b/docs/jwst/resample/arguments.rst @@ -15,52 +15,16 @@ image. image. Available kernels are `square`, `gaussian`, `point`, `turbo`, `lanczos2`, and `lanczos3`. - For spectral data, only the `square` and `point` kernels should be used. - The other kernels do not conserve spectral flux. - ``--pixel_scale_ratio`` (float, default=1.0) Ratio of input to output pixel scale. - For imaging data, a value of 0.5 means the output image would have 4 pixels sampling each input pixel. - - For spectral data, values greater than 1 indicate that the input - pixels have a larger spatial scale, so more output pixels will - sample the same input pixel. For example, a value of 2.0 - means the output image would have 2 pixels sampling each input - spatial pixel. If the input data has units of flux density (MJy/pixel), - the output flux per pixel will be half the input flux per pixel. - If the input data has units of surface brightness (MJy/sr), the output - flux per pixel is not scaled. - - Note that this parameter is only applied in the cross-dispersion - direction for spectral data: sampling wavelengths are not affected. - Ignored when ``pixel_scale`` or ``output_wcs`` are provided. - .. note:: - If this parameter is modified for spectral data, the extraction - aperture for the :ref:`extract_1d ` step must - also be modified, since it is specified in pixels. - ``--pixel_scale`` (float, default=None) Absolute pixel scale in ``arcsec``. When provided, overrides ``pixel_scale_ratio``. Ignored when ``output_wcs`` is provided. - For spectral data, if the input data has units of flux density - (MJy/pixel), the output flux per pixel will be scaled by the ratio - of the selected output pixel scale to an average input pixel scale. - If the input data has units of surface brightness (MJy/sr), - the output flux per pixel is not scaled. - - Note that this parameter is only applied in the cross-dispersion - direction for spectral data: sampling wavelengths are not affected. - - .. note:: - If this parameter is modified for spectral data, the extraction - aperture for the :ref:`extract_1d ` step must - also be modified, since it is specified in pixels. - ``--rotation`` (float, default=None) Position angle of output image’s Y-axis relative to North. A value of 0.0 would orient the final output image to be North up. @@ -68,20 +32,19 @@ image. but will instead be resampled in the default orientation for the camera with the x and y axes of the resampled image corresponding approximately to the detector axes. Ignored when ``pixel_scale`` - or ``output_wcs`` are provided. Also ignored for all spectral data. + or ``output_wcs`` are provided. ``--crpix`` (tuple of float, default=None) Position of the reference pixel in the image array in the ``x, y`` order. If ``crpix`` is not specified, it will be set to the center of the bounding box of the returned WCS object. When supplied from command line, it should be a comma-separated list of floats. Ignored when ``output_wcs`` - is provided. Also ignored for all spectral data. + is provided. ``--crval`` (tuple of float, default=None) Right ascension and declination of the reference pixel. Automatically computed if not provided. When supplied from command line, it should be a comma-separated list of floats. Ignored when ``output_wcs`` is provided. - Also ignored for all spectral data. ``--output_shape`` (tuple of int, default=None) Shape of the image (data array) using "standard" ``nx`` first and ``ny`` @@ -140,7 +103,7 @@ image. ``--single`` (bool, default=False) If set to `True`, resample each input image into a separate output. If `False` (the default), each input is resampled additively (with weights) to - a common output + a common output. ``--blendheaders`` (bool, default=True) Blend metadata from all input images into the resampled output image. @@ -155,8 +118,6 @@ image. For example, if set to ``0.5``, only resampled images that use less than half the available memory can be created. - This parameter is ignored for spectral data. - ``--in_memory`` (boolean, default=True) Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when diff --git a/docs/jwst/resample/resample.rst b/docs/jwst/resample/resample.rst index 0a2bba77e2..3e264d726e 100644 --- a/docs/jwst/resample/resample.rst +++ b/docs/jwst/resample/resample.rst @@ -1,7 +1,6 @@ .. resample_: -Python Interface to Drizzle: ResampleData() and ResampleSpecData() -================================================================== +Python Interface to Drizzle: ResampleData() +=========================================== .. automodapi:: jwst.resample.resample -.. automodapi:: jwst.resample.resample_spec diff --git a/docs/jwst/resample/resample_step.rst b/docs/jwst/resample/resample_step.rst index afa5d95df4..d0a87a5b75 100644 --- a/docs/jwst/resample/resample_step.rst +++ b/docs/jwst/resample/resample_step.rst @@ -1,7 +1,6 @@ .. resample_step_: -Python Step Interface: ResampleStep() and ResampleSpecStep() -============================================================ +Python Step Interface: ResampleStep() +===================================== .. automodapi:: jwst.resample.resample_step -.. automodapi:: jwst.resample.resample_spec_step diff --git a/docs/jwst/resample_spec/arguments.rst b/docs/jwst/resample_spec/arguments.rst index 5488149ce2..189979a3be 100644 --- a/docs/jwst/resample_spec/arguments.rst +++ b/docs/jwst/resample_spec/arguments.rst @@ -12,22 +12,50 @@ image. ``--kernel`` (str, default='square') The form of the kernel function used to distribute flux onto the output - image. Available kernels are `square` and `point`. + image. Available kernels for spectral data are `square` and `point`. + Other drizzle kernels, available for imaging data, do not conserve + spectral flux. ``--pixel_scale_ratio`` (float, default=1.0) - Ratio of input to output spatial pixel scale. Values greater than 1 - indicate that the input pixels have a larger spatial scale, so more output - pixels will sample the same input pixel. For example, a value of 2.0 - means the output image would have 4 pixels sampling each input pixel. - This parameter is only applied in the cross-dispersion + Ratio of input to output spatial pixel scale. + + For spectral data, values greater than 1 indicate that the input + pixels have a larger spatial scale, so more output pixels will + sample the same input pixel. For example, a value of 2.0 + means the output image would have 2 pixels sampling each input + spatial pixel. If the input data has units of flux density (MJy/pixel), + the output flux per pixel will be half the input flux per pixel. + If the input data has units of surface brightness (MJy/sr), the output + flux per pixel is not scaled. + + Note that this parameter is only applied in the cross-dispersion direction: sampling wavelengths are not affected. + Ignored when ``pixel_scale`` or ``output_wcs`` are provided. + .. note:: + If this parameter is modified from the default value, the extraction + aperture for the :ref:`extract_1d ` step must + also be modified, since it is specified in pixels. + ``--pixel_scale`` (float, default=None) - Absolute pixel scale in ``arcsec``. This parameter is only applied in - the cross-dispersion direction. When provided, overrides + Absolute pixel scale in ``arcsec``. When provided, overrides ``pixel_scale_ratio``. Ignored when ``output_wcs`` is provided. + For spectral data, if the input data has units of flux density + (MJy/pixel), the output flux per pixel will be scaled by the ratio + of the selected output pixel scale to an average input pixel scale. + If the input data has units of surface brightness (MJy/sr), + the output flux per pixel is not scaled. + + Note that this parameter is only applied in the cross-dispersion + direction: sampling wavelengths are not affected. + + .. note:: + If this parameter is modified from the default value, the extraction + aperture for the :ref:`extract_1d ` step must + also be modified, since it is specified in pixels. + ``--output_shape`` (tuple of int, default=None) Shape of the image (data array) using "standard" ``nx`` first and ``ny`` second (as opposite to the ``numpy.ndarray`` convention - ``ny`` first and @@ -84,7 +112,7 @@ image. ``--single`` (bool, default=False) If set to `True`, resample each input image into a separate output. If `False` (the default), each input is resampled additively (with weights) to - a common output + a common output. ``--blendheaders`` (bool, default=True) Blend metadata from all input images into the resampled output image. From f6c773640ba98fb3a077ce0004fcc05aa0740de9 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 31 Jul 2024 10:06:34 -0400 Subject: [PATCH 273/687] Fix links to resample_spec step --- docs/jwst/data_products/science_products.rst | 5 +- docs/jwst/master_background/description.rst | 2 +- docs/jwst/pipeline/calwebb_spec2.rst | 106 +++++++++---------- docs/jwst/pipeline/calwebb_spec3.rst | 5 +- docs/jwst/pixel_replace/main.rst | 2 +- docs/jwst/resample/arguments.rst | 4 +- docs/jwst/resample_spec/arguments.rst | 17 ++- 7 files changed, 71 insertions(+), 70 deletions(-) diff --git a/docs/jwst/data_products/science_products.rst b/docs/jwst/data_products/science_products.rst index eea9e2096a..8c51c1d954 100644 --- a/docs/jwst/data_products/science_products.rst +++ b/docs/jwst/data_products/science_products.rst @@ -368,7 +368,8 @@ which has the same structure and content as the calints_ product described above Resampled 2-D data: ``i2d`` and ``s2d`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Images and spectra that have been resampled by the :ref:`resample ` step use a +Images and spectra that have been resampled by the :ref:`resample ` +or :ref:`resample_spec ` steps use a different set of data arrays than other science products. Resampled 2-D images are stored in ``i2d`` products and resampled 2-D spectra are stored in ``s2d`` products. The FITS file structure for ``i2d`` and ``s2d`` products is as follows: @@ -855,4 +856,4 @@ The overall structure of the ``amilg`` FITS files is as follows: - RESID: A 3D image of the fit residuals. - N_RESID: A 3D image of RESID normalized by data peak. - SOLNS: A table of fringe coefficients. - - ADSF: The data model meta data. \ No newline at end of file + - ADSF: The data model meta data. diff --git a/docs/jwst/master_background/description.rst b/docs/jwst/master_background/description.rst index 6a5d39b10b..96f90e82aa 100644 --- a/docs/jwst/master_background/description.rst +++ b/docs/jwst/master_background/description.rst @@ -376,7 +376,7 @@ as follows: extended sources (appropriate for background signal), and saving the extended source correction arrays for each slit in an internal copy of the data model #. If a user-supplied master background spectrum is **not** given, the - :ref:`resample_spec ` and :ref:`extract_1d ` + :ref:`resample_spec ` and :ref:`extract_1d ` steps are applied to the calibrated background slits, resulting in extracted 1D background spectra #. The 1D background spectra are combined, using the diff --git a/docs/jwst/pipeline/calwebb_spec2.rst b/docs/jwst/pipeline/calwebb_spec2.rst index d2ad75724b..11d5a7aa8c 100644 --- a/docs/jwst/pipeline/calwebb_spec2.rst +++ b/docs/jwst/pipeline/calwebb_spec2.rst @@ -83,7 +83,7 @@ TSO exposures. The instrument mode abbreviations used in the table are as follow +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ | :ref:`pixel_replace ` \ :sup:`2` | |c| | |c| | |c| | |c| | |c| | | | |c| | |c| | | +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ -| :ref:`resample_spec ` | |c| | |c| | | |c| | | | | | | | +| :ref:`resample_spec ` | |c| | |c| | | |c| | | | | | | | +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ | :ref:`cube_build ` | | | |c| | | | |c| | | | | | +----------------------------------------------------------+-----+-----+-----+-----+-----+-----+-----------------+------+--------+-----+ @@ -109,8 +109,8 @@ Notice that NIRSpec MOS is the only mode to receive master background subtractio in the ``calwebb_spec2`` pipeline. All other spectral modes have master background subtraction applied in the :ref:`calwebb_spec3 ` pipeline. -The :ref:`resample_spec ` step produces a resampled/rectified product for -non-IFU modes of some spectroscopic exposures. If the :ref:`resample_spec ` step +The :ref:`resample_spec ` step produces a resampled/rectified product for +non-IFU modes of some spectroscopic exposures. If the :ref:`resample_spec ` step is not applied to a given exposure, the :ref:`extract_1d ` operation will be performed on the original (unresampled) data. The :ref:`cube_build ` step produces a resampled/rectified cube for IFU exposures, which is then used as input to @@ -148,7 +148,7 @@ The detailed processing flow is as follows: steps are saved, they will have an additional "_fs" suffix appended to their file names. - MOS and FS slits are recombined and processed together through the - :ref:`resample_spec ` step. + :ref:`resample_spec ` step. - MOS slits are processed through the :ref:`extract_1d ` step, then FS slits are processed through the same step. The extracted spectra are recombined into a final data product. @@ -220,49 +220,49 @@ abbreviations used in the table are as follows: - NIRSpec MOS = Multi-Object Spectroscopy - NIRSpec IFU = Integral Field Unit -+---------------------------------------+------------+--------------+-----------------+--------------+ -| Pipeline Step | NRS_LAMP | NRS_AUTOWAVE | NRS_AUTOFLAT | -+---------------------------------------+------------+--------------+-----------------+ + -| | LINE | FLAT | | (MOS only) | -+=======================================+============+==============+=================+==============+ -| :ref:`assign_wcs ` | ALL | ALL | ALL | ALL | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`msaflagopen ` | MOS, IFU | MOS, IFU | MOS, IFU | MOS | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`nsclean ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`imprint ` | NONE | IFU | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`background ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`extract_2d ` | MOS, FS | MOS, FS | MOS, FS | MOS | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`srctype ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`wavecorr ` | ALL | ALL | ALL | ALL | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`flat_field ` | | | | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| - D-FLAT | ALL | ALL | ALL | | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| - S-FLAT | ALL | NONE | ALL | | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| - F-FLAT | NONE | NONE | NONE | | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`pathloss ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`barshadow ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`photom ` | NONE | NONE | NONE | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`resample_spec ` | MOS, FS | NONE | MOS, FS | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`cube_build ` | IFU | NONE | IFU | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ -| :ref:`extract_1d ` | ALL | NONE | ALL | NONE | -+---------------------------------------+------------+--------------+-----------------+--------------+ - -In the :ref:`resample_spec ` and :ref:`cube_build ` steps, the spectra are ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| Pipeline Step | NRS_LAMP | NRS_AUTOWAVE | NRS_AUTOFLAT | ++--------------------------------------------+------------+--------------+-----------------+ + +| | LINE | FLAT | | (MOS only) | ++============================================+============+==============+=================+==============+ +| :ref:`assign_wcs ` | ALL | ALL | ALL | ALL | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`msaflagopen ` | MOS, IFU | MOS, IFU | MOS, IFU | MOS | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`nsclean ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`imprint ` | NONE | IFU | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`background ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`extract_2d ` | MOS, FS | MOS, FS | MOS, FS | MOS | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`srctype ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`wavecorr ` | ALL | ALL | ALL | ALL | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`flat_field ` | | | | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| - D-FLAT | ALL | ALL | ALL | | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| - S-FLAT | ALL | NONE | ALL | | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| - F-FLAT | NONE | NONE | NONE | | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`pathloss ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`barshadow ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`photom ` | NONE | NONE | NONE | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`resample_spec ` | MOS, FS | NONE | MOS, FS | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`cube_build ` | IFU | NONE | IFU | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ +| :ref:`extract_1d ` | ALL | NONE | ALL | NONE | ++--------------------------------------------+------------+--------------+-----------------+--------------+ + +In the :ref:`resample_spec ` and :ref:`cube_build ` steps, the spectra are transformed to a space of (wavelength, offset along the slit) without applying a tangent plane projection. Arguments @@ -304,7 +304,7 @@ in the association. If "_rateints" products are used as input, for modes other than NIRSpec Fixed Slit, each step applies its algorithm to each integration in the exposure, where appropriate. For the NIRSpec Fixed Slit mode the ``calwebb_spec2`` pipeline will currently -skip both the :ref:`resample_spec ` step and the +skip both the :ref:`resample_spec ` step and the :ref:`extract_1d ` step, because neither step supports multiple integration input products for this mode. @@ -348,7 +348,7 @@ The output is a fully calibrated, but unrectified, exposure, using the product type suffix "_cal" or "_calints", depending on the type of input, e.g. "jw80600012001_02101_00003_mirimage_cal.fits." This is the output of the :ref:`photom ` step, or whichever step is performed last before applying -either :ref:`resample_spec `, :ref:`cube_build `, or +either :ref:`resample_spec `, :ref:`cube_build `, or :ref:`extract_1d `. The output data model type can be any of the 4 listed above and is completely @@ -376,12 +376,12 @@ extracted slits/sources. :File suffix: _s2d If the input is a 2D exposure type that gets resampled/rectified by the -:ref:`resample_spec ` step, the rectified 2D spectral product is saved as a -"_s2d" file. This image is intended for use as a quick-look product only and is -not used in subsequent processing. The 2D unresampled, calibrated ("_cal") +:ref:`resample_spec ` step, the rectified 2D spectral product +is saved as a "_s2d" file. This image is intended for use as a quick-look product only +and is not used in subsequent processing. The 2D unresampled, calibrated ("_cal") products are passed along as input to subsequent Stage 3 processing. -If the input to the :ref:`resample_spec ` step is a `~jwst.datamodels.MultiSlitModel`, +If the input to the :ref:`resample_spec ` step is a `~jwst.datamodels.MultiSlitModel`, then the resampled output will be in the form of a `~jwst.datamodels.MultiSlitModel`, which contains an array of individual models, one per slit. Otherwise the output will be a single `~jwst.datamodels.SlitModel`. diff --git a/docs/jwst/pipeline/calwebb_spec3.rst b/docs/jwst/pipeline/calwebb_spec3.rst index 307311bc58..d1cd8de5dc 100644 --- a/docs/jwst/pipeline/calwebb_spec3.rst +++ b/docs/jwst/pipeline/calwebb_spec3.rst @@ -34,7 +34,7 @@ processed using the :ref:`calwebb_tso3 ` pipeline. +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ | :ref:`pixel_replace ` | |c| | |c| | |c| | |c| | |c| | | |c| | |c| | +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ -| :ref:`resample_spec ` | |c| | |c| | | |c| | | | | | +| :ref:`resample_spec ` | |c| | |c| | | |c| | | | | | +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ | :ref:`cube_build ` | | | |c| | | |c| | | | | +-------------------------------------------------------------+-----+-----+-----+-----+-----+------+------+--------+ @@ -157,7 +157,8 @@ new field in the original product root name, e.g. When processing non-IFU modes, a resampled/rectified 2D product of type "_s2d" is created containing the rectified and combined data for a given -slit/source, which is the output of the :ref:`resample_spec ` step. +slit/source, which is the output of the :ref:`resample_spec ` +step. 3D resampled and combined spectral data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/docs/jwst/pixel_replace/main.rst b/docs/jwst/pixel_replace/main.rst index e939c5a218..754c3b198e 100644 --- a/docs/jwst/pixel_replace/main.rst +++ b/docs/jwst/pixel_replace/main.rst @@ -12,7 +12,7 @@ the extracted spectrum. To avoid this defect in the 1-D spectrum, this step estimates the flux values of pixels flagged as ``DO_NOT_USE`` in 2-D extracted spectra using interpolation methods, -prior to rectification in the :ref:`resample_spec ` step. +prior to rectification in the :ref:`resample_spec ` step. ``pixel_replace`` inserts these estimates into the 2-D data array, unsets the ``DO_NOT_USE`` flag, and sets the ``FLUX_ESTIMATED`` flag for each affected pixel. Error values and variance components for the replaced pixels are similarly updated with diff --git a/docs/jwst/resample/arguments.rst b/docs/jwst/resample/arguments.rst index e2cc2e78f5..9332bc6dcd 100644 --- a/docs/jwst/resample/arguments.rst +++ b/docs/jwst/resample/arguments.rst @@ -48,7 +48,7 @@ image. ``--output_shape`` (tuple of int, default=None) Shape of the image (data array) using "standard" ``nx`` first and ``ny`` - second (as opposite to the ``numpy.ndarray`` convention - ``ny`` first and + second (opposite to the ``numpy.ndarray`` convention - ``ny`` first and ``nx`` second). This value will be assigned to ``pixel_shape`` and ``array_shape`` properties of the returned WCS object. When supplied from command line, it should be a comma-separated @@ -59,7 +59,7 @@ image. ``output_wcs`` does not have ``bounding_box`` property set. ``--output_wcs`` (str, default='') - File name of a ``ASDF`` file with a GWCS stored under the ``"wcs"`` key + File name of an ``ASDF`` file with a GWCS stored under the ``"wcs"`` key under the root of the file. The output image size is determined from the bounding box of the WCS (if any). Argument ``output_shape`` overrides computed image size and it is required when output WCS does not have diff --git a/docs/jwst/resample_spec/arguments.rst b/docs/jwst/resample_spec/arguments.rst index 189979a3be..e2b7dbe12d 100644 --- a/docs/jwst/resample_spec/arguments.rst +++ b/docs/jwst/resample_spec/arguments.rst @@ -19,10 +19,9 @@ image. ``--pixel_scale_ratio`` (float, default=1.0) Ratio of input to output spatial pixel scale. - For spectral data, values greater than 1 indicate that the input - pixels have a larger spatial scale, so more output pixels will - sample the same input pixel. For example, a value of 2.0 - means the output image would have 2 pixels sampling each input + Values greater than 1 indicate that the input pixels have a larger spatial + scale, so more output pixels will sample the same input pixel. For example, + a value of 2.0 means the output image would have 2 pixels sampling each input spatial pixel. If the input data has units of flux density (MJy/pixel), the output flux per pixel will be half the input flux per pixel. If the input data has units of surface brightness (MJy/sr), the output @@ -42,9 +41,9 @@ image. Absolute pixel scale in ``arcsec``. When provided, overrides ``pixel_scale_ratio``. Ignored when ``output_wcs`` is provided. - For spectral data, if the input data has units of flux density - (MJy/pixel), the output flux per pixel will be scaled by the ratio - of the selected output pixel scale to an average input pixel scale. + If the input data has units of flux density (MJy/pixel), the output flux per + pixel will be scaled by the ratio of the selected output pixel scale to an average + input pixel scale. If the input data has units of surface brightness (MJy/sr), the output flux per pixel is not scaled. @@ -58,7 +57,7 @@ image. ``--output_shape`` (tuple of int, default=None) Shape of the image (data array) using "standard" ``nx`` first and ``ny`` - second (as opposite to the ``numpy.ndarray`` convention - ``ny`` first and + second (opposite to the ``numpy.ndarray`` convention - ``ny`` first and ``nx`` second). This value will be assigned to ``pixel_shape`` and ``array_shape`` properties of the returned WCS object. When supplied from command line, it should be a comma-separated @@ -69,7 +68,7 @@ image. ``output_wcs`` does not have ``bounding_box`` property set. ``--output_wcs`` (str, default='') - File name of a ``ASDF`` file with a GWCS stored under the ``"wcs"`` key + File name of an ``ASDF`` file with a GWCS stored under the ``"wcs"`` key under the root of the file. The output image size is determined from the bounding box of the WCS (if any). Argument ``output_shape`` overrides computed image size and it is required when output WCS does not have From 11aef8ff212efa85b35ab682dabccee8fc634cfb Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 31 Jul 2024 10:29:23 -0400 Subject: [PATCH 274/687] Fix LaTeX math error in outlier detection docs --- docs/jwst/outlier_detection/outlier_detection_coron.rst | 2 +- docs/jwst/outlier_detection/outlier_detection_imaging.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 1a808861ef..72cbd79b80 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -26,7 +26,7 @@ Specifically, this routine performs the following operations: The core detection algorithm uses the following to generate an outlier mask - .. math:: | image\_input - image\_median | > SNR*input_\err + .. math:: | image\_input - image\_median | > SNR*input\_err #. Update input data model DQ arrays with mask of detected outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 6cf235c3f2..ced7d9599f 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -86,7 +86,7 @@ Specifically, this routine performs the following operations: will be ignored and instead the outlier mask will be computed using the following formula: - .. math:: | image\_input - image\_median | > SNR*input_err + .. math:: | image\_input - image\_median | > SNR * input\_err * When resampling is enabled, the comparison uses the original input images, the blotted median image, and the derivative of the blotted image to From 0e3006459571819ff717f140c37c692bd011a4d8 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 5 Aug 2024 15:21:42 -0400 Subject: [PATCH 275/687] Update NIRSpec IRS2 detector1 test to use flight data --- jwst/regtest/test_nirspec_irs2_detector1.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index 8af8bfbcee..d38d12b973 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -12,7 +12,7 @@ def run_detector1pipeline(rtdata_module): """Run calwebb_detector1 pipeline on NIRSpec data with IRS2 readout mode.""" rtdata = rtdata_module - rtdata.get_data("nirspec/irs2/jw0010010_11010_nrs1_chimera_uncal.fits") + rtdata.get_data("nirspec/irs2/jw01335004001_03101_00002_nrs2_uncal.fits") Step.from_cmdline([ "calwebb_detector1", @@ -33,7 +33,8 @@ def run_detector1pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ['dq_init', 'saturation', 'superbias', - 'refpix', 'linearity', 'dark_current', 'jump', '0_ramp_fit', 'gain_scale', + 'refpix', 'linearity', 'dark_current', 'jump', + '0_ramp_fit', 'gain_scale', 'rate']) def test_nirspec_irs2_detector1(run_detector1pipeline, rtdata_module, fitsdiff_default_kwargs, suffix): @@ -42,7 +43,7 @@ def test_nirspec_irs2_detector1(run_detector1pipeline, rtdata_module, """ rtdata = rtdata_module - output_filename = f"jw0010010_11010_nrs1_chimera_{suffix}.fits" + output_filename = f"jw01335004001_03101_00002_nrs2_{suffix}.fits" rtdata.output = output_filename rtdata.get_truth(f"truth/test_nirspec_irs2_detector1/{output_filename}") From a2f9e8b8a984428bc26e91dc800f35d2a07a5056 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 24 Jul 2024 12:22:01 -0700 Subject: [PATCH 276/687] update tso photometry to use ApertureStats --- jwst/tso_photometry/tso_photometry.py | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/jwst/tso_photometry/tso_photometry.py b/jwst/tso_photometry/tso_photometry.py index 463eabeea7..c3965e261c 100644 --- a/jwst/tso_photometry/tso_photometry.py +++ b/jwst/tso_photometry/tso_photometry.py @@ -6,7 +6,7 @@ from astropy.table import QTable from astropy.time import Time, TimeDelta import astropy.units as u -from photutils.aperture import CircularAperture, CircularAnnulus +from photutils.aperture import CircularAperture, CircularAnnulus, ApertureStats from stdatamodels.jwst.datamodels import CubeModel @@ -79,9 +79,9 @@ def tso_aperture_photometry(datamodel, xcenter, ycenter, radius, radius_inner, 'subarray. No background subtraction was performed.') for i in np.arange(nimg): - aperture_sum.append(np.sum(datamodel.data[i, :, :])) + aperture_sum.append(np.nansum(datamodel.data[i, :, :])) aperture_sum_err.append( - np.sqrt(np.sum(datamodel.err[i, :, :]**2))) + np.sqrt(np.nansum(datamodel.err[i, :, :]**2))) else: info = ('Photometry measured in a circular aperture of r={0} ' 'pixels. Background calculated as the mean in a ' @@ -89,15 +89,18 @@ def tso_aperture_photometry(datamodel, xcenter, ycenter, radius, radius_inner, 'r_outer={2} pixels.'.format(radius, radius_inner, radius_outer)) for i in np.arange(nimg): - aper_sum, aper_sum_err = phot_aper.do_photometry( - datamodel.data[i, :, :], error=datamodel.err[i, :, :]) - ann_sum, ann_sum_err = bkg_aper.do_photometry( - datamodel.data[i, :, :], error=datamodel.err[i, :, :]) - - aperture_sum.append(aper_sum[0]) - aperture_sum_err.append(aper_sum_err[0]) - annulus_sum.append(ann_sum[0]) - annulus_sum_err.append(ann_sum_err[0]) + aperstats = ApertureStats(datamodel.data[i, :, :], + phot_aper, + error=datamodel.err[i, :, :]) + + annstats = ApertureStats(datamodel.data[i, :, :], + bkg_aper, + error=datamodel.err[i, :, :]) + + aperture_sum.append(aperstats.sum) + aperture_sum_err.append(aperstats.sum_err) + annulus_sum.append(annstats.sum) + annulus_sum_err.append(annstats.sum_err) aperture_sum = np.array(aperture_sum) aperture_sum_err = np.array(aperture_sum_err) From b514bc4eecbfe83b1153ba03c92d7aba290843d0 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 25 Jul 2024 09:49:33 -0700 Subject: [PATCH 277/687] update change log --- CHANGES.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 5ea557e270..f85267c276 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -78,6 +78,13 @@ stpipe added a `query_step_status()` function to use as an alternative to checking `self.skip`. [#8600] +tso_photometry +-------------- + +- Replaced photutils.aperture do_photometry with photutils.ApertureStats to remove NaNs + when performing TSO photometry for non SUB64 WLP8 pupil data. For SUB64 WLP8 changed + summing data in the aperture to use np.nansum to ingore NaNs. [#8672] + tweakreg -------- From ab050331025fa7a47f1856db685678d15aad9b30 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 1 Aug 2024 09:35:09 -0700 Subject: [PATCH 278/687] added documentation update --- CHANGES.rst | 2 +- docs/jwst/tso_photometry/description.rst | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index f85267c276..a6edaeb8f9 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -83,7 +83,7 @@ tso_photometry - Replaced photutils.aperture do_photometry with photutils.ApertureStats to remove NaNs when performing TSO photometry for non SUB64 WLP8 pupil data. For SUB64 WLP8 changed - summing data in the aperture to use np.nansum to ingore NaNs. [#8672] + summing data in the aperture to use np.nansum to ignore NaNs. [#8672] tweakreg -------- diff --git a/docs/jwst/tso_photometry/description.rst b/docs/jwst/tso_photometry/description.rst index 5c3d1e2555..efc5321265 100644 --- a/docs/jwst/tso_photometry/description.rst +++ b/docs/jwst/tso_photometry/description.rst @@ -31,6 +31,14 @@ the times will be computed from the exposure start time, the integration time, and the number of integrations. In either case, the times are Modified Julian Date, time scale UTC. +If NaNs exist in the source or background annulus the NaNs are masked out +and the TSO mode photometry will not will not return NaN. +This is different than photometry for imaging mode where a NaN value is returned. +For TSO data it is easier for single pixels to be affected in a given integration +and science analysis will be focused on variability of one source. +In the imaging case the most likely cause of NaN pixels in the annuli +is NaN-valued central saturated pixels in an image with many sources. + The output table contains these fields: - MJD From 3cb6627b07b41d1a80dcb021e6a001ee914bab84 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 5 Aug 2024 12:36:18 -0700 Subject: [PATCH 279/687] updates to doc --- docs/jwst/tso_photometry/description.rst | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/docs/jwst/tso_photometry/description.rst b/docs/jwst/tso_photometry/description.rst index efc5321265..75b5a8fea9 100644 --- a/docs/jwst/tso_photometry/description.rst +++ b/docs/jwst/tso_photometry/description.rst @@ -31,13 +31,17 @@ the times will be computed from the exposure start time, the integration time, and the number of integrations. In either case, the times are Modified Julian Date, time scale UTC. -If NaNs exist in the source or background annulus the NaNs are masked out -and the TSO mode photometry will not will not return NaN. -This is different than photometry for imaging mode where a NaN value is returned. -For TSO data it is easier for single pixels to be affected in a given integration -and science analysis will be focused on variability of one source. -In the imaging case the most likely cause of NaN pixels in the annuli -is NaN-valued central saturated pixels in an image with many sources. +If NaNs exist in the source or background annulus, they are masked out and the value +returned is the sum over the real-valued data. +This is different from the convention for photometry in standard imaging mode: +in that case, a NaN value is returned if it is present in any of the pixels in +the source aperture. In the imaging case, the mostly likely cause of NaN pixels +in the aperture is NaN-valued central pixels for a saturating source in an image +with many sources. nFor TSO data it is more likely that single pixels are affected +in a given integration and science analysis will be focused on variability of one source. +If NaNs are present, the absolute flux will be underestimages, the relative values may still +be useful. + The output table contains these fields: From e13d34315c7371dd0378b7f08ef59009b339d69a Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 5 Aug 2024 15:48:27 -0400 Subject: [PATCH 280/687] Keep default jump detection threshold --- jwst/regtest/test_nirspec_irs2_detector1.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index d38d12b973..3a14b37cbd 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -27,7 +27,6 @@ def run_detector1pipeline(rtdata_module): "--steps.jump.save_results=True", "--steps.ramp_fit.save_results=True", "--steps.gain_scale.save_results=True", - "--steps.jump.rejection_threshold=200", ]) From 59bb5ff6ab79310cb130d3d473182fe75c6071d2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 6 Aug 2024 09:32:00 -0400 Subject: [PATCH 281/687] Add tolerances --- jwst/regtest/test_nirspec_irs2_detector1.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index 3a14b37cbd..407a8bd2dc 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -46,5 +46,8 @@ def test_nirspec_irs2_detector1(run_detector1pipeline, rtdata_module, rtdata.output = output_filename rtdata.get_truth(f"truth/test_nirspec_irs2_detector1/{output_filename}") + # Set tolerances so comparisons work across architectures + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 1e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From 6f1f2c04130ba08c83a754272da41a421a44f275 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 6 Aug 2024 09:38:11 -0400 Subject: [PATCH 282/687] Update docs/jwst/tso_photometry/description.rst --- docs/jwst/tso_photometry/description.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/jwst/tso_photometry/description.rst b/docs/jwst/tso_photometry/description.rst index 75b5a8fea9..7872f94c53 100644 --- a/docs/jwst/tso_photometry/description.rst +++ b/docs/jwst/tso_photometry/description.rst @@ -36,10 +36,10 @@ returned is the sum over the real-valued data. This is different from the convention for photometry in standard imaging mode: in that case, a NaN value is returned if it is present in any of the pixels in the source aperture. In the imaging case, the mostly likely cause of NaN pixels -in the aperture is NaN-valued central pixels for a saturating source in an image -with many sources. nFor TSO data it is more likely that single pixels are affected +in the aperture is NaN-valued central pixels for a saturated source in an image +with many sources. For TSO data, it is more likely that single pixels are affected in a given integration and science analysis will be focused on variability of one source. -If NaNs are present, the absolute flux will be underestimages, the relative values may still +If NaNs are present, the absolute flux will be underestimated, but the relative values may still be useful. From 355ed819f2ec3de788733aa4c4337027e2c0289e Mon Sep 17 00:00:00 2001 From: Brett Date: Sun, 21 Jul 2024 13:34:34 -0400 Subject: [PATCH 283/687] remove stsci.image dependency --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index b9e7a1b224..242dcbf8bf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,6 @@ dependencies = [ "stcal @ git+https://github.com/spacetelescope/stcal.git", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", - "stsci.image>=2.3.5", "stsci.imagestats>=1.6.3", "synphot>=1.2", "tweakwcs>=0.8.8", From 155408f3ff95fe38ce6e405615c77778c83bbfca Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 22 Jul 2024 08:46:29 -0400 Subject: [PATCH 284/687] add changelog --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 5ea557e270..f2cffc9971 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -28,6 +28,11 @@ emicorr - Fixed a bug where MIRI EMI correction step would return NaNs when it was unable to compute a correction. [#8675] +general +------- + +- Remove the unused ``stsci.image`` dependency. [#8663] + master_background ----------------- From f6c409555db2b1f49a6dfe497e7bc8df8c07eb35 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 6 Aug 2024 13:19:46 -0400 Subject: [PATCH 285/687] don't build for python 3.13 --- .github/workflows/build.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f94367e4b1..f842f29b8a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -25,12 +25,12 @@ jobs: with: upload_to_pypi: ${{ (github.event_name == 'release') && (github.event.action == 'released') }} targets: | - # Linux wheels - - cp3*-manylinux_x86_64 - # MacOS wheels - - cp3*-macosx_x86_64 - # Until we have arm64 runners, we can't automatically test arm64 wheels - - cp3*-macosx_arm64 + # Linux wheels (except python 313) + - cp31[!3]-manylinux_x86_64 + # MacOS wheels (except python 313) + - cp31[!3]-macosx_x86_64 + # MacOS arm64 wheels (except python 313) + - cp31[!3]-macosx_arm64 sdist: true test_command: python -c "from jwst.lib import winclip; from jwst.cube_build import cube_match_internal, cube_match_sky_pointcloud, cube_match_sky_driz, blot_median; from jwst.straylight import calc_xart" secrets: From 4ecb40c57aaf1ae99fd6d8d9be66b7530277cf40 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 8 Aug 2024 10:56:36 -0400 Subject: [PATCH 286/687] update test to allow easier deprecation of `ignore_version_mismatch` (#8693) --- jwst/assign_wcs/tests/test_schemas.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/jwst/assign_wcs/tests/test_schemas.py b/jwst/assign_wcs/tests/test_schemas.py index 7a91bfd22a..8d9e2bd522 100644 --- a/jwst/assign_wcs/tests/test_schemas.py +++ b/jwst/assign_wcs/tests/test_schemas.py @@ -2,6 +2,7 @@ import sys import warnings +from asdf.exceptions import AsdfDeprecationWarning from astropy.modeling import models from astropy import units as u import pytest @@ -55,6 +56,14 @@ def test_distortion_schema(distortion_model, tmp_path): with warnings.catch_warnings(): warnings.simplefilter("error") + # filter out the ignore_version_mismatch deprecation as it's + # unrelated to this test and will make the deprecation easier + # see https://github.com/spacetelescope/stdatamodels/pull/313 + warnings.filterwarnings( + "ignore", + message="ignore_version_mismatch is deprecated and has done nothing since asdf 3.0.0", + category=AsdfDeprecationWarning, + ) with DistortionModel(path) as dist1: assert dist1.meta.instrument.p_pupil == dist.meta.instrument.p_pupil assert dist1.meta.instrument.pupil == dist.meta.instrument.pupil From 9616df3a1d9c2c019e250b049a13ad3e795b471e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 8 Aug 2024 11:54:44 -0400 Subject: [PATCH 287/687] Update log messages containing newlines --- jwst/ami/leastsqnrm.py | 6 ++++-- jwst/assign_wcs/assign_wcs_step.py | 6 ++++-- jwst/associations/association.py | 3 ++- jwst/dark_current/dark_current_step.py | 4 ++-- jwst/extract_1d/extract.py | 13 ++++++------- jwst/extract_1d/extract1d.py | 4 ++-- jwst/lib/set_telescope_pointing.py | 18 +++++++++--------- jwst/lib/tests/stp_v1.py | 24 +++++++++--------------- jwst/pipeline/calwebb_spec2.py | 14 ++++++++------ jwst/pixel_replace/pixel_replace_step.py | 4 ++-- jwst/scripts/adjust_wcs.py | 11 +++++------ jwst/scripts/set_telescope_pointing.py | 4 +++- jwst/scripts/v1_calculate.py | 11 +++++------ jwst/tweakreg/tweakreg_step.py | 8 ++++---- 14 files changed, 65 insertions(+), 65 deletions(-) diff --git a/jwst/ami/leastsqnrm.py b/jwst/ami/leastsqnrm.py index a798ada376..fbbcd98b5f 100644 --- a/jwst/ami/leastsqnrm.py +++ b/jwst/ami/leastsqnrm.py @@ -542,7 +542,8 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): flatimg = img.reshape(np.shape(img)[0] * np.shape(img)[1]) flatdqm = dqm.reshape(np.shape(img)[0] * np.shape(img)[1]) log.info("fringefitting.leastsqnrm.matrix_operations(): ") - log.info(f"\timg {img.shape:} \n\tdqm {dqm.shape:}") + log.info(f"\timg {img.shape:}") + log.info(f"\tdqm {dqm.shape:}") log.info( f"\tL x W = {img.shape[0]:d} x {img.shape[1]:d} = {img.shape[0] * img.shape[1]:d}", ) @@ -550,7 +551,8 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): log.info(f"\tflatdqm {flatdqm.shape:}") - log.info("\n\ttype(dqm) %s", type(dqm)) + log.info("") + log.info("\ttype(dqm) %s", type(dqm)) if dqm is not None: nanlist = np.where(flatdqm) # where DO_NOT_USE up. else: diff --git a/jwst/assign_wcs/assign_wcs_step.py b/jwst/assign_wcs/assign_wcs_step.py index 42b0818d03..1bfa023ac2 100755 --- a/jwst/assign_wcs/assign_wcs_step.py +++ b/jwst/assign_wcs/assign_wcs_step.py @@ -118,7 +118,8 @@ def process(self, input, *args, **kwargs): except (ValueError, RuntimeError) as e: log.warning("Failed to update 'meta.wcsinfo' with FITS SIP " - f'approximation. Reported error is:\n"{e.args[0]}"') + "approximation. Reported error is:") + log.warning(f'"{e.args[0]}"') else: # WFSS modes try: # A bounding_box is needed for the imaging WCS @@ -137,6 +138,7 @@ def process(self, input, *args, **kwargs): ) except (ValueError, RuntimeError) as e: log.warning("Failed to update 'meta.wcsinfo' with FITS SIP " - f'approximation. Reported error is:\n"{e.args[0]}"') + "approximation. Reported error is:") + log.warning(f'"{e.args[0]}"') return result diff --git a/jwst/associations/association.py b/jwst/associations/association.py index f5d681954d..1f91602ef3 100644 --- a/jwst/associations/association.py +++ b/jwst/associations/association.py @@ -198,7 +198,8 @@ def validate(cls, asn): try: jsonschema.validate(asn_data, asn_schema) except (AttributeError, jsonschema.ValidationError) as err: - logger.debug('Validation failed:\n%s', err) + logger.debug('Validation failed:') + logger.debug(str(err)) raise AssociationNotValidError('Validation failed') from err # Validate no path data for expnames diff --git a/jwst/dark_current/dark_current_step.py b/jwst/dark_current/dark_current_step.py index 2a0eddfc80..a7ae3c2761 100755 --- a/jwst/dark_current/dark_current_step.py +++ b/jwst/dark_current/dark_current_step.py @@ -100,8 +100,8 @@ def set_average_dark_current(self, input_model, dark_model): if np.sum(dark_model.average_dark_current) == 0.0: input_model.average_dark_current[:, :] = dark_model.meta.exposure.average_dark_current elif np.shape(input_model.average_dark_current) != np.shape(dark_model.average_dark_current): - self.log.warning("DarkModel average_dark_current does not match shape of data.\n" - "Dark current from reference file cannot be applied.") + self.log.warning("DarkModel average_dark_current does not match shape of data.") + self.log.warning("Dark current from reference file cannot be applied.") else: input_model.average_dark_current = dark_model.average_dark_current diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 03b36b8968..7c0198bad7 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -805,13 +805,12 @@ def sanity_check_limits( """ if (ap_wcs.xstart >= ap_ref.xstop or ap_wcs.xstop <= ap_ref.xstart or ap_wcs.ystart >= ap_ref.ystop or ap_wcs.ystop <= ap_ref.ystart): - log.warning( - f"The WCS bounding box is outside the aperture:\n\t" - f"aperture: {ap_ref.xstart}, {ap_ref.xstop}, {ap_ref.ystart}, {ap_ref.ystop}\n\t" - f"wcs: {ap_wcs.xstart}, {ap_wcs.xstop}, {ap_wcs.ystart}, {ap_wcs.ystop}\n" - f"so the wcs bounding box will be ignored." - ) - + log.warning("The WCS bounding box is outside the aperture:") + log.warning(f"\taperture: {ap_ref.xstart}, {ap_ref.xstop}, " + f"{ap_ref.ystart}, {ap_ref.ystop}") + log.warning(f"\twcs: {ap_wcs.xstart}, {ap_wcs.xstop}, " + f"{ap_wcs.ystart}, {ap_wcs.ystop}") + log.warning("so the wcs bounding box will be ignored.") flag = False else: flag = True diff --git a/jwst/extract_1d/extract1d.py b/jwst/extract_1d/extract1d.py index e083497cd6..6f340b374b 100644 --- a/jwst/extract_1d/extract1d.py +++ b/jwst/extract_1d/extract1d.py @@ -269,8 +269,8 @@ def extract1d(image, var_poisson, var_rnoise, var_flat, lambdas, disp_range, elif len(bkg_model) < bkg_order: log.debug(f"Not enough valid pixels to determine background " f"with the required order for lambda={lam:.6f} " - f"(column {x:d})\n" - f"Lowering background order to {len(bkg_model)}") + f"(column {x:d})") + log.debug(f"Lowering background order to {len(bkg_model)}") # Extract the source, and optionally subtract background using the # fit to the background for this column. Even if diff --git a/jwst/lib/set_telescope_pointing.py b/jwst/lib/set_telescope_pointing.py index 111b415ce3..4f46a443ab 100644 --- a/jwst/lib/set_telescope_pointing.py +++ b/jwst/lib/set_telescope_pointing.py @@ -603,9 +603,9 @@ def add_wcs(filename, allow_any_file=False, force_level1bmodel=False, try: if type(model) not in EXPECTED_MODELS: - logger.warning(f'Input {model} is not of an expected type (uncal, rate, rateints)' - '\n Updating pointing may have no effect or detrimental effects on the WCS information,' - '\n especially if the input is the result of Level2b or higher calibration.') + logger.warning(f'Input {model} is not of an expected type (uncal, rate, rateints)') + logger.warning(' Updating pointing may have no effect or detrimental effects on the WCS information,') + logger.warning(' especially if the input is the result of Level2b or higher calibration.') if not allow_any_file: raise TypeError(f'Input model {model} is not one of {EXPECTED_MODELS} and `allow_any_file` is `False`.' '\n\tFailing WCS processing.') @@ -814,10 +814,10 @@ def update_wcs_from_fgs_guiding(model, t_pars, default_roll_ref=0.0, default_vpa model, t_pars, default_roll_ref, default_vparity, default_v3yangle ) - logger.info('WCS info:' - f'\n\tcrpix1: {crpix1} crpix2: {crpix2}' - f'\n\tcrval1: {crval1} crval2: {crval2}' - f'\n\tpc_matrix: {pc_matrix}') + logger.info('WCS info:') + logger.info(f'\tcrpix1: {crpix1} crpix2: {crpix2}') + logger.info(f'\tcrval1: {crval1} crval2: {crval2}') + logger.info(f'\tpc_matrix: {pc_matrix}') model.meta.wcsinfo.crpix1 = crpix1 model.meta.wcsinfo.crpix2 = crpix2 @@ -1242,8 +1242,8 @@ def calc_transforms_track_tr_202111(t_pars: TransformParameters): # Check on telemetry for FGS ID. If invalid, use either user-specified or default to 1. fgsid = t_pars.pointing.fgsid if fgsid not in FGSIDS: - logger.warning(f'Method {t_pars.method} requires a valid FGS ID in telementry.' - '\nHowever telemetry reports an invalid id of {fgsid}') + logger.warning(f'Method {t_pars.method} requires a valid FGS ID in telementry.') + logger.warning('However telemetry reports an invalid id of {fgsid}') if t_pars.fgsid in FGSIDS: fgsid = t_pars.fgsid logger.warning(f'Using user-specified ID of {fgsid}') diff --git a/jwst/lib/tests/stp_v1.py b/jwst/lib/tests/stp_v1.py index f6fcfb7b8b..625588398d 100644 --- a/jwst/lib/tests/stp_v1.py +++ b/jwst/lib/tests/stp_v1.py @@ -111,12 +111,10 @@ def add_wcs(filename): dec = pheader['TARG_DEC'] roll = 0 - logger.warning( - 'Cannot retrieve telescope pointing.' - '\n{}' - '\nUsing TARG_RA={}, TARG_DEC={} and PA_V3=0 ' - 'to set pointing.'.format(exception, ra, dec) - ) + logger.warning('Cannot retrieve telescope pointing.') + logger.warning(str(exception)) + logger.warning(f'Using TARG_RA={ra}, TARG_DEC={dec} and ' + f'PA_V3=0 to set pointing.') local_roll = compute_local_roll(roll, ra, dec, v2ref, v3ref) wcsinfo = (ra, dec, local_roll) @@ -136,10 +134,8 @@ def add_wcs(filename): v1_ra_deg, v1_dec_deg, v3_pa_deg = vinfo local_roll = compute_local_roll(v3_pa_deg, crval1, crval2, v2ref, v3ref) - logger.info( - 'Computed coordinates from quaternions:' - '\n\tRA = {} DEC={} PA_V3={}'.format(crval1, crval2, v3_pa_deg) - ) + logger.info('Computed coordinates from quaternions:') + logger.info(f'\tRA = {crval1} DEC={crval2} PA_V3={v3_pa_deg}') pheader['RA_V1'] = v1_ra_deg pheader['DEC_V1'] = v1_dec_deg @@ -339,11 +335,9 @@ def get_pointing(obsstart, obsend, result_type='first'): This will need be re-examined when more information is available. """ - logger.info( - 'Determining pointing between observations times (mjd):' - '\n\tobsstart = {}' - '\n\tobsend = {}'.format(obsstart, obsend) - ) + logger.info('Determining pointing between observations times (mjd):') + logger.info(f'\tobsstart = {obsstart}') + logger.info(f'\tobsend = {obsend}') try: engdb = ENGDB_Service() except Exception as exception: diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index d75f44dc72..37eafd0892 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -230,16 +230,18 @@ def process_exposure_product( assign_wcs_exception = exception if assign_wcs_exception is not None or \ calibrated.meta.cal_step.assign_wcs != 'COMPLETE': - message = ( - 'Assign_wcs processing was skipped.' - '\nAborting remaining processing for this exposure.' - '\nNo output product will be created.' + messages = ( + 'Assign_wcs processing was skipped.', + 'Aborting remaining processing for this exposure.', + 'No output product will be created.' ) if self.assign_wcs.skip: - self.log.warning(message) + for message in messages: + self.log.warning(message) return else: - self.log.error(message) + for message in messages: + self.log.error(message) if assign_wcs_exception is not None: raise assign_wcs_exception else: diff --git a/jwst/pixel_replace/pixel_replace_step.py b/jwst/pixel_replace/pixel_replace_step.py index 2c9b117e10..7aa07b3dfd 100644 --- a/jwst/pixel_replace/pixel_replace_step.py +++ b/jwst/pixel_replace/pixel_replace_step.py @@ -59,7 +59,7 @@ def process(self, input): self.log.debug('Input is a ModelContainer.') else: self.log.error(f'Input is of type {str(type(input_model))} for which') - self.log.error('pixel_replace does not have an algorithm.\n') + self.log.error('pixel_replace does not have an algorithm.') self.log.error('Pixel replacement will be skipped.') input_model.meta.cal_step.pixel_replace = 'SKIPPED' return input_model @@ -98,7 +98,7 @@ def process(self, input): self.log.debug('Input is a {model.meta.model_type}.') else: self.log.error(f'Input is of type {model.meta.model_type} for which') - self.log.error('pixel_replace does not have an algorithm.\n') + self.log.error('pixel_replace does not have an algorithm.') self.log.error('Pixel replacement will be skipped.') model.meta.cal_step.pixel_replace = 'SKIPPED' run_pixel_replace = False diff --git a/jwst/scripts/adjust_wcs.py b/jwst/scripts/adjust_wcs.py index d562745890..023d46971e 100755 --- a/jwst/scripts/adjust_wcs.py +++ b/jwst/scripts/adjust_wcs.py @@ -235,8 +235,8 @@ def main(): if wcs_pars == [0.0, 0.0, 0.0, 1.0]: logger.info( - "All WCS adjustment parameters ('ra_delta', 'dec_delta'," - "roll_delta, and scale_factor) have default values" + "All WCS adjustment parameters ('ra_delta', 'dec_delta', " + "'roll_delta', and 'scale_factor') have default values " "(identical transformation)." ) logger.info("Nothing to do. Quitting.") @@ -265,10 +265,9 @@ def main(): npoints=64 ) except (ValueError, RuntimeError) as e: - logger.warning( - "Failed to update 'meta.wcsinfo' with FITS SIP " - f'approximation. Reported error is:\n"{e.args[0]}"' - ) + logger.warning("Failed to update 'meta.wcsinfo' with " + "FITS SIP approximation. Reported error is:") + logger.warning(f'"{e.args[0]}"') if options.update: data_model.save(fname, overwrite=True) diff --git a/jwst/scripts/set_telescope_pointing.py b/jwst/scripts/set_telescope_pointing.py index 86f077e5e7..0b75ca0975 100755 --- a/jwst/scripts/set_telescope_pointing.py +++ b/jwst/scripts/set_telescope_pointing.py @@ -166,7 +166,9 @@ def main(): # Calculate WCS for all inputs. for filename in args.exposure: - logger.info('\n------' 'Setting pointing for {}'.format(filename)) + logger.info("") + logger.info('------') + logger.info('Setting pointing for {}'.format(filename)) # Create path for saving the transforms. transform_path = None diff --git a/jwst/scripts/v1_calculate.py b/jwst/scripts/v1_calculate.py index 91373a830c..567fa577fb 100755 --- a/jwst/scripts/v1_calculate.py +++ b/jwst/scripts/v1_calculate.py @@ -94,12 +94,11 @@ def main(): logger.info(f'Retrieving V1 over the time span {obsstart.isot} - {obsend.isot}') input_as_files = False if args.pointing != 'all': - logger.warning( - 'V1 pointings have been requested over a time range.' - ' However, the \'pointing\' option is not \'all\'.' - '\nThere will only be a single result returned. Is this what was desired?' - '\nSuggestion: Use \'--pointing=all\'' - ) + logger.warning("V1 pointings have been requested over a time range. " + "However, the 'pointing' option is not 'all'.") + logger.warning('There will only be a single result returned. ' + 'Is this what was desired?') + logger.warning("Suggestion: Use '--pointing=all'") else: input_as_files = True diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 0ae6a51eb0..5255769fa2 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -356,9 +356,9 @@ def _apply_tweakreg_solution(self, crpix=None ) except (ValueError, RuntimeError) as e: - msg = f"Failed to update 'meta.wcsinfo' with FITS SIP \ - approximation. Reported error is: \n {e.args[0]}" - self.log.warning(msg) + self.log.warning("Failed to update 'meta.wcsinfo' with FITS SIP " + "approximation. Reported error is:") + self.log.warning(f'"{e.args[0]}"') record_step_status(image_model, "tweakreg", success=True) return image_model @@ -494,4 +494,4 @@ def _rename_catalog_columns(catalog): "columns 'x' and 'y' or 'xcentroid' and " "'ycentroid'." ) - return catalog \ No newline at end of file + return catalog From a232ef953f53c06a0eef6bd50dd6d33c8821f987 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Mon, 12 Aug 2024 14:57:07 -0400 Subject: [PATCH 288/687] [SCSB-169] add user input to specify `CRDS_CONTEXT` (#8702) --- .github/workflows/ci.yml | 30 ++++++++++++++++++++++----- .github/workflows/ci_cron.yml | 32 +++++++++++++++++++++++++---- .github/workflows/tests_devdeps.yml | 30 ++++++++++++++++++++++----- 3 files changed, 78 insertions(+), 14 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index abb96ccb00..1b1f54cc37 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,9 +14,20 @@ on: # Weekly Monday 9AM build - cron: "0 9 * * 1" workflow_dispatch: + inputs: + crds_context: + description: CRDS context (leave blank for latest) + type: string + required: false + default: '' + crds_server: + description: CRDS server + type: string + required: false + default: https://jwst-crds.stsci.edu concurrency: - group: ${{ github.workflow }}-${{ github.ref }} + group: ${{ github.workflow }}-${{ github.ref }}-${{ inputs.crds_context }} cancel-in-progress: true jobs: @@ -32,19 +43,28 @@ jobs: default_python: "3.12" envs: | - linux: check-dependencies - crds_contexts: + latest_crds_contexts: uses: spacetelescope/crds/.github/workflows/contexts.yml@master + crds_context: + needs: [ latest_crds_contexts ] + runs-on: ubuntu-latest + steps: + - id: context + run: echo context=${{ github.event_name == 'workflow_dispatch' && (inputs.crds_context != '' && inputs.crds_context || needs.latest_crds_contexts.outputs.jwst) || needs.latest_crds_contexts.outputs.jwst }} >> $GITHUB_OUTPUT + outputs: + context: ${{ steps.context.outputs.context }} test: uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 - needs: [ crds_contexts ] + needs: [ crds_context ] with: setenv: | CRDS_PATH: /tmp/data/crds_cache - CRDS_SERVER_URL: https://jwst-crds.stsci.edu + CRDS_SERVER_URL: ${{ github.event_name == 'workflow_dispatch' && inputs.crds_server || 'https://jwst-crds.stsci.edu' }} + CRDS_CONTEXT: ${{ needs.crds_context.outputs.context }} CRDS_CLIENT_RETRY_COUNT: 3 CRDS_CLIENT_RETRY_DELAY_SECONDS: 20 cache-path: /tmp/data/crds_cache - cache-key: crds-${{ needs.crds_contexts.outputs.jwst }} + cache-key: crds-${{ needs.crds_context.outputs.context }} envs: | - linux: py310-oldestdeps-xdist-cov pytest-results-summary: true diff --git a/.github/workflows/ci_cron.yml b/.github/workflows/ci_cron.yml index 80ae34a8be..33addc0358 100644 --- a/.github/workflows/ci_cron.yml +++ b/.github/workflows/ci_cron.yml @@ -5,22 +5,46 @@ on: # Weekly Monday 6AM build - cron: "0 0 * * 1" workflow_dispatch: + inputs: + crds_context: + description: CRDS context (leave blank for latest) + type: string + required: false + default: '' + crds_server: + description: CRDS server + type: string + required: false + default: https://jwst-crds.stsci.edu + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ inputs.crds_context }} + cancel-in-progress: true jobs: - crds_contexts: + latest_crds_contexts: uses: spacetelescope/crds/.github/workflows/contexts.yml@master + crds_context: + needs: [ latest_crds_contexts ] + runs-on: ubuntu-latest + steps: + - id: context + run: echo context=${{ github.event_name == 'workflow_dispatch' && (inputs.crds_context != '' && inputs.crds_context || needs.latest_crds_contexts.outputs.jwst) || needs.latest_crds_contexts.outputs.jwst }} >> $GITHUB_OUTPUT + outputs: + context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run scheduled tests'))) uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 - needs: [ crds_contexts ] + needs: [ crds_context ] with: setenv: | CRDS_PATH: /tmp/crds_cache - CRDS_SERVER_URL: https://jwst-crds.stsci.edu + CRDS_SERVER_URL: ${{ github.event_name == 'workflow_dispatch' && inputs.crds_server || 'https://jwst-crds.stsci.edu' }} + CRDS_CONTEXT: ${{ needs.crds_context.outputs.context }} CRDS_CLIENT_RETRY_COUNT: 3 CRDS_CLIENT_RETRY_DELAY_SECONDS: 20 cache-path: /tmp/crds_cache - cache-key: crds-${{ needs.crds_context.outputs.jwst }} + cache-key: crds-${{ needs.crds_context.outputs.context }} envs: | - macos: py310-xdist - macos: py312-xdist diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index 49ad482d5b..6e0aef5081 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -14,26 +14,46 @@ on: # Weekly Monday 9AM build - cron: "0 9 * * 1" workflow_dispatch: + inputs: + crds_context: + description: CRDS context (leave blank for latest) + type: string + required: false + default: '' + crds_server: + description: CRDS server + type: string + required: false + default: https://jwst-crds.stsci.edu concurrency: - group: ${{ github.workflow }}-${{ github.ref }} + group: ${{ github.workflow }}-${{ github.ref }}-${{ inputs.crds_context }} cancel-in-progress: true jobs: - crds_contexts: + latest_crds_contexts: uses: spacetelescope/crds/.github/workflows/contexts.yml@master + crds_context: + needs: [ latest_crds_contexts ] + runs-on: ubuntu-latest + steps: + - id: context + run: echo context=${{ github.event_name == 'workflow_dispatch' && (inputs.crds_context != '' && inputs.crds_context || needs.latest_crds_contexts.outputs.jwst) || needs.latest_crds_contexts.outputs.jwst }} >> $GITHUB_OUTPUT + outputs: + context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run devdeps tests'))) uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 - needs: [ crds_contexts ] + needs: [ crds_context ] with: setenv: | CRDS_PATH: /tmp/data/crds_cache - CRDS_SERVER_URL: https://jwst-crds.stsci.edu + CRDS_SERVER_URL: ${{ github.event_name == 'workflow_dispatch' && inputs.crds_server || 'https://jwst-crds.stsci.edu' }} + CRDS_CONTEXT: ${{ needs.crds_context.outputs.context }} CRDS_CLIENT_RETRY_COUNT: 3 CRDS_CLIENT_RETRY_DELAY_SECONDS: 20 cache-path: /tmp/data/crds_cache - cache-key: crds-${{ needs.crds_contexts.outputs.jwst }} + cache-key: crds-${{ needs.crds_context.outputs.context }} envs: | - linux: py3-xdist pytest-results-summary: true From 2406bcf48c63b12d2dc4679c0f9940ab2af42db0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 8 Aug 2024 16:56:01 -0400 Subject: [PATCH 289/687] Add fixed slit name to level3 names if available --- jwst/associations/lib/dms_base.py | 6 ++---- jwst/associations/lib/rules_level3_base.py | 11 ++++++----- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/jwst/associations/lib/dms_base.py b/jwst/associations/lib/dms_base.py index d3b885174f..97882a880f 100644 --- a/jwst/associations/lib/dms_base.py +++ b/jwst/associations/lib/dms_base.py @@ -765,7 +765,7 @@ def _get_slit_name(self): else: values.sort(key=str.lower) value = format_list(values) - if value not in _EMPTY: + if value not in _EMPTY and value not in slit_names: slit_names.append(value) # Build the string. Sort the elements in order to @@ -773,9 +773,7 @@ def _get_slit_name(self): slit_names.sort(key=str.lower) slit_name = '-'.join(slit_names) - if slit_name == '': - slit_name = None - + # Slit name may be empty string return slit_name def _get_subarray(self): diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index b16a444aed..f914c4596a 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -158,6 +158,10 @@ def _dms_product_name(association): opt_elem = association._get_opt_element() + slit_name = association._get_slit_name() + if slit_name: + slit_name = '-' + slit_name + exposure = association._get_exposure() if exposure: exposure = '-' + exposure @@ -170,7 +174,7 @@ def _dms_product_name(association): 'jw{program}-{acid}' '_{target}' '_{instrument}' - '_{opt_elem}{subarray}' + '_{opt_elem}{slit_name}{subarray}' ) product_name = product_name.format( program=association.data['program'], @@ -178,6 +182,7 @@ def _dms_product_name(association): target=target, instrument=instrument, opt_elem=opt_elem, + slit_name=slit_name, subarray=subarray, exposure=exposure ) @@ -674,10 +679,6 @@ def dms_product_name_nrsfs_sources(asn): opt_elem = asn._get_opt_element() - slit_name = asn._get_slit_name() - if slit_name: - slit_name = '-' + slit_name - subarray = asn._get_subarray() if subarray: subarray = '-' + subarray From da148e64b3bbb46788379e2bbcc39f03c9fd2262 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 8 Aug 2024 18:29:31 -0400 Subject: [PATCH 290/687] More specific tests for level3 product names --- jwst/associations/lib/rules_level3_base.py | 7 +++- .../tests/test_level3_product_names.py | 32 ++++++++++++++----- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index f914c4596a..5cec88f0e6 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -638,6 +638,10 @@ def dms_product_name_sources(asn): opt_elem = asn._get_opt_element() + slit_name = asn._get_slit_name() + if slit_name: + slit_name = '-' + slit_name + subarray = asn._get_subarray() if subarray: subarray = '-' + subarray @@ -646,7 +650,7 @@ def dms_product_name_sources(asn): 'jw{program}-{acid}' '_{source_id}' '_{instrument}' - '_{opt_elem}{subarray}' + '_{opt_elem}{slit_name}{subarray}' ) product_name = format_product( product_name_format, @@ -654,6 +658,7 @@ def dms_product_name_sources(asn): acid=asn.acid.id, instrument=instrument, opt_elem=opt_elem, + slit_name=slit_name, subarray=subarray, ) diff --git a/jwst/associations/tests/test_level3_product_names.py b/jwst/associations/tests/test_level3_product_names.py index 5259bf2a76..88b2d65f94 100644 --- a/jwst/associations/tests/test_level3_product_names.py +++ b/jwst/associations/tests/test_level3_product_names.py @@ -117,11 +117,27 @@ def test_multiple_optelems(pool_file): if asn['asn_rule'] != 'Asn_Lv3MIRMRS': m = re.match(LEVEL3_PRODUCT_NAME_REGEX, product_name) assert m is not None - try: - value = '-'.join(asn.constraints['opt_elem2'].found_values) - except KeyError: - value = None - if value in EMPTY: - assert '-' not in m.groupdict()['opt_elem'] - else: - assert '-' in m.groupdict()['opt_elem'] + + # should always be an opt_elem + values = ['-'.join(asn.constraints['opt_elem'].found_values)] + + # may be an opt_elem2, fixed slit or 2, or a subarray + for extra in ['opt_elem2', 'fxd_slit', 'fxd_slit2', 'subarray']: + + # special rules for fixed slit for NRS FS: + # it gets a format placeholder instead of the value + if asn['asn_rule'] == 'Asn_Lv3NRSFSS': + if extra == 'fxd_slit': + values.append('{slit_name}') + continue + elif extra == 'fxd_slit2': + continue + + try: + value = '-'.join(asn.constraints[extra].found_values) + except KeyError: + value = '' + if value: + values.append(value) + + assert m.groupdict()['opt_elem'] == '-'.join(values) From f28d8f8d6f629757c608b9951e49f05aab63dcdf Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 9 Aug 2024 09:45:58 -0400 Subject: [PATCH 291/687] Update comments for clarity --- jwst/associations/lib/rules_level3_base.py | 6 ++++-- jwst/associations/tests/test_level3_product_names.py | 8 ++++---- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index 5cec88f0e6..b072f6cbea 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -666,8 +666,10 @@ def dms_product_name_sources(asn): def dms_product_name_nrsfs_sources(asn): - """Produce source-based product names for - NIRSpec fixed-slit observations. + """Produce source-based product names for NIRSpec fixed-slit observations. + + For this mode, the product names have a placeholder for the + slit name, to be filled in later by the pipeline. Parameters --------- diff --git a/jwst/associations/tests/test_level3_product_names.py b/jwst/associations/tests/test_level3_product_names.py index 88b2d65f94..1da85ce9a6 100644 --- a/jwst/associations/tests/test_level3_product_names.py +++ b/jwst/associations/tests/test_level3_product_names.py @@ -118,10 +118,10 @@ def test_multiple_optelems(pool_file): m = re.match(LEVEL3_PRODUCT_NAME_REGEX, product_name) assert m is not None - # should always be an opt_elem + # there should always be an opt_elem values = ['-'.join(asn.constraints['opt_elem'].found_values)] - # may be an opt_elem2, fixed slit or 2, or a subarray + # there may also be an opt_elem2, fixed slit or 2, or a subarray for extra in ['opt_elem2', 'fxd_slit', 'fxd_slit2', 'subarray']: # special rules for fixed slit for NRS FS: @@ -136,8 +136,8 @@ def test_multiple_optelems(pool_file): try: value = '-'.join(asn.constraints[extra].found_values) except KeyError: - value = '' - if value: + value = None + if value not in EMPTY: values.append(value) assert m.groupdict()['opt_elem'] == '-'.join(values) From b4a4ccf23f200b0ddf9e6a095706da3fb08fac75 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 9 Aug 2024 11:09:08 -0400 Subject: [PATCH 292/687] Update tests to check more conditions --- .../tests/test_level3_product_names.py | 38 +++++++++++++++++-- 1 file changed, 34 insertions(+), 4 deletions(-) diff --git a/jwst/associations/tests/test_level3_product_names.py b/jwst/associations/tests/test_level3_product_names.py index 1da85ce9a6..74213b024b 100644 --- a/jwst/associations/tests/test_level3_product_names.py +++ b/jwst/associations/tests/test_level3_product_names.py @@ -96,7 +96,7 @@ def test_level3_names(pool_file, global_constraints): rules = registry_level3_only( global_constraints=global_constraints ) - pool = AssociationPool.read(pool_file) + pool = combine_pools(pool_file) asns = generate(pool, rules) for asn in asns: product_name = asn['products'][0]['name'] @@ -110,8 +110,8 @@ def test_level3_names(pool_file, global_constraints): def test_multiple_optelems(pool_file): rules = registry_level3_only() - pool = AssociationPool.read(pool_file) - asns = generate(pool, rules) + pool = combine_pools(pool_file) + asns = generate(pool, rules, finalize=False) for asn in asns: product_name = asn['products'][0]['name'] if asn['asn_rule'] != 'Asn_Lv3MIRMRS': @@ -137,7 +137,37 @@ def test_multiple_optelems(pool_file): value = '-'.join(asn.constraints[extra].found_values) except KeyError: value = None - if value not in EMPTY: + + # empty values and subarray = full are not recorded + if value not in EMPTY and value != 'full': values.append(value) assert m.groupdict()['opt_elem'] == '-'.join(values) + + +def test_tso3_names(): + rules = registry_level3_only() + tso_pool = t_path('data/pool_021_tso.csv') + pool = combine_pools(tso_pool) + asns = generate(pool, rules, finalize=False) + for asn in asns: + product_name = asn['products'][0]['name'] + + m = re.match(LEVEL3_PRODUCT_NAME_REGEX, product_name) + assert m is not None + + # there should always be an opt_elem + values = ['-'.join(asn.constraints['opt_elem'].found_values)] + + # there may also be an opt_elem2, fixed slit or 2, or a subarray + for extra in ['opt_elem2', 'fxd_slit', 'fxd_slit2', 'subarray']: + try: + value = '-'.join(asn.constraints[extra].found_values) + except KeyError: + value = None + + # empty values and subarray = full are not recorded + if value not in EMPTY and value != 'full': + values.append(value) + + assert m.groupdict()['opt_elem'] == '-'.join(values) From 253e46eaa726bf1b2caa42c044ed6bd50002323d Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 9 Aug 2024 12:02:39 -0400 Subject: [PATCH 293/687] Update change log --- CHANGES.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 23ed8e6807..bac9999306 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -17,6 +17,12 @@ assign_wcs - Moved `update_s_region_imaging`, `update_s_region_keyword`, and `wcs_from_footprints` into stcal. [#8624] +associations +------------ + +- Restored slit name to level 3 product names for NIRSpec BOTS and background + fixed slit targets. [#8699] + cube_build ---------- From aeb7469b2cce0d1dc97a2f44c1791592b9a66fc4 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 9 Aug 2024 12:13:55 -0400 Subject: [PATCH 294/687] Code style fix --- jwst/associations/tests/test_level3_product_names.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/associations/tests/test_level3_product_names.py b/jwst/associations/tests/test_level3_product_names.py index 74213b024b..c77a146201 100644 --- a/jwst/associations/tests/test_level3_product_names.py +++ b/jwst/associations/tests/test_level3_product_names.py @@ -9,7 +9,7 @@ t_path, ) -from jwst.associations import (AssociationPool, generate) +from jwst.associations import generate from jwst.associations.lib.dms_base import DMSAttrConstraint From 38ea9b7e43f861cd36968feb562940266aae16d2 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Wed, 14 Aug 2024 09:06:46 -0400 Subject: [PATCH 295/687] [SCSB-170] pin Python<3.13 (#8703) --- .github/workflows/build.yml | 12 ++++++------ pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f842f29b8a..cc6c3dfd50 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -25,12 +25,12 @@ jobs: with: upload_to_pypi: ${{ (github.event_name == 'release') && (github.event.action == 'released') }} targets: | - # Linux wheels (except python 313) - - cp31[!3]-manylinux_x86_64 - # MacOS wheels (except python 313) - - cp31[!3]-macosx_x86_64 - # MacOS arm64 wheels (except python 313) - - cp31[!3]-macosx_arm64 + # Linux wheels + - cp3*-manylinux_x86_64 + # MacOS wheels + - cp3*-macosx_x86_64 + # MacOS arm64 wheels + - cp3*-macosx_arm64 sdist: true test_command: python -c "from jwst.lib import winclip; from jwst.cube_build import cube_match_internal, cube_match_sky_pointcloud, cube_match_sky_driz, blot_median; from jwst.straylight import calc_xart" secrets: diff --git a/pyproject.toml b/pyproject.toml index 242dcbf8bf..836047350e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "jwst" description = "Library for calibration of science observations from the James Webb Space Telescope" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.13" authors = [ { name = "JWST calibration pipeline developers" }, ] From 1a45e3480c6e7d6c103a188c04ce4041084fc644 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 14 Aug 2024 17:20:30 -0400 Subject: [PATCH 296/687] update dep for stcal to released version (#8706) --- CHANGES.rst | 2 ++ pyproject.toml | 3 +-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 23ed8e6807..b973b457be 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -34,6 +34,8 @@ general - Remove the unused ``stsci.image`` dependency. [#8663] +- Update required stcal version to 1.8.0. [#8706] + master_background ----------------- diff --git a/pyproject.toml b/pyproject.toml index 836047350e..8b00b35583 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,8 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - # "stcal>=1.7.3,<1.8.0", - "stcal @ git+https://github.com/spacetelescope/stcal.git", + "stcal>=1.8.0,<1.9.0", "stdatamodels>=2.0.0,<2.1.0", "stpipe>=0.6.0,<0.7.0", "stsci.imagestats>=1.6.3", From 84cfd56e89262812c962f3d29bab55d168805e13 Mon Sep 17 00:00:00 2001 From: "P. L. Lim" <2090236+pllim@users.noreply.github.com> Date: Mon, 19 Aug 2024 15:22:06 -0400 Subject: [PATCH 297/687] TST: Use photutils nightly wheel (#8701) --- requirements-dev-thirdparty.txt | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/requirements-dev-thirdparty.txt b/requirements-dev-thirdparty.txt index 88fcfa2ccc..51eaeb82e3 100644 --- a/requirements-dev-thirdparty.txt +++ b/requirements-dev-thirdparty.txt @@ -5,10 +5,12 @@ git+https://github.com/asdf-format/asdf-transform-schemas git+https://github.com/asdf-format/asdf-coordinates-schemas git+https://github.com/asdf-format/asdf-wcs-schemas -# Use nightly astropy dev build +# Use nightly Astropy dev builds git+https://github.com/astropy/asdf-astropy -git+https://github.com/astropy/photutils ---extra-index-url https://pypi.anaconda.org/astropy/simple astropy --pre +--extra-index-url https://pypi.anaconda.org/astropy/simple +pyerfa>=0.0.dev0 +astropy>=0.0.dev0 +photutils>=0.0.dev0 # Use Bi-weekly numpy/scipy dev builds --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple From addc2f29c22146f41c8b4ff234503d0f1b70cbf7 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Mon, 19 Aug 2024 15:23:24 -0400 Subject: [PATCH 298/687] Documentation typo (#8700) --- docs/jwst/data_products/guidestar_products.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/data_products/guidestar_products.rst b/docs/jwst/data_products/guidestar_products.rst index 7d8d6c27ea..2869fb9c5d 100644 --- a/docs/jwst/data_products/guidestar_products.rst +++ b/docs/jwst/data_products/guidestar_products.rst @@ -42,7 +42,7 @@ where the file name fields are: :ppppp: program id :ooo: observation number :vvv: visit number -:m: ID attempt counter (1-8) +:m: ID attempt counter (1-9) :yyyydddhhmmss: time stamp at the end of the data in the file Uncalibrated products use the "uncal" file name suffix as shown above, while calibrated From 8efc26fdbda7936199d1a05cb57707d2c1cb0665 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 20 Aug 2024 15:38:39 -0400 Subject: [PATCH 299/687] bump stpipe (#8713) --- CHANGES.rst | 2 ++ pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index b973b457be..d507be5fe6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -36,6 +36,8 @@ general - Update required stcal version to 1.8.0. [#8706] +- Increase minimum required stpipe. [#8713] + master_background ----------------- diff --git a/pyproject.toml b/pyproject.toml index 8b00b35583..ff6b557472 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ dependencies = [ "spherical-geometry>=1.2.22", "stcal>=1.8.0,<1.9.0", "stdatamodels>=2.0.0,<2.1.0", - "stpipe>=0.6.0,<0.7.0", + "stpipe>=0.7.0,<0.8.0", "stsci.imagestats>=1.6.3", "synphot>=1.2", "tweakwcs>=0.8.8", From f106f13ee1a48b3b59cbe632eff5630a8d9e6c4f Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 20 Aug 2024 16:08:39 -0400 Subject: [PATCH 300/687] add changelog to docs --- docs/index.rst | 1 + docs/jwst/changes.rst | 7 +++++++ 2 files changed, 8 insertions(+) create mode 100644 docs/jwst/changes.rst diff --git a/docs/index.rst b/docs/index.rst index a27a560828..431ba0abcb 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -78,3 +78,4 @@ Package Documentation :caption: Package Documentation jwst/package_index.rst + jwst/changes.rst diff --git a/docs/jwst/changes.rst b/docs/jwst/changes.rst new file mode 100644 index 0000000000..6529e8d57f --- /dev/null +++ b/docs/jwst/changes.rst @@ -0,0 +1,7 @@ +.. currentmodule:: jwst + +*********** +Change Log +*********** + +.. include:: ../../CHANGES.rst From d7f20aae6564da7c45e9b7024510674431c5a6c2 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 20 Aug 2024 16:17:46 -0400 Subject: [PATCH 301/687] fix errors in changelog formatting --- CHANGES.rst | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index d507be5fe6..5f814aadf6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1138,7 +1138,7 @@ datamodels ---------- - Added support for user-supplied ``group_id`` either via - ``model.meta.group_id`` attribute or as a member of the ASN table. [#7997] + ``model.meta.group_id`` attribute or as a member of the ASN table. [#7997] refpix ------ @@ -1343,7 +1343,7 @@ datamodels documentation ------------- -- Fixed a reference to the ``ramp_fitting` module in the user documentation. [#7898] +- Fixed a reference to the ``ramp_fitting`` module in the user documentation. [#7898] engdb_tools ----------- @@ -1745,6 +1745,7 @@ regtest ------- - Updated input filenames for NIRCam ``wfss_contam`` tests [#7595] + srctype ------- @@ -2479,7 +2480,7 @@ residual_fringe --------------- - Removed reading and saving data as a ModelContainer. Data now read in and saved -as an IFUImageModel. [#7051] + as an IFUImageModel. [#7051] set_telescope_pointing @@ -3541,7 +3542,7 @@ cube_build - Fix incorrect spatial footprint for single band MRS IFU cubes [#6478] dark_current ----------- +------------ - Refactored the code in preparation for moving the code to STCAL. [#6336] @@ -3583,7 +3584,7 @@ datamodels - Added SOSS-specific extraction parameters to core schema; add new datamodel to store SOSS model traces and aperture weights [#6422] -- Added the ``MirLrsPathlossModel`` for use in the ``pathloss` step. [#6435] +- Added the ``MirLrsPathlossModel`` for use in the ``pathloss`` step. [#6435] - Added new column 'reference_order' to 'planned_star_table' in guider_raw and guider_cal schemas [#6368] @@ -3677,7 +3678,7 @@ lib - Move setting of the default method to calc_transforms. [#6482] linearity --------- +--------- - Use the common code in STCAL for linearity correction. [#6386] @@ -4660,7 +4661,7 @@ cube_build appropriate channel/grating is skipped [#5347] - If outlier detection has flagged all the data on a input file as DO_NOT_USE, then - skip the file in creating an ifucube [*5347] + skip the file in creating an ifucube [#5347] - Refactor DataTypes handling of ModelContainer. [#5409] @@ -6279,7 +6280,7 @@ pipeline - ``calwebb_tso3`` was changed to allow processing of exposures with ``EXP_TYPE=MIR_IMAGE.`` [#3633] -- - ``calwebb_tso3`` was changed to allow tso photometry processing of exposures +- ``calwebb_tso3`` was changed to allow tso photometry processing of exposures with (``EXP_TYPE=MIR_IMAGE`` and tsovisit = True) or with (``EXP_TYPE=MIR_IMAGE``) [#3650] - Changed the default value of good_pixel from 4 to 6 for all outlier @@ -7439,7 +7440,7 @@ csv_tools --------- cube_build ---------- +---------- - Removed spaxel.py and replace class with set of arrays [#2472] - reworked in mapping of the detector pixel to the sky spaxel so that consistent @@ -7729,7 +7730,7 @@ csv_tools --------- cube_build ---------- +---------- cube_skymatch From ea6b58dd129c22c85f603748ef10ec4e0ab3a64b Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 20 Aug 2024 16:18:40 -0400 Subject: [PATCH 302/687] add changelog entry --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 5f814aadf6..969acee276 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -23,6 +23,11 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] +documentation +------------- + +- Add changelog to documentation. [#8716] + emicorr ------- From f860547f21cd1fff34f1291a94b1c513264ef61d Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 20 Aug 2024 16:28:41 -0400 Subject: [PATCH 303/687] fix another formatting issue --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 969acee276..51f99afa92 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1810,7 +1810,7 @@ other ----- - Remove use of deprecated ``pytest-openfiles`` plugin. This has been replaced by - catching ``ResourceWarning``s. [#7526] + catching ``ResourceWarning``. [#7526] - Remove use of ``codecov`` package. [#7543] From 3c053d10a1d4a04dbd642bbd61d17c706974cc43 Mon Sep 17 00:00:00 2001 From: Zhen-Kai Gao Date: Tue, 27 Aug 2024 00:50:03 +0800 Subject: [PATCH 304/687] Fix a typo in `load_custom_wcs` (#8698) Co-authored-by: Melanie Clarke --- CHANGES.rst | 7 + jwst/resample/resample_spec.py | 4 +- jwst/resample/resample_step.py | 16 +- jwst/resample/tests/test_resample_step.py | 214 +++++++++++++++++++++- 4 files changed, 232 insertions(+), 9 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index d507be5fe6..47b2856642 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -79,6 +79,13 @@ resample_spec so that the spectral resampling step only exposes parameters that are appropriate for spectral data. [#8622] +resample_step +------------- + +- Fixed a typo in ``load_custom_wcs`` from ``array_shape`` to ``pixel_shape`` and + changed to use values in the top-level ASDF structure if the values in the WCS + are ``None``. [#8698] + scripts ------- diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index f3f6414011..54791bfa5d 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -126,9 +126,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, log.warning("Unable to compute output pixel area " "from 'output_wcs'.") output_pix_area = None - else: # pragma: no cover - # This clause is not reachable under usual circumstances: - # gwcs WCS discards the pixel_area attribute when saved as ASDF + else: log.debug(f'Setting output pixel area from wcs.pixel_area: ' f'{output_wcs.pixel_area}') output_pix_area = output_wcs.pixel_area diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 444a2a9050..4c2d647985 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -176,9 +176,16 @@ def load_custom_wcs(asdf_wcs_file, output_shape=None): with asdf.open(asdf_wcs_file) as af: wcs = deepcopy(af.tree["wcs"]) - wcs.pixel_area = af.tree.get("pixel_area", None) - wcs.array_shape = af.tree.get("pixel_shape", None) - wcs.array_shape = af.tree.get("array_shape", None) + pixel_area = af.tree.get("pixel_area", None) + pixel_shape = af.tree.get("pixel_shape", None) + array_shape = af.tree.get("array_shape", None) + + if not hasattr(wcs, "pixel_area") or wcs.pixel_area is None: + wcs.pixel_area = pixel_area + if not hasattr(wcs, "pixel_shape") or wcs.pixel_shape is None: + wcs.pixel_shape = pixel_shape + if not hasattr(wcs, "array_shape") or wcs.array_shape is None: + wcs.array_shape = array_shape if output_shape is not None: wcs.array_shape = output_shape[::-1] @@ -192,10 +199,11 @@ def load_custom_wcs(asdf_wcs_file, output_shape=None): int(axs[1] + 0.5) for axs in wcs.bounding_box.bounding_box(order="C") ) + wcs.pixel_shape = wcs.array_shape[::-1] else: raise ValueError( "Step argument 'output_shape' is required when custom WCS " - "does not have neither of 'array_shape', 'pixel_shape', or " + "does not have 'array_shape', 'pixel_shape', or " "'bounding_box' attributes set." ) diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 7f8c479f99..199000c1f9 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -832,7 +832,7 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, refwcs = str(tmp_path / "resample_refwcs.asdf") result.meta.wcs.bounding_box = [(-0.5, 1204.5), (-0.5, 1099.5)] - asdf.AsdfFile({"wcs": result.meta.wcs}).write_to(tmp_path / refwcs) + asdf.AsdfFile({"wcs": result.meta.wcs}).write_to(refwcs) result = ResampleStep.call( im, @@ -865,6 +865,66 @@ def test_custom_refwcs_resample_imaging(nircam_rate, output_shape2, match, result.close() +def test_custom_refwcs_pixel_shape_imaging(nircam_rate, tmp_path): + + # make some data with a WCS and some random values + im = AssignWcsStep.call(nircam_rate, sip_approx=False) + rng = np.random.default_rng(seed=77) + im.data[:, :] = rng.random(im.data.shape) + + crpix = (600, 550) + crval = (22.04, 11.98) + rotation = 15 + ratio = 0.7 + + # first pass - create a reference output WCS: + result = ResampleStep.call( + im, + output_shape=(1205, 1100), + crpix=crpix, + crval=crval, + rotation=rotation, + pixel_scale_ratio=ratio + ) + + # make sure results are nontrivial + data1 = result.data + assert not np.all(np.isnan(data1)) + + # remove the bounding box so shape is set from pixel_shape + # and also set a top-level pixel area + pixel_area = 1e-13 + refwcs = str(tmp_path / "resample_refwcs.asdf") + result.meta.wcs.bounding_box = None + asdf.AsdfFile({"wcs": result.meta.wcs, + "pixel_area": pixel_area}).write_to(refwcs) + + result = ResampleStep.call(im, output_wcs=refwcs) + + data2 = result.data + assert not np.all(np.isnan(data2)) + + # test output image shape + assert data1.shape == data2.shape + assert np.allclose(data1, data2, equal_nan=True) + + # make sure pixel values are similar, accounting for scale factor + # (assuming inputs are in surface brightness units) + iscale = np.sqrt(im.meta.photometry.pixelarea_steradians + / compute_image_pixel_area(im.meta.wcs)) + input_mean = np.nanmean(im.data) + output_mean_1 = np.nanmean(data1) + output_mean_2 = np.nanmean(data2) + assert np.isclose(input_mean * iscale**2, output_mean_1, atol=1e-4) + assert np.isclose(input_mean * iscale**2, output_mean_2, atol=1e-4) + + # check that output pixel area is set from input + assert np.isclose(result.meta.photometry.pixelarea_steradians, pixel_area) + + im.close() + result.close() + + @pytest.mark.parametrize('ratio', [0.7, 1.0, 1.3]) def test_custom_refwcs_resample_miri(miri_cal, tmp_path, ratio): im = miri_cal @@ -936,7 +996,7 @@ def test_custom_refwcs_resample_nirspec(nirspec_cal, tmp_path, ratio): # save the wcs from the output refwcs = str(tmp_path / "resample_refwcs.asdf") - asdf.AsdfFile({"wcs": result.slits[0].meta.wcs}).write_to(tmp_path / refwcs) + asdf.AsdfFile({"wcs": result.slits[0].meta.wcs}).write_to(refwcs) # run again, this time using the created WCS as input result = ResampleSpecStep.call(im, output_wcs=refwcs) @@ -961,6 +1021,52 @@ def test_custom_refwcs_resample_nirspec(nirspec_cal, tmp_path, ratio): result.close() +def test_custom_refwcs_pixel_shape_nirspec(nirspec_cal, tmp_path): + im = nirspec_cal + for slit in im.slits: + slit.meta.bunit_data = "MJy/sr" + + # mock a spectrum by giving the first slit some random + # values at the center + rng = np.random.default_rng(seed=77) + new_values = rng.random(im.slits[0].data.shape) + + center = im.slits[0].data.shape[0] // 2 + im.slits[0].data[:] = 0.0 + im.slits[0].data[center - 2:center + 2, :] = new_values[center - 2:center + 2, :] + + # first pass: create a reference output WCS with a custom pixel scale + ratio = 0.7 + result = ResampleSpecStep.call(im, pixel_scale_ratio=ratio) + + # make sure results are nontrivial + data1 = result.slits[0].data + assert not np.all(np.isnan(data1)) + + # remove the bounding box from the WCS so shape is set from pixel_shape + # and also set a top-level pixel area + pixel_area = 1e-13 + refwcs = str(tmp_path / "resample_refwcs.asdf") + asdf.AsdfFile({"wcs": result.slits[0].meta.wcs, + "pixel_area": pixel_area}).write_to(refwcs) + + # run again, this time using the created WCS as input + result = ResampleSpecStep.call(im, output_wcs=refwcs) + + data2 = result.slits[0].data + assert not np.all(np.isnan(data2)) + + # check output data against first pass + assert data1.shape == data2.shape + assert np.allclose(data1, data2, equal_nan=True, rtol=1e-4) + + # check that output pixel area is set from output_wcs + assert np.isclose(result.slits[0].meta.photometry.pixelarea_steradians, pixel_area) + + im.close() + result.close() + + @pytest.mark.parametrize('ratio', [1.3, 1]) def test_custom_wcs_pscale_resample_imaging(nircam_rate, ratio): im = AssignWcsStep.call(nircam_rate, sip_approx=False) @@ -1000,6 +1106,7 @@ def test_custom_wcs_pscale_resample_miri(miri_cal, ratio): result.close() + @pytest.mark.parametrize('ratio', [1.3, 1]) def test_custom_wcs_pscale_resample_nirspec(nirspec_cal, ratio): im = nirspec_cal.slits[0] @@ -1019,6 +1126,109 @@ def test_custom_wcs_pscale_resample_nirspec(nirspec_cal, ratio): result.close() +@pytest.mark.parametrize('wcs_attr', ['pixel_shape', 'array_shape', 'bounding_box']) +def test_custom_wcs_input(tmp_path, nircam_rate, wcs_attr): + # make a valid WCS + im = AssignWcsStep.call(nircam_rate, sip_approx=False) + wcs = im.meta.wcs + + # store values in a dictionary + wcs_dict = {'array_shape': im.data.shape, + 'pixel_shape': im.data.shape[::-1], + 'bounding_box': wcs.bounding_box} + + # Set all attributes to None + for attr in ['pixel_shape', 'array_shape', 'bounding_box']: + setattr(wcs, attr, None) + + # Set the attribute to the correct value + setattr(wcs, wcs_attr, wcs_dict[wcs_attr]) + + # write the WCS to an asdf file + refwcs = str(tmp_path / 'test_wcs.asdf') + asdf.AsdfFile({"wcs": wcs}).write_to(refwcs) + + # load the WCS from the asdf file + loaded_wcs = ResampleStep.load_custom_wcs(refwcs) + + # check that the loaded WCS has the correct values + for attr in ['pixel_shape', 'array_shape']: + assert np.allclose(getattr(loaded_wcs, attr), wcs_dict[attr]) + + +@pytest.mark.parametrize('override,value', + [('pixel_area', 1e-13), ('pixel_shape', (300, 400)), + ('array_shape', (400, 300))]) +def test_custom_wcs_input_overrides(tmp_path, nircam_rate, override, value): + # make a valid WCS + im = AssignWcsStep.call(nircam_rate, sip_approx=False) + wcs = im.meta.wcs + + # remove existing shape keys if testing shape overrides + if override != 'pixel_area': + wcs.pixel_shape = None + wcs.bounding_box = None + + expected_array_shape = im.data.shape + expected_pixel_shape = im.data.shape[::-1] + expected_pixel_area = None + + # write the WCS to an asdf file with a top-level override + refwcs = str(tmp_path / 'test_wcs.asdf') + asdf.AsdfFile({"wcs": wcs, override: value}).write_to(refwcs) + + # check for expected values when read back in + keys = ['pixel_area', 'pixel_shape', 'array_shape'] + loaded_wcs = ResampleStep.load_custom_wcs(refwcs) + for key in keys: + if key == override: + assert np.allclose(getattr(loaded_wcs, key), value) + elif key == 'pixel_shape': + if override == 'array_shape': + assert np.allclose(getattr(loaded_wcs, key), value[::-1]) + else: + assert np.allclose(getattr(loaded_wcs, key), expected_pixel_shape) + elif key == 'array_shape': + if override == 'pixel_shape': + assert np.allclose(getattr(loaded_wcs, key), value[::-1]) + else: + assert np.allclose(getattr(loaded_wcs, key), expected_array_shape) + elif key == 'pixel_area': + assert getattr(loaded_wcs, key) == expected_pixel_area + + +def test_custom_wcs_input_error(tmp_path, nircam_rate): + # make a valid WCS + im = AssignWcsStep.call(nircam_rate, sip_approx=False) + wcs = im.meta.wcs + + # remove shape settings + wcs.pixel_shape = None + wcs.array_shape = None + wcs.bounding_box = None + + # write the WCS to an asdf file + refwcs = str(tmp_path / 'test_wcs.asdf') + asdf.AsdfFile({"wcs": wcs}).write_to(refwcs) + + # loading the file without shape info should produce an error + with pytest.raises(ValueError, match="'output_shape' is required"): + loaded_wcs = ResampleStep.load_custom_wcs(refwcs) + + # providing an output shape should succeed + output_shape = (300, 400) + loaded_wcs = ResampleStep.load_custom_wcs(refwcs, output_shape=output_shape) + + # array shape is opposite of input values (numpy convention) + assert np.all(loaded_wcs.array_shape == output_shape[::-1]) + + # pixel shape matches + assert np.all(loaded_wcs.pixel_shape == output_shape) + + # bounding box is not set + assert loaded_wcs.bounding_box is None + + def test_pixscale(nircam_rate): # check that if both 'pixel_scale_ratio' and 'pixel_scale' are passed in, From 386b74c38c1a4b962edb71c285f10d6fef67d84e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 26 Aug 2024 14:10:01 -0400 Subject: [PATCH 305/687] Fix MIRI array sizing for multiple inputs and pixel ratio != 1 --- jwst/resample/resample_spec.py | 7 +-- jwst/resample/tests/test_resample_step.py | 66 +++++++++++++++++++++++ 2 files changed, 70 insertions(+), 3 deletions(-) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 54791bfa5d..871068ba7f 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -509,6 +509,7 @@ def build_interpolated_output_wcs(self, input_models): all_wavelength = [] all_ra_slit = [] all_dec_slit = [] + xstop = 0 for im, model in enumerate(input_models): wcs = model.meta.wcs @@ -700,10 +701,10 @@ def build_interpolated_output_wcs(self, input_models): else: pix_to_xtan.intercept = -0.5 * (x_size - 1) * pix_to_xtan.slope - # single model use size of x_tan_array + # single model: use size of x_tan_array # to be consistent with method before if len(input_models) == 1: - x_size = len(x_tan_array) + x_size = int(np.ceil(xstop)) # define the output wcs transform = mapping | (pix_to_xtan & pix_to_ytan | undist2sky) & pix_to_wavelength @@ -723,7 +724,7 @@ def build_interpolated_output_wcs(self, input_models): # compute the output array size in WCS axes order, i.e. (x, y) output_array_size = [0, 0] output_array_size[spectral_axis] = int(np.ceil(len(wavelength_array))) - output_array_size[spatial_axis] = int(np.ceil(x_size * self.pscale_ratio)) + output_array_size[spatial_axis] = x_size # turn the size into a numpy shape in (y, x) order output_wcs.array_shape = output_array_size[::-1] diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 199000c1f9..15261f63d2 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -490,6 +490,72 @@ def test_pixel_scale_ratio_spec_miri(miri_cal, ratio, units): result3.close() +@pytest.mark.parametrize("units", ["MJy", "MJy/sr"]) +@pytest.mark.parametrize("ratio", [0.7, 1.0, 1.3]) +def test_pixel_scale_ratio_spec_miri_pair(miri_rate_pair, ratio, units): + im1, im2 = miri_rate_pair + _set_photom_kwd(im1) + _set_photom_kwd(im2) + im1.meta.bunit_data = units + im2.meta.bunit_data = units + im1.meta.filename = 'file1.fits' + im2.meta.filename = 'file2.fits' + im1.data += 1.0 + im2.data += 1.0 + + # Make an input pixel scale equivalent to the specified ratio + input_scale = compute_spectral_pixel_scale(im1.meta.wcs, disp_axis=2) + pscale = 3600.0 * input_scale / ratio + + result1 = ResampleSpecStep.call([im1, im2]) + result2 = ResampleSpecStep.call([im1, im2], pixel_scale_ratio=ratio) + result3 = ResampleSpecStep.call([im1, im2], pixel_scale=pscale) + + # pixel_scale and pixel_scale_ratio should be equivalent + nn = np.isnan(result2.data) | np.isnan(result3.data) + assert np.allclose(result2.data[~nn], result3.data[~nn]) + + # Check result2 for expected results + + # wavelength size does not change + assert result1.data.shape[0] == result2.data.shape[0] + + # spatial dimension is scaled + assert np.isclose(result1.data.shape[1], result2.data.shape[1] / ratio, atol=1) + + # data is non-trivial + assert np.nansum(result1.data) > 0.0 + assert np.nansum(result2.data) > 0.0 + + # flux is conserved + if 'sr' not in units: + # flux density conservation: sum over pixels in each row + # needs to be about the same, other than the edges + # Check the maximum sums, to avoid edges. + assert np.allclose(np.max(np.nansum(result1.data, axis=1)), + np.max(np.nansum(result1.data, axis=1)), rtol=0.05) + else: + # surface brightness conservation: mean values are the same + assert np.allclose(np.nanmean(result1.data, axis=1), + np.nanmean(result2.data, axis=1), rtol=0.05, + equal_nan=True) + + # output area is updated either way + area1 = result1.meta.photometry.pixelarea_steradians + area2 = result2.meta.photometry.pixelarea_steradians + area3 = result2.meta.photometry.pixelarea_steradians + assert np.isclose(area1 / area2, ratio) + assert np.isclose(area1 / area3, ratio) + + assert result1.meta.resample.pixel_scale_ratio == 1.0 + assert result2.meta.resample.pixel_scale_ratio == ratio + assert np.isclose(result3.meta.resample.pixel_scale_ratio, ratio) + + result1.close() + result2.close() + result3.close() + + @pytest.mark.parametrize("units", ["MJy", "MJy/sr"]) @pytest.mark.parametrize("ratio", [0.7, 1.0, 1.3]) def test_pixel_scale_ratio_spec_nirspec(nirspec_cal, ratio, units): From e90cafa198a780521b134756517af90f3592bd6e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 26 Aug 2024 14:19:30 -0400 Subject: [PATCH 306/687] Update change notes --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 467b890551..7ecfe3764a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -73,7 +73,7 @@ resample_spec - Updated handling for the ``pixel_scale_ratio`` parameter to apply only to the spatial dimension, to match the sense of the parameter application to the - documented intent, and to conserve spectral flux when applied. [#8596] + documented intent, and to conserve spectral flux when applied. [#8596, #8727] - Implemented handling for the ``pixel_scale`` parameter, which was previously ignored for spectral resampling. [#8596] From f470186cf680faf9e837f55d1b7d948bb595dd68 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 26 Aug 2024 13:02:03 -0700 Subject: [PATCH 307/687] fixed cube build and added a deprecated warning for mrs_imatch --- jwst/cube_build/ifu_cube.py | 8 ++++++-- jwst/mrs_imatch/mrs_imatch_step.py | 10 ++++++---- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 0085d30153..3cd627338d 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -826,7 +826,7 @@ def build_ifucube_single(self): coord1, coord2, corner_coord, wave, dwave, flux, err, slice_no, \ rois_pixel, roiw_pixel, weight_pixel, \ - softrad_pixel, scalerad_pixel = pixelresult + softrad_pixel, scalerad_pixel, _, _ = pixelresult build_cube = True if wave is None: # there is no valid data on the detector. Pixels are flagged as DO_NOT_USE. @@ -870,6 +870,9 @@ def build_ifucube_single(self): linear = 0 if self.linear_wavelength: linear = 1 + x_det = None + y_det = None + debug_cube = -1 result = cube_wrapper_driz(instrument, flag_dq_plane, start_region, end_region, self.overlap_partial, self.overlap_full, @@ -878,7 +881,8 @@ def build_ifucube_single(self): xi1, eta1, xi2, eta2, xi3, eta3, xi4, eta4, dwave, self.cdelt3_normal, - self.cdelt1, self.cdelt2, cdelt3_mean, linear) + self.cdelt1, self.cdelt2, cdelt3_mean, linear, + x_det, y_det, debug_cube) spaxel_flux, spaxel_weight, spaxel_var, spaxel_iflux, _ = result self.spaxel_flux = self.spaxel_flux + np.asarray(spaxel_flux, np.float64) diff --git a/jwst/mrs_imatch/mrs_imatch_step.py b/jwst/mrs_imatch/mrs_imatch_step.py index 56fc8b2676..dccd6efe3c 100644 --- a/jwst/mrs_imatch/mrs_imatch_step.py +++ b/jwst/mrs_imatch/mrs_imatch_step.py @@ -9,7 +9,7 @@ import numpy as np from jwst.datamodels import ModelContainer - +import warnings from .. stpipe import Step from wiimatch.match import match_lsq from astropy.stats import sigma_clipped_stats as sigclip @@ -29,16 +29,18 @@ class MRSIMatchStep(Step): # General sky matching parameters: bkg_degree = integer(min=0, default=1) # Degree of the polynomial for background fitting subtract = boolean(default=False) # subtract computed sky from 'images' cube data? - + skip = boolean(default=True) # Step must be turned on by parameter reference or user """ reference_file_types = [] def process(self, images): - all_models2d = ModelContainer(images) - chm = {} + # Provide warning to user this code is deprecated + warnings.warn("mrs_imatch deprecated", RuntimeWarning) + chm = {} + all_models2d = ModelContainer(images) for m in all_models2d: ch = m.meta.instrument.channel if ch not in chm: From e97b11b152ece39c3a363562f0ec6d94d75c8e4a Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 26 Aug 2024 13:20:58 -0700 Subject: [PATCH 308/687] updated change log --- CHANGES.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 62082b7580..76e147ae9f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -28,6 +28,7 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] +- Fixed a bug when cube_build was called from the mrs_imatch step. [#8728] documentation ------------- @@ -55,6 +56,11 @@ master_background - Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit as a background slit, instead of ``"bkg"`` only. [#8600] +mrs_imatch +---------- + +- Added a deprecation warning and set the default to skip=True for the step. [#8728] + outlier_detection ----------------- From 2e54815a2af6c7a355ed6c60a6b1b1fb8680d72d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 26 Aug 2024 13:31:44 -0700 Subject: [PATCH 309/687] updated docs to note deprecation and default is to skip --- docs/jwst/mrs_imatch/description.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/jwst/mrs_imatch/description.rst b/docs/jwst/mrs_imatch/description.rst index 6741a00438..33b380da7a 100644 --- a/docs/jwst/mrs_imatch/description.rst +++ b/docs/jwst/mrs_imatch/description.rst @@ -8,6 +8,10 @@ Description Overview -------- + +This step has been deprecated in JWST calibration pipeline version 11.1. +The mrs_imatch step is skipped in the default running of the calwebb_spec3 pipeline. + The ``mrs_imatch`` step "matches" image intensities of several input 2D MIRI MRS images by fitting polynomials to cube intensities (cubes built from the input 2D images), in such a way as to minimize - in the least squares From 7c0dbfb61017679602ac01bcb1993accc417ceca Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 28 Aug 2024 13:19:04 -0700 Subject: [PATCH 310/687] updates from review --- docs/jwst/mrs_imatch/description.rst | 2 +- jwst/mrs_imatch/mrs_imatch_step.py | 4 ++-- jwst/mrs_imatch/tests/test_configuration.py | 15 ++++++++++++++- 3 files changed, 17 insertions(+), 4 deletions(-) diff --git a/docs/jwst/mrs_imatch/description.rst b/docs/jwst/mrs_imatch/description.rst index 33b380da7a..18c61f5653 100644 --- a/docs/jwst/mrs_imatch/description.rst +++ b/docs/jwst/mrs_imatch/description.rst @@ -10,7 +10,7 @@ Overview -------- This step has been deprecated in JWST calibration pipeline version 11.1. -The mrs_imatch step is skipped in the default running of the calwebb_spec3 pipeline. +The ``mrs_imatch`` step is skipped by default in the calwebb_spec3 pipeline. The ``mrs_imatch`` step "matches" image intensities of several input 2D MIRI MRS images by fitting polynomials to cube intensities (cubes built diff --git a/jwst/mrs_imatch/mrs_imatch_step.py b/jwst/mrs_imatch/mrs_imatch_step.py index dccd6efe3c..40cefc54a3 100644 --- a/jwst/mrs_imatch/mrs_imatch_step.py +++ b/jwst/mrs_imatch/mrs_imatch_step.py @@ -37,8 +37,8 @@ class MRSIMatchStep(Step): def process(self, images): # Provide warning to user this code is deprecated - warnings.warn("mrs_imatch deprecated", RuntimeWarning) - + self.log.warning('mrs_imatch is deprecated') + chm = {} all_models2d = ModelContainer(images) for m in all_models2d: diff --git a/jwst/mrs_imatch/tests/test_configuration.py b/jwst/mrs_imatch/tests/test_configuration.py index 39dbe5cabe..5e6642a36c 100644 --- a/jwst/mrs_imatch/tests/test_configuration.py +++ b/jwst/mrs_imatch/tests/test_configuration.py @@ -70,9 +70,22 @@ def test_imatch_background_subtracted(tmp_cwd, miri_dither_ch12): # test if background subtracted - raise error with pytest.raises(ValueError): step = MRSIMatchStep() - step.run(new_container) + step.call(new_container, skip=False) +def test_imatch_default_run(tmp_cwd, miri_dither_ch12): + """ Test mrs_imatch test is skipped by default """ + + all_models = ModelContainer(miri_dither_ch12) + + # test if default running results in skipping step + step = MRSIMatchStep() + results = step.call(all_models) + n = len(all_models) + for i in range(n): + assert results[i].meta.cal_step.mrs_imatch =='SKIPPED' + + def test_imatch_background_reset(tmp_cwd, miri_dither_ch12): """ Test if background polynomial is already determined - reset it""" From 82911e35940798095443e898c4703c463c714770 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 28 Aug 2024 13:23:44 -0700 Subject: [PATCH 311/687] fix typo --- CHANGES.rst | 2 +- jwst/mrs_imatch/mrs_imatch_step.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 76e147ae9f..f3e2902767 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -28,7 +28,7 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] -- Fixed a bug when cube_build was called from the mrs_imatch step. [#8728] +- Fixed a bug when ``cube_build`` was called from the ``mrs_imatch`` step. [#8728] documentation ------------- diff --git a/jwst/mrs_imatch/mrs_imatch_step.py b/jwst/mrs_imatch/mrs_imatch_step.py index 40cefc54a3..6cf77f03f1 100644 --- a/jwst/mrs_imatch/mrs_imatch_step.py +++ b/jwst/mrs_imatch/mrs_imatch_step.py @@ -38,7 +38,7 @@ def process(self, images): # Provide warning to user this code is deprecated self.log.warning('mrs_imatch is deprecated') - + chm = {} all_models2d = ModelContainer(images) for m in all_models2d: From cccc198318542db4e6a981199cf677a885078ede Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 29 Aug 2024 09:10:22 -0700 Subject: [PATCH 312/687] remove import --- jwst/mrs_imatch/mrs_imatch_step.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/mrs_imatch/mrs_imatch_step.py b/jwst/mrs_imatch/mrs_imatch_step.py index 6cf77f03f1..543da7b208 100644 --- a/jwst/mrs_imatch/mrs_imatch_step.py +++ b/jwst/mrs_imatch/mrs_imatch_step.py @@ -9,7 +9,6 @@ import numpy as np from jwst.datamodels import ModelContainer -import warnings from .. stpipe import Step from wiimatch.match import match_lsq from astropy.stats import sigma_clipped_stats as sigclip From 43f772545aa69d32016a74ea83fd89f0ede443bf Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 28 Aug 2024 15:34:54 -0400 Subject: [PATCH 313/687] Fix naming issues for intermediate outlier detection files --- jwst/outlier_detection/imaging.py | 4 +- .../outlier_detection_step.py | 2 +- jwst/outlier_detection/spec.py | 13 +-- .../tests/test_outlier_detection.py | 99 ++++++++++++++++++- jwst/resample/resample.py | 10 +- jwst/resample/resample_spec.py | 3 +- jwst/resample/tests/test_resample_step.py | 8 +- 7 files changed, 120 insertions(+), 19 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index c6ba99da13..ad6466de82 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -60,8 +60,8 @@ def detect_outliers( if resample_data: # Start by creating resampled/mosaic images for # each group of exposures - output_path = make_output_path(basepath=input_models[0].meta.filename, - suffix='') + output_path = make_output_path( + basepath=input_models[0].meta.filename, suffix='') output_path = os.path.dirname(output_path) resamp = resample.ResampleData( input_models, diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 936ec3767b..a099509e22 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -193,7 +193,7 @@ def _guess_mode(self, input_models): if exptype in IFU_SPEC_MODES: return 'ifu' - self.log.error("Outlier detection failed for unknown/unsupported ", + self.log.error(f"Outlier detection failed for unknown/unsupported " f"exposure type: {exptype}") return None diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 7f35733aa6..88f541a4d9 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -2,6 +2,7 @@ Submodule for performing outlier detection on spectra. """ import copy +import os from stdatamodels.jwst import datamodels @@ -56,8 +57,12 @@ def detect_outliers( if resample_data is True: # Start by creating resampled/mosaic images for # each group of exposures + output_path = make_output_path( + basepath=input_models[0].meta.filename, suffix='') + output_path = os.path.dirname(output_path) resamp = resample_spec.ResampleSpecData( input_models, + output=output_path, single=True, blendheaders=False, wht_type=weight_type, @@ -70,14 +75,6 @@ def detect_outliers( ) median_wcs = resamp.output_wcs drizzled_models = resamp.do_drizzle(input_models) - if save_intermediate_results: - for model in drizzled_models: - model.meta.filename = make_output_path( - basepath=model.meta.filename, - suffix="_outlier_s2d.fits", - ) - log.info("Writing out resampled spectra...") - model.save(model.meta.filename) else: drizzled_models = input_models for i in range(len(input_models)): diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 622fc791ff..1c7bc0e9e4 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -6,6 +6,8 @@ from stdatamodels.jwst import datamodels +from jwst.assign_wcs import AssignWcsStep +from jwst.assign_wcs.pointing import create_fitswcs from jwst.datamodels import ModelContainer from jwst.outlier_detection import OutlierDetectionStep from jwst.outlier_detection.utils import flag_resampled_model_crs @@ -15,7 +17,7 @@ TSO_IMAGE_MODES, CORON_IMAGE_MODES, ) -from jwst.assign_wcs.pointing import create_fitswcs +from jwst.resample.tests.test_resample_step import miri_rate_model OUTLIER_DO_NOT_USE = np.bitwise_or( datamodels.dqflags.pixel["DO_NOT_USE"], datamodels.dqflags.pixel["OUTLIER"] @@ -210,7 +212,7 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Drop a CR on the science array container[0].data[12, 12] += 1 - # Verify that intermediary files are removed + # Verify that intermediate files are removed OutlierDetectionStep.call(container) i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) @@ -232,13 +234,104 @@ def test_outlier_step(we_three_sci, tmp_cwd): # Verify CR is flagged assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE - # Verify that intermediary files are saved at the specified location + # Verify that intermediate files are saved at the specified location i2d_files = glob(os.path.join(tmp_cwd, '*i2d.fits')) median_files = glob(os.path.join(tmp_cwd, '*median.fits')) assert len(i2d_files) != 0 assert len(median_files) != 0 +def test_outlier_step_spec(tmp_cwd, tmp_path): + """Test outlier step for spec data including saving intermediate results.""" + output_dir = tmp_path / 'output' + output_dir.mkdir(exist_ok=True) + output_dir = str(output_dir) + + # Make a MIRI model and assign a spectral wcs + miri_rate = miri_rate_model() + miri_cal = AssignWcsStep.call(miri_rate) + + # Make it an exposure type outlier detection expects + miri_cal.meta.exposure.type = "MIR_LRS-FIXEDSLIT" + + # Make a couple copies + container = ModelContainer([miri_cal, miri_cal.copy(), miri_cal.copy()]) + + # Give each image a unique name so output files don't overwrite + for i, model in enumerate(container): + model.meta.filename = f'test_{i}_cal.fits' + + # Drop a CR on the science array in the first image + container[0].data[209, 37] += 1 + + # Verify that intermediate files are removed when not saved + # (s2d files are expected, i2d files are not, but we'll check + # for them to make sure the imaging extension didn't creep back in) + OutlierDetectionStep.call(container, output_dir=output_dir, save_results=True) + for dirname in [output_dir, tmp_cwd]: + result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) + i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) + s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) + median_files = glob(os.path.join(dirname, '*median.fits')) + + # intermediate files are removed + assert len(i2d_files) == 0 + assert len(s2d_files) == 0 + assert len(median_files) == 0 + + # result files are written to the output directory + if dirname == output_dir: + assert len(result_files) == len(container) + else: + assert len(result_files) == 0 + + # Call again, but save intermediate to the output path + result = OutlierDetectionStep.call( + container, save_results=True, save_intermediate_results=True, + output_dir=output_dir + ) + + # Make sure nothing changed in SCI array + for image, corrected in zip(container, result): + np.testing.assert_allclose(image.data, corrected.data) + + # Verify CR is flagged + assert result[0].dq[209, 37] == OUTLIER_DO_NOT_USE + + # Verify that intermediate files are saved at the specified location + for dirname in [output_dir, tmp_cwd]: + all_files = glob(os.path.join(dirname, '*.fits')) + result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) + i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) + s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) + median_files = glob(os.path.join(dirname, '*median.fits')) + if dirname == output_dir: + # result files are written to the output directory + assert len(result_files) == len(container) + + # s2d and median files are written to the output directory + assert len(s2d_files) == len(container) + assert len(median_files) == 1 + + # i2d files not written + assert len(i2d_files) == 0 + + # nothing else was written + assert len(all_files) == len(s2d_files) + len(median_files) + len(result_files) + else: + # nothing should be written to the current directory + assert len(result_files) == 0 + assert len(s2d_files) == 0 + assert len(median_files) == 0 + assert len(i2d_files) == 0 + assert len(all_files) == 0 + + miri_rate.close() + result.close() + for model in container: + model.close() + + def test_outlier_step_on_disk(we_three_sci, tmp_cwd): """Test whole step with an outlier including saving intermediate and results files""" diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 245102b4b0..cffa46a8b1 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -73,6 +73,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None + self.output_suffix = 'i2d' self.pscale_ratio = pscale_ratio self.single = single @@ -276,6 +277,7 @@ def resample_many_to_many(self, input_models): """ for exposure in input_models.models_grouped: output_model = self.blank_output + # Determine output file type from input exposure filenames # Use this for defining the output filename indx = exposure[0].meta.filename.rfind('.') @@ -283,9 +285,13 @@ def resample_many_to_many(self, input_models): output_root = '_'.join(exposure[0].meta.filename.replace( output_type, '').split('_')[:-1]) if self.asn_id is not None: - output_model.meta.filename = f'{output_root}_{self.asn_id}_outlier_i2d{output_type}' + output_model.meta.filename = ( + f'{output_root}_{self.asn_id}_' + f'outlier_{self.output_suffix}{output_type}') else: - output_model.meta.filename = f'{output_root}_outlier_i2d{output_type}' + output_model.meta.filename = ( + f'{output_root}_' + f'outlier_{self.output_suffix}{output_type}') # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 871068ba7f..47701a44da 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -66,6 +66,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None + self.output_suffix = 's2d' self.pscale_ratio = pscale_ratio self.single = single @@ -78,7 +79,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.in_memory = kwargs.get('in_memory', True) self._recalc_pscale_ratio = False - log.info(f"Driz parameter kernal: {self.kernel}") + log.info(f"Driz parameter kernel: {self.kernel}") log.info(f"Driz parameter pixfrac: {self.pixfrac}") log.info(f"Driz parameter fillval: {self.fillval}") log.info(f"Driz parameter weight_type: {self.weight_type}") diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index 15261f63d2..fcd380a4b8 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -37,8 +37,7 @@ def _set_photom_kwd(im): ) -@pytest.fixture -def miri_rate(): +def miri_rate_model(): xsize = 72 ysize = 416 shape = (ysize, xsize) @@ -87,6 +86,11 @@ def miri_rate(): 'start_time': 58119.8333, 'type': 'MIR_LRS-SLITLESS', 'zero_frame': False} + return im + +@pytest.fixture +def miri_rate(): + im = miri_rate_model() yield im im.close() From b4569c2a7129119e23468a80de0456ac2291eb2e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 28 Aug 2024 16:22:41 -0400 Subject: [PATCH 314/687] Update change notes --- CHANGES.rst | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 14a97a26b6..6672bf030a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -61,14 +61,18 @@ outlier_detection - Fixed failures due to a missing ``wcs.array_shape`` attribute when the ``outlier_detection`` step was run standalone using e.g. ``strun`` [#8645] +- Refactored separate modes into submodules instead of inheriting from a base class. + Moved non-JWST-specific code to stcal. [#8613] + +- Fixed file names and output directory for intermediate resampled spectral + images. Intermediate files now have suffix ``outlier_s2d`` and are saved to + the output directory alongside final products. [#8735] + set_telescope_pointing ---------------------- - replace usage of ``copy_arrays=True`` with ``memmap=False`` [#8660] -- Refactored separate modes into submodules instead of inheriting from a base class. - Moved non-JWST-specific code to stcal. [#8613] - resample_spec ------------- From bf9b7367fbb5fabbc19161e231ea6a22b852e622 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 28 Aug 2024 16:30:56 -0400 Subject: [PATCH 315/687] Add suffix info to docs --- docs/jwst/outlier_detection/outlier_detection_spec.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index 6d8cd13de5..f66df77854 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -29,14 +29,14 @@ Specifically, this routine performs the following operations (modified from the #. Resample all input images into a :py:class:`~jwst.datamodels.ModelContainer` using :py:class:`~jwst.resample.resample_spec.ResampleSpecData` - - Resampled images are written out to disk if the ``save_intermediate_results`` - parameter is set to `True` + - Resampled images are written out to disk with suffix "outlier_s2d" + if the ``save_intermediate_results`` parameter is set to `True`. - **If resampling is turned off**, the original unrectified inputs are used to create - the median image for cosmic-ray detection + the median image for cosmic-ray detection. #. Create a median image from (possibly) resampled :py:class:`~jwst.datamodels.ModelContainer` - - The median image is written out to disk if the ``save_intermediate_results`` - parameter is set to `True` + - The median image is written out to disk with suffix "median" + if the ``save_intermediate_results`` parameter is set to `True` #. Blot median image to match each original input image - **If resampling is turned off**, the median image is used for comparison From e43af518b3a7035a1a8ea77b1201cd818db696a2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 29 Aug 2024 16:36:36 -0400 Subject: [PATCH 316/687] Clarify intermediate suffix attribute --- jwst/resample/resample.py | 6 +++--- jwst/resample/resample_spec.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index cffa46a8b1..681592f3aa 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -73,7 +73,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None - self.output_suffix = 'i2d' + self.intermediate_suffix = 'outlier_i2d' self.pscale_ratio = pscale_ratio self.single = single @@ -287,11 +287,11 @@ def resample_many_to_many(self, input_models): if self.asn_id is not None: output_model.meta.filename = ( f'{output_root}_{self.asn_id}_' - f'outlier_{self.output_suffix}{output_type}') + f'{self.intermediate_suffix}{output_type}') else: output_model.meta.filename = ( f'{output_root}_' - f'outlier_{self.output_suffix}{output_type}') + f'{self.intermediate_suffix}{output_type}') # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 47701a44da..e75d1c898d 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -66,7 +66,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, if output is not None and '.fits' not in str(output): self.output_dir = output self.output_filename = None - self.output_suffix = 's2d' + self.intermediate_suffix = 'outlier_s2d' self.pscale_ratio = pscale_ratio self.single = single From eb5fc1f66272c6fbe4ab3e3a8e4fb42b041d179b Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 30 Aug 2024 09:11:47 -0700 Subject: [PATCH 317/687] clean up --- CHANGES.rst | 1 + docs/jwst/mrs_imatch/description.rst | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index f3e2902767..590632e7c7 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -28,6 +28,7 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] + - Fixed a bug when ``cube_build`` was called from the ``mrs_imatch`` step. [#8728] documentation diff --git a/docs/jwst/mrs_imatch/description.rst b/docs/jwst/mrs_imatch/description.rst index 18c61f5653..4a8a1e37d9 100644 --- a/docs/jwst/mrs_imatch/description.rst +++ b/docs/jwst/mrs_imatch/description.rst @@ -9,8 +9,8 @@ Description Overview -------- -This step has been deprecated in JWST calibration pipeline version 11.1. -The ``mrs_imatch`` step is skipped by default in the calwebb_spec3 pipeline. +This step has been deprecated and will be +skipped by default in the calwebb_spec3 pipeline. The ``mrs_imatch`` step "matches" image intensities of several input 2D MIRI MRS images by fitting polynomials to cube intensities (cubes built From 2a49a7cd53188111dd2500ad621521b0b5090fe5 Mon Sep 17 00:00:00 2001 From: t-brandt Date: Fri, 30 Aug 2024 14:50:54 -0400 Subject: [PATCH 318/687] Masking reference pixels --- CHANGES.rst | 5 +++++ jwst/nsclean/lib.py | 1 - jwst/nsclean/nsclean.py | 12 ++++-------- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 6672bf030a..f9ce12d080 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -381,6 +381,11 @@ nsclean slits are defined in a MOS product, the central fixed slit quadrant is not automatically masked. [#8467] +- Changed subarray mode from operating on the entire array at once to + operating on sections of the array and smoothly combining these sections. + Due to the computational costs of matrix operations, this is a large + speedup that has little effect on the results. [#] + outlier_detection ----------------- diff --git a/jwst/nsclean/lib.py b/jwst/nsclean/lib.py index 08feaa045e..514be80240 100644 --- a/jwst/nsclean/lib.py +++ b/jwst/nsclean/lib.py @@ -1,4 +1,3 @@ -#-*- python -*- import numpy as np diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 24809e218f..a1dcbb4b03 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -1,4 +1,3 @@ -#-*- python -*- import logging import numpy as np import gwcs @@ -134,8 +133,11 @@ def create_mask(input_model, mask_spectral_regions, n_sigma): # Initialize mask to all True. Subsequent operations will mask # out pixels that contain signal. # Note: mask will be 3D for BOTS mode data - mask = np.full(np.shape(input_model.dq), True) + #mask = np.full(np.shape(input_model.dq), True) + # Similar to above, but don't use reference pixels. + mask = (input_model.dq>>31)&1 == 0 + # If IFU, mask all pixels contained in the IFU slices if exptype == 'nrs_ifu' and mask_spectral_regions: mask = mask_ifu_slices(input_model, mask) @@ -171,12 +173,6 @@ def create_mask(input_model, mask_spectral_regions, n_sigma): log.info("Fixed slits found in MSA definition; " "not masking the fixed slit region for MOS data.") - # Use left/right reference pixel columns (first and last 4). Can only be - # applied to data that uses all 2048 columns of the detector. - if mask.shape[-1] == 2048: - mask[..., :, :5] = True - mask[..., :, -4:] = True - # Mask outliers using sigma clipping stats. # For BOTS mode, which uses 3D data, loop over each integration separately. if len(input_model.data.shape) == 3: From f1444c88ddc0132acd8b6e8835337d3e1af387df Mon Sep 17 00:00:00 2001 From: t-brandt Date: Fri, 30 Aug 2024 14:56:05 -0400 Subject: [PATCH 319/687] Added PR issue number --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index f9ce12d080..01d219702b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -384,7 +384,7 @@ nsclean - Changed subarray mode from operating on the entire array at once to operating on sections of the array and smoothly combining these sections. Due to the computational costs of matrix operations, this is a large - speedup that has little effect on the results. [#] + speedup that has little effect on the results. [#8745] outlier_detection ----------------- From acb0c157326600b8cbd180c0a559482e648b47f7 Mon Sep 17 00:00:00 2001 From: t-brandt Date: Fri, 30 Aug 2024 14:57:54 -0400 Subject: [PATCH 320/687] Moved CHANGES comment to correct release --- CHANGES.rst | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 01d219702b..7059e903fe 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -55,6 +55,14 @@ master_background - Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit as a background slit, instead of ``"bkg"`` only. [#8600] +nsclean +------- + +- Changed subarray mode from operating on the entire array at once to + operating on sections of the array and smoothly combining these sections. + Due to the computational costs of matrix operations, this is a large + speedup that has little effect on the results. [#8745] + outlier_detection ----------------- @@ -381,11 +389,6 @@ nsclean slits are defined in a MOS product, the central fixed slit quadrant is not automatically masked. [#8467] -- Changed subarray mode from operating on the entire array at once to - operating on sections of the array and smoothly combining these sections. - Due to the computational costs of matrix operations, this is a large - speedup that has little effect on the results. [#8745] - outlier_detection ----------------- From 24fc8df299e779cec962b547eea83ffd9b8dc9e2 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 4 Sep 2024 11:07:03 -0400 Subject: [PATCH 321/687] changed psfalign product from quadmodel to cubemodel --- jwst/coron/imageregistration.py | 38 ++++++++++++++------------------- jwst/coron/klip.py | 14 +++++------- jwst/coron/tests/test_coron.py | 28 +++--------------------- 3 files changed, 24 insertions(+), 56 deletions(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 38eb4c068b..12fc185272 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -3,7 +3,7 @@ from scipy import optimize from scipy.ndimage import fourier_shift -from stdatamodels.jwst.datamodels import QuadModel +from stdatamodels.jwst.datamodels import CubeModel import logging log = logging.getLogger(__name__) @@ -212,13 +212,10 @@ def align_models(reference, target, mask): """ - # Get the number of integrations in the science exposure - nrefslices = reference.data.shape[0] - - # Create output QuadModel of required dimensions - quad_shape = (nrefslices, - target.shape[0], target.shape[1], target.shape[2]) - output_model = QuadModel(quad_shape) + # Create output CubeModel of required dimensions. Since all science integrations + # are assumed to have the same shift, the output is just a shifted copy of the + # 3-D PSF cube + output_model = CubeModel(target.shape) output_model.update(target) # Compute the shifts of the PSF ("target") images relative to @@ -228,21 +225,18 @@ def align_models(reference, target, mask): target.data.astype(np.float64), mask=mask.data, return_aligned=False) - # Loop over all integrations of the science exposure - for k in range(nrefslices): + # Apply the shifts to the PSF images + output_model.data = fourier_imshift( + target.data.astype(np.float64), + -shifts) - # Apply the shifts to the PSF images - output_model.data[k] = fourier_imshift( - target.data.astype(np.float64), + # Apply the same shifts to the PSF error arrays, if they exist + if target.err is not None: + output_model.err = fourier_imshift( + target.err.astype(np.float64), -shifts) - # Apply the same shifts to the PSF error arrays, if they exist - if target.err is not None: - output_model.err[k] = fourier_imshift( - target.err.astype(np.float64), - -shifts) - - # TODO: in the future we need to add shifts and other info (such as - # slice ID from the reference image to which target was aligned) - # to output cube metadata (or property). + # TODO: in the future we need to add shifts and other info (such as + # slice ID from the reference image to which target was aligned) + # to output cube metadata (or property). return output_model diff --git a/jwst/coron/klip.py b/jwst/coron/klip.py index ae6aab4d86..e99efc8cbe 100644 --- a/jwst/coron/klip.py +++ b/jwst/coron/klip.py @@ -22,14 +22,10 @@ def klip(target_model, refs_model, truncate): a single exposure are stacked along the first (NINTS) axis of the data arrays. - refs_model : QuadModel (NTARG x NINTS x NROWS x NCOLS) - The input 4D stack of reference images. The first (NTARG) axis - corresponds to the index of each target integration. The second - (NINTS) axis is the stack of aligned PSF integrations for that - target image. The length of the target_model first (NINTS) axis - should be equal to the length of the refs_model first (NTARG) - axis (i.e. one stack of aligned PSF images for each target - integration). + refs_model : CubeModel (NINTS_PSF x NROWS x NCOLS) + The input 3D stack of reference images. The first + (NINTS_PSF) axis is the stack of aligned PSF integrations for that + target image. truncate : int Indicates how many rows to keep in the Karhunen-Loeve transform. @@ -48,7 +44,7 @@ def klip(target_model, refs_model, truncate): target = target.reshape(-1) # Load the reference psf arrays and flatten them from 3-D to 2-D - refs = refs_model.data[i].astype(np.float64) + refs = refs_model.data.astype(np.float64) rshape = refs.shape nrefs = rshape[0] refs = refs.reshape(nrefs, rshape[1] * rshape[2]) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index c33b56fa60..6453578c49 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -178,15 +178,12 @@ def test_align_models(): ref_mod = datamodels.CubeModel(data=ref) am_results = imageregistration.align_models(ref_mod, targ_mod, mask_mod) - results_sub = am_results.data[:3, :2, 2, :3] + results_sub = am_results.data[:2, 2, :3] truth_results_sub = np.array( - [ - [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], - [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], - ] ) + print(results_sub.shape, truth_results_sub.shape) npt.assert_allclose(results_sub, truth_results_sub, atol=1e-6) @@ -313,7 +310,6 @@ def test_klip(): target_model = datamodels.CubeModel(data=target_model_data) refs_model_data = np.array( - [ [ [ [0.8174741, 0.74938107, 0.73527235, 1.3193785], @@ -331,28 +327,10 @@ def test_klip(): [1.419994, 1.1546139, 0.961317, 0.95088667], ], ], - [ - [ - [0.8174741, 0.74938107, 0.73527235, 1.3193785], - [1.0032778, 0.8247719, 0.78944355, 0.99227476], - [1.4609907, 1.1605016, 0.9564753, 0.9186427], - ], - [ - [0.86789674, 0.7998908, 0.8557136, 1.2926395], - [0.97756547, 0.7788742, 0.75892323, 0.9819151], - [1.495664, 1.1455023, 1.002115, 0.92159164], - ], - [ - [0.84426856, 0.7719569, 0.8088021, 1.2781427], - [0.98734635, 0.8125992, 0.77424014, 0.9934157], - [1.419994, 1.1546139, 0.961317, 0.95088667], - ], - ], - ], dtype=np.float32, ) - refs_model = datamodels.QuadModel(data=refs_model_data) + refs_model = datamodels.CubeModel(data=refs_model_data) # Call the KLIP routine truncate = 50 From 6dc6ab50b76dff29c2dfa9e7d8dae07c8c56e04a Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 4 Sep 2024 11:37:38 -0400 Subject: [PATCH 322/687] added changelog entry and modified docs where forgotten by previous PR --- CHANGES.rst | 9 +++++++++ docs/jwst/pipeline/calwebb_coron3.rst | 18 +++++++++++------- 2 files changed, 20 insertions(+), 7 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index cb5c7ea9dd..fbec6e44cf 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -6,6 +6,9 @@ align_refs - Compute alignment shifts from the first integration of the science exposure only. [#8643] +- Fixed a bug where the aligned PSF was being saved as a 4-D array with the first axis being + identical; now it is saved as a 3-D array. [#8747] + ami_average ----------- @@ -51,6 +54,12 @@ general - Increase minimum required stpipe. [#8713] +klip +---- + +- Allowed klip to ingest a single shifted 3-D PSF model instead of a 4-D structure + containing one shifted PSF per science integration. [#8747] + master_background ----------------- diff --git a/docs/jwst/pipeline/calwebb_coron3.rst b/docs/jwst/pipeline/calwebb_coron3.rst index 54965aa761..e9b5efb48a 100644 --- a/docs/jwst/pipeline/calwebb_coron3.rst +++ b/docs/jwst/pipeline/calwebb_coron3.rst @@ -32,7 +32,7 @@ The high-level processing provided by these steps is: 1) CR-flag all PSF and science target exposures 2) Accumulate all reference PSF images into a single product -3) Align every PSF image to every science target image +3) Align every PSF image to the first science integration image 4) Compute an optimal PSF fit and subtract it from every science target image 5) Combine the PSF-subtracted and CR-flagged images into a single resampled image @@ -115,17 +115,21 @@ steps. The stacked PSF data get written to disk in the form of a "_psfstack" pro The output file name is source-based, using the product name specified in the ASN file, e.g. "jw86073-a3001_t001_nircam_f140m-maskbar_psfstack.fits." -4D aligned PSF images +3D aligned PSF images ^^^^^^^^^^^^^^^^^^^^^ -:Data model: `~jwst.datamodels.QuadModel` +:Data model: `~jwst.datamodels.CubeModel` :File suffix: _psfalign -For each science target exposure, all of the reference PSF images in the -"_psfstack" product are aligned to each science target integration and saved to -a 4D "_psfalign" product by the :ref:`align_refs ` step. The output file +All of the reference PSF images in the +"_psfstack" product are aligned to the first science target integration and saved to +a 3D "_psfalign" product by the :ref:`align_refs ` step. The output file name is exposure-based, with the addition of the associated candidate ID, e.g. -"jw8607342001_02102_00001_nrcb3_a3001_psfalign.fits." +"jw8607342001_02102_00001_nrcb3_a3001_psfalign.fits." In-flight data show that +neither line-of-sight pointing drift nor PSF property changes are significant over the +timescales relevant to JWST coronagraphic observations; therefore it's sufficient to align +the PSF images to the first science target integration rather than separately for each science +target integration as was done in the pre-flight pipeline. 3D PSF-subtracted images ^^^^^^^^^^^^^^^^^^^^^^^^ From 8381a261d0b9a51d0ce2e7ac693032c76efac697 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 4 Sep 2024 14:55:45 -0400 Subject: [PATCH 323/687] removed extraneous print per @melanieclarke comment --- jwst/coron/tests/test_coron.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index 6453578c49..b113e9bb0d 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -183,7 +183,6 @@ def test_align_models(): truth_results_sub = np.array( [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], ) - print(results_sub.shape, truth_results_sub.shape) npt.assert_allclose(results_sub, truth_results_sub, atol=1e-6) From 1348581e748c88310456189067bcf189005987f4 Mon Sep 17 00:00:00 2001 From: David Law Date: Wed, 4 Sep 2024 15:31:05 -0400 Subject: [PATCH 324/687] Update association path warning message. --- jwst/associations/association.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/jwst/associations/association.py b/jwst/associations/association.py index 1f91602ef3..1baed4406f 100644 --- a/jwst/associations/association.py +++ b/jwst/associations/association.py @@ -208,9 +208,9 @@ def validate(cls, asn): for member in members: fpath, fname = os.path.split(member["expname"]) if len(fpath) > 0: - err_str = "'expname' contains path, but should only be a filename." - err_str += " All input files should be in a single directory" - err_str += ", so no path is needed." + err_str = "Input association file contains path information;" + err_str += " note that this can complicate usage and/or sharing" + err_str += " of such files." logger.debug(err_str) warnings.warn(err_str, UserWarning) return True From 04217a2bbdb8f142e45afeb9492b52231919c9c8 Mon Sep 17 00:00:00 2001 From: David Law Date: Wed, 4 Sep 2024 15:33:31 -0400 Subject: [PATCH 325/687] Update change log --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index cb5c7ea9dd..2c721e2aae 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -23,6 +23,8 @@ associations - Restored slit name to level 3 product names for NIRSpec BOTS and background fixed slit targets. [#8699] +- Update warning message about use of paths in associations. [#8752] + cube_build ---------- From 206fff6d546a33e9e7454eb8206817be363f05f1 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 5 Sep 2024 09:57:29 -0700 Subject: [PATCH 326/687] JP-3690: Switch from ModelContainer to ModelLibrary for image3 pipeline (#8683) Co-authored-by: Brett Co-authored-by: Tyler Pauly --- CHANGES.rst | 37 ++ .../outlier_detection_imaging.rst | 60 +-- docs/jwst/pipeline/calwebb_image3.rst | 4 +- docs/jwst/skymatch/arguments.rst | 6 + docs/jwst/tweakreg/README.rst | 18 +- jwst/assign_mtwcs/assign_mtwcs_step.py | 24 +- jwst/assign_mtwcs/moving_target_wcs.py | 80 ++-- jwst/assign_mtwcs/tests/test_mtwcs.py | 23 +- jwst/datamodels/__init__.py | 6 +- jwst/datamodels/library.py | 158 +++++++ jwst/datamodels/tests/test_library.py | 149 ++++++ jwst/lib/exposure_types.py | 8 +- jwst/outlier_detection/imaging.py | 70 +-- .../outlier_detection_step.py | 32 +- jwst/outlier_detection/spec.py | 35 +- .../tests/test_outlier_detection.py | 179 +++++-- jwst/outlier_detection/utils.py | 234 +++++++--- jwst/pipeline/calwebb_coron3.py | 9 +- jwst/pipeline/calwebb_image3.py | 91 ++-- jwst/pipeline/calwebb_spec3.py | 5 +- jwst/pipeline/tests/test_calwebb_image3.py | 114 +++++ jwst/regtest/test_niriss_image.py | 9 +- jwst/resample/resample.py | 439 +++++++++++------- jwst/resample/resample_spec.py | 4 +- jwst/resample/resample_spec_step.py | 36 +- jwst/resample/resample_step.py | 85 ++-- jwst/resample/resample_utils.py | 19 +- jwst/resample/tests/test_resample_step.py | 16 +- jwst/skymatch/skymatch_step.py | 136 +++--- jwst/skymatch/tests/test_skymatch.py | 132 +++--- jwst/stpipe/core.py | 9 +- jwst/stpipe/utilities.py | 8 +- jwst/tweakreg/tests/test_multichip_jwst.py | 45 +- jwst/tweakreg/tests/test_tweakreg.py | 81 ++-- jwst/tweakreg/tweakreg_step.py | 284 +++++------ 35 files changed, 1712 insertions(+), 933 deletions(-) create mode 100644 jwst/datamodels/library.py create mode 100644 jwst/datamodels/tests/test_library.py create mode 100644 jwst/pipeline/tests/test_calwebb_image3.py diff --git a/CHANGES.rst b/CHANGES.rst index fbec6e44cf..1c8995df2c 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -14,6 +14,12 @@ ami_average - Fix error in step spec that prevents step creation. [#8677] +assign_mtwcs +------------ + +- Step now uses `ModelLibrary` to handle accessing models consistently + whether they are in memory or on disk. [#8683] + assign_wcs ---------- @@ -34,6 +40,12 @@ cube_build - Fixed a bug when ``cube_build`` was called from the ``mrs_imatch`` step. [#8728] +datamodels +---------- + +- Added `ModelLibrary` class to allow passing on-disk models between steps in the + image3 pipeline. [#8683] + documentation ------------- @@ -84,11 +96,27 @@ outlier_detection images. Intermediate files now have suffix ``outlier_s2d`` and are saved to the output directory alongside final products. [#8735] +- For imaging modes, step now uses `ModelLibrary` to handle accessing models consistently + whether they are in memory or on disk. [#8683] + set_telescope_pointing ---------------------- - replace usage of ``copy_arrays=True`` with ``memmap=False`` [#8660] +pipeline +-------- + +- Updated `calwebb_image3` to use `ModelLibrary` instead of `ModelContainer`, added + optional `on_disk` parameter to govern whether models in the library should be stored + in memory or on disk. [#8683] + +resample +-------- + +- Step now uses `ModelLibrary` to handle accessing models consistently + whether they are in memory or on disk. [#8683] + resample_spec ------------- @@ -123,6 +151,12 @@ scripts - Removed many non-working and out-dated scripts. Including many scripts that were replaced by ``strun``. [#8619] +skymatch +-------- + +- Step now uses `ModelLibrary` to handle accessing models consistently + whether they are in memory or on disk. [#8683] + stpipe ------ @@ -150,6 +184,9 @@ tweakreg - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] +- Step now uses `ModelLibrary` to handle accessing models consistently + whether they are in memory or on disk. [#8683] + 1.15.1 (2024-07-08) =================== diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index ced7d9599f..15df877939 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -15,11 +15,11 @@ Specifically, this routine performs the following operations: #. Convert input data, as needed, to make sure it is in a format that can be processed. - * A :py:class:`~jwst.datamodels.ModelContainer` serves as the basic format for + * A :py:class:`~jwst.datamodels.ModelLibrary` serves as the basic format for all processing performed by this step, as each entry will be treated as an element of a stack of images to be processed to identify bad-pixels/cosmic-rays and other artifacts. - * If the input data is a :py:class:`~jwst.datamodels.CubeModel`, convert it into a ModelContainer. + * If the input data is a :py:class:`~jwst.datamodels.CubeModel`, convert it into a ModelLibrary. This allows each plane of the cube to be treated as a separate 2D image for resampling (if done) and for combining into a median image. @@ -62,13 +62,13 @@ Specifically, this routine performs the following operations: if the input model container has an , otherwise the suffix will be ``_outlier_i2d.fits`` by default. * **If resampling is turned off** through the use of the ``resample_data`` parameter, - a copy of the unrectified input images (as a ModelContainer) + a copy of the unrectified input images (as a ModelLibrary) will be used for subsequent processing. #. Create a median image from all grouped observation mosaics. * The median image is created by combining all grouped mosaic images or - non-resampled input data (as planes in a ModelContainer) pixel-by-pixel. + non-resampled input data (as planes in a ModelLibrary) pixel-by-pixel. * The ``maskpt`` parameter sets the percentage of the weight image values to use, and any pixel with a weight below this value gets flagged as "bad" and ignored when resampled. @@ -129,7 +129,7 @@ The outlier detection algorithm can end up using massive amounts of memory depending on the number of inputs, the size of each input, and the size of the final output product. Specifically, -#. The input :py:class:`~jwst.datamodels.ModelContainer` or +#. The input :py:class:`~jwst.datamodels.ModelLibrary` or :py:class:`~jwst.datamodels.CubeModel` for IFU data, by default, all input exposures would have been kept open in memory to make processing more efficient. @@ -152,56 +152,20 @@ memory usage at the expense of file I/O. The control over this memory model hap with the use of the ``in_memory`` parameter. The full impact of this parameter during processing includes: -#. The ``save_open`` parameter gets set to `False` - when opening the input :py:class:`~jwst.datamodels.ModelContainer` object. - This forces all input models in the input :py:class:`~jwst.datamodels.ModelContainer` or - :py:class:`~jwst.datamodels.CubeModel` to get written out to disk. The ModelContainer - then uses the filename of the input model during subsequent processing. +#. The input :py:class:`~jwst.datamodels.ModelLibrary` object is loaded with `on_disk=True`. + This ensures that input models are loaded into memory one at at time, + and saved to a temporary file when not in use; these read-write operations are handled by + the :py:class:`~jwst.datamodels.ModelLibrary` object. -#. The ``in_memory`` parameter gets passed to the :py:class:`~jwst.resample.ResampleStep` - to set whether or not to keep the resampled images in memory or not. By default, - the outlier detection processing sets this parameter to `False` so that each resampled - image gets written out to disk. +#. The ``on_disk`` status of the :py:class:`~jwst.datamodels.ModelLibrary` gets passed to the + :py:class:`~jwst.resample.ResampleStep` as well, to set whether or not to keep the + resampled images in memory or not. #. Computing the median image works section-by-section by only keeping 1Mb of each input in memory at a time. As a result, only the final output product array for the final median image along with a stack of 1Mb image sections are kept in memory. -#. The final resampling step also avoids keeping all inputs in memory by only reading - each input into memory 1 at a time as it gets resampled onto the final output product. - These changes result in a minimum amount of memory usage during processing at the obvious expense of reading and writing the products from disk. - -Outlier Detection for Coronagraphic Data ----------------------------------------- -Coronagraphic data is processed in a near-identical manner to direct imaging data, but -no resampling occurs. - - -Outlier Detection for TSO data -------------------------------- -Normal imaging data benefit from combining all integrations into a -single image. TSO data's value, however, comes from looking for variations from one -integration to the next. The outlier detection algorithm, therefore, gets run with -a few variations to accomodate the nature of these 3D data. See the -:ref:`TSO outlier detection ` documentation for details. - - -Outlier Detection for IFU data ------------------------------- -Integral Field Unit (IFU) data is handled as 2D images, similar to direct -imaging modes. The nature of the detection algorithm, however, is quite -different and involves measuring the differences between neighboring pixels -in the spatial (cross-dispersion) direction within the IFU slice images. -See the :ref:`IFU outlier detection ` documentation for -all the details. - - -Outlier Detection for Slit data -------------------------------- -See the :ref:`IFU outlier detection ` documentation for -details. - .. automodapi:: jwst.outlier_detection.imaging diff --git a/docs/jwst/pipeline/calwebb_image3.rst b/docs/jwst/pipeline/calwebb_image3.rst index 710948c3f5..1bad06273b 100644 --- a/docs/jwst/pipeline/calwebb_image3.rst +++ b/docs/jwst/pipeline/calwebb_image3.rst @@ -34,7 +34,9 @@ processed using the :ref:`calwebb_tso3 ` pipeline. Arguments --------- -The ``calwebb_image3`` pipeline does not have any optional arguments. +``--in_memory`` + Boolean governing whether to load all models in the input association to memory at once (faster) + or to save to temporary files when not in use (slower, less memory usage). Default is True. Inputs ------ diff --git a/docs/jwst/skymatch/arguments.rst b/docs/jwst/skymatch/arguments.rst index f686a90395..a3af4a5918 100644 --- a/docs/jwst/skymatch/arguments.rst +++ b/docs/jwst/skymatch/arguments.rst @@ -67,3 +67,9 @@ The ``skymatch`` step uses the following optional arguments: Bin width, in sigma, used to sample the distribution of pixel values in order to compute the sky background using statistics that require binning, such as `mode` and `midpt`. + +**Memory management parameters:** + +``in_memory`` (boolean, default=True) + If False, preserve memory using temporary files + at the expense of having to run many I/O operations. diff --git a/docs/jwst/tweakreg/README.rst b/docs/jwst/tweakreg/README.rst index 6c6811a86a..ea8069f0c0 100644 --- a/docs/jwst/tweakreg/README.rst +++ b/docs/jwst/tweakreg/README.rst @@ -86,7 +86,7 @@ models to the custom catalog file name, the ``tweakreg_step`` also supports two other ways of supplying custom source catalogs to the step: 1. Adding ``tweakreg_catalog`` attribute to the ``members`` of the input ASN - table - see `~jwst.datamodels.ModelContainer` for more details. + table - see `~jwst.datamodels.ModelLibrary` for more details. Catalog file names are relative to ASN file path. 2. Providing a simple two-column text file, specified via step's parameter @@ -165,17 +165,17 @@ telescope pointing will be identical in all these images and it is assumed that the relative positions of (e.g., NIRCam) detectors do not change. Identification of images that belong to the same "exposure" and therefore can be grouped together is based on several attributes described in -`~jwst.datamodels.ModelContainer`. This grouping is performed automatically +`~jwst.datamodels.ModelLibrary`. This grouping is performed automatically in the ``tweakreg`` step using the -`~jwst.datamodels.ModelContainer.models_grouped` property, which assigns -a group ID to each input image model in ``meta.group_id``. +`~jwst.datamodels.ModelLibrary.group_names` property. + However, when detector calibrations are not accurate, alignment of groups of images may fail (or result in poor alignment). In this case, it may be desirable to align each image independently. This can be achieved either by setting the ``image_model.meta.group_id`` attribute to a unique string or integer value for each image, or by adding the ``group_id`` attribute to the ``members`` of the input ASN -table - see `~jwst.datamodels.ModelContainer` for more details. +table - see `~jwst.datamodels.ModelLibrary` for more details. .. note:: Group ID (``group_id``) is used by both ``tweakreg`` and ``skymatch`` steps @@ -428,6 +428,14 @@ in the ``assign_wcs`` step. * ``sip_npoints``: Number of points for the SIP fit. (Default=12). +**stpipe general options:** + +* ``output_use_model``: A boolean indicating whether to use `DataModel.meta.filename` + when saving the results. (Default=True) + +* ``in_memory``: A boolean indicating whether to keep models in memory, or to save + temporary files on disk while not in use to save memory. (Default=True) + Further Documentation --------------------- The underlying algorithms as well as formats of source catalogs are described diff --git a/jwst/assign_mtwcs/assign_mtwcs_step.py b/jwst/assign_mtwcs/assign_mtwcs_step.py index bd312ccddd..a8e33e46f9 100755 --- a/jwst/assign_mtwcs/assign_mtwcs_step.py +++ b/jwst/assign_mtwcs/assign_mtwcs_step.py @@ -1,9 +1,8 @@ #! /usr/bin/env python import logging -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary +from jwst.stpipe.utilities import record_step_status from ..stpipe import Step from .moving_target_wcs import assign_moving_target_wcs @@ -32,17 +31,14 @@ class AssignMTWcsStep(Step): """ def process(self, input): - if isinstance(input, str): - input = datamodels.open(input) - - # Can't apply the step if we aren't given a ModelContainer as input - if not isinstance(input, ModelContainer): - log.warning("Input data type is not supported.") - # raise ValueError("Expected input to be an association file name or a ModelContainer.") - input.meta.cal_step.assign_mtwcs = 'SKIPPED' - return input - # Apply the step + if not isinstance(input, ModelLibrary): + try: + input = ModelLibrary(input) + except Exception: + log.warning("Input data type is not supported.") + record_step_status(input, "assign_mtwcs", False) + return input + result = assign_moving_target_wcs(input) - return result diff --git a/jwst/assign_mtwcs/moving_target_wcs.py b/jwst/assign_mtwcs/moving_target_wcs.py index 7f61b84e9c..be4a5a1b8b 100644 --- a/jwst/assign_mtwcs/moving_target_wcs.py +++ b/jwst/assign_mtwcs/moving_target_wcs.py @@ -16,7 +16,8 @@ from stdatamodels.jwst import datamodels -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary +from jwst.stpipe.utilities import record_step_status log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -24,49 +25,54 @@ __all__ = ["assign_moving_target_wcs"] -def assign_moving_target_wcs(input_model): +def assign_moving_target_wcs(input_models): - if not isinstance(input_model, ModelContainer): - raise ValueError("Expected a ModelContainer object") + if not isinstance(input_models, ModelLibrary): + raise ValueError("Expected a ModelLibrary object") - # get the indices of the science exposures in the ModelContainer - ind = input_model.ind_asn_type('science') - sci_models = np.asarray(input_model._models)[ind] - # Get the MT RA/Dec values from all the input exposures - mt_ra = np.array([model.meta.wcsinfo.mt_ra for model in sci_models]) - mt_dec = np.array([model.meta.wcsinfo.mt_dec for model in sci_models]) + # loop over only science exposures in the ModelLibrary + ind = input_models.indices_for_exptype("science") + mt_ra = np.empty(len(ind)) + mt_dec = np.empty(len(ind)) + with input_models: + for i in ind: + model = input_models.borrow(i) + mt_ra[i] = model.meta.wcsinfo.mt_ra + mt_dec[i] = model.meta.wcsinfo.mt_dec + input_models.shelve(model, i, modify=False) # Compute the mean MT RA/Dec over all exposures if None in mt_ra or None in mt_dec: log.warning("One or more MT RA/Dec values missing in input images") log.warning("Step will be skipped, resulting in target misalignment") - for model in sci_models: - model.meta.cal_step.assign_mtwcs = 'SKIPPED' - return input_model - else: - mt_avra = mt_ra.mean() - mt_avdec = mt_dec.mean() - - for model in sci_models: - model.meta.wcsinfo.mt_avra = mt_avra - model.meta.wcsinfo.mt_avdec = mt_avdec - if isinstance(model, datamodels.MultiSlitModel): - for ind, slit in enumerate(model.slits): - new_wcs = add_mt_frame(slit.meta.wcs, - mt_avra, mt_avdec, - slit.meta.wcsinfo.mt_ra, slit.meta.wcsinfo.mt_dec) - del model.slits[ind].meta.wcs - model.slits[ind].meta.wcs = new_wcs - else: - - new_wcs = add_mt_frame(model.meta.wcs, mt_avra, mt_avdec, - model.meta.wcsinfo.mt_ra, model.meta.wcsinfo.mt_dec) - del model.meta.wcs - model.meta.wcs = new_wcs - - model.meta.cal_step.assign_mtwcs = 'COMPLETE' - - return input_model + record_step_status(input_models, "assign_mtwcs", False) + return input_models + + mt_avra = mt_ra.mean() + mt_avdec = mt_dec.mean() + + with input_models: + for i in ind: + model = input_models.borrow(i) + model.meta.wcsinfo.mt_avra = mt_avra + model.meta.wcsinfo.mt_avdec = mt_avdec + if isinstance(model, datamodels.MultiSlitModel): + for ind, slit in enumerate(model.slits): + new_wcs = add_mt_frame(slit.meta.wcs, + mt_avra, mt_avdec, + slit.meta.wcsinfo.mt_ra, slit.meta.wcsinfo.mt_dec) + del model.slits[ind].meta.wcs + model.slits[ind].meta.wcs = new_wcs + else: + + new_wcs = add_mt_frame(model.meta.wcs, mt_avra, mt_avdec, + model.meta.wcsinfo.mt_ra, model.meta.wcsinfo.mt_dec) + del model.meta.wcs + model.meta.wcs = new_wcs + record_step_status(model, "assign_mtwcs", True) + input_models.shelve(model, i, modify=True) + + return input_models def add_mt_frame(wcs, ra_average, dec_average, mt_ra, mt_dec): diff --git a/jwst/assign_mtwcs/tests/test_mtwcs.py b/jwst/assign_mtwcs/tests/test_mtwcs.py index a33b848872..934869d4c5 100644 --- a/jwst/assign_mtwcs/tests/test_mtwcs.py +++ b/jwst/assign_mtwcs/tests/test_mtwcs.py @@ -2,7 +2,7 @@ from stdatamodels.jwst import datamodels -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary from jwst.assign_mtwcs import AssignMTWcsStep from jwst.assign_mtwcs.tests import data @@ -13,10 +13,17 @@ def test_mt_multislit(): file_path = os.path.join(data.__path__[0], 'test_mt_asn.json') with datamodels.open(file_path) as model: assert model[0].slits[0].meta.wcs.output_frame.name == 'world' - step = AssignMTWcsStep() - result = step.run(model) - assert isinstance(result, ModelContainer) - assert len(result[0].slits) == 1 - assert result[0].slits[0].meta.wcs.output_frame.name == 'moving_target' - assert len(result[1].slits) == 1 - assert result[1].slits[0].meta.wcs.output_frame.name == 'moving_target' + step = AssignMTWcsStep() + result = step.run(file_path) + assert isinstance(result, ModelLibrary) + with result: + zero = result.borrow(0) + one = result.borrow(1) + + assert len(zero.slits) == 1 + assert zero.slits[0].meta.wcs.output_frame.name == 'moving_target' + assert len(one.slits) == 1 + assert one.slits[0].meta.wcs.output_frame.name == 'moving_target' + + result.shelve(zero, 0, modify=False) + result.shelve(one, 1, modify=False) diff --git a/jwst/datamodels/__init__.py b/jwst/datamodels/__init__.py index 2bf017f093..1d3e0b23d6 100644 --- a/jwst/datamodels/__init__.py +++ b/jwst/datamodels/__init__.py @@ -8,6 +8,7 @@ from stdatamodels.jwst.datamodels.util import open from .container import ModelContainer +from .library import ModelLibrary from .source_container import SourceModelContainer import stdatamodels.jwst.datamodels @@ -19,14 +20,15 @@ __all__ = [ 'open', 'ModelContainer', 'SourceModelContainer', + 'ModelLibrary', ] + stdatamodels.jwst.datamodels.__all__ # Modules that are not part of stdatamodels -_jwst_modules = ["container", "source_container"] +_jwst_modules = ["container", "source_container", "library"] # Models that are not part of stdatamodels -_jwst_models = ["ModelContainer", "SourceModelContainer"] +_jwst_models = ["ModelContainer", "SourceModelContainer", "ModelLibrary"] # Deprecated modules in stdatamodels _deprecated_modules = ['schema'] diff --git a/jwst/datamodels/library.py b/jwst/datamodels/library.py new file mode 100644 index 0000000000..32e27aa117 --- /dev/null +++ b/jwst/datamodels/library.py @@ -0,0 +1,158 @@ +import io + +import asdf +from astropy.io import fits +from stdatamodels.jwst.datamodels.util import open as datamodels_open +from stpipe.library import AbstractModelLibrary, NoGroupID + +from jwst.associations import AssociationNotValidError, load_asn + +__all__ = ["ModelLibrary"] + + +class ModelLibrary(AbstractModelLibrary): + """ + JWST implementation of the ModelLibrary, a container designed to allow + efficient processing of datamodel instances created from an association. + See the `stpipe library documentation = 1 + + if has_groups: + with input_models: + model = input_models.borrow(0) + is_moving = is_moving_target(model) + input_models.shelve(model, 0, modify=False) + if is_moving: + input_models = self.assign_mtwcs(input_models) + else: + input_models = self.tweakreg(input_models) - # Check if input is single or multiple exposures - try: - has_groups = len(input_models.group_names) >= 1 - except (AttributeError, TypeError, KeyError): - has_groups = False + input_models = self.skymatch(input_models) + input_models = self.outlier_detection(input_models) - if isinstance(input_models, ModelContainer) and has_groups: - if is_moving_target(input_models): - input_models = self.assign_mtwcs(input_models) - else: - input_models = self.tweakreg(input_models) + elif self.skymatch.skymethod == 'match': + self.log.warning("Turning 'skymatch' step off for a single " + "input image when 'skymethod' is 'match'") - input_models = self.skymatch(input_models) - input_models = self.outlier_detection(input_models) + else: + input_models = self.skymatch(input_models) - elif self.skymatch.skymethod == 'match': - self.log.warning("Turning 'skymatch' step off for a single " - "input image when 'skymethod' is 'match'") + result = self.resample(input_models) + del input_models + if isinstance(result, datamodels.ImageModel) and result.meta.cal_step.resample == 'COMPLETE': + self.source_catalog(result) - else: - input_models = self.skymatch(input_models) - result = self.resample(input_models) - if isinstance(result, datamodels.ImageModel) and result.meta.cal_step.resample == 'COMPLETE': - self.source_catalog(result) + def _load_input_as_library(self, input): + """ + Load any valid input type into a ModelLibrary, including + single datamodels, associations, ModelLibrary instances, and + filenames pointing to those types. + """ + + if isinstance(input, ModelLibrary): + return input + + if isinstance(input, (str, dict)): + try: + # Try opening input as an association + return ModelLibrary(input, asn_exptypes=['science'], on_disk=not self.in_memory) + except OSError: + # Try opening input as a single cal file + input = datamodels.open(input) + input = [input,] + return ModelLibrary(input, asn_exptypes=['science'], on_disk=not self.in_memory) + elif isinstance(input, Sequence): + return ModelLibrary(input, asn_exptypes=['science'], on_disk=not self.in_memory) + elif isinstance(input, datamodels.JwstDataModel): + return ModelLibrary([input], asn_exptypes=['science'], on_disk=not self.in_memory) + else: + raise TypeError(f"Input type {type(input)} not supported.") \ No newline at end of file diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 96a5b25d68..7fd69e4373 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -138,9 +138,10 @@ def process(self, input): for member in product['members']: members_by_type[member['exptype'].lower()].append(member['expname']) - if is_moving_target(input_models): + if is_moving_target(input_models[0]): self.log.info("Assigning WCS to a Moving Target exposure.") - input_models = self.assign_mtwcs(input_models) + # assign_mtwcs modifies input_models in-place + self.assign_mtwcs(input_models) # If background data are present, call the master background step if members_by_type['background']: diff --git a/jwst/pipeline/tests/test_calwebb_image3.py b/jwst/pipeline/tests/test_calwebb_image3.py new file mode 100644 index 0000000000..8531674664 --- /dev/null +++ b/jwst/pipeline/tests/test_calwebb_image3.py @@ -0,0 +1,114 @@ +import pytest +import os +import shutil +from jwst.stpipe import Step +from jwst.assign_wcs import AssignWcsStep +from jwst.datamodels import ImageModel + + +INPUT_FILE = "dummy_cal.fits" +INPUT_FILE_2 = "dummy2_cal.fits" +INPUT_ASN = "dummy_asn.json" +OUTPUT_PRODUCT = "custom_name" +LOGFILE = "run_asn.log" +LOGCFG = "test_logs.cfg" +LOGCFG_CONTENT = f"[*] \n \ + handler = file:{LOGFILE}" + + +@pytest.fixture(scope='module') +def make_dummy_cal_file(tmp_cwd_module): + ''' + Make and save a dummy cal file in the temporary working directory + Partially copied from test_calwebb_image2.py + ''' + + image = ImageModel((2048, 2048)) + image.data[:, :] = 1 + image.meta.instrument.name = 'NIRCAM' + image.meta.instrument.filter = 'F210M' + image.meta.instrument.pupil = 'CLEAR' + image.meta.exposure.type = 'NRC_IMAGE' + image.meta.observation.date = '2024-02-27' + image.meta.observation.time = '13:37:18.548' + image.meta.date = '2024-02-27T13:37:18.548' + image.meta.subarray.xstart = 1 + image.meta.subarray.ystart = 1 + + image.meta.subarray.xsize = image.data.shape[-1] + image.meta.subarray.ysize = image.data.shape[-2] + + image.meta.instrument.channel = 'SHORT' + image.meta.instrument.module = 'A' + image.meta.instrument.detector = 'NRCA1' + + # bare minimum wcs info to get assign_wcs step to pass + image.meta.wcsinfo.crpix1 = 693.5 + image.meta.wcsinfo.crpix2 = 512.5 + image.meta.wcsinfo.v2_ref = -453.37849 + image.meta.wcsinfo.v3_ref = -373.810549 + image.meta.wcsinfo.roll_ref = 272.3237653262276 + image.meta.wcsinfo.ra_ref = 80.54724018120017 + image.meta.wcsinfo.dec_ref = -69.5081101864959 + + image = AssignWcsStep.call(image) + + with image as dm: + dm.save(INPUT_FILE) + + +@pytest.fixture(scope='module') +def make_dummy_association(make_dummy_cal_file): + + shutil.copy(INPUT_FILE, INPUT_FILE_2) + os.system(f"asn_from_list -o {INPUT_ASN} --product-name {OUTPUT_PRODUCT} -r DMS_Level3_Base {INPUT_FILE} {INPUT_FILE_2}") + + +@pytest.mark.parametrize("in_memory", [True, False]) +def test_run_image3_pipeline(make_dummy_association, in_memory): + ''' + Two-product association passed in, run pipeline, skipping most steps + ''' + # save warnings to logfile so can be checked later + with open(LOGCFG, 'w') as f: + f.write(LOGCFG_CONTENT) + + args = ["calwebb_image3", INPUT_ASN, + f"--logcfg={LOGCFG}", + "--steps.tweakreg.skip=true", + "--steps.skymatch.skip=true", + "--steps.outlier_detection.skip=true", + "--steps.resample.skip=true", + "--steps.source_catalog.skip=true", + f"--in_memory={str(in_memory)}",] + + Step.from_cmdline(args) + + _is_run_complete(LOGFILE) + + +def test_run_image3_single_file(make_dummy_cal_file): + + with open(LOGCFG, 'w') as f: + f.write(LOGCFG_CONTENT) + + args = ["calwebb_image3", INPUT_FILE, + f"--logcfg={LOGCFG}", + "--steps.tweakreg.skip=true", + "--steps.skymatch.skip=true", + "--steps.outlier_detection.skip=true", + "--steps.resample.skip=true", + "--steps.source_catalog.skip=true",] + + Step.from_cmdline(args) + _is_run_complete(LOGFILE) + + +def _is_run_complete(logfile): + ''' + Check that the pipeline runs to completion + ''' + msg = "Step Image3Pipeline done" + with open(LOGFILE, 'r') as f: + log = f.read() + assert msg in log diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 4cce41eda6..7e186986a2 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -71,11 +71,10 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): assert model.meta.cal_step.tweakreg != 'SKIPPED' result = TweakRegStep.call(mc) - - for model in result: - assert model.meta.cal_step.tweakreg == 'SKIPPED' - - result.close() + with result: + for model in result: + assert model.meta.cal_step.tweakreg == 'SKIPPED' + result.shelve(model, modify=False) def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix): diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 681592f3aa..97b2e3d766 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -1,6 +1,7 @@ import logging import os import warnings +import json import numpy as np import psutil @@ -10,7 +11,8 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.library.basic_utils import bytes2human -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary +from jwst.associations.asn_from_list import asn_from_list from . import gwcs_drizzle from jwst.resample import resample_utils @@ -49,8 +51,8 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, """ Parameters ---------- - input_models : list of objects - list of data models, one for each input image + input_models : library of objects + library of data models, one for each input image output : str filename for output @@ -133,7 +135,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, crpix=crpix, crval=crval ) - # Estimate output pixel area in Sr. NOTE: in principle we could # use the same algorithm as for when output_wcs is provided by the # user. @@ -180,14 +181,17 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, self.blank_output = datamodels.ImageModel(tuple(self.output_wcs.array_shape)) # update meta data and wcs - self.blank_output.update(input_models[0]) + with input_models: + example_model = input_models.borrow(0) + self.blank_output.update(example_model) + input_models.shelve(example_model, 0, modify=False) + del example_model self.blank_output.meta.wcs = self.output_wcs self.blank_output.meta.photometry.pixelarea_steradians = output_pix_area self.blank_output.meta.photometry.pixelarea_arcsecsq = ( output_pix_area * np.rad2deg(3600)**2 ) - self.output_models = ModelContainer(open_models=False) def do_drizzle(self, input_models): """Pick the correct drizzling mode based on self.single @@ -275,64 +279,77 @@ def resample_many_to_many(self, input_models): Used for outlier detection """ - for exposure in input_models.models_grouped: + output_models = [] + for group_id, indices in input_models.group_indices.items(): output_model = self.blank_output - # Determine output file type from input exposure filenames - # Use this for defining the output filename - indx = exposure[0].meta.filename.rfind('.') - output_type = exposure[0].meta.filename[indx:] - output_root = '_'.join(exposure[0].meta.filename.replace( - output_type, '').split('_')[:-1]) - if self.asn_id is not None: - output_model.meta.filename = ( - f'{output_root}_{self.asn_id}_' - f'{self.intermediate_suffix}{output_type}') - else: - output_model.meta.filename = ( - f'{output_root}_' - f'{self.intermediate_suffix}{output_type}') - - # Initialize the output with the wcs - driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, - kernel=self.kernel, fillval=self.fillval) - - log.info(f"{len(exposure)} exposures to drizzle together") - for img in exposure: - img = datamodels.open(img) - iscale = self._get_intensity_scale(img) - log.debug(f'Using intensity scale iscale={iscale}') - - inwht = resample_utils.build_driz_weight( - img, - weight_type=self.weight_type, - good_bits=self.good_bits - ) - - # apply sky subtraction - blevel = img.meta.background.level - if not img.meta.background.subtracted and blevel is not None: - data = img.data - blevel + copy_asn_info_from_library(input_models, output_model) + + with input_models: + example_image = input_models.borrow(indices[0]) + + # Determine output file type from input exposure filenames + # Use this for defining the output filename + indx = example_image.meta.filename.rfind('.') + output_type = example_image.meta.filename[indx:] + output_root = '_'.join(example_image.meta.filename.replace( + output_type, '').split('_')[:-1]) + if self.asn_id is not None: + output_model.meta.filename = ( + f'{output_root}_{self.asn_id}_' + f'{self.intermediate_suffix}{output_type}') else: - data = img.data + output_model.meta.filename = ( + f'{output_root}_' + f'{self.intermediate_suffix}{output_type}') + input_models.shelve(example_image, indices[0], modify=False) + del example_image + + # Initialize the output with the wcs + driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, + kernel=self.kernel, fillval=self.fillval) + + log.info(f"{len(indices)} exposures to drizzle together") + for index in indices: + img = input_models.borrow(index) + if isinstance(img, datamodels.SlitModel): + # must call this explicitly to populate area extension + # although the existence of this extension may not be necessary + img.area = img.area + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') + + inwht = resample_utils.build_driz_weight( + img, + weight_type=self.weight_type, + good_bits=self.good_bits + ) - xmin, xmax, ymin, ymax = resample_utils._resample_range( - data.shape, - img.meta.wcs.bounding_box - ) + # apply sky subtraction + blevel = img.meta.background.level + if not img.meta.background.subtracted and blevel is not None: + data = img.data - blevel + else: + data = img.data - driz.add_image( - data, - img.meta.wcs, - iscale=iscale, - inwht=inwht, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax - ) - del data - img.close() + xmin, xmax, ymin, ymax = resample_utils._resample_range( + data.shape, + img.meta.wcs.bounding_box + ) + + driz.add_image( + data, + img.meta.wcs, + iscale=iscale, + inwht=inwht, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax + ) + del data + input_models.shelve(img, index, modify=False) + del img if not self.in_memory: # Write out model to disk, then return filename @@ -341,13 +358,20 @@ def resample_many_to_many(self, input_models): output_name = os.path.join(self.output_dir, output_name) output_model.save(output_name) log.info(f"Saved model in {output_name}") - self.output_models.append(output_name) + output_models.append(output_name) else: - self.output_models.append(output_model.copy()) + output_models.append(output_model.copy()) output_model.data *= 0. output_model.wht *= 0. - return self.output_models + if not self.in_memory: + # build ModelLibrary as an association from the output files + # this saves memory if there are multiple groups + asn = asn_from_list(output_models, product_name='outlier_i2d') + asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict + return ModelLibrary(asn_dict, on_disk=True) + # otherwise just build it as a list of in-memory models + return ModelLibrary(output_models, on_disk=False) def resample_many_to_one(self, input_models): """Resample and coadd many inputs to a single output. @@ -359,45 +383,71 @@ def resample_many_to_one(self, input_models): output_model.meta.resample.weight_type = self.weight_type output_model.meta.resample.pointings = len(input_models.group_names) + # copy over asn information + copy_asn_info_from_library(input_models, output_model) + if self.blendheaders: - self.blend_output_metadata(output_model, input_models) + # right now this needs a list of input models, all in memory + # for now, just load the models as a list with empty data arrays + # but the blend_meta step itself should eventually be refactored + # to expect a list of metadata objects + # instead of a list of datamodels + input_list = [] + with input_models: + for i, model in enumerate(input_models): + empty_model = type(model)() + empty_model.meta = model.meta + copy_asn_info_from_library(input_models, empty_model) + empty_model.data = np.empty((1, 1)) + empty_model.dq = np.empty((1, 1)) + empty_model.err = np.empty((1, 1)) + empty_model.wht = np.empty((1, 1)) + empty_model.var_rnoise = np.empty((1, 1)) + empty_model.var_poisson = np.empty((1, 1)) + empty_model.var_flat = np.empty((1, 1)) + input_list.append(empty_model) + input_models.shelve(model, i, modify=False) + self.blend_output_metadata(output_model, input_list) + del input_list # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, kernel=self.kernel, fillval=self.fillval) log.info("Resampling science data") - for img in input_models: - iscale = self._get_intensity_scale(img) - log.debug(f'Using intensity scale iscale={iscale}') - img.meta.iscale = iscale - - inwht = resample_utils.build_driz_weight(img, - weight_type=self.weight_type, - good_bits=self.good_bits) - # apply sky subtraction - blevel = img.meta.background.level - if not img.meta.background.subtracted and blevel is not None: - data = img.data - blevel - else: - data = img.data.copy() + with input_models: + for img in input_models: + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') + img.meta.iscale = iscale - xmin, xmax, ymin, ymax = resample_utils._resample_range( - data.shape, - img.meta.wcs.bounding_box - ) + inwht = resample_utils.build_driz_weight(img, + weight_type=self.weight_type, + good_bits=self.good_bits) + # apply sky subtraction + blevel = img.meta.background.level + if not img.meta.background.subtracted and blevel is not None: + data = img.data - blevel + else: + data = img.data.copy() - driz.add_image( - data, - img.meta.wcs, - iscale=iscale, - inwht=inwht, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax - ) - del data, inwht + xmin, xmax, ymin, ymax = resample_utils._resample_range( + data.shape, + img.meta.wcs.bounding_box + ) + + driz.add_image( + data, + img.meta.wcs, + iscale=iscale, + inwht=inwht, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax + ) + del data, inwht + input_models.shelve(img) # Resample variance arrays in input_models to output_model self.resample_variance_arrays(output_model, input_models) @@ -414,12 +464,9 @@ def resample_many_to_one(self, input_models): output_model.err[all_nan] = np.nan self.update_exposure_times(output_model, input_models) - self.output_models.append(output_model) - for img in input_models: - del img.meta.iscale + return ModelLibrary([output_model,], on_disk=False) - return self.output_models def resample_variance_arrays(self, output_model, input_models): """Resample variance arrays from input_models to the output_model. @@ -438,82 +485,91 @@ def resample_variance_arrays(self, output_model, input_models): total_weight_rn_var = np.zeros_like(output_model.data) total_weight_pn_var = np.zeros_like(output_model.data) total_weight_flat_var = np.zeros_like(output_model.data) - for model in input_models: - # Do the read noise variance first, so it can be - # used for weights if needed - rn_var = self._resample_one_variance_array( - "var_rnoise", model, output_model) - - # Find valid weighting values in the variance - if rn_var is not None: - mask = (rn_var > 0) & np.isfinite(rn_var) - else: - mask = np.full_like(rn_var, False) - - # Set the weight for the image from the weight type - weight = np.ones(output_model.data.shape) - if self.weight_type == "ivm" and rn_var is not None: - weight[mask] = rn_var[mask] ** -1 - elif self.weight_type == "exptime": - if resample_utils.check_for_tmeasure(model): - weight[:] = model.meta.exposure.measurement_time + with input_models: + for i, model in enumerate(input_models): + # Do the read noise variance first, so it can be + # used for weights if needed + rn_var = self._resample_one_variance_array( + "var_rnoise", model, output_model) + + # Find valid weighting values in the variance + if rn_var is not None: + mask = (rn_var > 0) & np.isfinite(rn_var) else: - weight[:] = model.meta.exposure.exposure_time - - # Weight and add the readnoise variance - # Note: floating point overflow is an issue if variance weights - # are used - it can't be squared before multiplication - if rn_var is not None: - mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0) - weighted_rn_var[mask] = np.nansum( - [weighted_rn_var[mask], - rn_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_rn_var[mask] += weight[mask] - - # Now do poisson and flat variance, updating only valid new values - # (zero is a valid value; negative, inf, or NaN are not) - pn_var = self._resample_one_variance_array( - "var_poisson", model, output_model) - if pn_var is not None: - mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0) - weighted_pn_var[mask] = np.nansum( - [weighted_pn_var[mask], - pn_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_pn_var[mask] += weight[mask] - - flat_var = self._resample_one_variance_array( - "var_flat", model, output_model) - if flat_var is not None: - mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0) - weighted_flat_var[mask] = np.nansum( - [weighted_flat_var[mask], - flat_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_flat_var[mask] += weight[mask] + mask = np.full_like(rn_var, False) + + # Set the weight for the image from the weight type + weight = np.ones(output_model.data.shape) + if self.weight_type == "ivm" and rn_var is not None: + weight[mask] = rn_var[mask] ** -1 + elif self.weight_type == "exptime": + if resample_utils.check_for_tmeasure(model): + weight[:] = model.meta.exposure.measurement_time + else: + weight[:] = model.meta.exposure.exposure_time + + # Weight and add the readnoise variance + # Note: floating point overflow is an issue if variance weights + # are used - it can't be squared before multiplication + if rn_var is not None: + mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0) + weighted_rn_var[mask] = np.nansum( + [weighted_rn_var[mask], + rn_var[mask] * weight[mask] * weight[mask]], + axis=0 + ) + total_weight_rn_var[mask] += weight[mask] + + # Now do poisson and flat variance, updating only valid new values + # (zero is a valid value; negative, inf, or NaN are not) + pn_var = self._resample_one_variance_array( + "var_poisson", model, output_model) + if pn_var is not None: + mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0) + weighted_pn_var[mask] = np.nansum( + [weighted_pn_var[mask], + pn_var[mask] * weight[mask] * weight[mask]], + axis=0 + ) + total_weight_pn_var[mask] += weight[mask] + + flat_var = self._resample_one_variance_array( + "var_flat", model, output_model) + if flat_var is not None: + mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0) + weighted_flat_var[mask] = np.nansum( + [weighted_flat_var[mask], + flat_var[mask] * weight[mask] * weight[mask]], + axis=0 + ) + total_weight_flat_var[mask] += weight[mask] + + del model.meta.iscale + del weight + input_models.shelve(model, i) + + # We now have a sum of the weighted resampled variances. + # Divide by the total weights, squared, and set in the output model. + # Zero weight and missing values are NaN in the output. + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) + warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) + + output_variance = (weighted_rn_var + / total_weight_rn_var / total_weight_rn_var) + setattr(output_model, "var_rnoise", output_variance) - # We now have a sum of the weighted resampled variances. - # Divide by the total weights, squared, and set in the output model. - # Zero weight and missing values are NaN in the output. - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) - warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) + output_variance = (weighted_pn_var + / total_weight_pn_var / total_weight_pn_var) + setattr(output_model, "var_poisson", output_variance) - output_variance = (weighted_rn_var - / total_weight_rn_var / total_weight_rn_var) - setattr(output_model, "var_rnoise", output_variance) + output_variance = (weighted_flat_var + / total_weight_flat_var / total_weight_flat_var) + setattr(output_model, "var_flat", output_variance) - output_variance = (weighted_pn_var - / total_weight_pn_var / total_weight_pn_var) - setattr(output_model, "var_poisson", output_variance) + del weighted_rn_var, weighted_pn_var, weighted_flat_var + del total_weight_rn_var, total_weight_pn_var, total_weight_flat_var - output_variance = (weighted_flat_var - / total_weight_flat_var / total_weight_flat_var) - setattr(output_model, "var_flat", output_variance) def _resample_one_variance_array(self, name, input_model, output_model): """Resample one variance image from an input model. @@ -583,16 +639,19 @@ def update_exposure_times(self, output_model, input_models): duration = 0.0 total_measurement_time = 0.0 measurement_time_failures = [] - for exposure in input_models.models_grouped: - total_exposure_time += exposure[0].meta.exposure.exposure_time - if not resample_utils.check_for_tmeasure(exposure[0]): - measurement_time_failures.append(1) - else: - total_measurement_time += exposure[0].meta.exposure.measurement_time - measurement_time_failures.append(0) - exposure_times['start'].append(exposure[0].meta.exposure.start_time) - exposure_times['end'].append(exposure[0].meta.exposure.end_time) - duration += exposure[0].meta.exposure.duration + with input_models: + for _, indices in input_models.group_indices.items(): + model = input_models.borrow(indices[0]) + total_exposure_time += model.meta.exposure.exposure_time + if not resample_utils.check_for_tmeasure(model): + measurement_time_failures.append(1) + else: + total_measurement_time += model.meta.exposure.measurement_time + measurement_time_failures.append(0) + exposure_times['start'].append(model.meta.exposure.start_time) + exposure_times['end'].append(model.meta.exposure.end_time) + duration += model.meta.exposure.duration + input_models.shelve(model, indices[0], modify=False) # Update some basic exposure time values based on output_model output_model.meta.exposure.exposure_time = total_exposure_time @@ -833,7 +892,6 @@ def compute_image_pixel_area(wcs): spatial_idx = np.where(np.array(wcs.output_frame.axes_type) == 'SPATIAL')[0] ny, nx = wcs.array_shape - ((xmin, xmax), (ymin, ymax)) = wcs.bounding_box xmin = max(0, int(xmin + 0.5)) @@ -897,3 +955,28 @@ def compute_image_pixel_area(wcs): pix_area = sky_area / image_area return pix_area + + +def copy_asn_info_from_library(library, output_model): + """ + Transfer association information from the input library to the output model. + + Parameters + ---------- + library : ModelLibrary + The input library of data models. + + output_model : DataModel + The output data model to which the association information will be copied. + """ + if not hasattr(library, "asn"): + # No ASN table, occurs when input comes from ModelContainer in spectroscopic modes + # in this case do nothing; the asn info will be passed along later + # by code inside ResampleSpecStep + return + if (asn_pool := library.asn.get("asn_pool", None)) is not None: + output_model.meta.asn.pool_name = asn_pool + if ( + asn_table_name := library.asn.get("table_name", None) + ) is not None: + output_model.meta.asn.table_name = asn_table_name \ No newline at end of file diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index e75d1c898d..9316a838c0 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -16,7 +16,6 @@ from stdatamodels.jwst import datamodels from jwst.assign_wcs.util import compute_scale, wrap_ra -from jwst.datamodels import ModelContainer from jwst.resample import resample_utils from jwst.resample.resample import ResampleData @@ -192,7 +191,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.blank_output.meta.photometry.pixelarea_arcsecsq = ( output_pix_area * np.rad2deg(3600)**2) - self.output_models = ModelContainer() def build_nirspec_output_wcs(self, input_models, refmodel=None): """ @@ -946,4 +944,4 @@ def compute_spectral_pixel_scale(wcs, fiducial=None, disp_axis=1): fiducial = wcs(center_x, center_y) pixel_scale = compute_scale(wcs, fiducial, disp_axis=disp_axis) - return float(pixel_scale) + return float(pixel_scale) \ No newline at end of file diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index b529f5690b..bcc3d1a95c 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -3,11 +3,11 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import MultiSlitModel, ImageModel +from jwst.datamodels import ModelContainer, ModelLibrary from . import resample_spec, ResampleStep from ..exp_to_source import multislit_to_container from ..assign_wcs.util import update_s_region_spectral from ..stpipe import Step -from jwst.datamodels import ModelContainer from jwst.lib.wcs_utils import get_wavelengths @@ -95,6 +95,7 @@ def process(self, input): result = self._process_slit(input_models) # Update ASNTABLE in output + result.meta.cal_step.resample = "COMPLETE" result.meta.asn.table_name = input_models[0].meta.asn.table_name result.meta.asn.pool_name = input_models[0].meta.asn.pool_name @@ -133,20 +134,20 @@ def _process_multislit(self, input_models): for container in containers.values(): resamp = resample_spec.ResampleSpecData(container, **self.drizpars) - drizzled_models = resamp.do_drizzle(container) - - for model in drizzled_models: - self.update_slit_metadata(model) - update_s_region_spectral(model) - result.slits.append(model) + library = ModelLibrary(container, on_disk=False) + drizzled_library = resamp.do_drizzle(library) + with drizzled_library: + for i, model in enumerate(drizzled_library): + self.update_slit_metadata(model) + update_s_region_spectral(model) + result.slits.append(model) + drizzled_library.shelve(model, i, modify=False) + del library, drizzled_library # Keep the first computed pixel scale ratio for storage if self.pixel_scale is not None and pscale_ratio is None: pscale_ratio = resamp.pscale_ratio - result.meta.cal_step.resample = "COMPLETE" - result.meta.asn.pool_name = input_models.asn_pool_name - result.meta.asn.table_name = input_models.asn_table_name if self.pixel_scale is None or pscale_ratio is None: result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio else: @@ -206,13 +207,14 @@ def _process_slit(self, input_models): resamp = resample_spec.ResampleSpecData(input_models, **self.drizpars) - drizzled_models = resamp.do_drizzle(input_models) + library = ModelLibrary(input_models, on_disk=False) + drizzled_library = resamp.do_drizzle(library) + with drizzled_library: + result = drizzled_library.borrow(0) + drizzled_library.shelve(result, 0, modify=False) + del library, drizzled_library - result = drizzled_models[0] - result.meta.cal_step.resample = "COMPLETE" - result.meta.asn.pool_name = input_models.asn_pool_name - result.meta.asn.table_name = input_models.asn_table_name - result.meta.bunit_data = drizzled_models[0].meta.bunit_data + result.meta.bunit_data = input_models[0].meta.bunit_data if self.pixel_scale is None: result.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio else: @@ -241,4 +243,4 @@ def update_slit_metadata(self, model): pass else: if val is not None: - setattr(model, attr, val) + setattr(model, attr, val) \ No newline at end of file diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 4c2d647985..a2fd35db96 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -4,9 +4,7 @@ import asdf -from stdatamodels.jwst import datamodels - -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary, ImageModel from . import resample from ..stpipe import Step @@ -61,28 +59,34 @@ class ResampleStep(Step): def process(self, input): - input = datamodels.open(input) - - if isinstance(input, ModelContainer): + if isinstance(input, ModelLibrary): input_models = input - try: - output = input_models.meta.asn_table.products[0].name - except AttributeError: - # coron data goes through this path by the time it gets to - # resampling. - # TODO: figure out why and make sure asn_table is carried along - output = None - else: - input_models = ModelContainer([input]) - input_models.asn_pool_name = input.meta.asn.pool_name - input_models.asn_table_name = input.meta.asn.table_name + elif isinstance(input, (str, dict, list)): + input_models = ModelLibrary(input, on_disk=not self.in_memory) + elif isinstance(input, ImageModel): + input_models = ModelLibrary([input], on_disk=not self.in_memory) output = input.meta.filename self.blendheaders = False + else: + raise RuntimeError(f"Input {input} is not a 2D image.") + + try: + output = input_models.asn["products"][0]["name"] + except KeyError: + # coron data goes through this path by the time it gets to + # resampling. + # TODO: figure out why and make sure asn_table is carried along + output = None # Check that input models are 2D images - if len(input_models[0].data.shape) != 2: - # resample can only handle 2D images, not 3D cubes, etc - raise RuntimeError("Input {} is not a 2D image.".format(input_models[0])) + with input_models: + example_model = input_models.borrow(0) + data_shape = example_model.data.shape + input_models.shelve(example_model, 0, modify=False) + if len(data_shape) != 2: + # resample can only handle 2D images, not 3D cubes, etc + raise RuntimeError(f"Input {example_model} is not a 2D image.") + del example_model # Setup drizzle-related parameters kwargs = self.get_drizpars() @@ -91,26 +95,27 @@ def process(self, input): resamp = resample.ResampleData(input_models, output=output, **kwargs) result = resamp.do_drizzle(input_models) - for model in result: - model.meta.cal_step.resample = 'COMPLETE' - self.update_fits_wcs(model) - util.update_s_region_imaging(model) - model.meta.asn.pool_name = input_models.asn_pool_name - model.meta.asn.table_name = input_models.asn_table_name - - # if pixel_scale exists, it will override pixel_scale_ratio. - # calculate the actual value of pixel_scale_ratio based on pixel_scale - # because source_catalog uses this value from the header. - if self.pixel_scale is None: - model.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio - else: - model.meta.resample.pixel_scale_ratio = resamp.pscale_ratio - model.meta.resample.pixfrac = kwargs['pixfrac'] - - if len(result) == 1: - result = result[0] - - input_models.close() + with result: + for model in result: + model.meta.cal_step.resample = 'COMPLETE' + self.update_fits_wcs(model) + util.update_s_region_imaging(model) + + # if pixel_scale exists, it will override pixel_scale_ratio. + # calculate the actual value of pixel_scale_ratio based on pixel_scale + # because source_catalog uses this value from the header. + if self.pixel_scale is None: + model.meta.resample.pixel_scale_ratio = self.pixel_scale_ratio + else: + model.meta.resample.pixel_scale_ratio = resamp.pscale_ratio + model.meta.resample.pixfrac = kwargs['pixfrac'] + result.shelve(model) + + if len(result) == 1: + model = result.borrow(0) + result.shelve(model, 0, modify=False) + return model + return result @staticmethod diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index 0ba20c62c9..c527b450f1 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -27,7 +27,7 @@ def make_output_wcs(input_models, ref_wcs=None, Parameters ---------- - input_models : list of `~jwst.datamodel.JwstDataModel` + input_models : `~jwst.datamodel.ModelLibrary` Each datamodel must have a ~gwcs.WCS object. pscale_ratio : float, optional @@ -67,10 +67,16 @@ def make_output_wcs(input_models, ref_wcs=None, WCS object, with defined domain, covering entire set of input frames """ if ref_wcs is None: - wcslist = [i.meta.wcs for i in input_models] - for w, i in zip(wcslist, input_models): - if w.bounding_box is None: - w.bounding_box = wcs_bbox_from_shape(i.data.shape) + with input_models: + wcslist = [] + for i, model in enumerate(input_models): + w = model.meta.wcs + if w.bounding_box is None: + w.bounding_box = wcs_bbox_from_shape(model.data.shape) + wcslist.append(w) + if i == 0: + example_model = model + input_models.shelve(model) naxes = wcslist[0].output_frame.naxes if naxes != 2: @@ -81,7 +87,7 @@ def make_output_wcs(input_models, ref_wcs=None, output_wcs = util.wcs_from_footprints( wcslist, ref_wcs=wcslist[0], - ref_wcsinfo=input_models[0].meta.wcsinfo.instance, + ref_wcsinfo=example_model.meta.wcsinfo.instance, pscale_ratio=pscale_ratio, pscale=pscale, rotation=rotation, @@ -89,6 +95,7 @@ def make_output_wcs(input_models, ref_wcs=None, crpix=crpix, crval=crval ) + del example_model else: naxes = ref_wcs.output_frame.naxes diff --git a/jwst/resample/tests/test_resample_step.py b/jwst/resample/tests/test_resample_step.py index fcd380a4b8..75eddb8530 100644 --- a/jwst/resample/tests/test_resample_step.py +++ b/jwst/resample/tests/test_resample_step.py @@ -7,7 +7,7 @@ from stdatamodels.jwst.datamodels import ImageModel -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelContainer, ModelLibrary from jwst.assign_wcs import AssignWcsStep from jwst.assign_wcs.util import compute_fiducial, compute_scale from jwst.exp_to_source import multislit_to_container @@ -637,7 +637,7 @@ def test_weight_type(nircam_rate, tmp_cwd): im2.meta.observation.sequence_id = "2" im3.meta.observation.sequence_id = "3" - c = ModelContainer([im1, im2, im3]) + c = ModelLibrary([im1, im2, im3]) assert len(c.group_names) == 3 result1 = ResampleStep.call(c, weight_type="ivm", blendheaders=False, save_results=True) @@ -656,8 +656,10 @@ def test_weight_type(nircam_rate, tmp_cwd): # remove measurement time to force use of exposure time # this also implicitly shows that measurement time was indeed used above expected_ratio = im1.meta.exposure.exposure_time / im1.meta.exposure.measurement_time - for im in c: - del im.meta.exposure.measurement_time + with c: + for j, im in enumerate(c): + del im.meta.exposure.measurement_time + c.shelve(im, j) result3 = ResampleStep.call(c, weight_type="exptime", blendheaders=False) assert_allclose(result3.data[100:105, 100:105], 6.667, rtol=1e-2) @@ -791,9 +793,7 @@ def test_resample_variance(nircam_rate, n_images, weight_type): im.err += err im.meta.filename = "foo.fits" - c = ModelContainer() - for n in range(n_images): - c.append(im.copy()) + c = ModelLibrary([im.copy() for _ in range(n_images)]) result = ResampleStep.call(c, blendheaders=False, weight_type=weight_type) @@ -814,7 +814,7 @@ def test_resample_undefined_variance(nircam_rate, shape): im.var_poisson = np.ones(shape, dtype=im.var_poisson.dtype.type) im.var_flat = np.ones(shape, dtype=im.var_flat.dtype.type) im.meta.filename = "foo.fits" - c = ModelContainer([im]) + c = ModelLibrary([im]) with pytest.warns(RuntimeWarning, match="var_rnoise array not available"): result = ResampleStep.call(c, blendheaders=False) diff --git a/jwst/skymatch/skymatch_step.py b/jwst/skymatch/skymatch_step.py index 9723e0ef4d..c64b63c00d 100644 --- a/jwst/skymatch/skymatch_step.py +++ b/jwst/skymatch/skymatch_step.py @@ -18,12 +18,8 @@ ) from stdatamodels.jwst.datamodels.dqflags import pixel -from stdatamodels.jwst.datamodels.util import ( - open as datamodel_open, - is_association -) -from jwst.datamodels import ModelContainer +from jwst.datamodels import ModelLibrary from ..stpipe import Step @@ -62,33 +58,23 @@ class SkyMatchStep(Step): lsigma = float(min=0.0, default=4.0) # Lower clipping limit, in sigma usigma = float(min=0.0, default=4.0) # Upper clipping limit, in sigma binwidth = float(min=0.0, default=0.1) # Bin width for 'mode' and 'midpt' `skystat`, in sigma + + # Memory management: + in_memory = boolean(default=True) # If False, preserve memory using temporary files """ # noqa: E501 reference_file_types = [] def __init__(self, *args, **kwargs): - minimize_memory = kwargs.pop('minimize_memory', False) super().__init__(*args, **kwargs) - self.minimize_memory = minimize_memory def process(self, input): self.log.setLevel(logging.DEBUG) - # for now turn off memory optimization until we have better machinery - # to handle outputs in a consistent way. - - if hasattr(self, 'minimize_memory') and self.minimize_memory: - self._is_asn = ( - is_association(input) or isinstance(input, str) - ) + if isinstance(input, ModelLibrary): + library = input else: - self._is_asn = False - - img = ModelContainer( - input, - save_open=not self._is_asn, - return_open=not self._is_asn - ) + library = ModelLibrary(input, on_disk=not self.in_memory) self._dqbits = interpret_bit_flags(self.dqbits, flag_name_map=pixel) @@ -103,51 +89,45 @@ def process(self, input): binwidth=self.binwidth ) - # group images by their "group id": - grp_img = img.models_grouped - - # create a list of "Sky" Images and/or Groups: images = [] - grp_id = 1 - - for g in grp_img: - if len(g) > 1: - images.append( - SkyGroup( - list(map(self._imodel2skyim, g)), - id=grp_id - ) - ) - grp_id += 1 - elif len(g) == 1: - images.append(self._imodel2skyim(g[0])) - else: - raise AssertionError("Logical error in the pipeline code.") + with library: + for group_index, (group_id, group_inds) in enumerate(library.group_indices.items()): + sky_images = [] + for index in group_inds: + model = library.borrow(index) + try: + sky_images.append(self._imodel2skyim(model, index)) + finally: + library.shelve(model, index, modify=False) + if len(sky_images) == 1: + images.extend(sky_images) + else: + images.append(SkyGroup(sky_images, id=group_index)) # match/compute sky values: match(images, skymethod=self.skymethod, match_down=self.match_down, subtract=self.subtract) # set sky background value in each image's meta: - for im in images: - if isinstance(im, SkyImage): - self._set_sky_background( - im, - "COMPLETE" if im.is_sky_valid else "SKIPPED" - ) - else: - for gim in im: + with library: + for im in images: + if isinstance(im, SkyImage): self._set_sky_background( - gim, - "COMPLETE" if gim.is_sky_valid else "SKIPPED" + im, + library, + "COMPLETE" if im.is_sky_valid else "SKIPPED" ) + else: + for gim in im: + self._set_sky_background( + gim, + library, + "COMPLETE" if gim.is_sky_valid else "SKIPPED" + ) - return input if self._is_asn else img + return library - def _imodel2skyim(self, image_model): - input_image_model = image_model - if self._is_asn: - image_model = datamodel_open(image_model) + def _imodel2skyim(self, image_model, index): if self._dqbits is None: dqmask = np.isfinite(image_model.data).astype(dtype=np.uint8) @@ -163,9 +143,6 @@ def _imodel2skyim(self, image_model): # if 'subtract' mode has changed compared to the previous pass: if image_model.meta.background.subtracted is None: if image_model.meta.background.level is not None: - if self._is_asn: - image_model.close() - # report inconsistency: raise ValueError("Background level was set but the " "'subtracted' property is undefined (None).") @@ -179,9 +156,6 @@ def _imodel2skyim(self, image_model): # at this moment I think it is saver to quit and... # # report inconsistency: - if self._is_asn: - image_model.close() - raise ValueError("Background level was subtracted but the " "'level' property is undefined (None).") @@ -189,9 +163,6 @@ def _imodel2skyim(self, image_model): # cannot run 'skymatch' step on already "skymatched" images # when 'subtract' spec is inconsistent with # meta.background.subtracted: - if self._is_asn: - image_model.close() - raise ValueError("'subtract' step's specification is " "inconsistent with background info already " "present in image '{:s}' meta." @@ -206,30 +177,36 @@ def _imodel2skyim(self, image_model): pix_area=1.0, # TODO: pixel area convf=1.0, # TODO: conv. factor to brightness mask=dqmask, - id=image_model.meta.filename, # file name? + id=image_model.meta.filename, skystat=self._skystat, stepsize=self.stepsize, - reduce_memory_usage=self._is_asn, - meta={'image_model': input_image_model} + reduce_memory_usage=False, # this overwrote input files + meta={'index': index} ) - if self._is_asn: - image_model.close() - if self.subtract: sky_im.sky = level return sky_im - def _set_sky_background(self, sky_image, step_status): - image = sky_image.meta['image_model'] + def _set_sky_background(self, sky_image, library, step_status): + """ + Parameters + ---------- + sky_image : SkyImage + SkyImage object containing sky image data and metadata. + + library : ModelLibrary + Library of input data models, must be open + + step_status : str + Status of the sky subtraction step. Must be one of the following: + 'COMPLETE', 'SKIPPED'. + """ + index = sky_image.meta['index'] + dm = library.borrow(index) sky = sky_image.sky - if self._is_asn: - dm = datamodel_open(image) - else: - dm = image - if step_status == "COMPLETE": dm.meta.background.method = str(self.skymethod) dm.meta.background.level = sky @@ -238,7 +215,4 @@ def _set_sky_background(self, sky_image, step_status): dm.data[...] = sky_image.image[...] dm.meta.cal_step.skymatch = step_status - - if self._is_asn: - dm.save(image) - dm.close() + library.shelve(dm, index) diff --git a/jwst/skymatch/tests/test_skymatch.py b/jwst/skymatch/tests/test_skymatch.py index 774b04e2b7..2442819ffc 100644 --- a/jwst/skymatch/tests/test_skymatch.py +++ b/jwst/skymatch/tests/test_skymatch.py @@ -220,8 +220,10 @@ def test_skymatch(nircam_rate, skymethod, subtract, skystat, match_down, assert im.meta.background.subtracted is None # test that output models have original sky levels on failure: - for im, lev in zip(result, levels): - assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + with result: + for im, lev in zip(result, levels): + assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + result.shelve(im, modify=False) return @@ -243,19 +245,21 @@ def test_skymatch(nircam_rate, skymethod, subtract, skystat, match_down, sub_levels = np.subtract(levels, ref_levels) - for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): - # check that meta was set correctly: - assert im.meta.background.method == skymethod - assert im.meta.background.subtracted == subtract + with result: + for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): + # check that meta was set correctly: + assert im.meta.background.method == skymethod + assert im.meta.background.subtracted == subtract - # test computed/measured sky values: - assert abs(im.meta.background.level - rlev) < 0.01 + # test computed/measured sky values: + assert abs(im.meta.background.level - rlev) < 0.01 - # test - if subtract: - assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 - else: - assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + # test + if subtract: + assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 + else: + assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + result.shelve(im, modify=False) @pytest.mark.parametrize( @@ -334,33 +338,35 @@ def test_skymatch_overlap(nircam_rate, skymethod, subtract, skystat): sub_levels = np.subtract(levels, ref_levels) - for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): - # check that meta was set correctly: - assert im.meta.background.method == skymethod - assert im.meta.background.subtracted == subtract - - if skymethod in ['local', 'global']: - # These two sky methods must fail because they do not take - # into account (do not compute) overlap regions and use - # entire images: - - # test computed/measured sky values: - assert abs(im.meta.background.level - rlev) > 1000 # FAIL - - # test - if subtract: - assert abs(np.mean(im.data[dq_mask]) - slev) > 1000 # FAIL + with result: + for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): + # check that meta was set correctly: + assert im.meta.background.method == skymethod + assert im.meta.background.subtracted == subtract + + if skymethod in ['local', 'global']: + # These two sky methods must fail because they do not take + # into account (do not compute) overlap regions and use + # entire images: + + # test computed/measured sky values: + assert abs(im.meta.background.level - rlev) > 1000 # FAIL + + # test + if subtract: + assert abs(np.mean(im.data[dq_mask]) - slev) > 1000 # FAIL + else: + assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 # PASS else: - assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 # PASS - else: - # test computed/measured sky values: - assert abs(im.meta.background.level - rlev) < 0.01 + # test computed/measured sky values: + assert abs(im.meta.background.level - rlev) < 0.01 - # test - if subtract: - assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 - else: - assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + # test + if subtract: + assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 + else: + assert abs(np.mean(im.data[dq_mask]) - lev) < 0.01 + result.shelve(im, modify=False) def test_asn_input(tmp_cwd, nircam_rate, tmp_path): @@ -415,7 +421,6 @@ def test_asn_input(tmp_cwd, nircam_rate, tmp_path): # images are rotated and SATURATED pixels in the corners are not in the # common intersection of all input images. This is the purpose of this test step = SkyMatchStep( - minimize_memory=True, skymethod='match', match_down=True, subtract=True, @@ -426,23 +431,21 @@ def test_asn_input(tmp_cwd, nircam_rate, tmp_path): result = step.run(asn_out_fname) - assert isinstance(result, str) - ref_levels = np.subtract(levels, min(levels)) sub_levels = np.subtract(levels, ref_levels) - result = ModelContainer(result) + with result: + for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): + # check that meta was set correctly: + assert im.meta.background.method == 'match' + assert im.meta.background.subtracted is True - for im, lev, rlev, slev in zip(result, levels, ref_levels, sub_levels): - # check that meta was set correctly: - assert im.meta.background.method == 'match' - assert im.meta.background.subtracted is True - - # test computed/measured sky values: - assert abs(im.meta.background.level - rlev) < 0.01 + # test computed/measured sky values: + assert abs(im.meta.background.level - rlev) < 0.01 - # test - assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 + # test + assert abs(np.mean(im.data[dq_mask]) - slev) < 0.01 + result.shelve(im, modify=False) @pytest.mark.parametrize( @@ -498,7 +501,6 @@ def test_skymatch_2x(tmp_cwd, nircam_rate, tmp_path, skymethod, subtract): # images are rotated and SATURATED pixels in the corners are not in the # common intersection of all input images. This is the purpose of this test step = SkyMatchStep( - minimize_memory=True, skymethod=skymethod, match_down=True, subtract=subtract, @@ -529,19 +531,19 @@ def test_skymatch_2x(tmp_cwd, nircam_rate, tmp_path, skymethod, subtract): sub_levels = np.subtract(levels, ref_levels) - result2 = ModelContainer(result2) - # compare results - for im2, lev, rlev, slev in zip(result2, levels, ref_levels, sub_levels): - # check that meta was set correctly: - assert im2.meta.background.method == skymethod - assert im2.meta.background.subtracted == subtract + with result2: + for im2, lev, rlev, slev in zip(result2, levels, ref_levels, sub_levels): + # check that meta was set correctly: + assert im2.meta.background.method == skymethod + assert im2.meta.background.subtracted == subtract - # test computed/measured sky values: - assert abs(im2.meta.background.level - rlev) < 0.01 + # test computed/measured sky values: + assert abs(im2.meta.background.level - rlev) < 0.01 - # test - if subtract: - assert abs(np.mean(im2.data[dq_mask]) - slev) < 0.01 - else: - assert abs(np.mean(im2.data[dq_mask]) - lev) < 0.01 + # test + if subtract: + assert abs(np.mean(im2.data[dq_mask]) - slev) < 0.01 + else: + assert abs(np.mean(im2.data[dq_mask]) - lev) < 0.01 + result2.shelve(im2) diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 2c86df37c8..2a30139190 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -1,16 +1,17 @@ """ JWST-specific Step and Pipeline base classes. """ +import logging + from stdatamodels.jwst.datamodels import JwstDataModel from stdatamodels.jwst import datamodels - -from .. import __version_commit__, __version__ - from stpipe import crds_client from stpipe import Step from stpipe import Pipeline + +from .. import __version_commit__, __version__ from ..lib.suffix import remove_suffix -import logging + log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) diff --git a/jwst/stpipe/utilities.py b/jwst/stpipe/utilities.py index 67a4769113..ad62ba1468 100644 --- a/jwst/stpipe/utilities.py +++ b/jwst/stpipe/utilities.py @@ -36,6 +36,7 @@ import os import re from collections.abc import Sequence +from jwst import datamodels # Configure logging logger = logging.getLogger(__name__) @@ -155,7 +156,7 @@ def record_step_status(datamodel, cal_step, success=True): Parameters ---------- - datamodel : `~jwst.datamodels.JwstDataModel` instance + datamodel : `~jwst.datamodels.JwstDataModel`, `~jwst.datamodels.ModelContainer`, `~jwst.datamodels.ModelLibrary`, str, or Path instance This is the datamodel or container of datamodels to modify in place cal_step : str @@ -172,6 +173,11 @@ def record_step_status(datamodel, cal_step, success=True): if isinstance(datamodel, Sequence): for model in datamodel: model.meta.cal_step._instance[cal_step] = status + elif isinstance(datamodel, datamodels.ModelLibrary): + with datamodel: + for model in datamodel: + model.meta.cal_step._instance[cal_step] = status + datamodel.shelve(model) else: datamodel.meta.cal_step._instance[cal_step] = status diff --git a/jwst/tweakreg/tests/test_multichip_jwst.py b/jwst/tweakreg/tests/test_multichip_jwst.py index 952e61d81e..4402f7b897 100644 --- a/jwst/tweakreg/tests/test_multichip_jwst.py +++ b/jwst/tweakreg/tests/test_multichip_jwst.py @@ -291,7 +291,7 @@ def test_multichip_jwst_alignment(monkeypatch): assert rmse_dec < _REF_RMSE_DEC -def test_multichip_alignment_step(monkeypatch): +def test_multichip_alignment_step_rel(monkeypatch): monkeypatch.setattr(tweakreg_step.twk, 'align_wcs', _align_wcs) monkeypatch.setattr(tweakreg_step, 'make_tweakreg_catalog', _make_tweakreg_catalog) @@ -402,24 +402,31 @@ def test_multichip_alignment_step(monkeypatch): # Alternatively, disable this '_is_wcs_correction_small' test: # step._is_wcs_correction_small = lambda x, y: True - mr, m1, m2 = step.process(mc) - for im in [mr, m1, m2]: - assert im.meta.cal_step.tweakreg == 'COMPLETE' - - wc1 = m1.meta.wcs - wc2 = m2.meta.wcs - - ra1, dec1 = wc1(imcat1['x'], imcat1['y']) - ra2, dec2 = wc2(imcat2['x'], imcat2['y']) - ra = np.concatenate([ra1, ra2]) - dec = np.concatenate([dec1, dec2]) - rra = refcat['RA'] - rdec = refcat['DEC'] - rmse_ra = np.sqrt(np.mean((ra - rra)**2)) - rmse_dec = np.sqrt(np.mean((dec - rdec)**2)) - - assert rmse_ra < _REF_RMSE_RA - assert rmse_dec < _REF_RMSE_DEC + result = step.process(mc) + with result: + for im in result: + assert im.meta.cal_step.tweakreg == 'COMPLETE' + result.shelve(im, modify=False) + + with result: + m1 = result.borrow(1) + m2 = result.borrow(2) + wc1 = m1.meta.wcs + wc2 = m2.meta.wcs + + ra1, dec1 = wc1(imcat1['x'], imcat1['y']) + ra2, dec2 = wc2(imcat2['x'], imcat2['y']) + ra = np.concatenate([ra1, ra2]) + dec = np.concatenate([dec1, dec2]) + rra = refcat['RA'] + rdec = refcat['DEC'] + rmse_ra = np.sqrt(np.mean((ra - rra)**2)) + rmse_dec = np.sqrt(np.mean((dec - rdec)**2)) + + assert rmse_ra < _REF_RMSE_RA + assert rmse_dec < _REF_RMSE_DEC + result.shelve(m1, 1, modify=False) + result.shelve(m2, 2, modify=False) def test_multichip_alignment_step_abs(monkeypatch): diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index f17b378def..a038410e69 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -196,12 +196,18 @@ def test_tweakreg_step(example_input, with_shift): result = step(example_input) # check that step completed - for model in result: - assert model.meta.cal_step.tweakreg == 'COMPLETE' - - # and that the wcses differ by a small amount due to the shift above - # by projecting one point through each wcs and comparing the difference - abs_delta = abs(result[1].meta.wcs(0, 0)[0] - result[0].meta.wcs(0, 0)[0]) + with result: + for model in result: + assert model.meta.cal_step.tweakreg == 'COMPLETE' + result.shelve(model, modify=False) + + # and that the wcses differ by a small amount due to the shift above + # by projecting one point through each wcs and comparing the difference + r0 = result.borrow(0) + r1 = result.borrow(1) + abs_delta = abs(r1.meta.wcs(0, 0)[0] - r0.meta.wcs(0, 0)[0]) + result.shelve(r0, 0, modify=False) + result.shelve(r1, 1, modify=False) if with_shift: assert abs_delta > 1E-5 else: @@ -224,8 +230,10 @@ def test_src_confusion_pars(example_input, alignment_type): result = step(example_input) # check that step was skipped - for model in result: - assert model.meta.cal_step.tweakreg == 'SKIPPED' + with result: + for model in result: + assert model.meta.cal_step.tweakreg == 'SKIPPED' + result.shelve(model) @pytest.fixture() @@ -330,6 +338,7 @@ def test_custom_catalog(custom_catalog_path, example_input, catfile, asn, meta, kwargs = {'use_custom_catalogs': custom} if catfile != "no_catfile": kwargs["catfile"] = str(catfile_path) + step = tweakreg_step.TweakRegStep(**kwargs) # patch _construct_wcs_corrector to check the correct catalog was loaded @@ -349,6 +358,7 @@ def patched_construct_wcs_corrector(wcs, wcsinfo, catalog, group_id, _seen=[]): with pytest.raises(ValueError, match="done testing"): step(str(asn_path)) + @pytest.mark.parametrize("with_shift", [True, False]) def test_sip_approx(example_input, with_shift): """ @@ -376,31 +386,36 @@ def test_sip_approx(example_input, with_shift): # run the step on the example input modified above result = step(example_input) - # output wcs differs by a small amount due to the shift above: - # project one point through each wcs and compare the difference - abs_delta = abs(result[1].meta.wcs(0, 0)[0] - result[0].meta.wcs(0, 0)[0]) - if with_shift: - assert abs_delta > 1E-5 - else: - assert abs_delta < 1E-12 - - # the first wcs is identical to the input and - # does not have SIP approximation keywords -- - # they are normally set by assign_wcs - assert np.allclose(result[0].meta.wcs(0, 0)[0], example_input[0].meta.wcs(0, 0)[0]) - for key in ['ap_order', 'bp_order']: - assert key not in result[0].meta.wcsinfo.instance - - # for the second, SIP approximation should be present - for key in ['ap_order', 'bp_order']: - assert result[1].meta.wcsinfo.instance[key] == 3 - - # evaluate fits wcs and gwcs for the approximation, make sure they agree - wcs_info = result[1].meta.wcsinfo.instance - grid = grid_from_bounding_box(result[1].meta.wcs.bounding_box) - gwcs_ra, gwcs_dec = result[1].meta.wcs(*grid) - fits_wcs = WCS(wcs_info) - fitswcs_res = fits_wcs.pixel_to_world(*grid) + with result: + r0 = result.borrow(0) + r1 = result.borrow(1) + # output wcs differs by a small amount due to the shift above: + # project one point through each wcs and compare the difference + abs_delta = abs(r1.meta.wcs(0, 0)[0] - r0.meta.wcs(0, 0)[0]) + if with_shift: + assert abs_delta > 1E-5 + else: + assert abs_delta < 1E-12 + + # the first wcs is identical to the input and + # does not have SIP approximation keywords -- + # they are normally set by assign_wcs + assert np.allclose(r0.meta.wcs(0, 0)[0], example_input[0].meta.wcs(0, 0)[0]) + for key in ['ap_order', 'bp_order']: + assert key not in r0.meta.wcsinfo.instance + + # for the second, SIP approximation should be present + for key in ['ap_order', 'bp_order']: + assert r1.meta.wcsinfo.instance[key] == 3 + + # evaluate fits wcs and gwcs for the approximation, make sure they agree + wcs_info = r1.meta.wcsinfo.instance + grid = grid_from_bounding_box(r1.meta.wcs.bounding_box) + gwcs_ra, gwcs_dec = r1.meta.wcs(*grid) + fits_wcs = WCS(wcs_info) + fitswcs_res = fits_wcs.pixel_to_world(*grid) + result.shelve(r0, 0, modify=False) + result.shelve(r1, 1, modify=False) assert np.allclose(fitswcs_res.ra.deg, gwcs_ra) assert np.allclose(fitswcs_res.dec.deg, gwcs_dec) diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 5255769fa2..0d8363771b 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -12,9 +12,9 @@ import stcal.tweakreg.tweakreg as twk -from jwst.datamodels import ModelContainer from jwst.stpipe import record_step_status from jwst.assign_wcs.util import update_fits_wcsinfo, update_s_region_imaging +from jwst.datamodels import ModelLibrary # LOCAL from ..stpipe import Step @@ -123,12 +123,16 @@ class TweakRegStep(Step): # stpipe general options output_use_model = boolean(default=True) # When saving use `DataModel.meta.filename` + in_memory = boolean(default=True) # If False, preserve memory using temporary files at expense of runtime """ reference_file_types = [] def process(self, input): - images = ModelContainer(input) + if isinstance(input, ModelLibrary): + images = input + else: + images = ModelLibrary(input, on_disk=not self.in_memory) if len(images) == 0: raise ValueError("Input must contain at least one image model.") @@ -153,15 +157,15 @@ def process(self, input): ) use_custom_catalogs = False # else, load from association - elif hasattr(images.meta, "asn_table") and getattr(images, "asn_file_path", None) is not None: + elif images._asn_dir is not None: catdict = {} - asn_dir = path.dirname(images.asn_file_path) - for member in images.meta.asn_table.products[0].members: - if hasattr(member, "tweakreg_catalog"): - if member.tweakreg_catalog is None or not member.tweakreg_catalog.strip(): - catdict[member.expname] = None + for member in images.asn["products"][0]["members"]: + if "tweakreg_catalog" in member: + tweakreg_catalog = member["tweakreg_catalog"] + if tweakreg_catalog is None or not tweakreg_catalog.strip(): + catdict[member["expname"]] = None else: - catdict[member.expname] = path.join(asn_dir, member.tweakreg_catalog) + catdict[member["expname"]] = path.join(images._asn_dir, tweakreg_catalog) if self.abs_refcat is not None and self.abs_refcat.strip(): align_to_abs_refcat = True @@ -186,62 +190,64 @@ def process(self, input): # pre-allocate collectors (same length and order as images) correctors = [None] * len(images) - # Build the catalog for each input image - for (model_index, image_model) in enumerate(images): - # now that the model is open, check it's metadata for a custom catalog - # only if it's not listed in the catdict - if use_custom_catalogs and image_model.meta.filename not in catdict: - if (image_model.meta.tweakreg_catalog is not None and image_model.meta.tweakreg_catalog.strip()): - catdict[image_model.meta.filename] = image_model.meta.tweakreg_catalog - if use_custom_catalogs and catdict.get(image_model.meta.filename, None) is not None: - # FIXME this modifies the input_model - image_model.meta.tweakreg_catalog = catdict[image_model.meta.filename] - # use user-supplied catalog: - self.log.info("Using user-provided input catalog " - f"'{image_model.meta.tweakreg_catalog}'") - catalog = Table.read( - image_model.meta.tweakreg_catalog, - ) - save_catalog = False - else: - # source finding - catalog = self._find_sources(image_model) - - # only save if catalog was computed from _find_sources and - # the user requested save_catalogs - save_catalog = self.save_catalogs - - # if needed rename xcentroid to x, ycentroid to y - catalog = _rename_catalog_columns(catalog) - - # filter all sources outside the wcs bounding box - catalog = twk.filter_catalog_by_bounding_box( - catalog, - image_model.meta.wcs.bounding_box) - - # setting 'name' is important for tweakwcs logging - if catalog.meta.get('name') is None: - catalog.meta['name'] = path.splitext(image_model.meta.filename)[0].strip('_- ') - - # log results of source finding (or user catalog) - filename = image_model.meta.filename - nsources = len(catalog) - if nsources == 0: - self.log.warning('No sources found in {}.'.format(filename)) - else: - self.log.info('Detected {} sources in {}.' - .format(len(catalog), filename)) - - # save catalog (if requested) - if save_catalog: - # FIXME this modifies the input_model - image_model.meta.tweakreg_catalog = self._write_catalog(catalog, filename) - - # construct the corrector since the model is open (and already has a group_id) - correctors[model_index] = twk.construct_wcs_corrector(image_model.meta.wcs, - image_model.meta.wcsinfo.instance, - catalog, - image_model.meta.group_id,) + # Build the catalog and corrector for each input images + with images: + for (model_index, image_model) in enumerate(images): + # now that the model is open, check its metadata for a custom catalog + # only if it's not listed in the catdict + if use_custom_catalogs and image_model.meta.filename not in catdict: + if (image_model.meta.tweakreg_catalog is not None and image_model.meta.tweakreg_catalog.strip()): + catdict[image_model.meta.filename] = image_model.meta.tweakreg_catalog + if use_custom_catalogs and catdict.get(image_model.meta.filename, None) is not None: + image_model.meta.tweakreg_catalog = catdict[image_model.meta.filename] + # use user-supplied catalog: + self.log.info("Using user-provided input catalog " + f"'{image_model.meta.tweakreg_catalog}'") + catalog = Table.read( + image_model.meta.tweakreg_catalog, + ) + save_catalog = False + else: + # source finding + catalog = self._find_sources(image_model) + + # only save if catalog was computed from _find_sources and + # the user requested save_catalogs + save_catalog = self.save_catalogs + + # if needed rename xcentroid to x, ycentroid to y + catalog = _rename_catalog_columns(catalog) + + # filter all sources outside the wcs bounding box + catalog = twk.filter_catalog_by_bounding_box( + catalog, + image_model.meta.wcs.bounding_box) + + # setting 'name' is important for tweakwcs logging + if catalog.meta.get('name') is None: + catalog.meta['name'] = path.splitext(image_model.meta.filename)[0].strip('_- ') + + # log results of source finding (or user catalog) + filename = image_model.meta.filename + nsources = len(catalog) + if nsources == 0: + self.log.warning('No sources found in {}.'.format(filename)) + else: + self.log.info('Detected {} sources in {}.' + .format(len(catalog), filename)) + + # save catalog (if requested) + if save_catalog: + # FIXME this modifies the input_model + image_model.meta.tweakreg_catalog = self._write_catalog(catalog, filename) + + # construct the corrector since the model is open (and already has a group_id) + correctors[model_index] = \ + twk.construct_wcs_corrector(image_model.meta.wcs, + image_model.meta.wcsinfo.instance, + catalog, + image_model.meta.group_id,) + images.shelve(image_model, model_index) self.log.info('') self.log.info("Number of image groups to be aligned: {:d}." @@ -277,34 +283,36 @@ def process(self, input): # absolute alignment to the reference catalog # can (and does) occur after alignment between groups if align_to_abs_refcat: - try: - ref_image = images[0] - correctors = \ - twk.absolute_align(correctors, self.abs_refcat, - ref_wcs=ref_image.meta.wcs, - ref_wcsinfo=ref_image.meta.wcsinfo.instance, - epoch=Time(ref_image.meta.observation.date).decimalyear, - abs_minobj=self.abs_minobj, - abs_fitgeometry=self.abs_fitgeometry, - abs_nclip=self.abs_nclip, - abs_sigma=self.abs_sigma, - abs_searchrad=self.abs_searchrad, - abs_use2dhist=self.abs_use2dhist, - abs_separation=self.abs_separation, - abs_tolerance=self.abs_tolerance, - save_abs_catalog=self.save_abs_catalog, - abs_catalog_output_dir=self.output_dir, - ) - - except twk.TweakregError as e: - self.log.warning(str(e)) - for model in images: - model.meta.cal_step.tweakreg = "SKIPPED" - return images - - if local_align_failed and not align_to_abs_refcat: - for model in images: - record_step_status(model, "tweakreg", success=False) + with images: + ref_image = images.borrow(0) + try: + correctors = \ + twk.absolute_align(correctors, self.abs_refcat, + ref_wcs=ref_image.meta.wcs, + ref_wcsinfo=ref_image.meta.wcsinfo.instance, + epoch=Time(ref_image.meta.observation.date).decimalyear, + abs_minobj=self.abs_minobj, + abs_fitgeometry=self.abs_fitgeometry, + abs_nclip=self.abs_nclip, + abs_sigma=self.abs_sigma, + abs_searchrad=self.abs_searchrad, + abs_use2dhist=self.abs_use2dhist, + abs_separation=self.abs_separation, + abs_tolerance=self.abs_tolerance, + save_abs_catalog=self.save_abs_catalog, + abs_catalog_output_dir=self.output_dir, + ) + images.shelve(ref_image, 0, modify=False) + except twk.TweakregError as e: + self.log.warning(str(e)) + images.shelve(ref_image, 0, modify=False) + record_step_status(images, "tweakreg", success=False) + return images + finally: + del ref_image + + if local_align_failed and not align_to_abs_refcat: + record_step_status(images, "tweakreg", success=False) return images # one final pass through all the models to update them based @@ -315,53 +323,53 @@ def process(self, input): def _apply_tweakreg_solution(self, - images: ModelContainer, + images: ModelLibrary, correctors: list[JWSTWCSCorrector], align_to_abs_refcat: bool = False, - ) -> ModelContainer: - - for (image_model, corrector) in zip(images, correctors): - - # retrieve fit status and update wcs if fit is successful: - if ("fit_info" in corrector.meta and - "SUCCESS" in corrector.meta["fit_info"]["status"]): - - # Update/create the WCS .name attribute with information - # on this astrometric fit as the only record that it was - # successful: - if align_to_abs_refcat: - # NOTE: This .name attrib agreed upon by the JWST Cal - # Working Group. - # Current value is merely a place-holder based - # on HST conventions. This value should also be - # translated to the FITS WCSNAME keyword - # IF that is what gets recorded in the archive - # for end-user searches. - corrector.wcs.name = f"FIT-LVL3-{self.abs_refcat}" - - image_model.meta.wcs = corrector.wcs - update_s_region_imaging(image_model) - - # Also update FITS representation in input exposures for - # subsequent reprocessing by the end-user. - if self.sip_approx: - try: - update_fits_wcsinfo( - image_model, - max_pix_error=self.sip_max_pix_error, - degree=self.sip_degree, - max_inv_pix_error=self.sip_max_inv_pix_error, - inv_degree=self.sip_inv_degree, - npoints=self.sip_npoints, - crpix=None - ) - except (ValueError, RuntimeError) as e: - self.log.warning("Failed to update 'meta.wcsinfo' with FITS SIP " - "approximation. Reported error is:") - self.log.warning(f'"{e.args[0]}"') - record_step_status(image_model, "tweakreg", success=True) - - return image_model + ) -> ModelLibrary: + with images: + for (image_model, corrector) in zip(images, correctors): + + # retrieve fit status and update wcs if fit is successful: + if ("fit_info" in corrector.meta and + "SUCCESS" in corrector.meta["fit_info"]["status"]): + + # Update/create the WCS .name attribute with information + # on this astrometric fit as the only record that it was + # successful: + if align_to_abs_refcat: + # NOTE: This .name attrib agreed upon by the JWST Cal + # Working Group. + # Current value is merely a place-holder based + # on HST conventions. This value should also be + # translated to the FITS WCSNAME keyword + # IF that is what gets recorded in the archive + # for end-user searches. + corrector.wcs.name = f"FIT-LVL3-{self.abs_refcat}" + + image_model.meta.wcs = corrector.wcs + update_s_region_imaging(image_model) + + # Also update FITS representation in input exposures for + # subsequent reprocessing by the end-user. + if self.sip_approx: + try: + update_fits_wcsinfo( + image_model, + max_pix_error=self.sip_max_pix_error, + degree=self.sip_degree, + max_inv_pix_error=self.sip_max_inv_pix_error, + inv_degree=self.sip_inv_degree, + npoints=self.sip_npoints, + crpix=None + ) + except (ValueError, RuntimeError) as e: + self.log.warning("Failed to update 'meta.wcsinfo' with FITS SIP " + "approximation. Reported error is:") + self.log.warning(f'"{e.args[0]}"') + record_step_status(image_model, "tweakreg", success=True) + images.shelve(image_model) + return images def _write_catalog(self, catalog, filename): From da36be8a5e26d4448346ae51d666fe0f7b9c8ed9 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Thu, 5 Sep 2024 14:04:07 -0400 Subject: [PATCH 327/687] Update regression tests to remove missing files from tests (#8730) --- jwst/regtest/test_miri_image.py | 2 +- jwst/regtest/test_nircam_image.py | 2 +- jwst/regtest/test_nircam_subarray_4amp.py | 5 ++--- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index f7237c3d7f..5559daf121 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -85,7 +85,7 @@ def run_image3(run_image2, rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["dq_init", "saturation", "firstframe", "lastframe", "reset", - "linearity", "rscd", "dark_current", "refpix", "ramp", "rate", + "linearity", "rscd", "dark_current", "ramp", "rate", "rateints"]) def test_miri_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwargs, suffix): """Regression test of detector1 pipeline performed on MIRI imaging data.""" diff --git a/jwst/regtest/test_nircam_image.py b/jwst/regtest/test_nircam_image.py index 982e62914a..11283baf01 100644 --- a/jwst/regtest/test_nircam_image.py +++ b/jwst/regtest/test_nircam_image.py @@ -72,7 +72,7 @@ def run_image3pipeline(run_image2pipeline, rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["dq_init", "saturation", "superbias", - "refpix", "linearity", "trapsfilled", + "refpix", "linearity", "dark_current", "jump", "rate", "flat_field", "cal", "i2d"]) def test_nircam_image_stages12(run_image2pipeline, rtdata_module, fitsdiff_default_kwargs, suffix): diff --git a/jwst/regtest/test_nircam_subarray_4amp.py b/jwst/regtest/test_nircam_subarray_4amp.py index c0d7a77bb5..ef9a0932cb 100644 --- a/jwst/regtest/test_nircam_subarray_4amp.py +++ b/jwst/regtest/test_nircam_subarray_4amp.py @@ -28,9 +28,8 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("output", [ 'jw00617196001_02102_00001_nrca4_rate.fits', - 'jw00617196001_02102_00001_nrca4_rateints.fits', - 'jw00617196001_02102_00001_nrca4_trapsfilled.fits', ], - ids=['rate', 'rateints', 'trapsfilled']) + 'jw00617196001_02102_00001_nrca4_rateints.fits'], + ids=['rate', 'rateints']) def test_nircam_detector1_subarray(run_pipeline, fitsdiff_default_kwargs, output): """ Regression test of calwebb_detector1 pipeline performed on NIRSpec data. From 155956de9ff26f4b3263d739d07c7f33fe3ac202 Mon Sep 17 00:00:00 2001 From: Timothy D Brandt Date: Fri, 6 Sep 2024 11:38:43 -0400 Subject: [PATCH 328/687] Update jwst/nsclean/lib.py Co-authored-by: Melanie Clarke --- jwst/nsclean/lib.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/nsclean/lib.py b/jwst/nsclean/lib.py index 514be80240..d4bc05c0fd 100644 --- a/jwst/nsclean/lib.py +++ b/jwst/nsclean/lib.py @@ -541,4 +541,4 @@ def clean(self, weight_fit=True, return_model=False): return self.model else: self.data -= self.model # Overwrite data with cleaned data - return(self.data) + return self.data From 7dd944684a945522c3acbf5700f4da268ba6ab8c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 6 Sep 2024 11:58:08 -0400 Subject: [PATCH 329/687] Code style clean up --- jwst/nsclean/nsclean.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index a1dcbb4b03..8afc18ce46 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -133,10 +133,11 @@ def create_mask(input_model, mask_spectral_regions, n_sigma): # Initialize mask to all True. Subsequent operations will mask # out pixels that contain signal. # Note: mask will be 3D for BOTS mode data - #mask = np.full(np.shape(input_model.dq), True) + mask = np.full(np.shape(input_model.dq), True) - # Similar to above, but don't use reference pixels. - mask = (input_model.dq>>31)&1 == 0 + # Mask any reference pixels + ref_pix = input_model.dq & dqflags.pixel['REFERENCE_PIXEL'] + mask[ref_pix > 0] = False # If IFU, mask all pixels contained in the IFU slices if exptype == 'nrs_ifu' and mask_spectral_regions: @@ -278,20 +279,19 @@ def clean_subarray(detector, image, mask, npix_iter=512, if exclude_outliers: med = np.median(image[mask]) - std = 1.4825*np.median(np.abs((image - med)[mask])) - outlier = mask&(np.abs(image - med) > sigrej*std) + std = 1.4825 * np.median(np.abs((image - med)[mask])) + outlier = mask & (np.abs(image - med) > sigrej * std) mask = mask&(~outlier) # also get four nearest neighbors of flagged pixels - mask[1:] = mask[1:]&(~outlier[:-1]) - mask[:-1] = mask[:-1]&(~outlier[1:]) - mask[:, 1:] = mask[:, 1:]&(~outlier[:, :-1]) - mask[:, :-1] = mask[:, :-1]&(~outlier[:, 1:]) - + mask[1:] = mask[1:] & (~outlier[:-1]) + mask[:-1] = mask[:-1] & (~outlier[1:]) + mask[:, 1:] = mask[:, 1:] & (~outlier[:, :-1]) + mask[:, :-1] = mask[:, :-1] & (~outlier[:, 1:]) # Used to determine the fitting intervals along the slow scan - # direction. Pre-pend a zero so that we sum_mask[i] is equal + # direction. Pre-pend a zero so that sum_mask[i] is equal # to np.sum(mask[:i], axis=1). sum_mask = np.array([0] + list(np.cumsum(np.sum(mask, axis=1)))) @@ -305,9 +305,10 @@ def clean_subarray(detector, image, mask, npix_iter=512, # Want npix_iter available pixels in this section. If # there are fewer than 1.5*npix_iter available pixels in # the rest of the image, just go to the end. - + k = 0 for k in range(i1 + 1, image.shape[0] + 1): - if sum_mask[k] - sum_mask[i1] > npix_iter and sum_mask[-1] - sum_mask[i1] > 1.5*npix_iter: + if (sum_mask[k] - sum_mask[i1] > npix_iter + and sum_mask[-1] - sum_mask[i1] > 1.5 * npix_iter): break di = k - i1 @@ -327,7 +328,7 @@ def clean_subarray(detector, image, mask, npix_iter=512, models += [cleaner.clean(return_model=True)] else: log.warning("Insufficient reference pixels for NSClean around " - "row %d; no correction will be made here." % (i1)) + "row %d; no correction will be made here." % i1) models += [np.zeros(image[i1:i1 + di].shape)] # If we have reached the end of the array, we are finished. From facf275785e20a311ffd67d3e19032bf4f88c92e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 6 Sep 2024 12:04:29 -0400 Subject: [PATCH 330/687] Remove restriction on ALLSLITS data --- jwst/nsclean/nsclean.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 8afc18ce46..9c1fb60f7f 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -401,13 +401,6 @@ def do_correction(input_model, mask_spectral_regions, n_sigma, save_mask, user_m exp_type = input_model.meta.exposure.type log.info(f'Input exposure type is {exp_type}, detector={detector}') - # Check for a valid input that we can work on - if input_model.meta.subarray.name.upper() == "ALLSLITS": - log.warning("Step cannot be applied to ALLSLITS subarray images") - log.warning("Step will be skipped") - input_model.meta.cal_step.nsclean = 'SKIPPED' - return input_model, None - output_model = input_model.copy() # Check for a user-supplied mask image. If so, use it. From edd74c465cea761c146e4e4540e0f769241bbcb2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 6 Sep 2024 12:16:30 -0400 Subject: [PATCH 331/687] Update docs for ALLSLITS change --- docs/jwst/nsclean/main.rst | 5 ----- 1 file changed, 5 deletions(-) diff --git a/docs/jwst/nsclean/main.rst b/docs/jwst/nsclean/main.rst index c61e406094..161e84d9d1 100644 --- a/docs/jwst/nsclean/main.rst +++ b/docs/jwst/nsclean/main.rst @@ -21,11 +21,6 @@ IFU, MOS, fixed slit, and Bright Object Time Series (BOTS), in both full-frame and subarray readouts. Time series (3D) data are corrected one integration at a time. -.. note:: - - The step is currently not capable of processing images taken using the - "ALLSLITS" subarray. Other subarray types are allowed. - Details on the source of the correlated noise and the algorithm used in the ``nsclean`` step to fit and remove it can be found in `Rauscher 2023 `_. From 19d46a078e6e0ecdb63d75587159a9a348e0fb1f Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 6 Sep 2024 14:19:33 -0400 Subject: [PATCH 332/687] Fix parameter name --- jwst/nsclean/nsclean.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 9c1fb60f7f..1038acccfb 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -252,7 +252,7 @@ def clean_subarray(detector, image, mask, npix_iter=512, sigrej : float Number of sigma to clip when identifying outliers. Default 4. - minpct : float + minfrac : float Minimum fraction of pixels locally available in the mask in order to attempt a correction. Default 0.05 (i.e., 5%). From ae0eef44704f3c2ddbb2b1574ced74b2083539c0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 6 Sep 2024 14:34:48 -0400 Subject: [PATCH 333/687] JP-3570: Match NaNs and DO_NOT_USE flags (#8557) --- CHANGES.rst | 45 ++++-- jwst/cube_build/cube_build_step.py | 10 +- jwst/flatfield/flat_field.py | 35 ++--- jwst/flatfield/tests/test_flatfield.py | 13 +- jwst/lib/basic_utils.py | 26 ---- jwst/lib/pipe_utils.py | 77 +++++++++- jwst/lib/tests/test_pipe_utils.py | 132 ++++++++++++++++++ jwst/outlier_detection/ifu.py | 10 +- .../tests/test_outlier_detection.py | 77 +++++----- jwst/outlier_detection/utils.py | 13 +- jwst/pathloss/pathloss.py | 22 +-- jwst/photom/photom.py | 7 + jwst/regtest/test_nirspec_image2.py | 14 +- jwst/resample/resample_spec_step.py | 13 +- jwst/resample/resample_step.py | 7 + 15 files changed, 385 insertions(+), 116 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 1c8995df2c..661f78510e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -40,6 +40,9 @@ cube_build - Fixed a bug when ``cube_build`` was called from the ``mrs_imatch`` step. [#8728] +- Ensure that NaNs and DO_NOT_USE flags match up in all input data before + building a cube. [#8557] + datamodels ---------- @@ -57,6 +60,12 @@ emicorr - Fixed a bug where MIRI EMI correction step would return NaNs when it was unable to compute a correction. [#8675] +flat_field +---------- + +- Ensure that NaNs and DO_NOT_USE flags match up in all science, error, + variance, and DQ extensions for all modes. [#8557] + general ------- @@ -99,10 +108,20 @@ outlier_detection - For imaging modes, step now uses `ModelLibrary` to handle accessing models consistently whether they are in memory or on disk. [#8683] -set_telescope_pointing ----------------------- +- Ensure that NaNs and DO_NOT_USE flags match up in all output science, error, + variance, and DQ extensions. [#8557] + +pathloss +-------- + +- Ensure that NaNs and DO_NOT_USE flags match up in all output science, error, + variance, and DQ extensions. [#8557] + +photom +------ -- replace usage of ``copy_arrays=True`` with ``memmap=False`` [#8660] +- Ensure that NaNs and DO_NOT_USE flags match up in all output science, error, + variance, and DQ extensions. [#8557] pipeline -------- @@ -114,9 +133,16 @@ pipeline resample -------- +- Fixed a typo in ``load_custom_wcs`` from ``array_shape`` to ``pixel_shape`` and + changed to use values in the top-level ASDF structure if the values in the WCS + are ``None``. [#8698] + - Step now uses `ModelLibrary` to handle accessing models consistently whether they are in memory or on disk. [#8683] +- Ensure that NaNs and DO_NOT_USE flags match up in all input data before + resampling. [#8557] + resample_spec ------------- @@ -138,12 +164,8 @@ resample_spec so that the spectral resampling step only exposes parameters that are appropriate for spectral data. [#8622] -resample_step -------------- - -- Fixed a typo in ``load_custom_wcs`` from ``array_shape`` to ``pixel_shape`` and - changed to use values in the top-level ASDF structure if the values in the WCS - are ``None``. [#8698] +- Ensure that NaNs and DO_NOT_USE flags match up in all input data before + resampling. [#8557] scripts ------- @@ -151,6 +173,11 @@ scripts - Removed many non-working and out-dated scripts. Including many scripts that were replaced by ``strun``. [#8619] +set_telescope_pointing +---------------------- + +- Replace usage of ``copy_arrays=True`` with ``memmap=False`` [#8660] + skymatch -------- diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 7801169ba7..c675ae338f 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -1,14 +1,16 @@ """ This is the main ifu spectral cube building routine. """ +import time + from jwst.datamodels import ModelContainer +from jwst.lib.pipe_utils import match_nans_and_flags -from ..stpipe import Step, record_step_status from . import cube_build from . import ifu_cube from . import data_types from ..assign_wcs.util import update_s_region_keyword -import time +from ..stpipe import Step, record_step_status __all__ = ["CubeBuildStep"] @@ -281,6 +283,10 @@ def process(self, input): # ________________________________________________________________________________ # create an instance of class CubeData + # Make sure all input models have consistent NaN and DO_NOT_USE values + for model in self.input_models: + match_nans_and_flags(model) + cubeinfo = cube_build.CubeData( self.input_models, par_filename, diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 0b653d8d29..0ce1fcf805 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -10,7 +10,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags -from ..lib import reffile_utils, wcs_utils +from ..lib import pipe_utils, reffile_utils, wcs_utils from ..assign_wcs import nirspec log = logging.getLogger(__name__) @@ -234,6 +234,9 @@ def apply_flat_field(science, flat, inverse=False): flag_nonsci = np.bitwise_and(science.dq, dqflags.pixel['NON_SCIENCE']).astype(bool) science.dq[flag_nonsci] = np.bitwise_or(science.dq[flag_nonsci], dqflags.pixel['DO_NOT_USE']) + # Make sure all NaNs and flags match up in the output model + pipe_utils.match_nans_and_flags(science) + # # The following functions are for NIRSpec spectrographic data. @@ -438,14 +441,8 @@ def nirspec_fs_msa(output_model, f_flat_model, s_flat_model, d_flat_model, dispa # Combine the science and flat DQ arrays slit.dq |= slit_flat.dq - # Make sure all DO_NOT_USE pixels are set to NaN, - # including those flagged by this step - dnu = np.where(slit.dq & dqflags.pixel['DO_NOT_USE']) - slit.data[dnu] = np.nan - slit.err[dnu] = np.nan - slit.var_poisson[dnu] = np.nan - slit.var_rnoise[dnu] = np.nan - slit.var_flat[dnu] = np.nan + # Make sure all NaNs and flags match up in the output model + pipe_utils.match_nans_and_flags(slit) any_updated = True @@ -522,14 +519,8 @@ def nirspec_brightobj(output_model, f_flat_model, s_flat_model, d_flat_model, di output_model.var_poisson + output_model.var_rnoise + output_model.var_flat ) - # Make sure all DO_NOT_USE pixels are set to NaN, - # including those flagged by this step - dnu = np.where(output_model.dq & dqflags.pixel['DO_NOT_USE']) - output_model.data[dnu] = np.nan - output_model.err[dnu] = np.nan - output_model.var_poisson[dnu] = np.nan - output_model.var_rnoise[dnu] = np.nan - output_model.var_flat[dnu] = np.nan + # Make sure all NaNs and flags match up in the output model + pipe_utils.match_nans_and_flags(output_model) output_model.meta.cal_step.flat_field = 'COMPLETE' @@ -597,14 +588,8 @@ def nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, dispaxis output_model.var_poisson + output_model.var_rnoise + output_model.var_flat ) - # Make sure all DO_NOT_USE pixels are set to NaN, - # including those flagged by this step - dnu = np.where(output_model.dq & dqflags.pixel['DO_NOT_USE']) - output_model.data[dnu] = np.nan - output_model.err[dnu] = np.nan - output_model.var_poisson[dnu] = np.nan - output_model.var_rnoise[dnu] = np.nan - output_model.var_flat[dnu] = np.nan + # Make sure all NaNs and flags match up in the output model + pipe_utils.match_nans_and_flags(output_model) output_model.meta.cal_step.flat_field = 'COMPLETE' diff --git a/jwst/flatfield/tests/test_flatfield.py b/jwst/flatfield/tests/test_flatfield.py index a332632bf2..71a5f8b432 100644 --- a/jwst/flatfield/tests/test_flatfield.py +++ b/jwst/flatfield/tests/test_flatfield.py @@ -60,7 +60,18 @@ def test_flatfield_step_interface(instrument, exptype): finally: FlatFieldStep.reference_file_types = previous_reference_file_types - assert (result.data == data.data).all() + # flat is unity, so data should match up except where invalid + is_nan = np.isnan(result.data) + assert np.all(result.data[~is_nan] == data.data[~is_nan]) + + # where data is invalid, error and variance are also invalid + # and DQ is set to DO_NOT_USE + assert np.all(np.isnan(result.err[is_nan])) + assert np.all(np.isnan(result.var_rnoise[is_nan])) + assert np.all(np.isnan(result.var_poisson[is_nan])) + assert np.all(np.isnan(result.var_flat[is_nan])) + assert np.all(result.dq[is_nan] | datamodels.dqflags.pixel['DO_NOT_USE']) + assert result.var_flat.shape == shape assert result.meta.cal_step.flat_field == 'COMPLETE' diff --git a/jwst/lib/basic_utils.py b/jwst/lib/basic_utils.py index 92e77dcf46..28971ddb8e 100644 --- a/jwst/lib/basic_utils.py +++ b/jwst/lib/basic_utils.py @@ -1,31 +1,5 @@ """General utility objects""" -from stdatamodels.jwst.datamodels import dqflags -import numpy as np - - -def set_nans_to_donotuse(data, dq): - """Set all NaN values in the data that have an even value to - DO_NOT_USE. - - Parameters - ---------- - data : numpy array - The science data array to find NaN values and - check of these have a DQ flag=DO_NOT_USE, or - set it if not. - - dq : numpy array - The DQ array to be checked. - - Returns - ------- - dq : numpy array - The updated DQ array. - """ - dq[np.isnan(data)] |= dqflags.pixel['DO_NOT_USE'] - return dq - class LoggingContext: """Logging context manager diff --git a/jwst/lib/pipe_utils.py b/jwst/lib/pipe_utils.py index 168fa29d59..940b3bb1b2 100644 --- a/jwst/lib/pipe_utils.py +++ b/jwst/lib/pipe_utils.py @@ -1,8 +1,16 @@ """Pipeline utilities objects""" +import logging + import numpy as np +from stdatamodels.properties import ObjectNode +from stdatamodels.jwst.datamodels import dqflags, JwstDataModel + +from jwst.associations.lib.dms_base import TSO_EXP_TYPES + -from ..associations.lib.dms_base import TSO_EXP_TYPES +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) def is_tso(model): @@ -76,3 +84,70 @@ def is_irs2(model): return True else: return False + + +def match_nans_and_flags(input_model): + """Ensure data, error, variance, and DQ are marked consistently for invalid data. + + Invalid data is assumed to be any pixel set to NaN in any one of the + data, error, or variance arrays, or else set to the DO_NOT_USE flag + in the DQ array. + + The input model is updated in place with NaNs or DO_NOT_USE flags, as + appropriate, at all invalid data locations. + + Parameters + ---------- + input_model : DataModel + Input model containing some combination of data, dq, err, var_rnoise, + var_poisson, and var_flat extensions. These extensions must all have + matching dimensions if present. + """ + # Check for datamodel input or slit instance + if (not isinstance(input_model, JwstDataModel) + and not isinstance(input_model, ObjectNode)): + raise ValueError(f"Input {type(input_model)} is not a datamodel.") + + # Build up the invalid data flags from each available data extension. + is_invalid = None + data_shape = None + nan_extensions = ['data', 'err', 'var_rnoise', 'var_poisson', 'var_flat'] + for extension in nan_extensions: + if not hasattr(input_model, extension): + continue + data = getattr(input_model, extension) + if is_invalid is None: + is_invalid = np.isnan(data) + data_shape = data.shape + else: + if data.shape != data_shape: + log.warning(f"Mismatched data shapes; skipping invalid data " + f"updates for extension '{extension}'") + continue + is_invalid |= np.isnan(data) + + # Nothing to do if no extensions were found to update + if is_invalid is None: + return + + # Add in invalid flags from the DQ extension if present + if hasattr(input_model, 'dq'): + do_not_use = (input_model.dq & dqflags.pixel['DO_NOT_USE']).astype(bool) + if input_model.dq.shape != data_shape: + log.warning("Mismatched data shapes; skipping invalid data " + "updates for extension 'dq'") + else: + is_invalid |= do_not_use + + # Update all the data extensions + for extension in nan_extensions: + if not hasattr(input_model, extension): + continue + data = getattr(input_model, extension) + if data.shape != data_shape: + continue + data[is_invalid] = np.nan + + # Update the DQ extension + if input_model.dq.shape == data_shape: + input_model.dq[is_invalid] |= dqflags.pixel['DO_NOT_USE'] diff --git a/jwst/lib/tests/test_pipe_utils.py b/jwst/lib/tests/test_pipe_utils.py index 00a87688b8..cdd840ca3d 100644 --- a/jwst/lib/tests/test_pipe_utils.py +++ b/jwst/lib/tests/test_pipe_utils.py @@ -4,6 +4,7 @@ import numpy as np from stdatamodels.jwst import datamodels +from stdatamodels.properties import ObjectNode from jwst.lib import pipe_utils from jwst.associations.lib import dms_base @@ -84,3 +85,134 @@ def test_is_irs2_2(): x = np.ones((3200, 2), dtype=np.float32) model = datamodels.ImageModel(data=x) assert pipe_utils.is_irs2(model) + + +@pytest.mark.parametrize('shape', [(10,), (10, 10), (10, 10, 10)]) +@pytest.mark.parametrize('extensions', + [('data',), ('data', 'dq'), + ('data', 'err', 'var_rnoise'), + ('data', 'err', 'var_rnoise', 'var_poisson', + 'var_flat', 'dq')]) +def test_match_nans_and_flags(shape, extensions): + # Set up a model with matching data shapes, variable extensions + dnu = datamodels.dqflags.pixel['DO_NOT_USE'] + model = datamodels.SlitModel() + invalid = np.full(shape, False) + for i, extname in enumerate(extensions): + if extname == 'dq': + data = np.zeros(shape, dtype=np.uint32) + data.flat[i] = dnu + else: + data = np.ones(shape) + data.flat[i] = np.nan + setattr(model, extname, data) + invalid.flat[i] = True + + # Match flags and NaNs across all extensions + pipe_utils.match_nans_and_flags(model) + + # Check that all extensions have all invalid flags + for extname in extensions: + data = getattr(model, extname) + if extname == 'dq': + assert np.all(data[invalid] == dnu) + assert np.all(data[~invalid] == 0) + else: + assert np.all(np.isnan(data[invalid])) + assert np.all(data[~invalid] == 1) + + model.close() + + +def test_match_nans_and_flags_dq_only(): + # Set up a model with only a DQ array, to test edge conditions + dnu = datamodels.dqflags.pixel['DO_NOT_USE'] + model = datamodels.MaskModel() + shape = (10, 10) + invalid = np.full(shape, False) + + model.dq = np.zeros(shape, dtype=np.uint32) + model.dq.flat[5] = dnu + invalid.flat[5] = True + + # Match flags and NaNs across all extensions + dq_copy = model.dq.copy() + pipe_utils.match_nans_and_flags(model) + + # Check that DQ extension is unchanged + assert np.all(model.dq == dq_copy) + + model.close() + + +def test_match_nans_and_flags_no_data(caplog): + # Set up a model with no data, to test edge conditions + model = datamodels.JwstDataModel() + model_copy = model.copy() + pipe_utils.match_nans_and_flags(model) + + # nothing happens + assert model == model_copy + assert 'WARNING' not in caplog.text + assert 'ERROR' not in caplog.text + + model.close() + model_copy.close() + + +def test_match_nans_and_flags_not_a_model(): + # Make sure the function throws an error if input is not a model + model = "not_a_model" + with pytest.raises(ValueError, match="not a datamodel"): + pipe_utils.match_nans_and_flags(model) + + # empty JwstDataModel is okay + model = datamodels.JwstDataModel() + pipe_utils.match_nans_and_flags(model) + + # a datamodel object node is also okay + multislit = datamodels.MultiSlitModel() + multislit.slits.append(datamodels.SlitModel()) + assert not isinstance(multislit.slits[0], datamodels.JwstDataModel) + assert isinstance(multislit.slits[0], ObjectNode) + pipe_utils.match_nans_and_flags(multislit.slits[0]) + + +def test_match_nans_and_flags_shape_mismatch(): + # Set up a model with mismatched data shapes + dnu = datamodels.dqflags.pixel['DO_NOT_USE'] + model = datamodels.SlitModel() + + shape = (10, 10) + bad_shape = (10, 5) + invalid = np.full(shape, False) + extensions = ['data', 'err', 'dq', 'var_poisson'] + for i, extname in enumerate(extensions): + if extname == 'dq': + data = np.zeros(bad_shape, dtype=np.uint32) + data.flat[i] = dnu + elif extname == 'var_poisson': + data = np.ones(bad_shape, dtype=np.uint32) + data.flat[i] = dnu + else: + data = np.ones(shape) + data.flat[i] = np.nan + invalid.flat[i] = True + setattr(model, extname, data) + + # Match flags and NaNs across all extensions: + # will warn for data mismatch + model_copy = model.copy() + pipe_utils.match_nans_and_flags(model) + + # Only data and err are updated + for extname in extensions: + data = getattr(model, extname) + if extname == 'data' or extname == 'err': + assert np.all(np.isnan(data[invalid])) + assert np.all(data[~invalid] == 1) + else: + assert np.allclose(data, getattr(model_copy, extname)) + + model.close() + model_copy.close() diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index dfd7c7f6ed..6ab12a0157 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -23,7 +23,7 @@ 3. For each input file store the minimum of the pixel neighbor differences 4. Comparing all the differences from all the input data find the minimum neighbor difference 5. Normalize minimum difference to local median of difference array - 6. select outliers by flagging those normailzed minimum values > threshold_percent + 6. Select outliers by flagging those normalized minimum values > threshold_percent 7. Updates input ImageModel DQ arrays with mask of detected outliers. """ @@ -32,6 +32,7 @@ import numpy as np from jwst.datamodels import ModelContainer +from jwst.lib.pipe_utils import match_nans_and_flags from jwst.stpipe.utilities import record_step_status from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags @@ -105,6 +106,7 @@ def detect_outliers( make_output_path) return input_models + def flag_outliers(input_models, idet, uq_det, ndet_files, diffaxis, nx, ny, kern_size, threshold_percent, @@ -258,6 +260,11 @@ def flag_outliers(input_models, idet, uq_det, ndet_files, total_bad = num_above + nadditional percent_cr = total_bad / (model.data.shape[0] * model.data.shape[1]) * 100 log.info(f"Total # pixels flagged as outliers: {total_bad} ({percent_cr:.2f}%)") + + # Make sure all error and variance arrays also have matching + # NaNs and DQ flags + match_nans_and_flags(model) + # update model input_models[i] = model @@ -300,4 +307,3 @@ def create_optional_results_model(opt_info): opt_model.meta.kernel_ysize = kernsize_y opt_model.meta.threshold_percent = threshold_percent return opt_model - diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 7cb74f5a8c..b3b8c9b803 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -88,7 +88,10 @@ def test_flag_cr(sci_blot_image_pair): ) # Make sure science data array is unchanged after flag_cr() - np.testing.assert_allclose(sci.data, data_copy) + # except outliers are NaN + dnu = (sci.dq & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(sci.data[dnu])) + assert np.allclose(sci.data[~dnu], data_copy[~dnu]) # Verify that both DQ flags are set in the DQ array for all outliers assert sci.dq[3, 3] == OUTLIER_DO_NOT_USE @@ -224,21 +227,21 @@ def test_outlier_step_base(we_three_sci, tmp_cwd): assert len(median_files) == 0 # Save all the data into a separate array before passing into step - data_as_cube = container.map_function(lambda model, index: model.data.copy(), modify=False) + data_as_cube = list(container.map_function( + lambda model, index: model.data.copy(), modify=False)) result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True ) - # Make sure nothing changed in SCI array - result.map_function( - lambda model, index: np.testing.assert_allclose(data_as_cube[index], model.data), - modify=False, - ) - - # Verify source is not flagged with result: - for r in result: + for i, r in enumerate(result): + # Make sure nothing changed in SCI array except outliers are NaN + dnu = (r.dq & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(r.data[dnu])) + assert np.allclose(data_as_cube[i][~dnu], r.data[~dnu]) + + # Verify source is not flagged assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"] result.shelve(r, modify=False) @@ -378,21 +381,21 @@ def test_outlier_step_on_disk(three_sci_as_asn, tmp_cwd): container = ModelLibrary(three_sci_as_asn, on_disk=True) # Save all the data into a separate array before passing into step - data_as_cube = container.map_function(lambda model, index: model.data.copy(), modify=False) + data_as_cube = list(container.map_function( + lambda model, index: model.data.copy(), modify=False)) result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True, in_memory=False ) - # Make sure nothing changed in SCI array - result.map_function( - lambda model, index: np.testing.assert_allclose(data_as_cube[index], model.data), - modify=False, - ) - - # Verify source is not flagged with result: - for r in result: + for i, r in enumerate(result): + # Make sure nothing changed in SCI array except outliers are NaN + dnu = (r.dq & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(r.data[dnu])) + assert np.allclose(data_as_cube[i][~dnu], r.data[~dnu]) + + # Verify source is not flagged assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"] result.shelve(r, modify=False) @@ -440,7 +443,7 @@ def test_outlier_step_square_source_no_outliers(we_three_sci, tmp_cwd): @pytest.mark.parametrize("exptype", IMAGE_MODES) -def test_outlier_step_image_weak_CR_dither(exptype, tmp_cwd): +def test_outlier_step_image_weak_cr_dither(exptype, tmp_cwd): """Test whole step with an outlier for imaging modes""" bkg = 1.5 sig = 0.02 @@ -464,15 +467,14 @@ def test_outlier_step_image_weak_CR_dither(exptype, tmp_cwd): result = OutlierDetectionStep.call(container, in_memory=True) - # Make sure nothing changed in SCI array - result.map_function( - lambda model, index: np.testing.assert_allclose(data_as_cube[index], model.data), - modify=False, - ) - - # Verify source is not flagged with result: - for r in result: + for i, r in enumerate(result): + # Make sure nothing changed in SCI array except outliers are NaN + dnu = (r.dq & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(r.data[dnu])) + assert np.allclose(data_as_cube[i][~dnu], r.data[~dnu]) + + # Verify source is not flagged assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"] result.shelve(r, modify=False) @@ -484,7 +486,7 @@ def test_outlier_step_image_weak_CR_dither(exptype, tmp_cwd): @pytest.mark.parametrize("exptype, tsovisit", exptypes_coron) -def test_outlier_step_image_weak_CR_coron(exptype, tsovisit, tmp_cwd): +def test_outlier_step_image_weak_cr_coron(exptype, tsovisit, tmp_cwd): """Test whole step with an outlier for coronagraphic modes""" bkg = 1.5 sig = 0.02 @@ -503,15 +505,19 @@ def test_outlier_step_image_weak_CR_coron(exptype, tsovisit, tmp_cwd): result = OutlierDetectionStep.call(cube) - # Make sure nothing changed in SCI array + # Make sure nothing changed in SCI array except that + # outliers are NaN for i, image in enumerate(container): - np.testing.assert_allclose(image.data, result.data[i]) + dnu = (result.dq[i] & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(result.data[i][dnu])) + assert np.allclose(image.data[~dnu], result.data[i][~dnu]) # Verify source is not flagged assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) # Verify CR is flagged - assert np.all(result.dq[0, 12, 12] == OUTLIER_DO_NOT_USE) + assert result.dq[0, 12, 12] == OUTLIER_DO_NOT_USE + assert np.isnan(result.data[0, 12, 12]) @pytest.mark.parametrize("exptype, tsovisit", exptypes_tso) @@ -543,9 +549,12 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): result = OutlierDetectionStep.call(cube, rolling_window_width=rolling_window_width) - # Make sure nothing changed in SCI array + # Make sure nothing changed in SCI array except + # that outliers are NaN for i, model in enumerate(im): - np.testing.assert_allclose(model.data, result.data[i]) + dnu = (result.dq[i] & OUTLIER_DO_NOT_USE).astype(bool) + assert np.all(np.isnan(result.data[i][dnu])) + assert np.allclose(model.data[~dnu], result.data[i][~dnu]) # Verify source is not flagged assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index b14ab125c2..6491415715 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -5,6 +5,7 @@ import numpy as np +from jwst.lib.pipe_utils import match_nans_and_flags from jwst.resample.resample import compute_image_pixel_area from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_crs, flag_resampled_crs from stdatamodels.jwst import datamodels @@ -245,6 +246,10 @@ def _flag_resampled_model_crs( input_model.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") + # Make sure all data, error, and variance arrays have + # matching NaNs and DQ flags + match_nans_and_flags(input_model) + def flag_crs_in_models_with_resampling( input_models, @@ -262,6 +267,12 @@ def flag_crs_in_models_with_resampling( def flag_model_crs(image, blot, snr): cr_mask = flag_crs(image.data, image.err, blot, snr) - # update dq array in-place + + # Update dq array in-place image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) + + # Make sure all data, error, and variance arrays have + # matching NaNs and DQ flags + match_nans_and_flags(image) + log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") diff --git a/jwst/pathloss/pathloss.py b/jwst/pathloss/pathloss.py index 9586bc320e..c3aba3b324 100644 --- a/jwst/pathloss/pathloss.py +++ b/jwst/pathloss/pathloss.py @@ -10,7 +10,7 @@ from jwst.assign_wcs import nirspec, util from jwst.lib.wcs_utils import get_wavelengths -from jwst.lib.basic_utils import set_nans_to_donotuse +from jwst.lib.pipe_utils import match_nans_and_flags log = logging.getLogger(__name__) @@ -547,8 +547,8 @@ def do_correction_mos(data, pathloss, inverse=False, source_type=None, correctio slit.pathloss_point = correction.pathloss_point slit.pathloss_uniform = correction.pathloss_uniform - # check the dq flags have the correct value - slit.dq = set_nans_to_donotuse(slit.data, slit.dq) + # Make sure all NaNs and flags match up in the output slit model + match_nans_and_flags(slit) # Set step status to complete data.meta.cal_step.pathloss = 'COMPLETE' @@ -613,8 +613,8 @@ def do_correction_fixedslit(data, pathloss, inverse=False, source_type=None, cor slit.pathloss_point = correction.pathloss_point slit.pathloss_uniform = correction.pathloss_uniform - # check the dq flags have the correct value - slit.dq = set_nans_to_donotuse(slit.data, slit.dq) + # Make sure all NaNs and flags match up in the output slit model + match_nans_and_flags(slit) # Set step status to complete data.meta.cal_step.pathloss = 'COMPLETE' @@ -669,8 +669,8 @@ def do_correction_ifu(data, pathloss, inverse=False, source_type=None, correctio # This might be useful to other steps data.wavelength = correction.wavelength - # check the dq flags have the correct value - data.dq = set_nans_to_donotuse(data.data, data.dq) + # Make sure all NaNs and flags match up in the output data model + match_nans_and_flags(data) # Set the step status to complete data.meta.cal_step.pathloss = 'COMPLETE' @@ -713,8 +713,8 @@ def do_correction_lrs(data, pathloss, user_slit_loc): # This might be useful to other steps data.wavelength = correction.wavelength - # check the dq flags have the correct value - data.dq = set_nans_to_donotuse(data.data, data.dq) + # Make sure all NaNs and flags match up in the output data model + match_nans_and_flags(data) # Set the step status to complete data.meta.cal_step.pathloss = 'COMPLETE' @@ -808,8 +808,8 @@ def do_correction_soss(data, pathloss): data.var_flat /= pathloss_2d**2 data.pathloss_point = pathloss_2d - # check the dq flags have the correct value - data.dq = set_nans_to_donotuse(data.data, data.dq) + # Make sure all NaNs and flags match up in the output data model + match_nans_and_flags(data) # Set step status to complete data.meta.cal_step.pathloss = 'COMPLETE' diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py index 7628582e36..f8ec165674 100644 --- a/jwst/photom/photom.py +++ b/jwst/photom/photom.py @@ -7,6 +7,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags +from .. lib.pipe_utils import match_nans_and_flags from .. lib.wcs_utils import get_wavelengths from .. lib.dispaxis import get_dispersion_direction from . import miri_mrs @@ -940,6 +941,9 @@ def photom_io(self, tabdata, order=None): self.input.meta.bunit_data = 'DN/s' self.input.meta.bunit_err = 'DN/s' + # Make sure output model has consistent NaN and DO_NOT_USE values + match_nans_and_flags(slit) + elif isinstance(self.input, datamodels.MultiSpecModel): # Does this block need to address SB columns as well, or will # they (presumably) never be populated for SOSS? @@ -993,6 +997,9 @@ def photom_io(self, tabdata, order=None): self.input.meta.bunit_data = 'DN/s' self.input.meta.bunit_err = 'DN/s' + # Make sure output model has consistent NaN and DO_NOT_USE values + match_nans_and_flags(self.input) + return def create_2d_conversion(self, model, exptype, conversion, waves, relresps, diff --git a/jwst/regtest/test_nirspec_image2.py b/jwst/regtest/test_nirspec_image2.py index 5bb46cd75a..fba5f82eac 100644 --- a/jwst/regtest/test_nirspec_image2.py +++ b/jwst/regtest/test_nirspec_image2.py @@ -45,7 +45,12 @@ def test_ff_inv(rtdata, fitsdiff_default_kwargs): flatted = FlatFieldStep.call(data) unflatted = FlatFieldStep.call(flatted, inverse=True) - assert np.allclose(data.data, unflatted.data), 'Inversion failed' + # flat fielding may set some new NaN values - ignore these in test + is_nan = np.isnan(unflatted.data) + assert np.allclose(data.data[~is_nan], unflatted.data[~is_nan]), 'Inversion failed' + + # make sure NaNs are only at do_not_use pixels + assert np.all(unflatted.dq[is_nan] & dm.dqflags.pixel['DO_NOT_USE']) @pytest.mark.bigdata @@ -62,4 +67,9 @@ def test_correction_pars(rtdata, fitsdiff_default_kwargs): step.use_correction_pars = True reflatted = step.run(data) - assert np.allclose(flatted.data, reflatted.data), 'Re-run with correction parameters failed' + # flat fielding may set some new NaN values - ignore these in test + is_nan = np.isnan(reflatted.data) + assert np.allclose(flatted.data[~is_nan], reflatted.data[~is_nan]), 'Re-run with correction parameters failed' + + # make sure NaNs are only at do_not_use pixels + assert np.all(reflatted.dq[is_nan] & dm.dqflags.pixel['DO_NOT_USE']) diff --git a/jwst/resample/resample_spec_step.py b/jwst/resample/resample_spec_step.py index bcc3d1a95c..7aa406091b 100755 --- a/jwst/resample/resample_spec_step.py +++ b/jwst/resample/resample_spec_step.py @@ -4,11 +4,13 @@ from stdatamodels.jwst.datamodels import MultiSlitModel, ImageModel from jwst.datamodels import ModelContainer, ModelLibrary +from jwst.lib.pipe_utils import match_nans_and_flags +from jwst.lib.wcs_utils import get_wavelengths + from . import resample_spec, ResampleStep from ..exp_to_source import multislit_to_container from ..assign_wcs.util import update_s_region_spectral from ..stpipe import Step -from jwst.lib.wcs_utils import get_wavelengths # Force use of all DQ flagged data except for DO_NOT_USE and NON_SCIENCE @@ -132,6 +134,10 @@ def _process_multislit(self, input_models): pscale_ratio = None for container in containers.values(): + # Make sure all input models have consistent NaN and DO_NOT_USE values + for model in container: + match_nans_and_flags(model) + resamp = resample_spec.ResampleSpecData(container, **self.drizpars) library = ModelLibrary(container, on_disk=False) @@ -204,6 +210,9 @@ def _process_slit(self, input_models): result : `~jwst.datamodels.SlitModel` The resampled output """ + # Make sure all input models have consistent NaN and DO_NOT_USE values + for model in input_models: + match_nans_and_flags(model) resamp = resample_spec.ResampleSpecData(input_models, **self.drizpars) @@ -243,4 +252,4 @@ def update_slit_metadata(self, model): pass else: if val is not None: - setattr(model, attr, val) \ No newline at end of file + setattr(model, attr, val) diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index a2fd35db96..5790fb4f58 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -5,6 +5,7 @@ import asdf from jwst.datamodels import ModelLibrary, ImageModel +from jwst.lib.pipe_utils import match_nans_and_flags from . import resample from ..stpipe import Step @@ -88,6 +89,12 @@ def process(self, input): raise RuntimeError(f"Input {example_model} is not a 2D image.") del example_model + # Make sure all input models have consistent NaN and DO_NOT_USE values + for model in input_models: + match_nans_and_flags(model) + input_models.shelve(model) + del model + # Setup drizzle-related parameters kwargs = self.get_drizpars() From f4291171d8d31dd3cba8c6feb28eec6a2f6816d0 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Sat, 7 Sep 2024 05:13:42 -0400 Subject: [PATCH 334/687] update pull request checklist (#8670) Co-authored-by: Nadia Dencheva --- .github/pull_request_template.md | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 39941f867a..9da3b0c69f 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -8,13 +8,15 @@ Closes # This PR addresses ... -**Checklist for PR authors (skip items if you don't have permissions or they are not applicable)** -- [ ] added entry in `CHANGES.rst` within the relevant release section -- [ ] updated or added relevant tests -- [ ] updated relevant documentation -- [ ] added relevant milestone -- [ ] added relevant label(s) -- [ ] ran regression tests, post a link to the Jenkins job below. - [How to run regression tests on a PR](https://github.com/spacetelescope/jwst/wiki/Running-Regression-Tests-Against-PR-Branches) -- [ ] All comments are resolved -- [ ] Make sure the JIRA ticket is [resolved properly](https://github.com/spacetelescope/jwst/wiki/How-to-resolve-JIRA-issues) + +## Tasks +- [ ] **request a review from someone specific**, to avoid making the maintainers review every PR +- [ ] add a build milestone, i.e. `Build 11.3` (use the [latest build](https://github.com/spacetelescope/jwst/milestones) if not sure) +- [ ] Does this PR change user-facing code / API? + - [ ] add an entry to `CHANGES.rst` within the relevant release section (otherwise add the `no-changelog-entry-needed` label to this PR) + - [ ] update or add relevant tests + - [ ] update relevant docstrings and / or `docs/` page + - [ ] [start a regression test](https://github.com/spacetelescope/RegressionTests/actions/workflows/jwst.yml) and include a link to the running job ([click here for instructions](https://github.com/spacetelescope/RegressionTests/blob/main/docs/running_regression_tests.md)) + - [ ] Do truth files need to be updated ("okified")? + - [ ] **after the reviewer has approved these changes**, run `okify_regtests` to update the truth files +- [ ] if a JIRA ticket exists, [make sure it is resolved properly](https://github.com/spacetelescope/jwst/wiki/How-to-resolve-JIRA-issues) From e2286baad2737f8590e9057d36b340bbde70265b Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 11:52:09 -0400 Subject: [PATCH 335/687] log version and crds context at end of step/pipeline runs --- jwst/stpipe/core.py | 14 ++++++++------ jwst/stpipe/tests/test_step.py | 26 ++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 6 deletions(-) diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 2a30139190..4ff18dea7d 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -80,14 +80,18 @@ def finalize_result(self, result, reference_files_used): result.meta.calibration_software_revision = __version_commit__ or 'RELEASE' result.meta.calibration_software_version = __version__ + crds_context = crds_client.get_context_used(result.crds_observatory) + if len(reference_files_used) > 0: for ref_name, filename in reference_files_used: if hasattr(result.meta.ref_file, ref_name): getattr(result.meta.ref_file, ref_name).name = filename result.meta.ref_file.crds.sw_version = crds_client.get_svn_version() - result.meta.ref_file.crds.context_used = crds_client.get_context_used(result.crds_observatory) - if self.parent is None: - log.info(f"Results used CRDS context: {result.meta.ref_file.crds.context_used}") + result.meta.ref_file.crds.context_used = crds_context + + if self.parent is None: + log.info(f"Results used CRDS context: {crds_context}") + log.info(f"Results used jwst version: {__version__}") def remove_suffix(self, name): @@ -98,6 +102,4 @@ def remove_suffix(self, name): # be a subclass of JwstStep so that it will pass checks # when constructing a pipeline using JwstStep class methods. class JwstPipeline(Pipeline, JwstStep): - def finalize_result(self, result, reference_files_used): - if isinstance(result, JwstDataModel): - log.info(f"Results used CRDS context: {crds_client.get_context_used(result.crds_observatory)}") + pass diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 1c1d3a76d2..250fb14d81 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -1,3 +1,4 @@ +import logging import os from os.path import ( abspath, @@ -15,8 +16,10 @@ from stdatamodels.jwst import datamodels +from jwst import __version__ as jwst_version from jwst.white_light import WhiteLightStep from jwst.stpipe import Step +from jwst.tests.helpers import LogWatcher from jwst.stpipe.tests.steps import ( EmptyPipeline, MakeListPipeline, MakeListStep, @@ -39,6 +42,10 @@ CRDS_ERROR_STRING = 'PARS-WITHDEFAULTSSTEP: No parameters found' +# used in logging tests below to provide a deterministic crds +# context to look for in the log messages +FAKE_CRDS_CONTEXT = "0000.pmap" + @pytest.fixture(scope='module') def data_path(): @@ -607,3 +614,22 @@ def test_call_with_config(caplog, tmp_cwd): ProperPipeline.call(model, config_file=cfg) assert "newpar1" in caplog.text + + +@pytest.mark.parametrize( + "message", [ + f"Results used CRDS context: {FAKE_CRDS_CONTEXT}", + f"Results used jwst version: {jwst_version}", + ]) +def test_finalize_logging(monkeypatch, message): + """ + Check that the jwst version and crds context are logged + when a step/pipeline is run. + """ + pipeline = EmptyPipeline() + model = datamodels.ImageModel() + monkeypatch.setattr(crds_client, 'get_context_used', lambda observatory: FAKE_CRDS_CONTEXT) + watcher = LogWatcher(message) + monkeypatch.setattr(logging.getLogger("jwst.stpipe.core"), "info", watcher) + pipeline.run(model) + assert watcher.seen From ab9a9011d0e3693633930d53183d1f12c7275a8d Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 13:19:19 -0400 Subject: [PATCH 336/687] log even if result is not a datamodel --- jwst/stpipe/core.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 4ff18dea7d..094b4c1f4c 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -76,11 +76,16 @@ def load_as_level3_asn(self, obj): return asn def finalize_result(self, result, reference_files_used): + crds_context = crds_client.get_context_used('jwst') + + if self.parent is None: + log.info(f"Results used CRDS context: {crds_context}") + log.info(f"Results used jwst version: {__version__}") + if isinstance(result, JwstDataModel): result.meta.calibration_software_revision = __version_commit__ or 'RELEASE' result.meta.calibration_software_version = __version__ - crds_context = crds_client.get_context_used(result.crds_observatory) if len(reference_files_used) > 0: for ref_name, filename in reference_files_used: @@ -89,11 +94,6 @@ def finalize_result(self, result, reference_files_used): result.meta.ref_file.crds.sw_version = crds_client.get_svn_version() result.meta.ref_file.crds.context_used = crds_context - if self.parent is None: - log.info(f"Results used CRDS context: {crds_context}") - log.info(f"Results used jwst version: {__version__}") - - def remove_suffix(self, name): return remove_suffix(name) From e16c05831ac063348f661fe289b41f47dafd0cd7 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 13:22:24 -0400 Subject: [PATCH 337/687] add changelog --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 661f78510e..eb25ae7a58 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -191,6 +191,8 @@ stpipe added a `query_step_status()` function to use as an alternative to checking `self.skip`. [#8600] +- Log jwst version and crds context at the end of step/pipeline runs. [#8760] + tso_photometry -------------- From 0f4467440ba1a510a5305e5bed8b1bd4fd6957ca Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 5 Sep 2024 14:54:35 -0400 Subject: [PATCH 338/687] reenabled saving of blot models --- jwst/outlier_detection/imaging.py | 2 +- .../tests/test_outlier_detection.py | 24 +++++++++++++++++++ jwst/outlier_detection/utils.py | 23 +++++++++++++++++- 3 files changed, 47 insertions(+), 2 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 559db8cec0..3b1c4b861f 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -127,7 +127,7 @@ def detect_outliers( with input_models: for image in input_models: if resample_data: - flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg) + flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg, save_blot=save_intermediate_results, make_output_path=make_output_path) else: flag_model_crs(image, median_data, snr1) input_models.shelve(image, modify=True) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index b3b8c9b803..2355c2c1f0 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -405,6 +405,30 @@ def test_outlier_step_on_disk(three_sci_as_asn, tmp_cwd): assert zeroth.dq[12, 12] == OUTLIER_DO_NOT_USE result.shelve(zeroth, modify=False) + # Verify intermediate results were written to disk + dirname = tmp_cwd + all_files = glob(os.path.join(dirname, '*.fits')) + input_files = glob(os.path.join(dirname, '*_cal.fits')) + result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) + i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) + s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) + median_files = glob(os.path.join(dirname, '*median.fits')) + blot_files = glob(os.path.join(dirname, '*blot.fits')) + + assert len(result_files) == len(container) + + # i2d, median, blot files are written to the output directory + assert len(i2d_files) == len(container) + assert len(blot_files) == len(container) + assert len(median_files) == 1 + + # s2d files not written + assert len(s2d_files) == 0 + + # nothing else was written + assert len(all_files) == len(input_files) + len(i2d_files) + len(median_files) + len(result_files) + len(blot_files) + + def test_outlier_step_square_source_no_outliers(we_three_sci, tmp_cwd): """Test whole step with square source with sharp edges, no outliers""" diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 6491415715..fc0c23442f 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -206,6 +206,8 @@ def flag_resampled_model_crs( scale1, scale2, backg, + save_blot=False, + make_output_path=None, ): if 'SPECTRAL' not in input_model.meta.wcs.output_frame.axes_type: input_pixflux_area = input_model.meta.photometry.pixelarea_steradians @@ -217,6 +219,15 @@ def flag_resampled_model_crs( pix_ratio = 1.0 blot = gwcs_blot(median_data, median_wcs, input_model.data.shape, input_model.meta.wcs, pix_ratio) + if save_blot: + if make_output_path is None: + raise ValueError("make_output_path must be provided if save_blot is True") + model_path = make_output_path(input_model.meta.filename, suffix='blot') + blot_model = _make_blot_model(input_model, blot) + blot_model.meta.filename = model_path + blot_model.save(model_path) + log.info(f"Saved model in {model_path}") + del blot_model # dq flags will be updated in-place _flag_resampled_model_crs(input_model, blot, snr1, snr2, scale1, scale2, backg) @@ -260,9 +271,11 @@ def flag_crs_in_models_with_resampling( scale1, scale2, backg, + save_blot=False, + make_output_path=None, ): for image in input_models: - flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg) + flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg, save_blot=save_blot, make_output_path=make_output_path) def flag_model_crs(image, blot, snr): @@ -276,3 +289,11 @@ def flag_model_crs(image, blot, snr): match_nans_and_flags(image) log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") + + +def _make_blot_model(input_model, blot): + blot_model = input_model.copy() + blot_model.data = blot + blot_model.dq *= 0 + blot_model.err *= 0.0 + return blot_model \ No newline at end of file From 4a35cfde7ea1ba2368d9fac71ca79a39c5fcc96b Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 5 Sep 2024 15:09:16 -0400 Subject: [PATCH 339/687] added changelog, restored mentions of saving blot models in docs --- CHANGES.rst | 2 ++ docs/jwst/outlier_detection/outlier_detection_imaging.rst | 3 ++- docs/jwst/outlier_detection/outlier_detection_spec.rst | 2 ++ 3 files changed, 6 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index eb25ae7a58..101a221acb 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -496,6 +496,8 @@ outlier_detection - Fix errors in documentation describing arguments. [#8603] +- Re-enabled saving of blot models when `save_intermediate_results` is True. [#8758] + pathloss -------- diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 15df877939..c2cd6318b9 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -75,7 +75,8 @@ Specifically, this routine performs the following operations: * The median image is written out to disk as `__median.fits` by default. #. By default, the median image is blotted back (inverse of resampling) to - match each original input image. + match each original input image. Resampled/blotted images are written out to disk if + the ``save_intermediate_results`` parameter is set to `True`. * **If resampling is turned off**, the median image is compared directly to each input image. diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index f66df77854..6d5b432b2f 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -39,6 +39,8 @@ Specifically, this routine performs the following operations (modified from the if the ``save_intermediate_results`` parameter is set to `True` #. Blot median image to match each original input image + - Resampled/blotted images are written out to disk if the ``save_intermediate_results`` + parameter is set to `True` - **If resampling is turned off**, the median image is used for comparison with the original input models for detecting outliers #. Perform statistical comparison between blotted image and original image to identify outliers From fcb47cb059d032ac5951f15ae97f37d77cc587fc Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 6 Sep 2024 16:00:49 -0400 Subject: [PATCH 340/687] changes from review, incl carrying save option into spec modes --- jwst/outlier_detection/imaging.py | 11 ++++++++++- jwst/outlier_detection/spec.py | 6 +++++- jwst/outlier_detection/utils.py | 11 ++++++++++- 3 files changed, 25 insertions(+), 3 deletions(-) diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 3b1c4b861f..9f1aabf597 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -127,7 +127,16 @@ def detect_outliers( with input_models: for image in input_models: if resample_data: - flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg, save_blot=save_intermediate_results, make_output_path=make_output_path) + flag_resampled_model_crs(image, + median_data, + median_wcs, + snr1, + snr2, + scale1, + scale2, + backg, + save_blot=save_intermediate_results, + make_output_path=make_output_path) else: flag_model_crs(image, median_data, snr1) input_models.shelve(image, modify=True) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index 74a21965a4..c482aad8b9 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -130,7 +130,11 @@ def detect_outliers( scale1, scale2, backg, + save_blot=save_intermediate_results, + make_output_path=make_output_path ) else: - flag_crs_in_models(input_models, median_data, snr1) + flag_crs_in_models(input_models, + median_data, + snr1) return input_models diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index fc0c23442f..2a18ee4b56 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -275,7 +275,16 @@ def flag_crs_in_models_with_resampling( make_output_path=None, ): for image in input_models: - flag_resampled_model_crs(image, median_data, median_wcs, snr1, snr2, scale1, scale2, backg, save_blot=save_blot, make_output_path=make_output_path) + flag_resampled_model_crs(image, + median_data, + median_wcs, + snr1, + snr2, + scale1, + scale2, + backg, + save_blot=save_blot, + make_output_path=make_output_path) def flag_model_crs(image, blot, snr): From 0ce4c604a84d6873675e945b13c6dcc897a5da64 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 9 Sep 2024 09:08:15 -0400 Subject: [PATCH 341/687] updated spec unit test to account for blot files --- .../tests/test_outlier_detection.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 2355c2c1f0..ebd732437b 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -288,11 +288,13 @@ def test_outlier_step_spec(tmp_cwd, tmp_path): i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) median_files = glob(os.path.join(dirname, '*median.fits')) + blot_files = glob(os.path.join(dirname, '*blot.fits')) # intermediate files are removed assert len(i2d_files) == 0 assert len(s2d_files) == 0 assert len(median_files) == 0 + assert len(blot_files) == 0 # result files are written to the output directory if dirname == output_dir: @@ -320,25 +322,31 @@ def test_outlier_step_spec(tmp_cwd, tmp_path): i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) median_files = glob(os.path.join(dirname, '*median.fits')) + blot_files = glob(os.path.join(dirname, '*blot.fits')) if dirname == output_dir: # result files are written to the output directory assert len(result_files) == len(container) - # s2d and median files are written to the output directory + # s2d, median, and blot files are written to the output directory assert len(s2d_files) == len(container) + assert len(blot_files) == len(container) assert len(median_files) == 1 # i2d files not written assert len(i2d_files) == 0 # nothing else was written - assert len(all_files) == len(s2d_files) + len(median_files) + len(result_files) + assert len(all_files) == len(s2d_files) + \ + len(median_files) + \ + len(result_files) + \ + len(blot_files) else: # nothing should be written to the current directory assert len(result_files) == 0 assert len(s2d_files) == 0 assert len(median_files) == 0 assert len(i2d_files) == 0 + assert len(blot_files) == 0 assert len(all_files) == 0 miri_rate.close() From 42f33abfba2dd621c694a28982697b949e1eec14 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 9 Sep 2024 12:28:12 -0400 Subject: [PATCH 342/687] limit blotmodel to just sci array --- jwst/outlier_detection/utils.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 2a18ee4b56..9adccbba25 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -301,8 +301,6 @@ def flag_model_crs(image, blot, snr): def _make_blot_model(input_model, blot): - blot_model = input_model.copy() + blot_model = type(input_model)() blot_model.data = blot - blot_model.dq *= 0 - blot_model.err *= 0.0 return blot_model \ No newline at end of file From 8350208b53b706628825ca58b131d19705f0704b Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 9 Sep 2024 12:57:50 -0400 Subject: [PATCH 343/687] transfer metadata from input model to blot model --- jwst/outlier_detection/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 9adccbba25..4abdfbad1b 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -303,4 +303,5 @@ def flag_model_crs(image, blot, snr): def _make_blot_model(input_model, blot): blot_model = type(input_model)() blot_model.data = blot + blot_model.update(input_model) return blot_model \ No newline at end of file From 26082137d9fa79ee528db0fca672b280f39744f1 Mon Sep 17 00:00:00 2001 From: t-brandt Date: Mon, 9 Sep 2024 16:56:05 -0400 Subject: [PATCH 344/687] Small changes to avoid artifacts near detector edges. --- jwst/nsclean/nsclean.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 1038acccfb..eb6b3ec9d9 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -295,18 +295,24 @@ def clean_subarray(detector, image, mask, npix_iter=512, # to np.sum(mask[:i], axis=1). sum_mask = np.array([0] + list(np.cumsum(np.sum(mask, axis=1)))) + + # i1 will be the first row with a nonzero element in the mask + # imax will be the last row with a nonzero element in the mask - i1 = 0 + nonzero_mask_element = np.sum(mask, axis=1) > 0 + i1 = np.amin(np.arange(mask.shape[0])[nonzero_mask_element]) + imax = np.amax(np.arange(mask.shape[0])[nonzero_mask_element]) + i1_vals = [] di_list = [] models = [] - while i1 < image.shape[0] - 1: + while i1 <= imax: # Want npix_iter available pixels in this section. If # there are fewer than 1.5*npix_iter available pixels in # the rest of the image, just go to the end. - k = 0 - for k in range(i1 + 1, image.shape[0] + 1): + + for k in range(i1 + 1, imax + 2): if (sum_mask[k] - sum_mask[i1] > npix_iter and sum_mask[-1] - sum_mask[i1] > 1.5 * npix_iter): break @@ -332,7 +338,7 @@ def clean_subarray(detector, image, mask, npix_iter=512, models += [np.zeros(image[i1:i1 + di].shape)] # If we have reached the end of the array, we are finished. - if k == image.shape[0]: + if k == imax + 1: break # Step forward by half an interval so that we have @@ -340,8 +346,8 @@ def clean_subarray(detector, image, mask, npix_iter=512, i1 += max(int(np.round(di/2)), 1) - model = image*0 - tot_wgt = image*0 + model = np.zeros(image.shape) + tot_wgt = np.zeros(image.shape) # When we combine different corrections computed over # different intervals, each one the highest weight towards the @@ -354,7 +360,9 @@ def clean_subarray(detector, image, mask, npix_iter=512, wgt = 1.001 - np.abs(np.linspace(-1, 1, di_list[i]))[:, np.newaxis] model[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt*models[i] tot_wgt[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt - + + # don't divide by zero + tot_wgt[model == 0] = 1 model /= tot_wgt cleaned_image = image - model From 11c20f1d8bfa81112ed09ead9a418c5375b3bc71 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Tue, 10 Sep 2024 11:01:08 -0400 Subject: [PATCH 345/687] Changed frequencies with ALLSLITS and mask_spectral_regions. --- jwst/nsclean/nsclean.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index eb6b3ec9d9..52ee212d4f 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -226,6 +226,7 @@ def clean_full_frame(detector, image, mask): def clean_subarray(detector, image, mask, npix_iter=512, + fc=(1061, 1211, 49943, 49957), exclude_outliers=True, sigrej=4, minfrac=0.05): """Clean a subarray image. @@ -246,6 +247,15 @@ def clean_subarray(detector, image, mask, npix_iter=512, where most pixels are available for fitting. Previous default behavior corresponds to npix_iter of infinity. + fc : tuple + Apodizing filter definition. These parameters are tunable. The + defaults happen to work well for NIRSpec BOTS exposures. + 1) Unity gain for f < fc[0] + 2) Cosine roll-off from fc[0] to fc[1] + 3) Zero gain from fc[1] to fc[2] + 4) Cosine roll-on from fc[2] to fc[3] + Default (1061, 1211, 49943, 49957) + exclude_outliers : bool Find and mask outliers in the fit? Default True @@ -330,7 +340,7 @@ def clean_subarray(detector, image, mask, npix_iter=512, if np.mean(mask[i1:i1 + di]) > minfrac: cleaner = NSCleanSubarray(image[i1:i1 + di], mask[i1:i1 + di], - exclude_outliers=False) + fc=fc, exclude_outliers=False) models += [cleaner.clean(return_model=True)] else: log.warning("Insufficient reference pixels for NSClean around " @@ -467,8 +477,17 @@ def do_correction(input_model, mask_spectral_regions, n_sigma, save_mask, user_m # Clean a full-frame image cleaned_image = clean_full_frame(detector, image, mask) else: + # BOTS and ALLSLITS exposures should be fitting different + # ranges of 1/f frequencies. If the spectral regions are + # masked in ALLSLITS mode, we cannot fit the higher + # frequencies. + if input_model.meta.subarray.name.upper() == "ALLSLITS" and mask_spectral_regions: + fc = (150, 200, 49943, 49957) + else: + fc = (1061, 1211, 49943, 49957) + # Clean a subarray image - cleaned_image = clean_subarray(detector, image, mask) + cleaned_image = clean_subarray(detector, image, mask, fc=fc) # Check for failure if cleaned_image is None: From e0008ab08c693ffdb3b1921532023668b9f5b7d5 Mon Sep 17 00:00:00 2001 From: Timothy Brandt Date: Tue, 10 Sep 2024 11:17:24 -0400 Subject: [PATCH 346/687] Changed default frequencies with ALLSLITS. --- jwst/nsclean/nsclean.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py index 52ee212d4f..27c6509548 100644 --- a/jwst/nsclean/nsclean.py +++ b/jwst/nsclean/nsclean.py @@ -478,10 +478,9 @@ def do_correction(input_model, mask_spectral_regions, n_sigma, save_mask, user_m cleaned_image = clean_full_frame(detector, image, mask) else: # BOTS and ALLSLITS exposures should be fitting different - # ranges of 1/f frequencies. If the spectral regions are - # masked in ALLSLITS mode, we cannot fit the higher - # frequencies. - if input_model.meta.subarray.name.upper() == "ALLSLITS" and mask_spectral_regions: + # ranges of 1/f frequencies. Be less aggressive with + # fitting higher frequencies in ALLSLITS mode. + if input_model.meta.subarray.name.upper() == "ALLSLITS": fc = (150, 200, 49943, 49957) else: fc = (1061, 1211, 49943, 49957) From ae222ceac1cdec437bdce59248b5d7791b347cba Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 10 Sep 2024 13:07:43 -0400 Subject: [PATCH 347/687] Revert "JP-3729: log version and crds context at end of step/pipeline runs" --- CHANGES.rst | 2 -- jwst/stpipe/core.py | 16 +++++++--------- jwst/stpipe/tests/test_step.py | 26 -------------------------- 3 files changed, 7 insertions(+), 37 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 101a221acb..a921bb2099 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -191,8 +191,6 @@ stpipe added a `query_step_status()` function to use as an alternative to checking `self.skip`. [#8600] -- Log jwst version and crds context at the end of step/pipeline runs. [#8760] - tso_photometry -------------- diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 094b4c1f4c..2a30139190 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -76,23 +76,19 @@ def load_as_level3_asn(self, obj): return asn def finalize_result(self, result, reference_files_used): - crds_context = crds_client.get_context_used('jwst') - - if self.parent is None: - log.info(f"Results used CRDS context: {crds_context}") - log.info(f"Results used jwst version: {__version__}") - if isinstance(result, JwstDataModel): result.meta.calibration_software_revision = __version_commit__ or 'RELEASE' result.meta.calibration_software_version = __version__ - if len(reference_files_used) > 0: for ref_name, filename in reference_files_used: if hasattr(result.meta.ref_file, ref_name): getattr(result.meta.ref_file, ref_name).name = filename result.meta.ref_file.crds.sw_version = crds_client.get_svn_version() - result.meta.ref_file.crds.context_used = crds_context + result.meta.ref_file.crds.context_used = crds_client.get_context_used(result.crds_observatory) + if self.parent is None: + log.info(f"Results used CRDS context: {result.meta.ref_file.crds.context_used}") + def remove_suffix(self, name): return remove_suffix(name) @@ -102,4 +98,6 @@ def remove_suffix(self, name): # be a subclass of JwstStep so that it will pass checks # when constructing a pipeline using JwstStep class methods. class JwstPipeline(Pipeline, JwstStep): - pass + def finalize_result(self, result, reference_files_used): + if isinstance(result, JwstDataModel): + log.info(f"Results used CRDS context: {crds_client.get_context_used(result.crds_observatory)}") diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 250fb14d81..1c1d3a76d2 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -1,4 +1,3 @@ -import logging import os from os.path import ( abspath, @@ -16,10 +15,8 @@ from stdatamodels.jwst import datamodels -from jwst import __version__ as jwst_version from jwst.white_light import WhiteLightStep from jwst.stpipe import Step -from jwst.tests.helpers import LogWatcher from jwst.stpipe.tests.steps import ( EmptyPipeline, MakeListPipeline, MakeListStep, @@ -42,10 +39,6 @@ CRDS_ERROR_STRING = 'PARS-WITHDEFAULTSSTEP: No parameters found' -# used in logging tests below to provide a deterministic crds -# context to look for in the log messages -FAKE_CRDS_CONTEXT = "0000.pmap" - @pytest.fixture(scope='module') def data_path(): @@ -614,22 +607,3 @@ def test_call_with_config(caplog, tmp_cwd): ProperPipeline.call(model, config_file=cfg) assert "newpar1" in caplog.text - - -@pytest.mark.parametrize( - "message", [ - f"Results used CRDS context: {FAKE_CRDS_CONTEXT}", - f"Results used jwst version: {jwst_version}", - ]) -def test_finalize_logging(monkeypatch, message): - """ - Check that the jwst version and crds context are logged - when a step/pipeline is run. - """ - pipeline = EmptyPipeline() - model = datamodels.ImageModel() - monkeypatch.setattr(crds_client, 'get_context_used', lambda observatory: FAKE_CRDS_CONTEXT) - watcher = LogWatcher(message) - monkeypatch.setattr(logging.getLogger("jwst.stpipe.core"), "info", watcher) - pipeline.run(model) - assert watcher.seen From 5e39dbf6128092049ac0c412b19342a361b9aab8 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 10 Sep 2024 14:13:09 -0400 Subject: [PATCH 348/687] Subtract pedestal dark for MIRI MRS when constructing selfcal minimum --- jwst/badpix_selfcal/badpix_selfcal_step.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index a8cde5d888..18f7042284 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -103,7 +103,15 @@ def process(self, input, selfcal_list=None, bkg_list=None): selfcal_list = [input_sci] + selfcal_list selfcal_3d = [] for i, selfcal_model in enumerate(selfcal_list): - selfcal_3d.append(selfcal_model.data) + # If working with MIRI MRS data, subtract any pedestal residual dark + # using the median of count rates from between the channels + if (input_sci.meta.instrument.detector.upper() == 'MIRIFUSHORT'): + selfcal_3d.append(selfcal_model.data - np.nanmedian(selfcal_model.data[50:1024-50, 503:516])) + elif (input_sci.meta.instrument.detector.upper() == 'MIRIFULONG'): + selfcal_3d.append(selfcal_model.data - np.nanmedian(selfcal_model.data[50:1024-50, 474:507])) + else: + selfcal_3d.append(selfcal_model.data) + with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) From 2aa4c185c3161a3cb9090c5211c5b948874be6de Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 10 Sep 2024 14:16:06 -0400 Subject: [PATCH 349/687] Add change log entry --- CHANGES.rst | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 101a221acb..46094da659 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -32,6 +32,12 @@ associations - Restored slit name to level 3 product names for NIRSpec BOTS and background fixed slit targets. [#8699] +badpix_selfcal +-------------- + +- Subtract pedestal dark when constructing min array across selfcal exposures + for MIRI MRS data. [#8771] + cube_build ---------- From 10eb08facbbfd7c4a38c3378b39937d09005bba0 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 10 Sep 2024 14:03:16 -0400 Subject: [PATCH 350/687] report jwst version at end of Step.run --- jwst/stpipe/core.py | 8 ++++++++ jwst/stpipe/tests/test_step.py | 16 ++++++++++++++++ 2 files changed, 24 insertions(+) diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 2a30139190..66103dfef3 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -1,6 +1,7 @@ """ JWST-specific Step and Pipeline base classes. """ +from functools import wraps import logging from stdatamodels.jwst.datamodels import JwstDataModel @@ -93,6 +94,13 @@ def finalize_result(self, result, reference_files_used): def remove_suffix(self, name): return remove_suffix(name) + @wraps(Step.run) + def run(self, *args, **kwargs): + result = super().run(*args, **kwargs) + if not self.parent: + log.info(f"Results used jwst version: {__version__}") + return result + # JwstPipeline needs to inherit from Pipeline, but also # be a subclass of JwstStep so that it will pass checks diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 1c1d3a76d2..735b474a91 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -1,3 +1,4 @@ +import logging import os from os.path import ( abspath, @@ -15,8 +16,10 @@ from stdatamodels.jwst import datamodels +from jwst import __version__ as jwst_version from jwst.white_light import WhiteLightStep from jwst.stpipe import Step +from jwst.tests.helpers import LogWatcher from jwst.stpipe.tests.steps import ( EmptyPipeline, MakeListPipeline, MakeListStep, @@ -607,3 +610,16 @@ def test_call_with_config(caplog, tmp_cwd): ProperPipeline.call(model, config_file=cfg) assert "newpar1" in caplog.text + + +def test_finalize_logging(monkeypatch): + """ + Check that the jwst version and crds context are logged + when a step/pipeline is run. + """ + pipeline = EmptyPipeline() + model = datamodels.ImageModel() + watcher = LogWatcher(f"Results used jwst version: {jwst_version}") + monkeypatch.setattr(logging.getLogger("jwst.stpipe.core"), "info", watcher) + pipeline.run(model) + assert watcher.seen From 8f6814f490ec3016bb51598f027b80f2bc9b2716 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 10 Sep 2024 14:04:21 -0400 Subject: [PATCH 351/687] add changelog --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 7ff7b4d961..e388054df0 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -193,6 +193,8 @@ stpipe added a `query_step_status()` function to use as an alternative to checking `self.skip`. [#8600] +- Log jwst version at end of `Step.run`. [#8769] + tso_photometry -------------- From eb5e69558859d838b0f2a1c61ed790a1fe7a13df Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 10 Sep 2024 16:15:32 -0400 Subject: [PATCH 352/687] Use variable for pedestal region --- jwst/badpix_selfcal/badpix_selfcal_step.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 18f7042284..7d9ad62c9f 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -6,6 +6,10 @@ __all__ = ["BadpixSelfcalStep"] +# Define the regions between MRS channels with which to estimate +# the pedestal dark. Format is (Y1, Y2, X1, X2) +PEDESTAL_INDX_MIRIFULONG = (50, 974, 474, 507) +PEDESTAL_INDX_MIRIFUSHORT = (50, 974, 503, 516) class BadpixSelfcalStep(Step): """ @@ -106,9 +110,13 @@ def process(self, input, selfcal_list=None, bkg_list=None): # If working with MIRI MRS data, subtract any pedestal residual dark # using the median of count rates from between the channels if (input_sci.meta.instrument.detector.upper() == 'MIRIFUSHORT'): - selfcal_3d.append(selfcal_model.data - np.nanmedian(selfcal_model.data[50:1024-50, 503:516])) + selfcal_3d.append(selfcal_model.data - np.nanmedian( + selfcal_model.data[PEDESTAL_INDX_MIRIFUSHORT[0]:PEDESTAL_INDX_MIRIFUSHORT[1], + PEDESTAL_INDX_MIRIFUSHORT[2]:PEDESTAL_INDX_MIRIFUSHORT[3]])) elif (input_sci.meta.instrument.detector.upper() == 'MIRIFULONG'): - selfcal_3d.append(selfcal_model.data - np.nanmedian(selfcal_model.data[50:1024-50, 474:507])) + selfcal_3d.append(selfcal_model.data - np.nanmedian( + selfcal_model.data[PEDESTAL_INDX_MIRIFULONG[0]:PEDESTAL_INDX_MIRIFULONG[1], + PEDESTAL_INDX_MIRIFULONG[2]:PEDESTAL_INDX_MIRIFULONG[3]])) else: selfcal_3d.append(selfcal_model.data) From 8baaab270736449c9929440d43b60e92fa231dda Mon Sep 17 00:00:00 2001 From: David Law Date: Wed, 11 Sep 2024 12:47:02 -0400 Subject: [PATCH 353/687] Add info to the step description --- docs/jwst/badpix_selfcal/description.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/jwst/badpix_selfcal/description.rst b/docs/jwst/badpix_selfcal/description.rst index fe68e21e64..293c7aeeb8 100644 --- a/docs/jwst/badpix_selfcal/description.rst +++ b/docs/jwst/badpix_selfcal/description.rst @@ -38,6 +38,9 @@ the spectral axis. The algorithm proceeds as follows: If no additional exposures are available, the science data itself is passed in without modification, serving as the "background image" for the rest of the procedure, i.e., true self-calibration. +* For MIRI MRS, any residual pedestal in the effective dark current is subtracted from + each exposure using the unilluminated region of the detector between spectral channels + prior to performing the pixelwise minimum combination. * The combined background image is median-filtered, ignoring NaNs, along the spectral axis with a user-specified kernel size. The default kernel size is 15 pixels. * The difference between the original background image and the median-filtered background image From 291b375300e3d19d6bf5ded74cd694dae1b5e188 Mon Sep 17 00:00:00 2001 From: Ken MacDonald <68608562+kmacdonald-stsci@users.noreply.github.com> Date: Thu, 12 Sep 2024 09:17:15 -0400 Subject: [PATCH 354/687] JP-3669: 'OLS' Conditional Read Noise Recalculation for CHARGELOSS (#8697) Co-authored-by: Tyler Pauly --- CHANGES.rst | 6 ++ jwst/ramp_fitting/ramp_fit_step.py | 89 +++++++++++++++--------------- 2 files changed, 51 insertions(+), 44 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index cb8f45114a..3e5d7a4927 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -145,6 +145,12 @@ pipeline - Updated `calwebb_image3` to use `ModelLibrary` instead of `ModelContainer`, added optional `on_disk` parameter to govern whether models in the library should be stored in memory or on disk. [#8683] + +ramp_fitting +------------ + +- Moved the read noise variance recalculation for CHARGELOSS flagging to the C + implementation, when the algorithm is ``OLS_C``. [#8697, spacetelescope/stcal#278] resample -------- diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index baf0e95aa8..1d4291730a 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -453,50 +453,51 @@ def process(self, input): suppress_one_group=self.suppress_one_group) # Create a gdq to modify if there are charge_migrated groups - gdq = input_model_W.groupdq.copy() - - # Locate groups where that are flagged with CHARGELOSS - wh_chargeloss = np.where(np.bitwise_and(gdq.astype(np.uint32), dqflags.group['CHARGELOSS'])) - - if len(wh_chargeloss[0]) > 0: - # Unflag groups flagged as both CHARGELOSS and DO_NOT_USE - gdq[wh_chargeloss] -= (dqflags.group['DO_NOT_USE'] + dqflags.group['CHARGELOSS']) - - # Flag SATURATED groups as DO_NOT_USE for later segment determination - where_sat = np.where(np.bitwise_and(gdq, dqflags.group['SATURATED'])) - gdq[where_sat] = np.bitwise_or(gdq[where_sat], dqflags.group['DO_NOT_USE']) - - # Get group_time for readnoise variance calculation - group_time = input_model.meta.exposure.group_time - - # Using the modified GROUPDQ array, create new readnoise variance arrays - image_var_RN, integ_var_RN, opt_var_RN = \ - compute_RN_variances(gdq, readnoise_2d, gain_2d, group_time) - - # Create new ramp fitting array tuples, by inserting the new - # readnoise variances into copies of the original ramp fitting - # tuples. - image_info_new, integ_info_new = None, None - ch_int, ch_grp, ch_row, ch_col = wh_chargeloss - if image_info is not None and image_var_RN is not None: - rnoise = image_info[3] - rnoise[ch_row, ch_col] = image_var_RN[ch_row, ch_col] - image_info_new = (image_info[0], image_info[1], image_info[2], rnoise, image_info[4]) - - if integ_info is not None and integ_var_RN is not None: - rnoise = integ_info[3] - rnoise[ch_int, ch_row, ch_col] = integ_var_RN[ch_int, ch_row, ch_col] - integ_info_new = (integ_info[0], integ_info[1], integ_info[2], rnoise, integ_info[4]) - - image_info = image_info_new - integ_info = integ_info_new - - opt_info_new = None - if opt_info is not None and opt_var_RN is not None: - opt_info_new = (opt_info[0], opt_info[1], opt_info[2], opt_var_RN, - opt_info[4], opt_info[5], opt_info[6], opt_info[7], opt_info[8]) - - opt_info = opt_info_new + if self.algorithm == "OLS": + gdq = input_model_W.groupdq.copy() + + # Locate groups where that are flagged with CHARGELOSS + wh_chargeloss = np.where(np.bitwise_and(gdq.astype(np.uint32), dqflags.group['CHARGELOSS'])) + + if len(wh_chargeloss[0]) > 0: + # Unflag groups flagged as both CHARGELOSS and DO_NOT_USE + gdq[wh_chargeloss] -= (dqflags.group['DO_NOT_USE'] + dqflags.group['CHARGELOSS']) + + # Flag SATURATED groups as DO_NOT_USE for later segment determination + where_sat = np.where(np.bitwise_and(gdq, dqflags.group['SATURATED'])) + gdq[where_sat] = np.bitwise_or(gdq[where_sat], dqflags.group['DO_NOT_USE']) + + # Get group_time for readnoise variance calculation + group_time = input_model.meta.exposure.group_time + + # Using the modified GROUPDQ array, create new readnoise variance arrays + image_var_RN, integ_var_RN, opt_var_RN = \ + compute_RN_variances(gdq, readnoise_2d, gain_2d, group_time) + + # Create new ramp fitting array tuples, by inserting the new + # readnoise variances into copies of the original ramp fitting + # tuples. + image_info_new, integ_info_new = None, None + ch_int, ch_grp, ch_row, ch_col = wh_chargeloss + if image_info is not None and image_var_RN is not None: + rnoise = image_info[3] + rnoise[ch_row, ch_col] = image_var_RN[ch_row, ch_col] + image_info_new = (image_info[0], image_info[1], image_info[2], rnoise, image_info[4]) + + if integ_info is not None and integ_var_RN is not None: + rnoise = integ_info[3] + rnoise[ch_int, ch_row, ch_col] = integ_var_RN[ch_int, ch_row, ch_col] + integ_info_new = (integ_info[0], integ_info[1], integ_info[2], rnoise, integ_info[4]) + + image_info = image_info_new + integ_info = integ_info_new + + opt_info_new = None + if opt_info is not None and opt_var_RN is not None: + opt_info_new = (opt_info[0], opt_info[1], opt_info[2], opt_var_RN, + opt_info[4], opt_info[5], opt_info[6], opt_info[7], opt_info[8]) + + opt_info = opt_info_new # Save the OLS optional fit product, if it exists. if opt_info is not None: From 50c689eb9038e1640ed9aa7b4f76c2b6e59a8a32 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 7 Aug 2024 13:38:12 -0400 Subject: [PATCH 355/687] remove MultilineLogger --- jwst/associations/lib/log_config.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/jwst/associations/lib/log_config.py b/jwst/associations/lib/log_config.py index a3c1dcdb27..ec3047ad3c 100644 --- a/jwst/associations/lib/log_config.py +++ b/jwst/associations/lib/log_config.py @@ -5,8 +5,6 @@ from logging.config import dictConfig from collections import defaultdict -from functools import partialmethod - __all__ = ['log_config'] @@ -147,28 +145,6 @@ def format(self, record): } -class MultilineLogger(logging.getLoggerClass()): - """Split multilines so that each line is logged separately""" - - def __init__(self, *args, **kwargs): - super(MultilineLogger, self).__init__(*args, **kwargs) - - def log(self, level, msg, *args, **kwargs): - if self.isEnabledFor(level): - for line in msg.split('\n'): - self._log(level, line, args, **kwargs) - - debug = partialmethod(log, logging.DEBUG) - info = partialmethod(log, logging.INFO) - warning = partialmethod(log, logging.WARNING) - error = partialmethod(log, logging.ERROR) - critical = partialmethod(log, logging.CRITICAL) - fatal = critical - - -logging.setLoggerClass(MultilineLogger) - - def log_config(name=None, user_name=None, logger_config=None, From 9f40eb812e9dedab3926c19be6d8ed8e3d15805d Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 7 Aug 2024 13:52:28 -0400 Subject: [PATCH 356/687] add changelog --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 3e5d7a4927..0d52ede1f2 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,6 +1,11 @@ 1.15.2 (unreleased) =================== +associations +------------ + +- remove ``MultilineLogger`` and no longer set it as the default logger. [#8694] + align_refs ---------- From eb6a3e0f1289700325c0b4be301926d3b8229f3c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 12 Sep 2024 15:22:54 -0400 Subject: [PATCH 357/687] Update change notes --- CHANGES.rst | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 0d52ede1f2..b279ddfa96 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,11 +1,6 @@ 1.15.2 (unreleased) =================== -associations ------------- - -- remove ``MultilineLogger`` and no longer set it as the default logger. [#8694] - align_refs ---------- @@ -39,6 +34,8 @@ associations - Update warning message about use of paths in associations. [#8752] +- Remove ``MultilineLogger`` and no longer set it as the default logger. [#8781] + badpix_selfcal -------------- From 3190272bedbdcff66a9951dbccc7fd9ee71d67ec Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 12 Sep 2024 13:13:01 -0700 Subject: [PATCH 358/687] JP-3743: Make outlier detection respect weights for in-memory models (#8777) --- CHANGES.rst | 3 ++ jwst/outlier_detection/imaging.py | 3 +- .../tests/test_outlier_detection.py | 12 +++++- jwst/outlier_detection/utils.py | 38 +++++++++++-------- jwst/regtest/test_nircam_mtimage.py | 5 ++- 5 files changed, 40 insertions(+), 21 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3e5d7a4927..a17ab3914b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -520,6 +520,9 @@ outlier_detection - Re-enabled saving of blot models when `save_intermediate_results` is True. [#8758] +- Fixed a bug that caused different results from the median calculation when the + `in_memory` parameter was set to `True` vs `False`. [#8777] + pathloss -------- diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 9f1aabf597..5678b45802 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -100,14 +100,13 @@ def detect_outliers( input_models.shelve(model, modify=True) # Perform median combination on set of drizzled mosaics - median_data = create_median(drizzled_models, maskpt, on_disk=not in_memory) + median_data = create_median(drizzled_models, maskpt) if save_intermediate_results: # make a median model with drizzled_models: example_model = drizzled_models.borrow(0) drizzled_models.shelve(example_model, modify=False) - #with datamodels.open(example_model) as dm0: median_model = datamodels.ImageModel(median_data) median_model.update(example_model) median_model.meta.wcs = median_wcs diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index ebd732437b..ec469f4fb4 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -600,8 +600,18 @@ def test_create_median(three_sci_as_asn, tmp_cwd): lib_on_disk = ModelLibrary(three_sci_as_asn, on_disk=True) lib_in_memory = ModelLibrary(three_sci_as_asn, on_disk=False) + # make this test meaningful w.r.t. handling of weights + with (lib_on_disk, lib_in_memory): + for lib in [lib_on_disk, lib_in_memory]: + for model in lib: + model.wht = np.ones_like(model.data) + model.wht[4,9] = 0.5 + lib.shelve(model, modify=True) + median_on_disk = create_median(lib_on_disk, 0.7) median_in_memory = create_median(lib_in_memory, 0.7) + assert np.isnan(median_in_memory[4,9]) + # Make sure the median library is the same for on-disk and in-memory - assert np.allclose(median_on_disk, median_in_memory) + assert np.allclose(median_on_disk, median_in_memory, equal_nan=True) \ No newline at end of file diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 4abdfbad1b..2bc6b2ff75 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -32,7 +32,7 @@ def create_cube_median(cube_model, maskpt): return median -def create_median(resampled_models, maskpt, on_disk=True, buffer_size=10.0): +def create_median(resampled_models, maskpt, buffer_size=10.0): """Create a median image from the singly resampled images. Parameters @@ -43,36 +43,42 @@ def create_median(resampled_models, maskpt, on_disk=True, buffer_size=10.0): maskpt : float The weight threshold for masking out low weight pixels. - on_disk : bool - If True, the input models are on disk and will be read in chunks. - buffer_size : float The size of chunk in MB, per input model, that will be read into memory. - This parameter has no effect if on_disk is False. + This parameter has no effect if the input library has its on_disk attribute + set to False. Returns ------- median_image : ndarray The median image. """ + on_disk = resampled_models._on_disk + # Compute the weight threshold for each input model weight_thresholds = [] + model_list = [] with resampled_models: for resampled in resampled_models: weight_threshold = compute_weight_threshold(resampled.wht, maskpt) - weight_thresholds.append(weight_threshold) + if not on_disk: + # handle weights right away for in-memory case + data = resampled.data.copy() + data[resampled.wht < weight_threshold] = np.nan + model_list.append(data) + del data + else: + weight_thresholds.append(weight_threshold) resampled_models.shelve(resampled, modify=False) - - # compute median over all models + del resampled + + # easier case: all models in library can be loaded into memory at once if not on_disk: - # easier case: all models in library can be loaded into memory at once - model_list = [] - with resampled_models: - for resampled in resampled_models: - model_list.append(resampled.data) - resampled_models.shelve(resampled, modify=False) - del resampled - return np.nanmedian(np.array(model_list), axis=0) + with warnings.catch_warnings(): + warnings.filterwarnings(action="ignore", + message="All-NaN slice encountered", + category=RuntimeWarning) + return np.nanmedian(np.array(model_list), axis=0) else: # set up buffered access to all input models with resampled_models: diff --git a/jwst/regtest/test_nircam_mtimage.py b/jwst/regtest/test_nircam_mtimage.py index 961d34914d..21b5013042 100644 --- a/jwst/regtest/test_nircam_mtimage.py +++ b/jwst/regtest/test_nircam_mtimage.py @@ -8,11 +8,12 @@ @pytest.mark.bigdata -def test_nircam_image_moving_target_i2d(rtdata, fitsdiff_default_kwargs): +@pytest.mark.parametrize("in_memory", [True, False]) +def test_nircam_image_moving_target_i2d(rtdata, fitsdiff_default_kwargs, in_memory): """Test resampled i2d of moving target exposures for NIRCam imaging""" rtdata.get_asn("nircam/image/mt_asn.json") rtdata.output = "mt_assoc_i2d.fits" - args = ["calwebb_image3", rtdata.input] + args = ["calwebb_image3", rtdata.input, "--in_memory=" + str(in_memory)] Step.from_cmdline(args) rtdata.get_truth("truth/test_nircam_mtimage/mt_assoc_i2d.fits") From 041ecc86806639b345cd94a0ff06f13f6a09a039 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 10:14:21 -0400 Subject: [PATCH 359/687] remove unused textutil --- jwst/model_blender/textutil.py | 72 ---------------------------------- 1 file changed, 72 deletions(-) delete mode 100644 jwst/model_blender/textutil.py diff --git a/jwst/model_blender/textutil.py b/jwst/model_blender/textutil.py deleted file mode 100644 index b5b14a541d..0000000000 --- a/jwst/model_blender/textutil.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Text output-related utilities.""" - - -import textwrap - - -def wrap(text, width, *args, **kwargs): - """ - Like :func:`textwrap.wrap` but preserves existing newlines which - :func:`textwrap.wrap` does not otherwise handle well. - - See Also - -------- - :func:`textwrap.wrap` - """ - - return sum([textwrap.wrap(line, width, *args, **kwargs) - if line else [''] for line in text.splitlines()], []) - - -def textbox(text, width=78, boxchar='#', indent=0): - """ - Outputs line-wrapped text wrapped in a box drawn with a repeated (usually - ASCII) character. - - For example: - - >>> print(textbox('Text to wrap', width=16)) - ################ - # # - # Text to wrap # - # # - ################ - - Parameters - ---------- - text : string - The text to wrap - - width : int - The width of the entire box, including the perimeter and - the indentation space. Because the - wrapped text is padded with an additional column of whitespace on each - side, the minimum width is 5--any width less than that is - is automatically increased to 5 (default: 78) - - boxchar : string - (No pun intended.) The character to draw the box with. May also - be a string of multiple characters (default: '#') - - indent : int - Amount of space by which the box should be indented. (default: 0) - - """ - - min_width = len(boxchar) * 2 + 3 - width = max(width - indent, min_width) - indentspace = indent * ' ' - - wrap_width = width - min_width + 1 - - q, r = divmod(width, len(boxchar)) - # The top/bottom border - top_border = indentspace + boxchar * q + boxchar[:r] - top_padding = indentspace + boxchar + ' ' * (width - len(boxchar) * 2) + boxchar - - lines = ['%s%s %s %s' % (indentspace, boxchar, line.ljust(wrap_width), - boxchar) - for line in wrap(text, wrap_width)] - top = [top_border, top_padding] - bottom = [top_padding, top_border] - return '\n'.join(top + lines + bottom) From ae2e861e0ac350c0c34941d9fdb4e7fde35df627 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 12:45:31 -0400 Subject: [PATCH 360/687] remove unused blend rules --- jwst/model_blender/blendrules.py | 62 ++------------------------------ 1 file changed, 3 insertions(+), 59 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 32bfd570af..353b3aac80 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -20,10 +20,10 @@ # with this code __rules_version__ = 2.1 -__all__ = ['find_keywords_in_section', 'first', 'float_one', 'int_one', +__all__ = ['find_keywords_in_section', 'first', 'interpret_attr_line', 'interpret_entry', 'last', 'maxdate', 'maxdatetime', 'maxtime', 'mindate', 'mindatetime', 'mintime', - 'multi', 'multi1', 'zero', 'KeywordRules', 'KwRule'] + 'multi', 'KeywordRules', 'KwRule'] # Custom blending functions @@ -42,39 +42,6 @@ def multi(vals): return "MULTIPLE" -def multi1(vals): - """ - This will either return the common value from a list of identical values - or the single character '?' - """ - uniq_vals = list(set(vals)) - num_vals = len(uniq_vals) - if num_vals == 0: - return None - if num_vals == 1: - return uniq_vals[0] - if num_vals > 1: - return "?" - - -def float_one(vals): - """ Return a constant floating point value of 1.0 - """ - return 1.0 - - -def int_one(vals): - """ Return an integer value of 1 - """ - return int(1) - - -def zero(vals): - """ Return a value of 0 - """ - return 0 - - def first(items): """ Return first item from list of values""" if len(items): @@ -133,16 +100,11 @@ def _isotime(time_str): # translation dictionary for function entries from rules files blender_funcs = {'first': first, 'last': last, - 'float_one': float_one, - 'int_one': int_one, - 'zero': zero, 'multi': multi, - 'multi?': multi1, 'mean': np.mean, 'sum': np.sum, 'max': np.max, 'min': np.min, - 'stddev': np.std, 'mintime': mintime, 'maxtime': maxtime, 'mindate': mindate, @@ -159,11 +121,8 @@ def __init__(self, model): instrument image header. """ self.instrument = model.meta.instrument.name.lower() - self.new_header = None - self.rules_version = __rules_version__ - self.rules_file = model._schema - self.rule_specs = _build_schema_rules_dict(self.rules_file) + self.rule_specs = _build_schema_rules_dict(model._schema) self.rule_objects = [] self.rules = [] @@ -289,21 +248,6 @@ def apply(self, models, tabhdu=False): new_table = None return new_model, new_table - def add_rules_kws(self, hdr): - """ Update metadata with - WARNING - ------- - Needs to be modified to work with metadata. - - Update PRIMARY header with HISTORY cards that report the exact - rules used to create this header. Only non-comment lines from the - rules file will be reported. - """ - hdr['meta.rules_version'] = (__rules_version__, - 'Version ID for header kw rules file') - hdr['meta.blender_version'] = (__version__, - 'Version of blendheader software used') - def index_of(self, kw): """ Reports the index of the specified kw.""" indx = [] From 2c0bca1ea177bac13cb143c7a6410e28c9391483 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 12:47:33 -0400 Subject: [PATCH 361/687] remove unused error_type error_value --- jwst/model_blender/blender.py | 35 ++------------------------------ jwst/model_blender/blendrules.py | 2 +- 2 files changed, 3 insertions(+), 34 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index bb4afe4870..d1e8a8e934 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -40,8 +40,7 @@ class _KeywordMapping: entry. """ - def __init__(self, src_kwd, dst_name, agg_func=None, error_type="ignore", - error_value=np.nan): + def __init__(self, src_kwd, dst_name, agg_func=None): if not isinstance(src_kwd, str): raise TypeError( "The source keyword name must be a string") @@ -65,15 +64,9 @@ def __init__(self, src_kwd, dst_name, agg_func=None, error_type="ignore", "None or a sequence of callables") self.agg_func_is_sequence = False - if error_type not in ('ignore', 'raise', 'constant'): - raise ValueError( - "The error type must be either 'ignore', 'raise' or 'constant'") - self.src_kwd = src_kwd self.dst_name = dst_name self.agg_func = agg_func - self.error_type = error_type - self.error_value = error_value def metablender(input_models, spec): @@ -94,7 +87,7 @@ def metablender(input_models, spec): aggregated and how. Each element in the list should be a sequence with 2 to 5 elements of the form: - (*src_keyword*, *dst_name*, *function*, *error_type*, *error_value*) + (*src_keyword*, *dst_name*, *function*) - *src_keyword* is the keyword to pull values from. It is case-insensitive. @@ -139,24 +132,6 @@ def metablender(input_models, spec): minimum and maximum values, use the following as *function*: ``(numpy.min, numpy.max)``. - - *error_type* (optional) defines how missing or syntax-errored - values are handled. It may be one of the following: - - - 'ignore': missing or unparsable values are ignored. They - are not included in the list of values passed to the - aggregating function. In the result *table*, missing values - are masked out. - - - 'raise': missing or unparsable values raise a `ValueError` - exception. - - - 'constant': missing or unparsable values are replaced with a - constant, given by the *error_value* field. - - - *error_value* (optional) is the constant value to be used for - missing or unparsable values when *error_type* is set to - 'constant'. When not provided, it defaults to `NaN`. - **Returns:** A 2-tuple of the form (*aggregate_dict*, *table*) where: @@ -187,12 +162,6 @@ def metablender(input_models, spec): for i, mapping in enumerate(mappings): if mapping.src_kwd in header: value = model[mapping.src_kwd] - elif mapping.error_type == 'raise': - raise ValueError( - "%s is missing keyword '%s'" % - (filename, mapping.src_kwd)) - elif mapping.error_type == 'constant': - value = mapping.error_value else: value = None diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 353b3aac80..8c491867da 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -441,7 +441,7 @@ def interpret_attr_line(attr, line_spec): lrule = None # build separate rule for each kw for kw1, kw2 in zip(kws, kws2): - new_rule = (kw1, kw2, lrule, "ignore") + new_rule = (kw1, kw2, lrule) if new_rule not in rules: rules.append(new_rule) else: From 58ba93cde81e98bd79f673b1b02e914f55f0d95c Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 12:48:16 -0400 Subject: [PATCH 362/687] remove __rules_version__ --- jwst/model_blender/blendrules.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 8c491867da..c505966121 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -14,11 +14,6 @@ from jwst import __version__ from . import blender -# Version of rules file format supported by this version of the code -# All changes should be backwards compatible to older rules versions -# so any rules file with Version >= __rules_version__ should work -# with this code -__rules_version__ = 2.1 __all__ = ['find_keywords_in_section', 'first', 'interpret_attr_line', 'interpret_entry', 'last', 'maxdate', From f3fabf257039e9b87b845b7d283a4c601dedf8e2 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 12:52:14 -0400 Subject: [PATCH 363/687] remove unused agg_func_is_sequence support --- jwst/model_blender/blender.py | 25 +++++-------------------- 1 file changed, 5 insertions(+), 20 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index d1e8a8e934..89886c6500 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -50,19 +50,10 @@ def __init__(self, src_kwd, dst_name, agg_func=None): "The destination name must be a string") if agg_func is not None: - try: - for i in agg_func: - if not hasattr(i, '__call__'): - raise TypeError( - "The aggregating function must be a callable " + - "object, None or a sequence of callables") - self.agg_func_is_sequence = True - except TypeError: - if not hasattr(agg_func, '__call__'): - raise TypeError( - "The aggregating function must be a callable object, " - "None or a sequence of callables") - self.agg_func_is_sequence = False + if not hasattr(agg_func, '__call__'): + raise TypeError( + "The aggregating function must be a callable object, " + "None or a sequence of callables") self.src_kwd = src_kwd self.dst_name = dst_name @@ -183,13 +174,7 @@ def metablender(input_models, spec): result = None continue if mapping.agg_func is not None: - if mapping.agg_func_is_sequence: - result = [] - for func in mapping.agg_func: - result.append(func(data[i])) - result = tuple(result) - else: - result = mapping.agg_func(data[i]) + result = mapping.agg_func(data[i]) if result is not None: results[mapping.dst_name] = result From c436cf54599fc9665d282aa32fda8873b4226e7f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:08:43 -0400 Subject: [PATCH 364/687] remove unnecessary date and time aggregation functions --- jwst/model_blender/blendrules.py | 70 ++++++++------------------------ 1 file changed, 16 insertions(+), 54 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index c505966121..ff500f11ed 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -5,20 +5,20 @@ from collections import OrderedDict import numpy as np -from datetime import time from astropy.io import fits -from astropy.time import Time from stdatamodels import schema as dm_schema from stdatamodels import properties -from jwst import __version__ from . import blender -__all__ = ['find_keywords_in_section', 'first', - 'interpret_attr_line', 'interpret_entry', 'last', 'maxdate', - 'maxdatetime', 'maxtime', 'mindate', 'mindatetime', 'mintime', - 'multi', 'KeywordRules', 'KwRule'] +__all__ = [ + 'find_keywords_in_section', + 'interpret_attr_line', + 'interpret_entry', + 'KeywordRules', + 'KwRule', +] # Custom blending functions @@ -51,47 +51,6 @@ def last(items): return None -def mindate(items): - """Return the minimum date from a list of date strings in yyyy-mm-dd format.""" - time_list = Time(items, format="iso", in_subfmt="date", out_subfmt="date") - return str(time_list.min()) - - -def maxdate(items): - """Return the maximum date from a list of date strings in yyyy-mm-dd format.""" - time_list = Time(items, format="iso", in_subfmt="date", out_subfmt="date") - return str(time_list.max()) - - -def mindatetime(items): - """Return the minimum datetime from a list of datetime strings in ISO-8601 format.""" - time_list = Time(items, format="isot") - return str(time_list.min()) - - -def maxdatetime(items): - """Return the maximum datetime from a list of datetime strings in ISO-8601 format.""" - time_list = Time(items, format="isot") - return str(time_list.max()) - - -def mintime(items): - times = [_isotime(time_str) for time_str in items] - return min(times).isoformat() - - -def maxtime(items): - times = [_isotime(time_str) for time_str in items] - return max(times).isoformat() - - -def _isotime(time_str): - hms = [float(i) for i in time_str.split(':')] - sec_ms = hms[2] - int(hms[2]) - isotime = time(int(hms[0]), int(hms[1]), int(hms[2]), int(sec_ms * 1000000)) - return isotime - - # translation dictionary for function entries from rules files blender_funcs = {'first': first, 'last': last, @@ -100,12 +59,15 @@ def _isotime(time_str): 'sum': np.sum, 'max': np.max, 'min': np.min, - 'mintime': mintime, - 'maxtime': maxtime, - 'mindate': mindate, - 'maxdate': maxdate, - 'mindatetime': mindatetime, - 'maxdatetime': maxdatetime} + # retained date/time names for backwards compatibility + # as these all assume ISO8601 format the lexical and + # chronological sorting match + 'mintime': min, + 'maxtime': max, + 'mindate': min, + 'maxdate': max, + 'mindatetime': min, + 'maxdatetime': max} # Classes for managing keyword rules From 5dc769788d5e800e461d3b6ca75b085c8a2cc83c Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:13:05 -0400 Subject: [PATCH 365/687] remove unused find_keywords_in_section and tabhdu --- jwst/model_blender/blendrules.py | 42 ++------------------------------ 1 file changed, 2 insertions(+), 40 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index ff500f11ed..338915ea80 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -5,7 +5,6 @@ from collections import OrderedDict import numpy as np -from astropy.io import fits from stdatamodels import schema as dm_schema from stdatamodels import properties @@ -13,7 +12,6 @@ __all__ = [ - 'find_keywords_in_section', 'interpret_attr_line', 'interpret_entry', 'KeywordRules', @@ -161,7 +159,7 @@ def merge(self, kwrules): # extend self.rules with additional rules self.rules.extend(k) - def apply(self, models, tabhdu=False): + def apply(self, models): """ For a full list of metadata objects, apply the specified rules to generate a dictionary of new values and a table using blender. @@ -196,11 +194,7 @@ def apply(self, models, tabhdu=False): # Create summary table if len(tabcols) > 0: - if tabhdu: - new_table = fits.BinTableHDU.from_columns(fbtab) - new_table.header['EXTNAME'] = 'HDRTAB' - else: - new_table = fbtab + new_table = fbtab else: new_table = None return new_model, new_table @@ -408,35 +402,3 @@ def interpret_attr_line(attr, line_spec): rules.append(new_rule) return rules - - -def find_keywords_in_section(hdr, title): - """Return a list of keyword names. - - The list will be derived from the section - with the specified section title identified in the hdr. - """ - # Identify card indices of start and end of specified section - sect_start = None - sect_end = None - for i, kw in enumerate(hdr.cards): - if sect_start is None: - if title in str(hdr[i]): - sect_start = i - else: - if '/' in str(hdr[i]) and hdr[i] not in ['N/A', ' ', '']: - sect_end = i - break - if sect_end is None: - sect_end = len(hdr) - if sect_start is None: - return None - - # Now, extract the keyword names from this section - # section_keys = hdr.ascard[sect_start+1:sect_end-1].keys() - section_keys = list(hdr[sect_start + 1:sect_end - 1].keys()) - # remove any blank keywords - while section_keys.count('') > 0: - section_keys.remove('') - - return section_keys From 7fec0ff3bb91a06028a2e2246ee46d54eda64cca Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:14:05 -0400 Subject: [PATCH 366/687] remove unused index_of --- jwst/model_blender/blendrules.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 338915ea80..61f7cd57bc 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -199,14 +199,6 @@ def apply(self, models): new_table = None return new_model, new_table - def index_of(self, kw): - """ Reports the index of the specified kw.""" - indx = [] - for r, i in zip(self.rules, list(range(len(self.rules)))): - if r[0] == kw: - indx.append(i) - return indx - class KwRule(): """ From e6b494ae2537fb9ddd7e7bf457bb82191079d65f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:19:04 -0400 Subject: [PATCH 367/687] remove unreachable conditional --- jwst/model_blender/blendrules.py | 22 +++------------------- 1 file changed, 3 insertions(+), 19 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 61f7cd57bc..5118532d0d 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -6,7 +6,6 @@ import numpy as np from stdatamodels import schema as dm_schema -from stdatamodels import properties from . import blender @@ -325,6 +324,7 @@ def interpret_entry(line, hdr): 'output' is assumed to be the same as attribute_name if not present """ + breakpoint() # Interpret raw input line attr = list(line.keys())[0] line_spec = line[attr] @@ -337,24 +337,8 @@ def interpret_entry(line, hdr): section_name = None # Parse the line - if isinstance(hdr[attr], properties.ObjectNode): - section_name = attr - # Datamodel sections are just parent Nodes for each attribute - keys = hdr[section_name].instance.keys() - kws = ['{}.{}'.format(section_name, k) for k in keys] - # - if kws is not None: - for kw1 in kws: - if attr_column: - # Interpret each attribute as a new column in Table - rules.append((kw1, kw1)) - else: - # Interpret each attribute in Node with same rule - attr_rules = interpret_attr_line(kw1, line_spec) - rules.extend(attr_rules) - else: - attr_rules = interpret_attr_line(attr, line_spec) - rules.extend(attr_rules) + attr_rules = interpret_attr_line(attr, line_spec) + rules.extend(attr_rules) return rules, section_name From 5003ead85c70dfc568db14d0edf38820f904f52f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:21:48 -0400 Subject: [PATCH 368/687] remove unused output rule attribute --- jwst/model_blender/blendrules.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 5118532d0d..6e756f14b8 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -212,7 +212,7 @@ class KwRule(): Example:: Interpreting rule from - {'meta.attribute': { 'rule': 'first', 'output': 'meta.attribute'}} + {'meta.attribute': { 'rule': 'first'}} --or-- {'meta.attribute': 'meta.attribute'} # Table column specification @@ -228,7 +228,7 @@ def __init__(self, line): ========== line : dict Line should be dict with attribute name as the key, and - a dict as the value specifying 'rule' and (optionally)'output'. + a dict as the value specifying 'rule'. """ self.rule_spec = line # dict read in from rules file self.rules = [] @@ -317,14 +317,10 @@ def interpret_entry(line, hdr): Notes ----- The entry should always be a dict with format: - {attribute_name : {'rule':'some_rule', 'output':''}} + {attribute_name : {'rule':'some_rule'}} -- or (for table column specification)-- {attribute_name: attribute_name} - where - 'output' is assumed to be the same as attribute_name if not present - """ - breakpoint() # Interpret raw input line attr = list(line.keys())[0] line_spec = line[attr] @@ -349,10 +345,7 @@ def interpret_attr_line(attr, line_spec): kws = [attr] if isinstance(line_spec, dict): - if 'output' in line_spec: - kws2 = [line_spec['output']] - else: - kws2 = kws + kws2 = kws else: kws2 = [line_spec] From 7d33f3554ed995674e7f07ce4a1dee0da1ac6f3f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:24:30 -0400 Subject: [PATCH 369/687] remove unused section_name --- jwst/model_blender/blendrules.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 6e756f14b8..2a470aed7c 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -80,7 +80,6 @@ def __init__(self, model): self.rule_objects = [] self.rules = [] - self.section_names = [] def interpret_rules(self, hdrs): """ Convert specifications for rules from rules file @@ -217,8 +216,6 @@ class KwRule(): {'meta.attribute': 'meta.attribute'} # Table column specification into rule [('meta.attribute', 'meta.attribute', , 'ignore')] - and sname None - """ def __init__(self, line): @@ -233,7 +230,6 @@ def __init__(self, line): self.rule_spec = line # dict read in from rules file self.rules = [] self.delete_kws = [] - self.section_name = [] def interpret(self, hdr): """Use metadata to interpret rule.""" @@ -241,11 +237,7 @@ def interpret(self, hdr): # If self.rules has already been defined for this rule, do not try # to interpret it any further with additional headers return - irules, sname = interpret_entry(self.rule_spec, hdr) - - # keep track of any section name identified for this rule - if sname: - self.section_name.append(sname) + irules = interpret_entry(self.rule_spec, hdr) # Now, interpret rule based on presence of kw in hdr if irules: @@ -330,13 +322,12 @@ def interpret_entry(line, hdr): # Initialize output values rules = [] - section_name = None # Parse the line attr_rules = interpret_attr_line(attr, line_spec) rules.extend(attr_rules) - return rules, section_name + return rules def interpret_attr_line(attr, line_spec): From afbe3d91970081ee7117a219de5a617eb82036b5 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:27:10 -0400 Subject: [PATCH 370/687] remove unused cat_headers --- jwst/model_blender/blendmeta.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 9d68886140..72824a77d9 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -244,18 +244,6 @@ def get_blended_metadata(input_models, verbose=False): return new_meta, new_table -def cat_headers(hdr1, hdr2): - """ - Create new `astropy.io.fits.Header` object from - concatenating 2 input Headers - """ - nhdr = hdr1.copy() - for c in hdr2.cards: - nhdr.append(c) - - return fits.Header(nhdr) - - def extract_filenames_from_product(product): """ Returns the list of filenames with extensions of input observations that From 0b278ff20e4576c297cf0c17175eb45537ad9814 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:34:18 -0400 Subject: [PATCH 371/687] make inputs required drop unused verbose --- docs/jwst/model_blender/main.rst | 2 +- jwst/cube_build/ifu_cube.py | 2 +- jwst/model_blender/blendmeta.py | 35 +++++--------------------------- jwst/pipeline/calwebb_coron3.py | 2 +- jwst/resample/resample.py | 2 +- 5 files changed, 9 insertions(+), 34 deletions(-) diff --git a/docs/jwst/model_blender/main.rst b/docs/jwst/model_blender/main.rst index fc970dba30..8238993eb0 100644 --- a/docs/jwst/model_blender/main.rst +++ b/docs/jwst/model_blender/main.rst @@ -51,7 +51,7 @@ The simplest way to run model blender only requires calling a single interface: .. code-block:: python from jwst.model_blender import blendmeta - blendmeta.blendmodels(product, inputs=input_list) + blendmeta.blendmodels(product, input_list) where diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 3cd627338d..83c6d24e5f 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -2364,7 +2364,7 @@ def blend_output_metadata(self, IFUCube): """Create new output metadata based on blending all input metadata.""" # Run fitsblender on output product output_file = IFUCube.meta.filename - blendmeta.blendmodels(IFUCube, inputs=self.input_models_this_cube, + blendmeta.blendmodels(IFUCube, self.input_models_this_cube, output=output_file) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 72824a77d9..b7af9356fa 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -18,14 +18,12 @@ from .blendrules import KeywordRules -EMPTY_LIST = [None, '', ' ', 'INDEF', 'None'] - __doctest_skip__ = ['blendmodels'] # Primary functional interface for the code -def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): +def blendmodels(product, inputs, output=None, ignore=None): """ Run main interface for blending metadata from multiple models. @@ -33,9 +31,6 @@ def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): new metadata instance with a table that contains attribute values from all input datamodels. - The product will be used to determine the names of the input models, - should no filenames be provided in the 'inputs' parameter. - The product will be updated 'in-place' with the new metadata attributes and FITS BinTableHDU table. The blended FITS table, with extname=HDRTAB, has 1 column for each metadata attribute recorded from the input models, @@ -59,12 +54,8 @@ def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): product : str Name of combined product with metadata that needs updating. This can be specified as a single filename. - When no value for `inputs` has been provided, this file - will also evaluate `meta.asn` to determine the names of the input - datamodels whose metadata need to be blended to create - the new combined metadata. - inputs : list, optional + inputs : list This can be either a list of filenames or a list of DataModels objects. If provided, the filenames provided in this list will be used to get the metadata which will be blended into the final output metadata. @@ -77,9 +68,6 @@ def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): A list of string the meta attribute names which, if provided, will show which attributes should not be blended. - verbose : bool, optional [Default: False] - Print out additional messages during processing when specified. - Example ------- This example shows how to blend the metadata from a set of DataModels @@ -92,22 +80,9 @@ def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): >>> asnfile = "jw99999-a3001_20170327t121212_coron3_001_asn.json" >>> data = datamodels.open(asnfile) >>> input_models = [data[3], data[4]] # we know the last datasets are SCIENCE - >>> blendmodels(data.meta.asn_table.products[0].name, inputs=input_models) + >>> blendmodels(data.meta.asn_table.products[0].name, input_models) """ - if inputs in EMPTY_LIST: - input_filenames = extract_filenames_from_product(product) - inputs = [datamodels.open(i) for i in inputs] # return datamodels - else: - if isinstance(inputs, datamodels.JwstDataModel): - input_filenames = [i.meta.filename for i in inputs] - else: - input_filenames = inputs # assume list of filenames as input - if verbose: - print('Creating blended metadata from: ') - for i in input_filenames: - print('\t{}'.format(i)) - - newmeta, newtab = get_blended_metadata(inputs, verbose=verbose) + newmeta, newtab = get_blended_metadata(inputs) # open product to update metadata from input models used to create product if isinstance(product, str): @@ -159,7 +134,7 @@ def blendmodels(product, inputs=None, output=None, ignore=None, verbose=False): del newmeta, newtab -def get_blended_metadata(input_models, verbose=False): +def get_blended_metadata(input_models): """ Return a blended metadata instance and table based on the input datamodels. This will serve as the primary interface for blending datamodels. diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index 04673418a9..d40c2421ad 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -213,7 +213,7 @@ def process(self, user_input): completed = 'SKIPPED' if completed == 'COMPLETE': self.log.debug(f'Blending metadata for {result}') - blendmeta.blendmodels(result, inputs=targ_files) + blendmeta.blendmodels(result, targ_files) try: result.meta.asn.pool_name = input_models.meta.asn_table.asn_pool diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 97b2e3d766..b5f1254850 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -214,7 +214,7 @@ def blend_output_metadata(self, output_model, input_models): log.info('Blending metadata for {}'.format(output_file)) blendmeta.blendmodels( output_model, - inputs=input_models, + input_models, output=output_file, ignore=ignore_list ) From dd7f5f2dc27aa7b2b85f20224339151b7b6cb463 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 13:56:49 -0400 Subject: [PATCH 372/687] restrict blending to one model type --- jwst/model_blender/blendmeta.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index b7af9356fa..acc49d4bcc 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -180,8 +180,13 @@ def get_blended_metadata(input_models): # from the PRIMARY header of each input # icache = {} + model_type = None for i in range(num_files): model = input_models[i] + if model_type is None: + model_type = type(model) + elif type(model) != model_type: + raise ValueError("blending only works for a single model type") inst = model.meta.instrument.name.lower() if inst not in icache: From 181aa25f303182906116db4c77626eacdf9cf0cd Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 4 Sep 2024 16:26:48 -0400 Subject: [PATCH 373/687] remove unnecessary output argument --- jwst/model_blender/blendmeta.py | 30 ++---------------------------- jwst/resample/resample.py | 8 +++----- 2 files changed, 5 insertions(+), 33 deletions(-) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index acc49d4bcc..e2297b208a 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -9,7 +9,6 @@ from stdatamodels import fits_support from stdatamodels import schema as dm_schema -from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer @@ -23,7 +22,7 @@ # Primary functional interface for the code -def blendmodels(product, inputs, output=None, ignore=None): +def blendmodels(product, inputs, ignore=None): """ Run main interface for blending metadata from multiple models. @@ -60,10 +59,6 @@ def blendmodels(product, inputs, output=None, ignore=None): If provided, the filenames provided in this list will be used to get the metadata which will be blended into the final output metadata. - output : str, optional - If provided, update `meta.filename` in the blended `product` - to define what file this model will get written out to. - ignore : list of str, None, optional A list of string the meta attribute names which, if provided, will show which attributes should not be blended. @@ -85,10 +80,7 @@ def blendmodels(product, inputs, output=None, ignore=None): newmeta, newtab = get_blended_metadata(inputs) # open product to update metadata from input models used to create product - if isinstance(product, str): - output_model = datamodels.open(product) - else: - output_model = product + output_model = product ''' NOTE 17-Jan-2017: @@ -116,13 +108,6 @@ def blendmodels(product, inputs, output=None, ignore=None): # Ignore keys that are in the asdf tree but not in the schema pass - # Apply any user-specified filename for output product - if output: - output_model.meta.filename = output - else: - # Otherwise, determine output filename from metadata - output = output_model.meta.filename - # Now, append HDRTAB as new element in datamodel newtab_schema = build_tab_schema(newtab) if hasattr(output_model, 'hdrtab'): @@ -162,13 +147,6 @@ def get_blended_metadata(input_models): not isinstance(input_models, ModelContainer): input_models = [input_models] - # Turn input filenames into a set of metadata objects - close_models = False - if isinstance(input_models[0], str): - # convert `input_models` to a list of datamodels - input_models = [datamodels.open(i) for i in input_models] - close_models = True - num_files = len(input_models) # Determine what blending rules need to be merged to create the final @@ -217,10 +195,6 @@ def get_blended_metadata(input_models): else: new_table = None - if close_models: - for model in input_models: - model.close() - return new_meta, new_table diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index b5f1254850..c19a67bf11 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -203,9 +203,8 @@ def do_drizzle(self, input_models): def blend_output_metadata(self, output_model, input_models): """Create new output metadata based on blending all input metadata.""" - # Run fitsblender on output product - output_file = output_model.meta.filename - + # ignore blending for a few metadata items that + # are already defined ignore_list = [ 'meta.photometry.pixelarea_steradians', 'meta.photometry.pixelarea_arcsecsq', @@ -215,7 +214,6 @@ def blend_output_metadata(self, output_model, input_models): blendmeta.blendmodels( output_model, input_models, - output=output_file, ignore=ignore_list ) @@ -979,4 +977,4 @@ def copy_asn_info_from_library(library, output_model): if ( asn_table_name := library.asn.get("table_name", None) ) is not None: - output_model.meta.asn.table_name = asn_table_name \ No newline at end of file + output_model.meta.asn.table_name = asn_table_name From 49ee9104db3234b9e2406b854cec11d7841b3efb Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 08:19:15 -0400 Subject: [PATCH 374/687] test blendmodels in unit test --- jwst/model_blender/tests/test_blend.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index dc0e578b79..4998d97fdd 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -88,8 +88,10 @@ def blend(make_data): or from the results of `_make_data` directly. """ models, input_values, output_values = make_data - newmeta, newtab = blendmeta.get_blended_metadata(models) - return newmeta, newtab, models, input_values, output_values + output = ImageModel() + blendmeta.blendmodels(output, models) + newmeta = {k: v for k, v in output.to_flat_dict().items() if k.startswith('meta')} + return newmeta, output.hdrtab, models, input_values, output_values def test_blendmeta(blend): @@ -156,5 +158,5 @@ def test_blendtab(blend): ) # Ensure all the expected FITS keywords are in the table. - table = Table(newtab.data) + table = Table(newtab) assert not fits_expected.difference(table.colnames) From a46ec3a77c7f5e672664ab3c4a155e9d6c0a284b Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 09:51:22 -0400 Subject: [PATCH 375/687] combine ignore schema traversal with rule traversal prior to this commit the schema was first traversed to find keywords to blend then traversed again to find keywords to ignore (which were only array/ListNode) items. With this commit the 2 traversals are combined where the array/ListNode items are skipped during the finding traversal instead of found then ignored. The unit tests are also updated to test that this combination works (and to test blendmodels, the only "public" API for this submodule). --- jwst/model_blender/blendmeta.py | 34 +------------------------- jwst/model_blender/blendrules.py | 2 ++ jwst/model_blender/tests/test_blend.py | 11 ++++++--- 3 files changed, 11 insertions(+), 36 deletions(-) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index e2297b208a..9313524dc6 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -91,8 +91,7 @@ def blendmodels(product, inputs, ignore=None): ''' # Start by identifying elements of the model which need to be ignored - ignore_list = _build_schema_ignore_list(newmeta._schema) - ignore_list += ['meta.wcs'] # Necessary since meta.wcs is not in schema + ignore_list = ['meta.wcs'] # Necessary since meta.wcs is not in schema if ignore: ignore_list.extend(ignore) @@ -239,34 +238,3 @@ def convert_dtype(value): new_dtype = str(value) return new_dtype - - -def _build_schema_ignore_list(schema): - """ Create a list of metadata that should be ignored when blending. - - Parameters - ---------- - schema : JSON schema fragment - The schema in which to search. - - Returns - ------- - results : list - List with schema attributes that needs to be ignored - - """ - def build_rules_list(subschema, path, combiner, ctx, recurse): - # Only interpret elements of the meta component of the model - if len(path) > 1 and path[0] == 'meta' and 'items' not in path: - attr = '.'.join(path) - if subschema.get('properties'): - return # Ignore ObjectNodes - kwtype = subschema.get('type') - if kwtype == 'array': - results.append(attr) - else: - return - - results = [] - dm_schema.walk_schema(schema, build_rules_list, results) - return results diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index 2a470aed7c..ada9ee3344 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -268,6 +268,8 @@ def build_rules_dict(subschema, path, combiner, ctx, recurse): path = path[:path.index(combiner)] break attr = '.'.join(path) + if subschema.get('type') == 'array': + return # Ignore ListNodes if subschema.get('properties'): return # Ignore ObjectNodes diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 4998d97fdd..7c94bedf92 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -19,8 +19,9 @@ DATETIMES = ['2017-11-30T13:52:20.367', '2017-11-11T15:14:29.176', '2017-11-11T15:15:06.118'] DATES = ['2017-11-30', '2017-11-11', '2017-12-10'] -INSTRUMENT_NAMES = ['NIRCAM'] * 3 +INSTRUMENT_NAMES = ['NIRCAM'] * N_MODELS CORONAGRAPHS = ['4QPM', '4QPM_1065', '4QPM_1140'] +POLYNOMIAL_INFO = [[{'degree': [1]}]] * N_MODELS def _make_data(): @@ -49,6 +50,7 @@ def _make_data(): 'meta.date': DATETIMES, 'meta.observation.date': DATES, 'meta.observation.date_beg': DATETIMES, + 'meta.background.polynomial_info': POLYNOMIAL_INFO, } output_values = { 'meta.exposure.start_time': START_TIMES[0], @@ -105,7 +107,10 @@ def test_blendmeta(blend): newmeta, newtab, models, input_values, output_values = blend for attr in input_values: - assert newmeta[attr] == output_values[attr] + if attr in output_values: + assert newmeta[attr] == output_values[attr] + else: + assert attr not in newmeta def build_fits_dict(schema): @@ -154,7 +159,7 @@ def test_blendtab(blend): meta_to_fits = dict(map(reversed, fits_to_meta.items())) fits_expected = set( meta_to_fits[meta] - for meta in input_values + for meta in output_values ) # Ensure all the expected FITS keywords are in the table. From 86442b276f96eff4ad59e6f53e6599988d4dce42 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 09:56:26 -0400 Subject: [PATCH 376/687] remove unused extract_filenames_from_products --- jwst/model_blender/blendmeta.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 9313524dc6..83ab741cdc 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -197,19 +197,6 @@ def get_blended_metadata(input_models): return new_meta, new_table -def extract_filenames_from_product(product): - """ - Returns the list of filenames with extensions of input observations that - were used to generate the product. - """ - asn_table = product.meta.asn.table_name - asn = associations.load_asn(asn_table) - prod = asn['products'][0] - fnames = [m['expname'] for m in prod['members'] - if m['exptype'].upper() == 'SCIENCE'] - return fnames - - def build_tab_schema(new_table): """Return new schema definition that describes the input table.""" hdrtab = OrderedDict() From 067d7b1e275e2f3c7f5104d65c929ae236a97b81 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 13:52:51 -0400 Subject: [PATCH 377/687] test a few quirks of the model blender --- jwst/cube_build/ifu_cube.py | 12 +++++++---- jwst/model_blender/blendrules.py | 3 --- jwst/model_blender/tests/test_blend.py | 29 +++++++++++++++++++++++--- 3 files changed, 34 insertions(+), 10 deletions(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 83c6d24e5f..ed034949d1 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -2362,10 +2362,14 @@ def setup_final_ifucube_model(self, model_ref): # ******************************************************************************** def blend_output_metadata(self, IFUCube): """Create new output metadata based on blending all input metadata.""" - # Run fitsblender on output product - output_file = IFUCube.meta.filename - blendmeta.blendmodels(IFUCube, self.input_models_this_cube, - output=output_file) + # Run blendmodels on output product + blendmeta.blendmodels( + IFUCube, + self.input_models_this_cube, + ignore=[ + "meta.filename", + ], + ) class IncorrectInput(Exception): diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py index ada9ee3344..c3a60eeb11 100644 --- a/jwst/model_blender/blendrules.py +++ b/jwst/model_blender/blendrules.py @@ -318,9 +318,6 @@ def interpret_entry(line, hdr): # Interpret raw input line attr = list(line.keys())[0] line_spec = line[attr] - attr_column = True # Determine whether this rule defines a table column - if isinstance(line_spec, dict): - attr_column = False # If not, turn this off # Initialize output values rules = [] diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 7c94bedf92..cd6b00ef09 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -23,6 +23,18 @@ CORONAGRAPHS = ['4QPM', '4QPM_1065', '4QPM_1140'] POLYNOMIAL_INFO = [[{'degree': [1]}]] * N_MODELS +# even though this won't be in the schema the blender +# copies the metadata from the first model so the +# meta from the first model will appear in the result +UNKNOWN_IN_FIRST = ['foo', 'bar', 'bam'] + +# but... if the metadata isn't in the first model it +# won't appear in the output +UNKNOWN_MISSING_FIRST = [None, 'fizz', 'buzz'] + +# even if the metadata is defined in the schema +KNOWN_MISSING_FIRST = [None, '1', '2'] + def _make_data(): """Create a set of input models to blendmeta @@ -51,17 +63,27 @@ def _make_data(): 'meta.observation.date': DATES, 'meta.observation.date_beg': DATETIMES, 'meta.background.polynomial_info': POLYNOMIAL_INFO, + + # test a few "quirks" of the blender including... + # unknown (not in schema) attribute in the first model + 'meta.unknown_in_first': UNKNOWN_IN_FIRST, + # unknown attribute missing from the first model + 'meta.unknown_missing_first': UNKNOWN_MISSING_FIRST, + # known (in schema) attribute missing from the first model + 'meta.observation.visit_id': KNOWN_MISSING_FIRST, } output_values = { 'meta.exposure.start_time': START_TIMES[0], 'meta.exposure.exposure_time': np.sum(EXP_TIMES), 'meta.exposure.end_time': END_TIMES[-1], - 'meta.filename': FILENAMES[0], + # skip filename as it will be ignored below 'meta.instrument.coronagraph': CORONAGRAPHS[0], 'meta.instrument.name': INSTRUMENT_NAMES[0], 'meta.date': DATETIMES[0], 'meta.observation.date': DATES[1], - 'meta.observation.date_beg': DATETIMES[1] + 'meta.observation.date_beg': DATETIMES[1], + # this is the only "quirk" that will appear in the output + 'meta.unknown_in_first': UNKNOWN_IN_FIRST[0], } for i, model in enumerate(models): @@ -91,7 +113,7 @@ def blend(make_data): """ models, input_values, output_values = make_data output = ImageModel() - blendmeta.blendmodels(output, models) + blendmeta.blendmodels(output, models, ignore=["meta.filename"]) newmeta = {k: v for k, v in output.to_flat_dict().items() if k.startswith('meta')} return newmeta, output.hdrtab, models, input_values, output_values @@ -160,6 +182,7 @@ def test_blendtab(blend): fits_expected = set( meta_to_fits[meta] for meta in output_values + if meta in meta_to_fits ) # Ensure all the expected FITS keywords are in the table. From edd1b33d43d955c91038858cd1cc8217af1f77c2 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 16:31:34 -0400 Subject: [PATCH 378/687] overhaul blendmodels --- jwst/model_blender/blender.py | 277 ++++++---------------- jwst/model_blender/blendmeta.py | 221 +----------------- jwst/model_blender/blendrules.py | 363 ----------------------------- jwst/model_blender/rules.py | 78 +++++++ jwst/model_blender/schemautil.py | 54 +++++ jwst/model_blender/tablebuilder.py | 60 +++++ 6 files changed, 268 insertions(+), 785 deletions(-) delete mode 100644 jwst/model_blender/blendrules.py create mode 100644 jwst/model_blender/rules.py create mode 100644 jwst/model_blender/schemautil.py create mode 100644 jwst/model_blender/tablebuilder.py diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 89886c6500..45eee87302 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -1,208 +1,73 @@ -# Copyright (C) 2010 Association of Universities for Research in Astronomy(AURA) -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. -# -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -import numpy as np -from numpy import ma - -from stdatamodels.jwst.datamodels import JwstDataModel -from stdatamodels.jwst import datamodels - - -class _KeywordMapping: - """ - A simple class to verify and store information about each mapping - entry. - """ - - def __init__(self, src_kwd, dst_name, agg_func=None): - if not isinstance(src_kwd, str): - raise TypeError( - "The source keyword name must be a string") - - if not isinstance(dst_name, str): - raise TypeError( - "The destination name must be a string") - - if agg_func is not None: - if not hasattr(agg_func, '__call__'): - raise TypeError( - "The aggregating function must be a callable object, " - "None or a sequence of callables") - - self.src_kwd = src_kwd - self.dst_name = dst_name - self.agg_func = agg_func - - -def metablender(input_models, spec): - """ - Given a list of datamodels, aggregate metadata attribute values and - create a table made up of values from a number of metadata instances, - according to the given specification. - - **Parameters:** - - - *input_models* is a sequence where each element is either: - - - a `datamodels.JwstDataModel` instance or sub-class - - - a string giving the *filename* for the input_model - - - *spec* is a list defining which keyword arguments are to be - aggregated and how. Each element in the list should be a - sequence with 2 to 5 elements of the form: - - (*src_keyword*, *dst_name*, *function*) - - - *src_keyword* is the keyword to pull values from. It is - case-insensitive. - - - *dst_name* is the name to use as a dictionary key or column - name for the destination values. - - - *function* (optional). If function is not None, the values - from the source are aggregated and returned in the - *aggregate_dict*. If function is None (or the tuple contains - only 2 elements), all values are stored as a column with the - name *dst_name* in the result *table*. - - If not None, *function* should be a callable object that takes - a sequence of values and returns an aggregate result. If the - function returns None, no values will be added to the - aggregate dictionary. There are many functions in Numpy that - are directly useful as an aggregating function, for example: - - - mean: `numpy.mean` - - - median: `numpy.median` - - - maximum: `numpy.max` - - - minimum: `numpy.min` - - - sum: `numpy.sum` - - - standard deviation: `numpy.std` - - Lambda functions are also often useful: - - - first: ``lambda x: x[0]`` - - - last: ``lambda x: x[-1]`` - - Additionally, *function* may be a tuple, where each member is - itself a callable object. The result will be a tuple - containing results from each of the given functions. For - instance, to aggregate a range of values, i.e. both the - minimum and maximum values, use the following as *function*: - ``(numpy.min, numpy.max)``. - - **Returns:** - - A 2-tuple of the form (*aggregate_dict*, *table*) where: - - - *aggregate_dict* is a dictionary of where the keys come from - *dst_name* and the values are the aggregated values as run_KeywordMapping - through *function*. - - - *table* is a masked Numpy structured array where the column - names come from *dst_name* and the column contains the values - from *src_keyword* for all of the given headers. Missing values - are masked out. - """ - mappings = [_KeywordMapping(*x) for x in spec] - data = [[] for x in spec] - data_masks = [[] for x in spec] - - # Read in data - for model in input_models: - if not isinstance(model, JwstDataModel): - if not isinstance(model, str): - raise TypeError( - "Each entry in the headers list must be either a " + - "datamodels.JwstDataModel instance or a filename (str)") - model = datamodels.open(model) +from .rules import make_blender +from .schemautil import parse_schema +from .tablebuilder import TableBuilder, table_to_schema + + +class ModelBlender: + def __init__(self, blend_ignore_attrs=None): + self._first_header_meta = None + self._blenders = None + self._table_builder = None + # TODO for now hard-code the polynomial_info ignore + self._blend_ignore_attrs = ['meta.wcs', 'meta.background.polynomial_info'] + if self._blend_ignore_attrs is not None: + self._blend_ignore_attrs.extend(blend_ignore_attrs) + + def accumulate(self, model): + if self._first_header_meta is None: + # capture the entire contents of the first model metadata + self._first_header_meta = { + attr: v + for attr, v in model.to_flat_dict().items() + if attr.startswith('meta') and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) + } + + # search the schema for other metadata to "blend" and to add to the table + attr_to_columns, attr_to_blend_rules = parse_schema(model.schema) + + # make "blenders" for the metadata with special rules + self._blenders = { + attr: make_blender(rule) + for attr, rule in attr_to_blend_rules.items() + # first is implied + if rule != 'first' and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) + } + + # make a table builder using the mapping from the schema + self._table_builder = TableBuilder(attr_to_columns) + + # convert the model to a flat header header = model.to_flat_dict() - filename = header['meta.filename'] - for i, mapping in enumerate(mappings): - if mapping.src_kwd in header: - value = model[mapping.src_kwd] - else: - value = None - - if mapping.agg_func is None: - if value is None: - data[i].append(np.nan) - data_masks[i].append(True) - else: - data[i].append(value) - data_masks[i].append(False) - else: - if value is not None: - data[i].append(value) - - # Aggregate data into dictionary - results = {} - for i, mapping in enumerate(mappings): - if data[i] == []: - result = None - continue - if mapping.agg_func is not None: - result = mapping.agg_func(data[i]) - if result is not None: - results[mapping.dst_name] = result - - # Aggregate data into table - dtype = [] - arrays = [] - - # Use Numpy to "guess" a data type for each of the columns - for i, mapping in enumerate(mappings): - if mapping.agg_func is None: - array = np.array(data[i]) - if np.issubdtype(np.int32, array.dtype): - # see about recasting as int32 - if not np.any(array / (2**31 - 1) > 1.): - array = array.astype(np.int32) - dtype.append((mapping.dst_name, array.dtype)) - arrays.append(array) - - if len(dtype): - # Combine the columns into a structured array - table = ma.empty((len(input_models),), dtype=dtype) - j = 0 - for i, mapping in enumerate(mappings): - if mapping.agg_func is None: - table.data[mapping.dst_name] = arrays[j] - table.mask[mapping.dst_name] = data_masks[i] - j += 1 - else: - table = np.empty((0,)) - return results, table + # add the header to the table + self._table_builder.header_to_row(header) + + # and perform any special blending + for attr, blender in self._blenders.items(): + if attr in header: + blender.accumulate(header[attr]) + + def _finalize_metadata(self): + # start with the entire contents of the first model + meta = self._first_header_meta.copy() + for attr, blender in self._blenders.items(): + meta[attr] = blender.finalize() + return meta + + def _finalize_table(self): + return self._table_builder.build_table() + + def finalize_model(self, model): + # update metadata of the output model based on the results + # of the "blenders" + for attr, val in self._finalize_metadata().items(): + try: + model[attr] = val + except KeyError: + # Ignore keys that are in the asdf tree but not in the schema + pass + + # patch the table into the output model and the schema + table = self._finalize_table() + model.add_schema_entry('hdrtab', table_to_schema(table)) + model.hdrtab = table diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 83ab741cdc..2e5dc2787e 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -3,225 +3,14 @@ This module will create a new metadata instance and table from a list of input datamodels or filenames. """ -from collections import OrderedDict -from astropy.io import fits +from .blender import ModelBlender -from stdatamodels import fits_support -from stdatamodels import schema as dm_schema - -from jwst.datamodels import ModelContainer - -from .. import associations - -from .blendrules import KeywordRules - - -__doctest_skip__ = ['blendmodels'] # Primary functional interface for the code - def blendmodels(product, inputs, ignore=None): - """ - Run main interface for blending metadata from multiple models. - - Blend models that went into creating the original drzfile into a - new metadata instance with a table that contains attribute values from - all input datamodels. - - The product will be updated 'in-place' with the new metadata attributes - and FITS BinTableHDU table. The blended FITS table, with extname=HDRTAB, - has 1 column for each metadata attribute recorded from the input models, - one row for each input model, and column names are the FITS keywords for - that metadata attribute. For example, values from `meta.observation.time` - would be stored in the `TIME-OBS` column. - - Rules for what function to use to determine the blended output attribute - value and what metadata attributes should be used as columns in the - blended FITS table are defined in the datamodel schema. - - - NOTE - ==== - Custom rules for a metadata value should be computed by the calling routine - and used to update the metadata in the output model AFTER - calling this function. - - Parameters - ---------- - product : str - Name of combined product with metadata that needs updating. This can - be specified as a single filename. - - inputs : list - This can be either a list of filenames or a list of DataModels objects. - If provided, the filenames provided in this list will be used to get - the metadata which will be blended into the final output metadata. - - ignore : list of str, None, optional - A list of string the meta attribute names which, if provided, - will show which attributes should not be blended. - - Example - ------- - This example shows how to blend the metadata from a set of DataModels - already read in memory for the product created by the `resample` step. - This example relies on the Association file used as the input to the - `resample` step to specify all the inputs for blending using the - following syntax:: - - >>> from stdatamodels.jwst import datamodels - >>> asnfile = "jw99999-a3001_20170327t121212_coron3_001_asn.json" - >>> data = datamodels.open(asnfile) - >>> input_models = [data[3], data[4]] # we know the last datasets are SCIENCE - >>> blendmodels(data.meta.asn_table.products[0].name, input_models) - """ - newmeta, newtab = get_blended_metadata(inputs) - - # open product to update metadata from input models used to create product - output_model = product - - ''' - NOTE 17-Jan-2017: - Effort needs to be made to insure that new blended values conform - to the definitions of the attribute as provided by the schema. - This will address #1650 for the jwst package. - https://github.com/STScI-JWST/jwst/issues/1650 - ''' - - # Start by identifying elements of the model which need to be ignored - ignore_list = ['meta.wcs'] # Necessary since meta.wcs is not in schema - if ignore: - ignore_list.extend(ignore) - - # Now assign values from new_hdrs to output_model.meta - flat_new_metadata = newmeta.to_flat_dict() - - for attr in flat_new_metadata: - attr_use = not [attr.startswith(i) for i in ignore_list].count(True) - if attr.startswith('meta') and attr_use: - try: - output_model[attr] = newmeta[attr] - except KeyError: - # Ignore keys that are in the asdf tree but not in the schema - pass - - # Now, append HDRTAB as new element in datamodel - newtab_schema = build_tab_schema(newtab) - if hasattr(output_model, 'hdrtab'): - del output_model.hdrtab - output_model.add_schema_entry('hdrtab', newtab_schema) - output_model.hdrtab = fits_support.from_fits_hdu(newtab, newtab_schema) - - # Clean up for the next run - del newmeta, newtab - - -def get_blended_metadata(input_models): - """ - Return a blended metadata instance and table based on the input datamodels. - This will serve as the primary interface for blending datamodels. - - Parameters - ---------- - input_models : list - Either a single list of filenames from which to extract the metadata to - be blended, or a list of `datamodels.JwstDataModel` objects to be blended. - The input models are assumed to have the blending rules defined as - an integral part of the schema definition for the model. - - Returns - ------- - metadata : list - A list of blended metadata instances, one for each i - - new_table : object - Single fits.TableHDU object that contains the combined results from - all input headers(extension). Each row will correspond to an image, - and each column corresponds to a single keyword listed in the rules. - - """ - if not isinstance(input_models, list) and \ - not isinstance(input_models, ModelContainer): - input_models = [input_models] - - num_files = len(input_models) - - # Determine what blending rules need to be merged to create the final - # blended headers. There will be a separate set of rules for each - # instrument, and all rules get merged into a composite set of rules that - # get applied to all input headers regardless of instrument. - # - # Instrument identification will be extracted from the INSTRUME keyword - # from the PRIMARY header of each input - # - icache = {} - model_type = None - for i in range(num_files): - model = input_models[i] - if model_type is None: - model_type = type(model) - elif type(model) != model_type: - raise ValueError("blending only works for a single model type") - inst = model.meta.instrument.name.lower() - - if inst not in icache: - # initialize the appropriate class for this data's instrument - inst_class = KeywordRules(model) - # Interpret rules for this class based on image that - # initialized this instrument's rules - inst_class.interpret_rules(model) - - # Now add this interpreted class to the cache - icache[inst] = inst_class - - # Create final merged set of rules - final_rules = None - for inst in icache: - if final_rules is None: - final_rules = icache[inst] - else: - final_rules.merge(icache[inst]) - - # apply rules to datamodels metadata - new_meta, newtab = final_rules.apply(input_models) - - if len(newtab) > 0: - # Now merge the results for all the tables into single table extension - new_table = fits.BinTableHDU.from_columns(newtab) - new_table.header['EXTNAME'] = 'HDRTAB' - else: - new_table = None - - return new_meta, new_table - - -def build_tab_schema(new_table): - """Return new schema definition that describes the input table.""" - hdrtab = OrderedDict() - hdrtab['title'] = 'Combined header table' - hdrtab['fits_hdu'] = 'HDRTAB' - datatype = [] - for col in new_table.columns: - cname = col.name - ctype = convert_dtype(str(col.dtype)) - c = OrderedDict() - c['name'] = cname - c['datatype'] = ctype - datatype.append(c) - hdrtab['datatype'] = datatype - - return hdrtab - - -def convert_dtype(value): - """Convert numarray column dtype into YAML-compatible format description""" - if 'S' in value: - # working with a string description - str_len = int(value[value.find('S') + 1:]) - new_dtype = ['ascii', str_len] - else: - new_dtype = str(value) - - return new_dtype + blender = ModelBlender(blend_ignore_attrs=ignore) + for model in inputs: + blender.accumulate(model) + blender.finalize_model(product) diff --git a/jwst/model_blender/blendrules.py b/jwst/model_blender/blendrules.py deleted file mode 100644 index c3a60eeb11..0000000000 --- a/jwst/model_blender/blendrules.py +++ /dev/null @@ -1,363 +0,0 @@ -""" blendmeta - Merge metadata from multiple models to create - a new metadata instance and table - -""" -from collections import OrderedDict - -import numpy as np -from stdatamodels import schema as dm_schema - -from . import blender - - -__all__ = [ - 'interpret_attr_line', - 'interpret_entry', - 'KeywordRules', - 'KwRule', -] - - -# Custom blending functions -def multi(vals): - """ - This will either return the common value from a list of identical values - or 'MULTIPLE' - """ - uniq_vals = list(set(vals)) - num_vals = len(uniq_vals) - if num_vals == 0: - return None - if num_vals == 1: - return uniq_vals[0] - if num_vals > 1: - return "MULTIPLE" - - -def first(items): - """ Return first item from list of values""" - if len(items): - return items[0] - return None - - -def last(items): - """ Return last item from list of values""" - if len(items): - return items[-1] - return None - - -# translation dictionary for function entries from rules files -blender_funcs = {'first': first, - 'last': last, - 'multi': multi, - 'mean': np.mean, - 'sum': np.sum, - 'max': np.max, - 'min': np.min, - # retained date/time names for backwards compatibility - # as these all assume ISO8601 format the lexical and - # chronological sorting match - 'mintime': min, - 'maxtime': max, - 'mindate': min, - 'maxdate': max, - 'mindatetime': min, - 'maxdatetime': max} - - -# Classes for managing keyword rules -class KeywordRules(): - - def __init__(self, model): - """ Read in the rules used to interpret the keywords from the specified - instrument image header. - """ - self.instrument = model.meta.instrument.name.lower() - - self.rule_specs = _build_schema_rules_dict(model._schema) - - self.rule_objects = [] - self.rules = [] - - def interpret_rules(self, hdrs): - """ Convert specifications for rules from rules file - into specific rules for this header(instrument/detector). - - Notes - ----- - This allows for expansion rules to be applied to rules - from the rules files (such as any wildcards or section titles). - - Output will be 'self.rules' that contains a list of tuples: - - a tuple of 2 values for each column in the table - - a tuple of 4 values for each attribute identified in metadata - Partial sample from HST to show format: - [('CTYPE1O', 'CTYPE1O'), - ('CTYPE2O', 'CTYPE2O'), - ('CUNIT1O', 'CUNIT1O'), - ('CUNIT2O', 'CUNIT2O'), - ('APERTURE', 'APERTURE', , 'ignore'), - ('DETECTOR', 'DETECTOR', , 'ignore'), - ('EXPEND', 'EXPEND', , 'ignore'), - ('EXPSTART', 'EXPSTART', , 'ignore'), - ('EXPTIME', 'TEXPTIME', , 'ignore'), - ('EXPTIME', 'EXPTIME', , 'ignore')] - - This rules format will allow the algorithm, logic and code from - the original fitsblender to be used with as little change as - possible. It will need to be derived (as with HST) from the - input models metadata for expansion of attribute sections or - wildcards in attributes specified in the rules. - - """ - if isinstance(hdrs, tuple): - hdrs = list(hdrs) - if not isinstance(hdrs, list): - hdrs = [hdrs] - - # apply rules to headers - for attr in self.rule_specs: - speclist = self.rule_specs[attr] - - for rule in speclist: - # Create KwRule input equivalent to HST rules input - kwr = KwRule(rule) - duplicate_rule = False - - for robj in self.rule_objects: - if kwr.rule_spec == robj.rule_spec: - duplicate_rule = True - break - if not duplicate_rule: - for hdr in hdrs: - kwr.interpret(hdr) - - self.rules.extend(kwr.rules) - - def merge(self, kwrules): - """ - Merge a new set of interpreted rules into the current set - The new rules, kwrules, can either be a new class or a whole new - set of rules (like those obtained from using self.interpret_rules with - a new header). - """ - if isinstance(kwrules, KeywordRules): - kwrules = kwrules.rules - - # Determine what rules are specified in kwrules that - # are NOT in self.rules - k = [] - # Delete these extraneous rules from input kwrules - for r in kwrules: - if r not in self.rules: - k.append(r) - - # extend self.rules with additional rules - self.rules.extend(k) - - def apply(self, models): - """ For a full list of metadata objects, apply the specified rules to - generate a dictionary of new values and a table using - blender. - - This method returns the new metadata object and summary table - as `datamodels.model.ndmodel` and fits.binTableHDU objects. - """ - # Apply rules to headers - fbdict, fbtab = blender.metablender(models, self.rules) - - # Determine which keywords are included in the table but not - # the new dict(header). These will be removed from the output - # header altogether - tabcols = fbtab.dtype.names - - # Start with a copy of the template as the new header - # This will define what keywords need to be updated, as the rules - # and input headers often include headers for multiple extensions in - # order to build the complete table for all the keywords in the file - # in one run - new_model = models[0].copy() - - # Apply updated/blended values into new header, but only those - # keywords which are already present in the 'template' new header - # this allows the rules to be used on all extensions at once yet - # update each extension separately without making copies of kws from - # one extension to another. - # new_model.update(fbdict) - for attr in new_model.to_flat_dict(): - if 'meta' in attr and attr in fbdict: - new_model[attr] = fbdict[attr] - - # Create summary table - if len(tabcols) > 0: - new_table = fbtab - else: - new_table = None - return new_model, new_table - - -class KwRule(): - """ - This class encapsulates the logic needed for interpreting a single keyword - rule from a text file. - - Notes - ----- - The ``.rules`` attribute contains the interpreted set of rules that corresponds - to this line. - - Example:: - - Interpreting rule from - {'meta.attribute': { 'rule': 'first'}} - --or-- - {'meta.attribute': 'meta.attribute'} # Table column specification - - into rule [('meta.attribute', 'meta.attribute', , 'ignore')] - """ - - def __init__(self, line): - """Initialize new keyword rule. - - Parameters - ========== - line : dict - Line should be dict with attribute name as the key, and - a dict as the value specifying 'rule'. - """ - self.rule_spec = line # dict read in from rules file - self.rules = [] - self.delete_kws = [] - - def interpret(self, hdr): - """Use metadata to interpret rule.""" - if self.rules: - # If self.rules has already been defined for this rule, do not try - # to interpret it any further with additional headers - return - irules = interpret_entry(self.rule_spec, hdr) - - # Now, interpret rule based on presence of kw in hdr - if irules: - self.rules = irules - - -# Utility functions. -def _build_schema_rules_dict(schema): - """ Create a dict that extracts blend rules from an input schema. - - Parameters - ---------- - schema : JSON schema fragment - The schema in which to search. - - Returns - ------- - results : OrderedDict - Dictionary with schema attributes as keys and blend rules - as values - - """ - def build_rules_dict(subschema, path, combiner, ctx, recurse): - # Only interpret elements of the meta component of the model - if len(path) > 1 and path[0] == 'meta' and 'items' not in path: - for combiner in ['anyOf', 'oneOf']: - if combiner in path: - path = path[:path.index(combiner)] - break - attr = '.'.join(path) - if subschema.get('type') == 'array': - return # Ignore ListNodes - if subschema.get('properties'): - return # Ignore ObjectNodes - - # Get blending info - kwrule = subschema.get('blend_rule') - kwtab = subschema.get('blend_table') - kwname = subschema.get('fits_keyword', attr) - - # If rules had already been set, only modify - # the rules if there are explicit settings. - rule_spec = None - result = results.get(attr, []) - if kwrule: - rule_spec = {attr: {'rule': kwrule}} - elif not results.get(attr): - rule_spec = {attr: {'rule': 'first'}} - if rule_spec: - result.append(rule_spec) - - # Add keyword to table if specified. - if kwtab: - result.append({attr: kwname}) - - # Add the results back. - if len(result): - results[attr] = result - - else: - return - - results = OrderedDict() - dm_schema.walk_schema(schema, build_rules_dict, results) - return results - - -def interpret_entry(line, hdr): - """ Generate the rule(s) specified by the entry from the rules file. - - Notes - ----- - The entry should always be a dict with format: - {attribute_name : {'rule':'some_rule'}} - -- or (for table column specification)-- - {attribute_name: attribute_name} - """ - # Interpret raw input line - attr = list(line.keys())[0] - line_spec = line[attr] - - # Initialize output values - rules = [] - - # Parse the line - attr_rules = interpret_attr_line(attr, line_spec) - rules.extend(attr_rules) - - return rules - - -def interpret_attr_line(attr, line_spec): - """ Generate rule for single attribute from input line from rules file.""" - rules = [] - - kws = [attr] - if isinstance(line_spec, dict): - kws2 = kws - else: - kws2 = [line_spec] - - lrule = None - if 'rule' in line_spec: - lrule = line_spec['rule'] - - # Interpret short-hand rules using dict - if lrule is not None and len(lrule) > 0: - if lrule in blender_funcs: - lrule = blender_funcs[lrule] - else: - lrule = None - # build separate rule for each kw - for kw1, kw2 in zip(kws, kws2): - new_rule = (kw1, kw2, lrule) - if new_rule not in rules: - rules.append(new_rule) - else: - for kw1, kw2 in zip(kws, kws2): - new_rule = (kw1, kw2) - if new_rule not in rules: - rules.append(new_rule) - - return rules diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py new file mode 100644 index 0000000000..c06f61991c --- /dev/null +++ b/jwst/model_blender/rules.py @@ -0,0 +1,78 @@ +from datetime import time + +import numpy as np + + +def multi(vals): + """ + This will either return the common value from a list of identical values + or 'MULTIPLE' + """ + uniq_vals = list(set(vals)) + num_vals = len(uniq_vals) + if num_vals == 0: + return None + if num_vals == 1: + return uniq_vals[0] + if num_vals > 1: + return "MULTIPLE" + + +def _isotime(time_str): + hms = [float(i) for i in time_str.split(':')] + sec_ms = hms[2] - int(hms[2]) + isotime = time(int(hms[0]), int(hms[1]), int(hms[2]), int(sec_ms * 1000000)) + return isotime + + +def bad_mintime(vals): + # this introduces floating point error for backwards compatibility + # it should be replaced with min + return _isotime(min(vals)).isoformat() + + +def bad_maxtime(vals): + # this introduces floating point error for backwards compatibility + # it should be replaced with max + return _isotime(max(vals)).isoformat() + + +RULE_FUNCTIONS = { + 'multi': multi, + 'mean': np.mean, + 'sum': np.sum, + 'max': np.max, + 'min': np.min, + + # these intentionally introduce floating point errors + # for backwards compatibility but would be more accurate + # using the builtin min/max + 'mintime': bad_mintime, + 'maxtime': bad_maxtime, + + # retained date/time names for backwards compatibility + # as these all assume ISO8601 format the lexical and + # chronological sorting match + 'mindate': min, + 'maxdate': max, + 'mindatetime': min, + 'maxdatetime': max +} + + +class Blender: + def __init__(self, blend_function): + self.blend_function = blend_function + self.values = [] + + def accumulate(self, value): + self.values.append(value) + + def finalize(self): + if not self.values: + return None + return self.blend_function(self.values) + + +def make_blender(rule): + return Blender(RULE_FUNCTIONS[rule]) diff --git a/jwst/model_blender/schemautil.py b/jwst/model_blender/schemautil.py new file mode 100644 index 0000000000..13991aa428 --- /dev/null +++ b/jwst/model_blender/schemautil.py @@ -0,0 +1,54 @@ +from stdatamodels import schema as dm_schema + +def parse_schema(schema): + """ + Returns + ------- + attr_to_column: dict + one-to-one mapping of metadata attributes + (full dotted-paths) to table columns. For example + {'meta.observation.time': 'TIME-OBS'} for storing + the 'meta.observation.time' attributes in a 'TIME-OBS' + column. + + attr_to_rule: dict + mapping of metadata attribute to blend rule + type. For example {'meta.observation.time': 'mintime'} + will combine all 'meta.observation.time' attributes + using the 'mintime' rule. + """ + def callback(subschema, path, combiner, ctx, recurse): + if len(path) <= 1: + return # ignore top-level (non-meta) attributes + if path[0] != 'meta': + return # ignore non-meta attributes + if 'items' in path: + return # ignore attributes in arrays + if subschema.get('type') == 'array': + return # ignore ListNodes + if subschema.get('properties'): + return # ignore ObjectNodes + + # strip trailing path if there's a combiner + for combiner in ['anyOf', 'oneOf']: + if combiner in path: + path = path[:path.index(combiner)] + break + + # construct the metadata attribute path + attr = '.'.join(path) + + # if 'blend_rule' is defined, make a 'blend' + if 'blend_rule' in subschema: + ctx['blends'][attr] = subschema['blend_rule'] + + # if 'blend_table' is defined (and truthy), add a column + if subschema.get('blend_table'): + ctx['columns'][attr] = subschema.get('fits_keyword', attr) + + ctx = { + 'columns': {}, + 'blends': {}, + } + dm_schema.walk_schema(schema, callback, ctx) + return ctx['columns'], ctx['blends'] diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py new file mode 100644 index 0000000000..f5250f84bc --- /dev/null +++ b/jwst/model_blender/tablebuilder.py @@ -0,0 +1,60 @@ +import numpy as np + + +def _convert_dtype(value): + """Convert numarray column dtype into YAML-compatible format description""" + if 'U' in value: + # working with a string description + str_len = int(value[value.find('U') + 1:]) + new_dtype = ['ascii', str_len] + else: + new_dtype = str(value) + + return new_dtype + + +def table_to_schema(table): + return { + 'title': 'Combined header table', + 'fits_hdu': 'HDRTAB', + 'datatype': [ + { + 'name': col_name, + 'datatype': _convert_dtype(str(table.dtype[col_name])), + } + for col_name in table.dtype.fields + ], + } + + +class TableBuilder: + def __init__(self, attr_to_column): + self.attr_to_column = attr_to_column + self.columns = {col: [] for col in self.attr_to_column.values()} + self.masks = {col: [] for col in self.columns} + if len(attr_to_column) != len(self.columns): + raise ValueError(f"Invalid attr_to_column, mapping is not 1-to-1: {attr_to_column}") + + def header_to_row(self, header): + row = {} + for attr in self.attr_to_column: + if attr in header: + row[attr] = header[attr] + self._add_row(row) + + def _add_row(self, row): + for attr, col in self.attr_to_column.items(): + if attr in row: + self.columns[col].append(row[attr]) + self.masks[col].append(False) + else: + self.columns[col].append(np.nan) + self.masks[col].append(True) + + def build_table(self): + arrays = [] + table_dtype = [] + for col, items in self.columns.items(): + arrays.append(np.ma.array(items, mask=self.masks[col])) + table_dtype.append((col, arrays[-1].dtype)) + return np.rec.fromarrays(arrays, dtype=table_dtype) From 7172782500ac90e8f7ff686b5ee752cbdd3d4669 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 16:51:19 -0400 Subject: [PATCH 379/687] fix bool datatype --- jwst/model_blender/tablebuilder.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index f5250f84bc..89eca633fb 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -7,6 +7,8 @@ def _convert_dtype(value): # working with a string description str_len = int(value[value.find('U') + 1:]) new_dtype = ['ascii', str_len] + elif value == 'bool': + new_dtype = 'bool8' else: new_dtype = str(value) From 82f45a7c17f2e9270499a287cfa731c394b4d70f Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 5 Sep 2024 16:51:35 -0400 Subject: [PATCH 380/687] use ModelBlender for incremental model blending --- jwst/resample/resample.py | 52 ++++++++++----------------------------- 1 file changed, 13 insertions(+), 39 deletions(-) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index c19a67bf11..6350c3496e 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -15,8 +15,8 @@ from jwst.associations.asn_from_list import asn_from_list from . import gwcs_drizzle +from jwst.model_blender.blender import ModelBlender from jwst.resample import resample_utils -from ..model_blender import blendmeta log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -201,22 +201,6 @@ def do_drizzle(self, input_models): else: return self.resample_many_to_one(input_models) - def blend_output_metadata(self, output_model, input_models): - """Create new output metadata based on blending all input metadata.""" - # ignore blending for a few metadata items that - # are already defined - ignore_list = [ - 'meta.photometry.pixelarea_steradians', - 'meta.photometry.pixelarea_arcsecsq', - ] - - log.info('Blending metadata for {}'.format(output_file)) - blendmeta.blendmodels( - output_model, - input_models, - ignore=ignore_list - ) - def _get_intensity_scale(self, img): """ Compute an intensity scale from the input and output pixel area. @@ -385,28 +369,13 @@ def resample_many_to_one(self, input_models): copy_asn_info_from_library(input_models, output_model) if self.blendheaders: - # right now this needs a list of input models, all in memory - # for now, just load the models as a list with empty data arrays - # but the blend_meta step itself should eventually be refactored - # to expect a list of metadata objects - # instead of a list of datamodels - input_list = [] - with input_models: - for i, model in enumerate(input_models): - empty_model = type(model)() - empty_model.meta = model.meta - copy_asn_info_from_library(input_models, empty_model) - empty_model.data = np.empty((1, 1)) - empty_model.dq = np.empty((1, 1)) - empty_model.err = np.empty((1, 1)) - empty_model.wht = np.empty((1, 1)) - empty_model.var_rnoise = np.empty((1, 1)) - empty_model.var_poisson = np.empty((1, 1)) - empty_model.var_flat = np.empty((1, 1)) - input_list.append(empty_model) - input_models.shelve(model, i, modify=False) - self.blend_output_metadata(output_model, input_list) - del input_list + blender = ModelBlender( + blend_ignore_attrs=[ + 'meta.photometry.pixelarea_steradians', + 'meta.photometry.pixelarea_arcsecsq', + 'meta.filename', + ] + ) # Initialize the output with the wcs driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, @@ -415,6 +384,8 @@ def resample_many_to_one(self, input_models): log.info("Resampling science data") with input_models: for img in input_models: + if self.blendheaders: + blender.accumulate(img) iscale = self._get_intensity_scale(img) log.debug(f'Using intensity scale iscale={iscale}') img.meta.iscale = iscale @@ -447,6 +418,9 @@ def resample_many_to_one(self, input_models): del data, inwht input_models.shelve(img) + if self.blendheaders: + blender.finalize_model(output_model) + # Resample variance arrays in input_models to output_model self.resample_variance_arrays(output_model, input_models) var_components = [ From 7c5088480807d6da7d783dc759cd6449f20da43d Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 07:52:35 -0400 Subject: [PATCH 381/687] test output table values --- jwst/model_blender/blender.py | 10 ++++++--- jwst/model_blender/tablebuilder.py | 1 + jwst/model_blender/tests/test_blend.py | 30 +++++++++++++++----------- 3 files changed, 26 insertions(+), 15 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 45eee87302..7d0446adb4 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -1,3 +1,6 @@ +from astropy.io import fits +from stdatamodels import fits_support + from .rules import make_blender from .schemautil import parse_schema from .tablebuilder import TableBuilder, table_to_schema @@ -10,7 +13,7 @@ def __init__(self, blend_ignore_attrs=None): self._table_builder = None # TODO for now hard-code the polynomial_info ignore self._blend_ignore_attrs = ['meta.wcs', 'meta.background.polynomial_info'] - if self._blend_ignore_attrs is not None: + if blend_ignore_attrs is not None: self._blend_ignore_attrs.extend(blend_ignore_attrs) def accumulate(self, model): @@ -69,5 +72,6 @@ def finalize_model(self, model): # patch the table into the output model and the schema table = self._finalize_table() - model.add_schema_entry('hdrtab', table_to_schema(table)) - model.hdrtab = table + schema = table_to_schema(table) + model.add_schema_entry('hdrtab', schema) + model.hdrtab = fits_support.from_fits_hdu(fits.BinTableHDU.from_columns(table), schema) diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index 89eca633fb..ca251663cf 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -59,4 +59,5 @@ def build_table(self): for col, items in self.columns.items(): arrays.append(np.ma.array(items, mask=self.masks[col])) table_dtype.append((col, arrays[-1].dtype)) + # TODO loses masks... return np.rec.fromarrays(arrays, dtype=table_dtype) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index cd6b00ef09..29caaf22a8 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -1,7 +1,6 @@ """Test blend_models""" import pytest -from astropy.table import Table import numpy as np from stdatamodels.schema import walk_schema @@ -21,19 +20,20 @@ DATES = ['2017-11-30', '2017-11-11', '2017-12-10'] INSTRUMENT_NAMES = ['NIRCAM'] * N_MODELS CORONAGRAPHS = ['4QPM', '4QPM_1065', '4QPM_1140'] -POLYNOMIAL_INFO = [[{'degree': [1]}]] * N_MODELS +EXP_ONLYS = [True] * N_MODELS +POLYNOMIAL_INFOS = [[{'degree': [1]}]] * N_MODELS # even though this won't be in the schema the blender # copies the metadata from the first model so the # meta from the first model will appear in the result -UNKNOWN_IN_FIRST = ['foo', 'bar', 'bam'] +UNKNOWN_IN_FIRSTS = ['foo', 'bar', 'bam'] # but... if the metadata isn't in the first model it # won't appear in the output -UNKNOWN_MISSING_FIRST = [None, 'fizz', 'buzz'] +UNKNOWN_MISSING_FIRSTS = [None, 'fizz', 'buzz'] # even if the metadata is defined in the schema -KNOWN_MISSING_FIRST = [None, '1', '2'] +KNOWN_MISSING_FIRSTS = [None, '1', '2'] def _make_data(): @@ -62,15 +62,16 @@ def _make_data(): 'meta.date': DATETIMES, 'meta.observation.date': DATES, 'meta.observation.date_beg': DATETIMES, - 'meta.background.polynomial_info': POLYNOMIAL_INFO, + 'meta.visit.exp_only': EXP_ONLYS, + 'meta.background.polynomial_info': POLYNOMIAL_INFOS, # test a few "quirks" of the blender including... # unknown (not in schema) attribute in the first model - 'meta.unknown_in_first': UNKNOWN_IN_FIRST, + 'meta.unknown_in_first': UNKNOWN_IN_FIRSTS, # unknown attribute missing from the first model - 'meta.unknown_missing_first': UNKNOWN_MISSING_FIRST, + 'meta.unknown_missing_first': UNKNOWN_MISSING_FIRSTS, # known (in schema) attribute missing from the first model - 'meta.observation.visit_id': KNOWN_MISSING_FIRST, + 'meta.observation.visit_id': KNOWN_MISSING_FIRSTS, } output_values = { 'meta.exposure.start_time': START_TIMES[0], @@ -82,8 +83,10 @@ def _make_data(): 'meta.date': DATETIMES[0], 'meta.observation.date': DATES[1], 'meta.observation.date_beg': DATETIMES[1], + 'meta.visit.exp_only': EXP_ONLYS[0], + # this is the only "quirk" that will appear in the output - 'meta.unknown_in_first': UNKNOWN_IN_FIRST[0], + 'meta.unknown_in_first': UNKNOWN_IN_FIRSTS[0], } for i, model in enumerate(models): @@ -186,5 +189,8 @@ def test_blendtab(blend): ) # Ensure all the expected FITS keywords are in the table. - table = Table(newtab) - assert not fits_expected.difference(table.colnames) + colnames = set(newtab.dtype.fields) + assert not fits_expected.difference(colnames) + for col in colnames: + if col in input_values: + assert newtab[col] == input_values[col] From 0b8268b5d8c43dca626d30cf474ad25c6f5cda47 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 08:11:20 -0400 Subject: [PATCH 382/687] fix boolean column and work around astropy data mangling --- jwst/model_blender/blender.py | 4 ++++ jwst/model_blender/tablebuilder.py | 5 +++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 7d0446adb4..a74d708710 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -74,4 +74,8 @@ def finalize_model(self, model): table = self._finalize_table() schema = table_to_schema(table) model.add_schema_entry('hdrtab', schema) + + # because astropy will silently mangle boolean columns on write + # we roundtrip the data through a BinTableHDU here to allow + # stdatamodels to correct the data in a way that won't result in mangling model.hdrtab = fits_support.from_fits_hdu(fits.BinTableHDU.from_columns(table), schema) diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index ca251663cf..8a21fa711c 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -8,7 +8,8 @@ def _convert_dtype(value): str_len = int(value[value.find('U') + 1:]) new_dtype = ['ascii', str_len] elif value == 'bool': - new_dtype = 'bool8' + # cast all bool to int8 to avoid issues on write + new_dtype = 'int8' else: new_dtype = str(value) @@ -59,5 +60,5 @@ def build_table(self): for col, items in self.columns.items(): arrays.append(np.ma.array(items, mask=self.masks[col])) table_dtype.append((col, arrays[-1].dtype)) - # TODO loses masks... + # TODO loses masks... but so did the old code? return np.rec.fromarrays(arrays, dtype=table_dtype) From b9204853e25d50f890c5cb0829be8956f8b1ee53 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 08:22:59 -0400 Subject: [PATCH 383/687] remove unused masks --- jwst/model_blender/tablebuilder.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index 8a21fa711c..05ecb298bd 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -34,7 +34,6 @@ class TableBuilder: def __init__(self, attr_to_column): self.attr_to_column = attr_to_column self.columns = {col: [] for col in self.attr_to_column.values()} - self.masks = {col: [] for col in self.columns} if len(attr_to_column) != len(self.columns): raise ValueError(f"Invalid attr_to_column, mapping is not 1-to-1: {attr_to_column}") @@ -47,18 +46,12 @@ def header_to_row(self, header): def _add_row(self, row): for attr, col in self.attr_to_column.items(): - if attr in row: - self.columns[col].append(row[attr]) - self.masks[col].append(False) - else: - self.columns[col].append(np.nan) - self.masks[col].append(True) + self.columns[col].append(row[attr] if attr in row else np.nan) def build_table(self): arrays = [] table_dtype = [] for col, items in self.columns.items(): - arrays.append(np.ma.array(items, mask=self.masks[col])) + arrays.append(np.array(items)) table_dtype.append((col, arrays[-1].dtype)) - # TODO loses masks... but so did the old code? return np.rec.fromarrays(arrays, dtype=table_dtype) From de8f26bba1ed1a0065680d3d89fc3996dda4ac17 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 09:24:16 -0400 Subject: [PATCH 384/687] working on docs --- docs/jwst/model_blender/blender_rules.rst | 39 ++----- docs/jwst/model_blender/index.rst | 1 - docs/jwst/model_blender/main.rst | 127 +++++++++++----------- jwst/model_blender/__init__.py | 5 + jwst/model_blender/blender.py | 3 + jwst/model_blender/blendmeta.py | 2 + jwst/model_blender/rules.py | 3 + 7 files changed, 81 insertions(+), 99 deletions(-) diff --git a/docs/jwst/model_blender/blender_rules.rst b/docs/jwst/model_blender/blender_rules.rst index edafb70618..8a952c6516 100644 --- a/docs/jwst/model_blender/blender_rules.rst +++ b/docs/jwst/model_blender/blender_rules.rst @@ -5,39 +5,14 @@ Model Blender Rules Blending models relies on rules to define how to evaluate all the input values for a model attribute in order to determine the final output value. These rules -then get specified in the model schema for each attribute. +are derived from the model schema for each attribute. -The rules get interpreted and applied as list or array operations that work on -the set of input values for each attribute. The full set of pre-defined rules -includes +The rules are applied to a collection of all values to be blended (for example +if blending models that have different exposure times, the blended exposure +time will be the sum of all exposure times). -.. code-block:: python +Supported rule names and corresponding functions are listed in +`jwst.model_blender.rules.RULE_FUNCTIONS`. - import numpy as np - # translation dictionary for function entries from rules files - blender_funcs = {'first': first, - 'last': last, - 'float_one': float_one, - 'int_one': int_one, - 'zero': zero, - 'multi': multi, - 'multi?': multi1, - 'mean': np.mean, - 'sum': np.sum, - 'max': np.max, - 'min': np.min, - 'stddev': np.std, - 'mintime': mintime, - 'maxtime': maxtime, - 'mindate': mindate, - 'maxdate': maxdate, - 'mindatetime': mindatetime, - 'maxdatetime': maxdatetime} - -The rules that should be referenced in the model schema definition are the -keys defined for `jwst.model_blender.blender_rules.blender_funcs` listed -above. This definition illustrates how several rules are simply interfaces for -numpy array operations, while others are defined internally to `model_blender`. - -.. automodapi:: jwst.model_blender.blendrules +.. automodapi:: jwst.model_blender.rules :no-inheritance-diagram: diff --git a/docs/jwst/model_blender/index.rst b/docs/jwst/model_blender/index.rst index fd76d52ea8..ae90222145 100644 --- a/docs/jwst/model_blender/index.rst +++ b/docs/jwst/model_blender/index.rst @@ -10,4 +10,3 @@ Model Blender blender_rules.rst .. automodapi:: jwst.model_blender - :skip: "OrderedDict" diff --git a/docs/jwst/model_blender/main.rst b/docs/jwst/model_blender/main.rst index 8238993eb0..d9c2b7ae0c 100644 --- a/docs/jwst/model_blender/main.rst +++ b/docs/jwst/model_blender/main.rst @@ -3,6 +3,14 @@ Role of Model Blender ====================== +Model Blender performs 2 operations: + + - Accumulate, blend and set metadata when combining several models (like + as is done during resampling) using combination rules defined in the datamodel + schemas. + - Record the metadata of input models in a "table" added to the + combined model. + One problem with combining data from multiple exposures stems from not being able to keep track of what kind of data was used to create the final product. The final product only reports one value for each of the metadata attributes from the @@ -10,7 +18,7 @@ model schema used to describe the science data, where each of multiple inputs ma have had different values for each attribute. The model_blender package solves this problem by allowing the user to define rules that can be used to determine a final single value from all the input values for each attribute, using separate -rules for each attribute as appropriate. This package also creates a FITS binary +rules for each attribute as appropriate. This package also creates a table that records the input attribute values for all the input models used to create the final product, allowing the user to select what attributes to keep in this table. @@ -27,24 +35,6 @@ This code works by Using model_blender =================== -The model blender package requires - - - all the input models be available - - the output product has already been generated - -Both the input models and output product could be provided as either a datamodel -instance in memory or as the name of a FITS file on disk. The primary advantage -to working strictly in-memory with datamodel instances comes from minimizing the -amount of disk I/O needed for this operation which can result in significantly -more efficient (read that: faster) processing. - -.. note:: - - The generated output model will be considered to contain a default - (or perhaps even empty) set of :ref:`metadata` based on some - model defined in :ref:`stdatamodels` - This metadata will be replaced - **in-place** when running :ref:`blender_api`. The simplest way to run model blender only requires calling a single interface: @@ -55,58 +45,63 @@ The simplest way to run model blender only requires calling a single interface: where - - `product`: the datamodel (or FITS filename) for the already combined product - - `input_list`: list of input datamodels or FITS filenames for all inputs used - to create the `product` + - `product`: the datamodel for the already combined product + - `input_list`: list of input datamodels used to create the `product` -The output product will end up with new metadata attribute values and a new HDRTAB -FITS binary table extension in the FITS file when the product gets saved to disk. +The output product will end up with new metadata attribute values and a new ``hdrtab`` +attribute that will produce a ``HDRTAB`` table extension when the product is saved +to disk. Customizing the behavior ======================== -By default, `blendmodels` will not write out the updated product model to disk. -This allows the user or calling program to revise or apply data-specific logic -to redefine the output value for any of the output product's metadata attributes. -For example, when combining multiple images, the WCS information does not represent -any combination of the input WCS attributes. Instead, the user can have -their own processing code replace the *blended* WCS attributes with one that was -computed separately using a complex, accurate algorithm. This is, in fact, what -the resample step does to create the final resampled output product whenever it is -called by steps in the JWST pipeline. - -Additional control over the behavior of model_blender comes from editing the -schema for the input datamodels where the rules for each attribute are defined. -A sample definition from the core schema demonstrates the basic syntax used for -any model blending definitions:: - - time_end: - title: UTC time at end of exposure - type: string - fits_keyword: TIME-END - blend_rule: last + + +Combined model metadata +----------------------- + +The metadata assigned to the combined (output) model will be determined from: + + - the first combined model metadata + - any metadata with a ``blend_rule`` + +Looking at this sample from the core schema:: + + start_time: + title: "[d] exposure start time in MJD" + type: number + fits_keyword: EXPSTART blend_table: True + blend_rule: min + +Since ``start_time`` uses ``blend_rule: min`` the ``start_time`` for the combined +model will be the minimum of all input models. If ``blend_rule`` was not defined +``start_time`` for the combined model would be the ``start_time`` of the first model. + +.. warning:: + + Since model blender copies the metadata of the first model, all metadata + from that model will be added to combined model. This is true even for + metadata that is not defined in the schema. + +.. warning:: + + Metadata nested in arrays will not be blended. For example, + ``meta.background.polynomial_info`` contains a list/array of + dictionaries. Model blender will not blend this attribute. + +Input model metadata Table +-------------------------- + +As seen in the above example, ``start_time`` defines ``blend_table: True``. +This tells model blender to include this attribute in the table added to the +combined model that lists attributes from input models. If the attribute +has a ``fits_keyword`` the resulting column will use the keyword for the +column name (if not defined, the attribute name will be used). So for +the above example, the resulting table will contain a ``EXPSTART`` column +(and not a ``meta.exposure.start_time`` column) with a row listing +the ``start_time`` for each input model. -Any attribute without an entry for `blend_rule` will use the default rule of -`first` which selects the first value from all inputs in the order provided as the -final output value. Any attribute with a `blend_table` rule will insure that -the specific attribute will be included in the output HDRTAB binary table appended -to the product model when it gets written out to disk as a FITS file. - -The full set of rules included in the package are described in -:ref:`blender_rules` and include common list/array operations such as -(but not limited to): - - - minimum - - maximum - - first - - last - - mean - - zero - -These can then be used to customize the output value for any given attribute -should the rule provided by default with the schema installed with the -JWST environment not be correct for the user's input data. The user can simply -edit the schema definition installed in their JWST environment to apply custom -rules for blending the data being processed. +If an input model is missing an attribute a ``nan`` will be stored in the +corresponding table cell. diff --git a/jwst/model_blender/__init__.py b/jwst/model_blender/__init__.py index e69de29bb2..a2128990dd 100644 --- a/jwst/model_blender/__init__.py +++ b/jwst/model_blender/__init__.py @@ -0,0 +1,5 @@ +from .blender import ModelBlender +from .blendmeta import blendmodels + + +__all__ = ['ModelBlender', 'blendmodels'] diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index a74d708710..8cd83f8d90 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -6,6 +6,9 @@ from .tablebuilder import TableBuilder, table_to_schema +__all__ = ['ModelBlender'] + + class ModelBlender: def __init__(self, blend_ignore_attrs=None): self._first_header_meta = None diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 2e5dc2787e..5f890368aa 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -7,6 +7,8 @@ from .blender import ModelBlender +__all__ = ['blendmodels'] + # Primary functional interface for the code def blendmodels(product, inputs, ignore=None): diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index c06f61991c..06e9f7a415 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -3,6 +3,9 @@ import numpy as np +__all__ = ['RULE_FUNCTIONS', 'Blender', 'make_blender'] + + def multi(vals): """ This will either return the common value from a list of identical values From 9b5e4d87ceefe2af9762d38d1626b53e1e9f04d2 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 09:36:29 -0400 Subject: [PATCH 385/687] remove hard-coded polynomial_info ignore --- jwst/model_blender/blender.py | 16 +++++++++------- jwst/model_blender/schemautil.py | 12 +++++++++--- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 8cd83f8d90..69077b1c86 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -14,23 +14,25 @@ def __init__(self, blend_ignore_attrs=None): self._first_header_meta = None self._blenders = None self._table_builder = None - # TODO for now hard-code the polynomial_info ignore - self._blend_ignore_attrs = ['meta.wcs', 'meta.background.polynomial_info'] + self._blend_ignore_attrs = ['meta.wcs'] if blend_ignore_attrs is not None: self._blend_ignore_attrs.extend(blend_ignore_attrs) def accumulate(self, model): if self._first_header_meta is None: + # search the schema for other metadata to "blend" and to add to the table + attr_to_columns, attr_to_blend_rules, schema_ignores = parse_schema(model.schema) + + # update ignores list for items in schema that can't be blended + self._blend_ignore_attrs.extend(schema_ignores) + # capture the entire contents of the first model metadata self._first_header_meta = { attr: v - for attr, v in model.to_flat_dict().items() + for attr, v in model.to_flat_dict(include_arrays=False).items() if attr.startswith('meta') and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) } - # search the schema for other metadata to "blend" and to add to the table - attr_to_columns, attr_to_blend_rules = parse_schema(model.schema) - # make "blenders" for the metadata with special rules self._blenders = { attr: make_blender(rule) @@ -43,7 +45,7 @@ def accumulate(self, model): self._table_builder = TableBuilder(attr_to_columns) # convert the model to a flat header - header = model.to_flat_dict() + header = model.to_flat_dict(include_arrays=False) # add the header to the table self._table_builder.header_to_row(header) diff --git a/jwst/model_blender/schemautil.py b/jwst/model_blender/schemautil.py index 13991aa428..bb589acfab 100644 --- a/jwst/model_blender/schemautil.py +++ b/jwst/model_blender/schemautil.py @@ -16,6 +16,9 @@ def parse_schema(schema): type. For example {'meta.observation.time': 'mintime'} will combine all 'meta.observation.time' attributes using the 'mintime' rule. + + schema_ignores: lsit + list of attributes that will not be blended """ def callback(subschema, path, combiner, ctx, recurse): if len(path) <= 1: @@ -24,8 +27,6 @@ def callback(subschema, path, combiner, ctx, recurse): return # ignore non-meta attributes if 'items' in path: return # ignore attributes in arrays - if subschema.get('type') == 'array': - return # ignore ListNodes if subschema.get('properties'): return # ignore ObjectNodes @@ -38,6 +39,10 @@ def callback(subschema, path, combiner, ctx, recurse): # construct the metadata attribute path attr = '.'.join(path) + if subschema.get('type') == 'array': + ctx['ignores'].append(attr) + return # ignore ListNodes + # if 'blend_rule' is defined, make a 'blend' if 'blend_rule' in subschema: ctx['blends'][attr] = subschema['blend_rule'] @@ -49,6 +54,7 @@ def callback(subschema, path, combiner, ctx, recurse): ctx = { 'columns': {}, 'blends': {}, + 'ignores': [], } dm_schema.walk_schema(schema, callback, ctx) - return ctx['columns'], ctx['blends'] + return ctx['columns'], ctx['blends'], ctx['ignores'] From e937f019fdc542f5949d336b08c64aec913afe29 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 6 Sep 2024 13:29:17 -0400 Subject: [PATCH 386/687] use ModelBlender in coron3 --- jwst/pipeline/calwebb_coron3.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index d40c2421ad..b35cceecf8 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -7,7 +7,7 @@ from jwst.datamodels import ModelContainer, ModelLibrary -from ..model_blender import blendmeta +from ..model_blender import ModelBlender # step imports from ..coron import stack_refs_step @@ -161,8 +161,10 @@ def process(self, user_input): # Call the sequence of steps: outlier_detection, align_refs, and klip # once for each input target exposure resample_input = ModelContainer() + model_blender = ModelBlender() for target_file in targ_files: with datamodels.open(target_file) as target: + model_blender.accumulate(target) # Remove outliers from the target if not skip_outlier_detection: @@ -213,7 +215,7 @@ def process(self, user_input): completed = 'SKIPPED' if completed == 'COMPLETE': self.log.debug(f'Blending metadata for {result}') - blendmeta.blendmodels(result, targ_files) + model_blender.finalize_model(result) try: result.meta.asn.pool_name = input_models.meta.asn_table.asn_pool From 83f7aef7eec506555eca1b0569851abe1252ce26 Mon Sep 17 00:00:00 2001 From: Brett Date: Sat, 7 Sep 2024 11:14:56 -0400 Subject: [PATCH 387/687] access all blend_table attributes --- jwst/model_blender/blender.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 69077b1c86..aa75849c01 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -23,6 +23,14 @@ def accumulate(self, model): # search the schema for other metadata to "blend" and to add to the table attr_to_columns, attr_to_blend_rules, schema_ignores = parse_schema(model.schema) + # the previous model blending code accessed every attribute + # that will be added to the table. This causes those attributes + # to fill in defaults (if they don't have a defined value). + # Access all those attributes to allow the new model blender to + # behave in the same way. + for attr in attr_to_columns: + model[attr] + # update ignores list for items in schema that can't be blended self._blend_ignore_attrs.extend(schema_ignores) From 796c64e4c8b040a913266e14ba596a8db6b9be86 Mon Sep 17 00:00:00 2001 From: Brett Date: Sat, 7 Sep 2024 15:19:33 -0400 Subject: [PATCH 388/687] add changelog --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index a17ab3914b..4108daff15 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -95,6 +95,11 @@ master_background - Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit as a background slit, instead of ``"bkg"`` only. [#8600] +model_blender +------------- + +- Allow incremental blending of models. [#8759] + mrs_imatch ---------- From ed2dde3956952eeef631f59dad6fe74c84bbdcd7 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Mon, 9 Sep 2024 15:34:19 -0400 Subject: [PATCH 389/687] Update docs from review Co-authored-by: Ned Molter --- docs/jwst/model_blender/main.rst | 10 +++++----- jwst/model_blender/blender.py | 2 +- jwst/model_blender/schemautil.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/jwst/model_blender/main.rst b/docs/jwst/model_blender/main.rst index d9c2b7ae0c..91e1d31c7c 100644 --- a/docs/jwst/model_blender/main.rst +++ b/docs/jwst/model_blender/main.rst @@ -5,10 +5,10 @@ Role of Model Blender Model Blender performs 2 operations: - - Accumulate, blend and set metadata when combining several models (like + - Accumulate, blend and set metadata when combining several models ( as is done during resampling) using combination rules defined in the datamodel schemas. - - Record the metadata of input models in a "table" added to the + - Record the metadata of input models in a table added to the combined model. One problem with combining data from multiple exposures stems from not being able @@ -76,7 +76,7 @@ Looking at this sample from the core schema:: blend_rule: min Since ``start_time`` uses ``blend_rule: min`` the ``start_time`` for the combined -model will be the minimum of all input models. If ``blend_rule`` was not defined +model will be the minimum of all input models. If ``blend_rule`` was not defined, the ``start_time`` for the combined model would be the ``start_time`` of the first model. .. warning:: @@ -97,9 +97,9 @@ Input model metadata Table As seen in the above example, ``start_time`` defines ``blend_table: True``. This tells model blender to include this attribute in the table added to the combined model that lists attributes from input models. If the attribute -has a ``fits_keyword`` the resulting column will use the keyword for the +has a ``fits_keyword``, the resulting column will use the keyword for the column name (if not defined, the attribute name will be used). So for -the above example, the resulting table will contain a ``EXPSTART`` column +the above example, the resulting table will contain an ``EXPSTART`` column (and not a ``meta.exposure.start_time`` column) with a row listing the ``start_time`` for each input model. diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index aa75849c01..ae40a7c32d 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -45,7 +45,7 @@ def accumulate(self, model): self._blenders = { attr: make_blender(rule) for attr, rule in attr_to_blend_rules.items() - # first is implied + # 'first' is the default behavior so these rules need not be made explicitly if rule != 'first' and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) } diff --git a/jwst/model_blender/schemautil.py b/jwst/model_blender/schemautil.py index bb589acfab..815d30a653 100644 --- a/jwst/model_blender/schemautil.py +++ b/jwst/model_blender/schemautil.py @@ -17,7 +17,7 @@ def parse_schema(schema): will combine all 'meta.observation.time' attributes using the 'mintime' rule. - schema_ignores: lsit + schema_ignores: list list of attributes that will not be blended """ def callback(subschema, path, combiner, ctx, recurse): From f2955eda4c7b6cab3ec6c53c92dac528350504fa Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 16:01:45 -0400 Subject: [PATCH 390/687] add multi test --- jwst/model_blender/tests/test_blend.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 29caaf22a8..0436306732 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -19,6 +19,7 @@ '2017-11-11T15:15:06.118'] DATES = ['2017-11-30', '2017-11-11', '2017-12-10'] INSTRUMENT_NAMES = ['NIRCAM'] * N_MODELS +DETECTOR_NAMES = ['NRCA1', 'NRCA2', 'NRCA3'] CORONAGRAPHS = ['4QPM', '4QPM_1065', '4QPM_1140'] EXP_ONLYS = [True] * N_MODELS POLYNOMIAL_INFOS = [[{'degree': [1]}]] * N_MODELS @@ -59,6 +60,7 @@ def _make_data(): 'meta.filename': FILENAMES, 'meta.instrument.coronagraph': CORONAGRAPHS, 'meta.instrument.name': INSTRUMENT_NAMES, + 'meta.instrument.detector': DETECTOR_NAMES, 'meta.date': DATETIMES, 'meta.observation.date': DATES, 'meta.observation.date_beg': DATETIMES, @@ -80,6 +82,7 @@ def _make_data(): # skip filename as it will be ignored below 'meta.instrument.coronagraph': CORONAGRAPHS[0], 'meta.instrument.name': INSTRUMENT_NAMES[0], + 'meta.instrument.detector': 'MULTIPLE', 'meta.date': DATETIMES[0], 'meta.observation.date': DATES[1], 'meta.observation.date_beg': DATETIMES[1], From 6e1c672514ecca0c8671ffa52068b66e29d2482c Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 17:16:07 -0400 Subject: [PATCH 391/687] add docstrings to TableBuilder and ModelBlender --- jwst/model_blender/blender.py | 56 +++++++++++++++++++++++++++++ jwst/model_blender/tablebuilder.py | 58 ++++++++++++++++++++++++++++++ 2 files changed, 114 insertions(+) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index ae40a7c32d..601d8cf623 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -10,7 +10,36 @@ class ModelBlender: + """ + Class to "blend" metadata from several datamodels + into: + + - metadata for a combined model + - a table with metadata of each datamodel + + Input models can be added to the blender using `ModelBlender.accumulate` + and the output/combined model updated using `ModelBlender.finalize_model`. + + >>> blender = ModelBlender() + >>> blender.accumulate(input_model_a) # doctest: +SKIP + >>> blender.accumulate(input_model_b) # doctest: +SKIP + >>> blender.finalize_model(combined_model) # doctest: +SKIP + + """ def __init__(self, blend_ignore_attrs=None): + """ + Create a new `ModelBlender` using an optional list of + attributes to ignore. + + Parameters + ---------- + blend_ignore_attrs: list or None + A list of metadata attributes to ignore during blending. + These attributes will not be set on the output/combined. + These attributes must be strings containing the dotted + path of each attribute (for example "meta.filename"). + (Note that "meta.wcs" will always be ignored). + """ self._first_header_meta = None self._blenders = None self._table_builder = None @@ -19,6 +48,16 @@ def __init__(self, blend_ignore_attrs=None): self._blend_ignore_attrs.extend(blend_ignore_attrs) def accumulate(self, model): + """ + Process a datamodel, adding it's metadata to the blended + metadata and the metadata table. + + Parameters + ---------- + + model: `jwst.datamodels.JwstDataModel` + The datatamodel to blend. + """ if self._first_header_meta is None: # search the schema for other metadata to "blend" and to add to the table attr_to_columns, attr_to_blend_rules, schema_ignores = parse_schema(model.schema) @@ -74,6 +113,23 @@ def _finalize_table(self): return self._table_builder.build_table() def finalize_model(self, model): + """ + Add blended metadata and the accumulated metadata table to + the provided datamodel. The update process involves: + + - setting the model metadata to the blended metadata values + - adding an "hdrtab" attribute (containing the metadata table) + - updating the model schema to save "hdrtab" + + The provided model will be update in-place. + + Parameters + ---------- + model: `jwst.datamodels.JwstDataModel` + A datamodel that will have its metadata set + to the blended metadata and have the metadata + table assigned to the "hdrtab" attribute. + """ # update metadata of the output model based on the results # of the "blenders" for attr, val in self._finalize_metadata().items(): diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index 05ecb298bd..2d257f12ac 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -17,6 +17,21 @@ def _convert_dtype(value): def table_to_schema(table): + """ + Convert a "table" (a structured ndarray) to a stdatamodels + sub-schema that will allow the "table" to be stored to a fits + extension HDRTAB. + + Parameters + ---------- + table: ndarray + The structured array containing the data (and datatype). + + Returns + ------- + subschema: dict + stdatamodels for the "table" datatype. + """ return { 'title': 'Combined header table', 'fits_hdu': 'HDRTAB', @@ -31,13 +46,47 @@ def table_to_schema(table): class TableBuilder: + """ + Class to incrementally build a metadat "table" (a numpy + structured array) containing metadata from several models. + + >>> tb = TableBuilder({"meta.filename": "FN"}) + >>> tb.header_to_row({"meta.filename": "foo.fits"}) + >>> tb.build_table() + """ def __init__(self, attr_to_column): + """ + Construct a `TableBuilder` instance using the mapping + provided in ``attr_to_column``. + + Parameters + ---------- + attr_to_column: dict + A one-to-one mapping of attribute names (as + dotted paths like "meta.filename") to column + names (strings). + """ self.attr_to_column = attr_to_column self.columns = {col: [] for col in self.attr_to_column.values()} if len(attr_to_column) != len(self.columns): raise ValueError(f"Invalid attr_to_column, mapping is not 1-to-1: {attr_to_column}") def header_to_row(self, header): + """ + Add a datamodel header metadata dictionary + as a row to the table. + + This function will add a complete row for each + header. If header is missing a required attribute + (as defined in the mapping provided to + `TableBuilder.__init__`) the column with this + missing value for this row will contain a ``nan``. + + Parameters + ---------- + header: dict + Often produced from ``Datamodel.to_flat_dict``. + """ row = {} for attr in self.attr_to_column: if attr in header: @@ -49,6 +98,15 @@ def _add_row(self, row): self.columns[col].append(row[attr] if attr in row else np.nan) def build_table(self): + """ + Build a "table" containing the previously + added rows. + + Returns + ------- + table: numpy.ndarray + Structured array containing fields with datatypes + """ arrays = [] table_dtype = [] for col, items in self.columns.items(): From db0fb20fe8fdfe430f846098e1b1268638f44912 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 17:27:00 -0400 Subject: [PATCH 392/687] check that all models are the same type --- docs/jwst/model_blender/main.rst | 4 ++++ jwst/model_blender/blender.py | 10 ++++++++++ jwst/model_blender/tests/test_blend.py | 10 +++++++++- 3 files changed, 23 insertions(+), 1 deletion(-) diff --git a/docs/jwst/model_blender/main.rst b/docs/jwst/model_blender/main.rst index 91e1d31c7c..20b5210c4f 100644 --- a/docs/jwst/model_blender/main.rst +++ b/docs/jwst/model_blender/main.rst @@ -79,6 +79,10 @@ Since ``start_time`` uses ``blend_rule: min`` the ``start_time`` for the combine model will be the minimum of all input models. If ``blend_rule`` was not defined, the ``start_time`` for the combined model would be the ``start_time`` of the first model. +.. warning:: + + All blended models must be of the same type. + .. warning:: Since model blender copies the metadata of the first model, all metadata diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 601d8cf623..abe68dcbdb 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -20,6 +20,8 @@ class ModelBlender: Input models can be added to the blender using `ModelBlender.accumulate` and the output/combined model updated using `ModelBlender.finalize_model`. + All input/accumulated models must be of the same type. + >>> blender = ModelBlender() >>> blender.accumulate(input_model_a) # doctest: +SKIP >>> blender.accumulate(input_model_b) # doctest: +SKIP @@ -40,6 +42,7 @@ def __init__(self, blend_ignore_attrs=None): path of each attribute (for example "meta.filename"). (Note that "meta.wcs" will always be ignored). """ + self._model_type = None self._first_header_meta = None self._blenders = None self._table_builder = None @@ -59,6 +62,7 @@ def accumulate(self, model): The datatamodel to blend. """ if self._first_header_meta is None: + self._model_type = type(model) # search the schema for other metadata to "blend" and to add to the table attr_to_columns, attr_to_blend_rules, schema_ignores = parse_schema(model.schema) @@ -90,6 +94,12 @@ def accumulate(self, model): # make a table builder using the mapping from the schema self._table_builder = TableBuilder(attr_to_columns) + else: + if type(model) != self._model_type: # noqa: E721 + raise ValueError( + f"model of type {type(model)} does not match previous type({self._model_type}). " + "ModelBlender only supports blending models of the same model type." + ) # convert the model to a flat header header = model.to_flat_dict(include_arrays=False) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 0436306732..37c9d705dc 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -4,9 +4,10 @@ import numpy as np from stdatamodels.schema import walk_schema -from stdatamodels.jwst.datamodels import ImageModel +from stdatamodels.jwst.datamodels import ImageModel, CubeModel from jwst.model_blender import blendmeta +from jwst.model_blender.blender import ModelBlender # Setup various input meta data @@ -197,3 +198,10 @@ def test_blendtab(blend): for col in colnames: if col in input_values: assert newtab[col] == input_values[col] + + +def test_wrong_type_failure(): + blender = ModelBlender() + blender.accumulate(ImageModel()) + with pytest.raises(ValueError, match="model of type"): + blender.accumulate(CubeModel()) From fe17d55603a536e1fa1add796f458b2dbe9f5981 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 18:00:32 -0400 Subject: [PATCH 393/687] remove bad_min/maxtime, replace with min/max --- jwst/model_blender/rules.py | 20 ++------------------ 1 file changed, 2 insertions(+), 18 deletions(-) diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index 06e9f7a415..b8036fa64f 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -28,18 +28,6 @@ def _isotime(time_str): return isotime -def bad_mintime(vals): - # this introduces floating point error for backwards compatibility - # it should be replaced with min - return _isotime(min(vals)).isoformat() - - -def bad_maxtime(vals): - # this introduces floating point error for backwards compatibility - # it should be replaced with max - return _isotime(max(vals)).isoformat() - - RULE_FUNCTIONS = { 'multi': multi, 'mean': np.mean, @@ -47,15 +35,11 @@ def bad_maxtime(vals): 'max': np.max, 'min': np.min, - # these intentionally introduce floating point errors - # for backwards compatibility but would be more accurate - # using the builtin min/max - 'mintime': bad_mintime, - 'maxtime': bad_maxtime, - # retained date/time names for backwards compatibility # as these all assume ISO8601 format the lexical and # chronological sorting match + 'mintime': min, + 'maxtime': max, 'mindate': min, 'maxdate': max, 'mindatetime': min, From 967715786430e9dbba11c544a7f622e9ad4c1d53 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 18:01:55 -0400 Subject: [PATCH 394/687] don't create missing metdata --- jwst/model_blender/blender.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index abe68dcbdb..3578d00a12 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -66,14 +66,6 @@ def accumulate(self, model): # search the schema for other metadata to "blend" and to add to the table attr_to_columns, attr_to_blend_rules, schema_ignores = parse_schema(model.schema) - # the previous model blending code accessed every attribute - # that will be added to the table. This causes those attributes - # to fill in defaults (if they don't have a defined value). - # Access all those attributes to allow the new model blender to - # behave in the same way. - for attr in attr_to_columns: - model[attr] - # update ignores list for items in schema that can't be blended self._blend_ignore_attrs.extend(schema_ignores) From 6bd3367fc0ae6472a8da18c158b00d82a7b282a2 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 9 Sep 2024 22:03:03 -0400 Subject: [PATCH 395/687] add output for doctest --- jwst/model_blender/tablebuilder.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index 2d257f12ac..3a93cf9ae5 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -53,6 +53,8 @@ class TableBuilder: >>> tb = TableBuilder({"meta.filename": "FN"}) >>> tb.header_to_row({"meta.filename": "foo.fits"}) >>> tb.build_table() + rec.array([('foo.fits',)], + dtype=[('FN', ' Date: Thu, 12 Sep 2024 12:32:52 -0400 Subject: [PATCH 396/687] Update docs from review Co-authored-by: Melanie Clarke --- jwst/model_blender/blender.py | 6 +++--- jwst/model_blender/tablebuilder.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 3578d00a12..e2cc50f927 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -52,14 +52,14 @@ def __init__(self, blend_ignore_attrs=None): def accumulate(self, model): """ - Process a datamodel, adding it's metadata to the blended + Process a datamodel, adding its metadata to the blended metadata and the metadata table. Parameters ---------- model: `jwst.datamodels.JwstDataModel` - The datatamodel to blend. + The datamodel to blend. """ if self._first_header_meta is None: self._model_type = type(model) @@ -123,7 +123,7 @@ def finalize_model(self, model): - adding an "hdrtab" attribute (containing the metadata table) - updating the model schema to save "hdrtab" - The provided model will be update in-place. + The provided model will be updated in-place. Parameters ---------- diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index 3a93cf9ae5..f74a0db179 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -47,7 +47,7 @@ def table_to_schema(table): class TableBuilder: """ - Class to incrementally build a metadat "table" (a numpy + Class to incrementally build a metadata "table" (a numpy structured array) containing metadata from several models. >>> tb = TableBuilder({"meta.filename": "FN"}) From a3719fb8bda0371ee1d1082f4757a31359bfa8f7 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 12:36:26 -0400 Subject: [PATCH 397/687] remove unused _isotime --- jwst/model_blender/rules.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index b8036fa64f..026ddb4707 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -21,13 +21,6 @@ def multi(vals): return "MULTIPLE" -def _isotime(time_str): - hms = [float(i) for i in time_str.split(':')] - sec_ms = hms[2] - int(hms[2]) - isotime = time(int(hms[0]), int(hms[1]), int(hms[2]), int(sec_ms * 1000000)) - return isotime - - RULE_FUNCTIONS = { 'multi': multi, 'mean': np.mean, From fad727c83813223eaf0b0bdfd785df241b149587 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 12:39:57 -0400 Subject: [PATCH 398/687] break up long comprehensions --- jwst/model_blender/blender.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index e2cc50f927..ad8ad30b84 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -70,19 +70,22 @@ def accumulate(self, model): self._blend_ignore_attrs.extend(schema_ignores) # capture the entire contents of the first model metadata - self._first_header_meta = { - attr: v - for attr, v in model.to_flat_dict(include_arrays=False).items() - if attr.startswith('meta') and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) - } + self._first_header_meta = {} + for attr, v in model.to_flat_dict(include_arrays=False).items(): + if not attr.startswith('meta'): + continue + if any((attr.startswith(i) for i in self._blend_ignore_attrs)): + continue + self._first_header_meta[attr] = v # make "blenders" for the metadata with special rules - self._blenders = { - attr: make_blender(rule) - for attr, rule in attr_to_blend_rules.items() - # 'first' is the default behavior so these rules need not be made explicitly - if rule != 'first' and not any((attr.startswith(i) for i in self._blend_ignore_attrs)) - } + self._blenders = {} + for attr, rule in attr_to_blend_rules.items(): + if rule == 'first': + continue + if any((attr.startswith(i) for i in self._blend_ignore_attrs)): + continue + self._blenders[attr] = make_blender(rule) # make a table builder using the mapping from the schema self._table_builder = TableBuilder(attr_to_columns) From c8da30b1da28ae4c31bd252127f66c4561458317 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 12:56:02 -0400 Subject: [PATCH 399/687] update docstrings --- jwst/model_blender/blender.py | 14 +++++++++----- jwst/model_blender/blendmeta.py | 20 ++++++++++++++++++++ jwst/model_blender/rules.py | 1 - jwst/model_blender/schemautil.py | 7 +++++++ jwst/model_blender/tablebuilder.py | 15 ++++++++------- 5 files changed, 44 insertions(+), 13 deletions(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index ad8ad30b84..1903d27fe0 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -11,8 +11,9 @@ class ModelBlender: """ - Class to "blend" metadata from several datamodels - into: + Class to "blend" metadata from several datamodels. + + Tht output "blended" model will contain: - metadata for a combined model - a table with metadata of each datamodel @@ -30,8 +31,7 @@ class ModelBlender: """ def __init__(self, blend_ignore_attrs=None): """ - Create a new `ModelBlender` using an optional list of - attributes to ignore. + Create a new `ModelBlender`. Parameters ---------- @@ -52,7 +52,9 @@ def __init__(self, blend_ignore_attrs=None): def accumulate(self, model): """ - Process a datamodel, adding its metadata to the blended + Blend metadata for model. + + Process model adding its metadata to the blended metadata and the metadata table. Parameters @@ -119,6 +121,8 @@ def _finalize_table(self): def finalize_model(self, model): """ + Update model with the blend results. + Add blended metadata and the accumulated metadata table to the provided datamodel. The update process involves: diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 5f890368aa..49790f156d 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -12,6 +12,26 @@ # Primary functional interface for the code def blendmodels(product, inputs, ignore=None): + """ + Blend datamodel metadata. + + Properties + ---------- + product: JwstDataModel + A datamodel that will have its metadata set + to the blended metadata and have the metadata + table assigned to the "hdrtab" attribute. + + inputs: list of JwstDataModel + Input datamodels with metadata to blend. + + ignore: list of str, optional + A list of metadata attributes to ignore during blending. + These attributes will not be set on the output/combined. + These attributes must be strings containing the dotted + path of each attribute (for example "meta.filename"). + (Note that "meta.wcs" will always be ignored). + """ blender = ModelBlender(blend_ignore_attrs=ignore) for model in inputs: blender.accumulate(model) diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index 026ddb4707..3abc7f9085 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -20,7 +20,6 @@ def multi(vals): if num_vals > 1: return "MULTIPLE" - RULE_FUNCTIONS = { 'multi': multi, 'mean': np.mean, diff --git a/jwst/model_blender/schemautil.py b/jwst/model_blender/schemautil.py index 815d30a653..be84a9c4e2 100644 --- a/jwst/model_blender/schemautil.py +++ b/jwst/model_blender/schemautil.py @@ -2,6 +2,13 @@ def parse_schema(schema): """ + Parse an ASDF schema for model blending instructions. + + Parameters + ---------- + schema : dict + Dictionary containing an ASDF schema. + Returns ------- attr_to_column: dict diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/tablebuilder.py index f74a0db179..e677d0699e 100644 --- a/jwst/model_blender/tablebuilder.py +++ b/jwst/model_blender/tablebuilder.py @@ -18,6 +18,8 @@ def _convert_dtype(value): def table_to_schema(table): """ + Construct a schema for a table. + Convert a "table" (a structured ndarray) to a stdatamodels sub-schema that will allow the "table" to be stored to a fits extension HDRTAB. @@ -47,7 +49,9 @@ def table_to_schema(table): class TableBuilder: """ - Class to incrementally build a metadata "table" (a numpy + Class to build a metadata table. + + Used to incrementally build a metadata "table" (a numpy structured array) containing metadata from several models. >>> tb = TableBuilder({"meta.filename": "FN"}) @@ -58,8 +62,7 @@ class TableBuilder: """ def __init__(self, attr_to_column): """ - Construct a `TableBuilder` instance using the mapping - provided in ``attr_to_column``. + Create a new `TableBuilder`. Parameters ---------- @@ -75,8 +78,7 @@ def __init__(self, attr_to_column): def header_to_row(self, header): """ - Add a datamodel header metadata dictionary - as a row to the table. + Add metadata in header to the table. This function will add a complete row for each header. If header is missing a required attribute @@ -101,8 +103,7 @@ def _add_row(self, row): def build_table(self): """ - Build a "table" containing the previously - added rows. + Build the final "table". Returns ------- From ee1743b3db2ea31249509008bdcf38e8a62cddca Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 13:07:40 -0400 Subject: [PATCH 400/687] add docs to rules --- jwst/model_blender/rules.py | 60 +++++++++++++++++++++++++++++++++++-- 1 file changed, 58 insertions(+), 2 deletions(-) diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index 3abc7f9085..da5b4a8895 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -6,7 +6,7 @@ __all__ = ['RULE_FUNCTIONS', 'Blender', 'make_blender'] -def multi(vals): +def _multi(vals): """ This will either return the common value from a list of identical values or 'MULTIPLE' @@ -21,7 +21,7 @@ def multi(vals): return "MULTIPLE" RULE_FUNCTIONS = { - 'multi': multi, + 'multi': _multi, 'mean': np.mean, 'sum': np.sum, 'max': np.max, @@ -37,21 +37,77 @@ def multi(vals): 'mindatetime': min, 'maxdatetime': max } +""" +Mapping of rule names to functions. + +Used for `make_blender`. + +The following rules are considered deprecated +and should not be used for new schemas. + + - mintime + - maxtime + - mindate + - maxdate + - mindatetime + - maxdatetime +""" class Blender: + """ + Single attribute metadata blender + """ def __init__(self, blend_function): + """ + Create a new metadata attribute blender. + + Parameters + ---------- + blend_function: callable + Function to blend accumulated metadata values + """ self.blend_function = blend_function self.values = [] def accumulate(self, value): + """ + Add a metadata value for blending. + + Parameters + ---------- + value: + Value for this metadata attribute to use + when blending. + """ self.values.append(value) def finalize(self): + """ + Blend the accumulated metadata values. + + Returns + ------- + value: + The blended result. + """ if not self.values: return None return self.blend_function(self.values) def make_blender(rule): + """ + Make a new Blender instance using the provided rule + + Parameters + ---------- + rule: string + Name of the blending rule. Must be in ``RULE_FUNCTIONS``. + + Returns + ------- + blender: Blender + Blender instance using the provided rule. + """ return Blender(RULE_FUNCTIONS[rule]) From 97222cdc4717b45ee754429f70dbfe02338fbfcb Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 13:20:23 -0400 Subject: [PATCH 401/687] make some things private, document rules --- docs/jwst/model_blender/blender_rules.rst | 1 + .../model_blender/{schemautil.py => _schemautil.py} | 0 .../{tablebuilder.py => _tablebuilder.py} | 0 jwst/model_blender/blender.py | 4 ++-- jwst/model_blender/rules.py | 13 +++++++------ 5 files changed, 10 insertions(+), 8 deletions(-) rename jwst/model_blender/{schemautil.py => _schemautil.py} (100%) rename jwst/model_blender/{tablebuilder.py => _tablebuilder.py} (100%) diff --git a/docs/jwst/model_blender/blender_rules.rst b/docs/jwst/model_blender/blender_rules.rst index 8a952c6516..55d5e6d67e 100644 --- a/docs/jwst/model_blender/blender_rules.rst +++ b/docs/jwst/model_blender/blender_rules.rst @@ -16,3 +16,4 @@ Supported rule names and corresponding functions are listed in .. automodapi:: jwst.model_blender.rules :no-inheritance-diagram: + :include-all-objects: diff --git a/jwst/model_blender/schemautil.py b/jwst/model_blender/_schemautil.py similarity index 100% rename from jwst/model_blender/schemautil.py rename to jwst/model_blender/_schemautil.py diff --git a/jwst/model_blender/tablebuilder.py b/jwst/model_blender/_tablebuilder.py similarity index 100% rename from jwst/model_blender/tablebuilder.py rename to jwst/model_blender/_tablebuilder.py diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 1903d27fe0..5f0147e267 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -2,8 +2,8 @@ from stdatamodels import fits_support from .rules import make_blender -from .schemautil import parse_schema -from .tablebuilder import TableBuilder, table_to_schema +from ._schemautil import parse_schema +from ._tablebuilder import TableBuilder, table_to_schema __all__ = ['ModelBlender'] diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index da5b4a8895..67da7912ef 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -3,7 +3,7 @@ import numpy as np -__all__ = ['RULE_FUNCTIONS', 'Blender', 'make_blender'] +__all__ = ['RULE_FUNCTIONS', 'AttributeBlender', 'make_blender'] def _multi(vals): @@ -20,6 +20,7 @@ def _multi(vals): if num_vals > 1: return "MULTIPLE" + RULE_FUNCTIONS = { 'multi': _multi, 'mean': np.mean, @@ -54,7 +55,7 @@ def _multi(vals): """ -class Blender: +class AttributeBlender: """ Single attribute metadata blender """ @@ -98,16 +99,16 @@ def finalize(self): def make_blender(rule): """ - Make a new Blender instance using the provided rule + Make a `AttributeBlender` instance using the provided rule Parameters ---------- rule: string - Name of the blending rule. Must be in ``RULE_FUNCTIONS``. + Name of the blending rule. Must be in `RULE_FUNCTIONS`. Returns ------- - blender: Blender + attr_blender: `AttrBlender` Blender instance using the provided rule. """ - return Blender(RULE_FUNCTIONS[rule]) + return AttributeBlender(RULE_FUNCTIONS[rule]) From 81f26ca3cc2605a668385beb9e521558ff1694a9 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 13:29:11 -0400 Subject: [PATCH 402/687] add ModelBlender to docs --- docs/jwst/model_blender/main.rst | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/docs/jwst/model_blender/main.rst b/docs/jwst/model_blender/main.rst index 20b5210c4f..97b8bb5611 100644 --- a/docs/jwst/model_blender/main.rst +++ b/docs/jwst/model_blender/main.rst @@ -36,6 +36,10 @@ This code works by Using model_blender =================== + +Using blendmodels +----------------- + The simplest way to run model blender only requires calling a single interface: .. code-block:: python @@ -53,6 +57,23 @@ The output product will end up with new metadata attribute values and a new ``hd attribute that will produce a ``HDRTAB`` table extension when the product is saved to disk. +Using ModelBlender +------------------ + +Alternatively, metadata blending can be done incrementally by using `ModelBlender` +to accumulate metadata from several models and then blending the result into an +output model. + +.. code-block:: python + + from jwst.model_blender import ModelBlender + blender = ModelBlender() + for model in input_models: + blender.accumulate(model) + blender.finalize_model(product) + +This produces a ``product`` identical to a call to `blendmodels` described above. + Customizing the behavior ======================== From 31f7fd746cd6bd1900b6e191cdd95f2c28234fe6 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 12 Sep 2024 13:29:28 -0400 Subject: [PATCH 403/687] remove unused import --- jwst/model_blender/rules.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/jwst/model_blender/rules.py b/jwst/model_blender/rules.py index 67da7912ef..799d6ffba2 100644 --- a/jwst/model_blender/rules.py +++ b/jwst/model_blender/rules.py @@ -1,5 +1,3 @@ -from datetime import time - import numpy as np From f986dc1380fec60a335961daf65268cd108a2ca4 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 12 Sep 2024 13:55:12 -0400 Subject: [PATCH 404/687] Update jwst/model_blender/blender.py Co-authored-by: Melanie Clarke --- jwst/model_blender/blender.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/model_blender/blender.py b/jwst/model_blender/blender.py index 5f0147e267..732eb6455e 100644 --- a/jwst/model_blender/blender.py +++ b/jwst/model_blender/blender.py @@ -59,7 +59,6 @@ def accumulate(self, model): Parameters ---------- - model: `jwst.datamodels.JwstDataModel` The datamodel to blend. """ From 418d9a8a6180d138dbd6718a84795cfb0b9cb54a Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 12 Sep 2024 13:55:49 -0400 Subject: [PATCH 405/687] Update jwst/model_blender/blendmeta.py Co-authored-by: Melanie Clarke --- jwst/model_blender/blendmeta.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/model_blender/blendmeta.py b/jwst/model_blender/blendmeta.py index 49790f156d..d9a5434e08 100644 --- a/jwst/model_blender/blendmeta.py +++ b/jwst/model_blender/blendmeta.py @@ -15,7 +15,7 @@ def blendmodels(product, inputs, ignore=None): """ Blend datamodel metadata. - Properties + Parameters ---------- product: JwstDataModel A datamodel that will have its metadata set From 7a82b784ebe90843e154bb15e0d639f6275a24c9 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 13 Sep 2024 11:15:47 -0400 Subject: [PATCH 406/687] rename variable to silence sonarscan --- jwst/model_blender/_schemautil.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/jwst/model_blender/_schemautil.py b/jwst/model_blender/_schemautil.py index be84a9c4e2..2aa50c6ff2 100644 --- a/jwst/model_blender/_schemautil.py +++ b/jwst/model_blender/_schemautil.py @@ -38,9 +38,9 @@ def callback(subschema, path, combiner, ctx, recurse): return # ignore ObjectNodes # strip trailing path if there's a combiner - for combiner in ['anyOf', 'oneOf']: - if combiner in path: - path = path[:path.index(combiner)] + for schema_combiner in ['anyOf', 'oneOf']: + if schema_combiner in path: + path = path[:path.index(schema_combiner)] break # construct the metadata attribute path From 47ec50b5fa375eac5cb4a5de19f540dd4b5eff12 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 16 Sep 2024 13:07:12 -0400 Subject: [PATCH 407/687] Remove extra spaces --- jwst/associations/association.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/associations/association.py b/jwst/associations/association.py index 1baed4406f..e6edce4b75 100644 --- a/jwst/associations/association.py +++ b/jwst/associations/association.py @@ -209,8 +209,8 @@ def validate(cls, asn): fpath, fname = os.path.split(member["expname"]) if len(fpath) > 0: err_str = "Input association file contains path information;" - err_str += " note that this can complicate usage and/or sharing" - err_str += " of such files." + err_str += " note that this can complicate usage and/or sharing" + err_str += " of such files." logger.debug(err_str) warnings.warn(err_str, UserWarning) return True From fa9a6dd0c3753dfa43786a9031fe307b736d6e7c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 16 Sep 2024 13:07:59 -0400 Subject: [PATCH 408/687] Resolve autosummary warning about missing stub file --- docs/jwst/associations/association_api.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/jwst/associations/association_api.rst b/docs/jwst/associations/association_api.rst index 68071c8752..3a82f4ffef 100644 --- a/docs/jwst/associations/association_api.rst +++ b/docs/jwst/associations/association_api.rst @@ -1,3 +1,4 @@ .. _asn-full-api: .. automodapi:: jwst.associations + :skip: main From 7f04369032d6f0da013bd6c6560456853a868b43 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 16 Sep 2024 13:08:35 -0400 Subject: [PATCH 409/687] Fix typos --- jwst/associations/asn_from_list.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/associations/asn_from_list.py b/jwst/associations/asn_from_list.py index e7c9f5009a..792daea0d4 100644 --- a/jwst/associations/asn_from_list.py +++ b/jwst/associations/asn_from_list.py @@ -9,7 +9,7 @@ def asn_from_list(items, rule=DMS_Level3_Base, **kwargs): - """Creat an association from a list + """Create an association from a list Parameters ---------- @@ -32,7 +32,7 @@ def asn_from_list(items, rule=DMS_Level3_Base, **kwargs): ----- This is a lower-level tool for artificially creating an association. As such, the association created may not be valid. - It is presume the user knows what they are doing. + It is presumed the user knows what they are doing. """ asn = rule() From dc25467e9e79f2b8455efc43ddb0960b46a2c28c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 16 Sep 2024 13:09:43 -0400 Subject: [PATCH 410/687] Update docs for path recommendation in expname --- docs/jwst/associations/level2_asn_technical.rst | 12 ++++++++---- docs/jwst/associations/level3_asn_technical.rst | 9 ++++++++- docs/jwst/associations/overview.rst | 17 ++++++++++------- 3 files changed, 26 insertions(+), 12 deletions(-) diff --git a/docs/jwst/associations/level2_asn_technical.rst b/docs/jwst/associations/level2_asn_technical.rst index ee32678fe3..ec71adc71d 100644 --- a/docs/jwst/associations/level2_asn_technical.rst +++ b/docs/jwst/associations/level2_asn_technical.rst @@ -133,10 +133,14 @@ members *required* association. Each member has the following keywords. expname *required* - The exposure file name. This must be a filename only, with no path. This - file must be in the same directory as the association file, so path is not - needed. If path data is included, an exception is raised when loading the - association file. + The exposure file name. + + It is recommended that this value be set to a filename only, + with no path. In this case, the file must be in the same directory as the + association file, so the path is not needed. If valid absolute or relative + path data is included, the input file is still found and used by the pipeline, + but a warning is raised: the association file in this case may be less portable + than if the path data is not included. exptype *required* Type of information represented by the exposure. Possible diff --git a/docs/jwst/associations/level3_asn_technical.rst b/docs/jwst/associations/level3_asn_technical.rst index 98d872a6d4..5e08f6c54a 100644 --- a/docs/jwst/associations/level3_asn_technical.rst +++ b/docs/jwst/associations/level3_asn_technical.rst @@ -135,7 +135,14 @@ members *required* association. Each member has the following keywords. expname *required* - The exposure file name + The exposure file name. + + It is recommended that this value be set to a filename only, + with no path. In this case, the file must be in the same directory as the + association file, so the path is not needed. If valid absolute or relative + path data is included, the input file is still found and used by the pipeline, + but a warning is raised: the association file in this case may be less portable + than if the path data is not included. exptype *required* Type of information represented by the exposure. Possible values are diff --git a/docs/jwst/associations/overview.rst b/docs/jwst/associations/overview.rst index e90554e44a..4b3f0ffa7c 100644 --- a/docs/jwst/associations/overview.rst +++ b/docs/jwst/associations/overview.rst @@ -84,13 +84,16 @@ Usage Users should not need to run the generator. Instead, it is expected that one edits an already existing association that accompanies the user's JWST data. -Care should be taken if editing an association file. Keep in mind all input -files listed in the association file are in the same directory as the -association file and no path information can be put in ``expname``, only the -file name. Or, if need be, an association can be created based on the existing -:ref:`Stage 2 ` or :ref:`Stage 3 ` examples. -If, however, the user *does* need to run the generator, :ref:`Association Generator -` documentation will be helpful. + +Care should be taken if editing an association file. Keep in mind that generally +all input files listed in the association files are assumed to be in the same +directory as the association file and no path information is put in ``expname``. + +If need be, an association can be created based on the existing +:ref:`Stage 2 ` or :ref:`Stage 3 ` examples, +or using the command line tool :ref:`asn-from-list`. +If, however, the user *does* need to run the generator, the :ref:`Association Generator +` description and :ref:`asn-generate` documentation will be helpful. Once an association is in-hand, one can pass it as input to a pipeline routine. For example:: From bc0abc27c261f9fe35125026f66fce0bc7e6b51c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 16 Sep 2024 13:18:17 -0400 Subject: [PATCH 411/687] Update change log --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index b9f075c9c6..bd82a9d40d 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -64,6 +64,9 @@ documentation - Add changelog to documentation. [#8716] +- Updated description of association keyword `expname`: including path information + in addition to the filename is discouraged, but allowed. [#8789] + emicorr ------- From 05d3c960fca68340232c6cd59c67662bdae7770b Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 17 Sep 2024 09:46:13 -0400 Subject: [PATCH 412/687] JP-3639: Implement 1/f noise correction for ramp data (#8669) --- CHANGES.rst | 16 + docs/jwst/clean_flicker_noise/arguments.rst | 71 + docs/jwst/clean_flicker_noise/index.rst | 13 + docs/jwst/clean_flicker_noise/main.rst | 253 +++ .../clean_flicker_noise/reference_files.rst | 3 + docs/jwst/nsclean/arguments.rst | 62 +- docs/jwst/nsclean/main.rst | 168 +- docs/jwst/package_index.rst | 1 + docs/jwst/pipeline/calwebb_detector1.rst | 88 +- jwst/clean_flicker_noise/__init__.py | 3 + .../clean_flicker_noise.py | 1522 +++++++++++++++++ .../clean_flicker_noise_step.py | 146 ++ jwst/{nsclean => clean_flicker_noise}/lib.py | 3 +- jwst/clean_flicker_noise/tests/__init__.py | 0 .../tests/test_clean_flicker_noise.py | 844 +++++++++ .../tests/test_clean_flicker_noise_step.py | 82 + jwst/lib/suffix.py | 2 + jwst/msaflagopen/msaflag_open.py | 21 +- jwst/msaflagopen/tests/test_msa_open.py | 96 +- jwst/nsclean/nsclean.py | 508 ------ jwst/nsclean/nsclean_step.py | 103 +- jwst/pipeline/calwebb_detector1.py | 5 + jwst/pipeline/calwebb_spec2.py | 4 +- jwst/pipeline/clean_noise.cfg | 2 + jwst/pipeline/tests/test_calwebb_spec2.py | 2 +- jwst/regtest/test_miri_image.py | 43 + jwst/regtest/test_nircam_image.py | 41 + jwst/regtest/test_niriss_image.py | 38 +- jwst/regtest/test_nirspec_irs2_detector1.py | 41 + jwst/step.py | 2 + jwst/stpipe/integration.py | 1 + 31 files changed, 3397 insertions(+), 787 deletions(-) create mode 100644 docs/jwst/clean_flicker_noise/arguments.rst create mode 100644 docs/jwst/clean_flicker_noise/index.rst create mode 100644 docs/jwst/clean_flicker_noise/main.rst create mode 100644 docs/jwst/clean_flicker_noise/reference_files.rst create mode 100644 jwst/clean_flicker_noise/__init__.py create mode 100644 jwst/clean_flicker_noise/clean_flicker_noise.py create mode 100644 jwst/clean_flicker_noise/clean_flicker_noise_step.py rename jwst/{nsclean => clean_flicker_noise}/lib.py (99%) create mode 100644 jwst/clean_flicker_noise/tests/__init__.py create mode 100644 jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py create mode 100644 jwst/clean_flicker_noise/tests/test_clean_flicker_noise_step.py delete mode 100644 jwst/nsclean/nsclean.py create mode 100644 jwst/pipeline/clean_noise.cfg diff --git a/CHANGES.rst b/CHANGES.rst index b9f075c9c6..51fdfe145b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -42,6 +42,18 @@ badpix_selfcal - Subtract pedestal dark when constructing min array across selfcal exposures for MIRI MRS data. [#8771] +calwebb_detector1 +----------------- + +- Added the optional ``clean_flicker_noise`` step between ``jump`` and + ``ramp_fit``. [#8669] + +clean_flicker_noise +------------------- + +- Implemented this new optional step to clean transient flicker noise (e.g. 1/f noise) + from group images in ramp data. [#8669] + cube_build ---------- @@ -115,6 +127,10 @@ nsclean Due to the computational costs of matrix operations, this is a large speedup that has little effect on the results. [#8745] +- Merged implementation with the new ``clean_flicker_noise`` step. This step + can still be called from the ``calwebb_spec2`` pipeline on NIRSpec rate + data, but it is now deprecated. [#8669] + outlier_detection ----------------- diff --git a/docs/jwst/clean_flicker_noise/arguments.rst b/docs/jwst/clean_flicker_noise/arguments.rst new file mode 100644 index 0000000000..8fc131709e --- /dev/null +++ b/docs/jwst/clean_flicker_noise/arguments.rst @@ -0,0 +1,71 @@ +.. _clean_flicker_noise_arguments: + +Step Arguments +============== + +The ``clean_flicker_noise`` step has the following optional arguments to control +the behavior of the processing. + +``--fit_method`` (str, default='median') + The noise fitting algorithm to use. Options are 'fft' and 'median'. + +``--fit_by_channel`` (boolean, default=False) + If set, flicker noise is fit independently for each detector channel. + Ignored for MIRI, for subarray data, and for `fit_method` = 'fft'. + +``--background_method`` (str, default='median') + If 'median', the preliminary background to remove and restore + after fitting the noise is a simple median of the background data. + If 'model', the background data is fit with a low-resolution model + via `~photutils.background.Background2D`. + If None, the background value is set to 0.0. + +``--background_box_size`` (list of int, default=(32,32)) + Box size for the data grid used by `~photutils.background.Background2D` + when `background_method` = 'model'. For best results, use a + box size that evenly divides the input image shape. + +``--mask_science_regions`` (boolean, default=False) + For NIRSpec, mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. For MIRI imaging, mask + regions of the detector not used for science. + +``--n_sigma`` (float, default=2.0) + The sigma-clipping threshold to use when searching for outliers + and illuminated pixels to be excluded from use in the background + and noise fitting processes. + +``--fit_histogram`` (boolean, default=False) + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + +``--single_mask`` (boolean, default=True) + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. + +``--user_mask`` (string, default=None) + Path to a user-supplied mask file. If supplied, the mask is used + directly and the process of creating a scene mask in the step is + skipped. + + The mask file must contain either a `~jwst.datamodels.ImageModel` + or a `~jwst.datamodels.CubeModel`, with image dimensions matching + the input science data. If an ImageModel is provided, the same + mask will be used for all integrations. If a CubeModel is provided, + the number of slices must equal the number of integrations in + the input science data. + +``--save_mask`` (boolean, default=False) + If set, the mask constructed by the step will be saved to a file + with suffix 'mask'. + +``--save_background`` (boolean, default=False) + If set, the background fit to the group diff images will be saved + to a file with suffix 'flicker_bkg'. + +``--save_noise`` (boolean, default=False) + If set, the residual noise fit and removed from the input data + will be saved to a file with suffix 'flicker_noise'. diff --git a/docs/jwst/clean_flicker_noise/index.rst b/docs/jwst/clean_flicker_noise/index.rst new file mode 100644 index 0000000000..aa8b5e426d --- /dev/null +++ b/docs/jwst/clean_flicker_noise/index.rst @@ -0,0 +1,13 @@ +.. _clean_flicker_noise_step: + +=================== +Clean Flicker Noise +=================== +.. toctree:: + :maxdepth: 2 + + main.rst + reference_files.rst + arguments.rst + +.. automodapi:: jwst.clean_flicker_noise diff --git a/docs/jwst/clean_flicker_noise/main.rst b/docs/jwst/clean_flicker_noise/main.rst new file mode 100644 index 0000000000..c7356c690a --- /dev/null +++ b/docs/jwst/clean_flicker_noise/main.rst @@ -0,0 +1,253 @@ +Description +=========== + +:Class: `jwst.clean_flicker_noise.CleanFlickerNoiseStep` +:Alias: clean_flicker_noise + +Overview +-------- +The ``clean_flicker_noise`` step removes flicker noise from calibrated +ramp images, after the :ref:`jump ` step and prior to +performing the :ref:`ramp_fitting ` step. + +For NIR detectors, the noise addressed by this step is 1/f noise, which +appears as faint banding along the detector slow axis. For NIRSpec and +NIRISS, 1/f noise looks like vertical stripes; for NIRCam, it appears +as horizontal stripes. + +MIRI images also often show a faint vertical banding, similar to 1/f noise +but from a different physical source. This type of flicker noise can be +corrected by similar methods, so it is also addressed by this step. + +To correct for flicker noise, the algorithm requires that the noise +generated between one group readout and the next be isolated as much +as possible from the astronomical signal. The residual noise may then +be fit in frequency space, via an FFT, or may be characterized by a +median along the rows or columns, as appropriate for the detector. +The modeled noise is then directly subtracted from each group readout. + +The correction is available for any type of NIRSpec, NIRISS, or NIRCam +exposure. For MIRI, it is available only for imaging exposures. + +Creation of a scene mask +------------------------ +One of the key components of the correction is knowing which pixels can +be used to fit the background noise. The step builds a scene mask +on the fly from a draft rate image, generated from the input ramp data, +which is used to mark usable and unusable pixels. The mask is a 2D +Boolean array, having the same size as the image, with +pixels set to True interpreted as being OK to use. + +The process of building the mask varies somewhat depending on the +observing mode of the image being processed. Some features are common +to all modes, while others are mode-specific. The following sections +describe each type of masking that can be applied. At the end, there +is a summary of the types applied to each instrument mode. + +The user-settable step parameter `save_mask` can be used to save the +scene mask to a file, if desired (see the +:ref:`step arguments `). + +Note that a user may also supply their own mask image as input to the step, +in which case the process of creating a mask is skipped. The step parameter +`user_mask` is used to specify an input mask. If specified, the input +mask must contain a datamodel matching the shape of a 'rate' or 'rateints' +file generated for the input science data (either `~jwst.datamodels.ImageModel` +or `~jwst.datamodels.CubeModel`). To generate a custom mask, it may be +easiest to save the mask output from a default run of the ``clean_flicker_noise`` +step on the input data, then modify it as needed. + +NIRSpec IFU Slices +^^^^^^^^^^^^^^^^^^ +For IFU images, the majority of the mask is based on knowing which +pixels are contained within the footprints of the IFU slices. To do +this, the image's World Coordinate System (WCS) object is queried in +order to determine which pixels are contained within each of the 30 +slices. Pixels within each slice are set to False (do not use) in the +mask. + +NIRSpec MOS/FS Slits +^^^^^^^^^^^^^^^^^^^^ +The footprints of each open MOS slitlet or fixed slit are flagged in +a similar way as IFU slices. For MOS and FS images, the WCS object is +queried to determine which pixels are contained within each open +slit/slitlet and they are set to False in the mask. + +NIRSpec MSA Failed Open Shutters +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Pixels affected by stuck open MSA shutters are masked, because they +may contain signal. This is accomplished by setting all pixels flagged by the +:ref:`msaflagopen ` step with DQ value "MSA_FAILED_OPEN" +to False in the mask. + +NIRSpec Fixed-Slit Region Pixels +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Full-frame MOS and IFU images may contain signal from the always open +fixed slits, which appear in a fixed region in the middle of each image. +The entire region containing the fixed slits is masked out when +processing MOS and IFU images. The masked region is currently hardwired +in the step to image indexes [1:2048, 923:1116], where the indexes are +in x, y order and in 1-indexed values. + +Note, however, that it is possible to plan one or more fixed slit targets +alongside MSA slitlets in MOS observations. In this situation, the fixed +slit region is not automatically masked. + +MIRI Imaging Metering Structure +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The MIRI imager has a metering structure covering a large part of the +detector area. These regions must be masked in order to fit and +remove the background data in the science areas of the detector. +Regions marked with DQ value "DO_NOT_USE" by the +:ref:`flat_field ` step are set to False in the +scene mask. + +Missing Data +^^^^^^^^^^^^ +Any pixel in the draft rate image that has a value of NaN or exactly zero +is flagged as False in the mask. This typically includes any reference +pixels that are present in the exposure. + +Outliers +^^^^^^^^ +For imaging modes, bright, compact sources must be distinguished +from the background and masked in order to fit and remove the +smooth background level. + +For spectral modes that already have significant masking applied, +pixels in the unilluminated areas of the region can still contain anomalous +signal, due to uncaught cosmic rays, hot pixels, etc. + +For both modes, a sigma-clipping routine is employed to find such outliers +within the draft rate image and set them to False in the mask. All pixels with +values greater than :math:`median+n\_sigma*sigma` are assumed to contain +signal and are set to False in the scene mask. In addition, all pixels +with values less than :math:`median-3.0*sigma` are assumed to be bad pixels, +and are also set to False in the scene mask. + +Mode-Specific Masking Steps +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The following table indicates which flavors of masking are applied to +images from each instrument and observing mode. + +.. |c| unicode:: U+2713 .. checkmark + ++--------------------------+-----+-----+-----+-------+--------+--------+ +| | NIRSpec | MIRI | NIRCam | NIRISS | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| | IFU | MOS | FS | Image | All | All | ++==========================+=====+=====+=====+=======+========+========+ +| IFU Slices\ :sup:`1` | |c| | | | | | | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| Slits/Slitlets\ :sup:`1` | | |c| | |c| | | | | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| MSA_FAILED_OPEN\ :sup:`1`| |c| | |c| | | | | | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| Non-science\ :sup:`1` | | | | |c| | | | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| FS Region\ :sup:`1` | |c| | |c| | | | | | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| Missing Data | |c| | |c| | |c| | |c| | |c| | |c| | ++--------------------------+-----+-----+-----+-------+--------+--------+ +| Outliers | |c| | |c| | |c| | |c| | |c| | |c| | ++--------------------------+-----+-----+-----+-------+--------+--------+ + +:sup:`1`\ These steps are only applied if the +:ref:`step parameter ` +`mask_science_regions` is set to True. + +Correction Algorithm +-------------------- + +The detailed process for fitting and removing flicker noise is as follows. +See the :ref:`step arguments ` for more +information on all referenced parameters. + +#. From the calibrated ramp input, make a draft rate (`single_mask` = True) + or rateints (`single_mask` = False) file. + +#. Create a scene mask from the rate data. + + #. If `mask_science_regions` is set and the input is NIRSpec data, + run :ref:`assign_wcs ` and + :ref:`msaflagopen ` on the draft rate data, + then mask any known science areas or failed-open MSA shutters. + + This will mask out regions that are likely to contain significant + astronomical signal. + + #. If `mask_science_regions` is set and the input is MIRI imaging data, + run :ref:`flat_field ` on the draft rate data, + and extract just the DQ plane from the output. Pixels flagged + as 'DO_NOT_USE' by the flat fielding process are masked. + + This will mask out regions of the detector under the metering + structure. + + #. Iteratively sigma clip the data to get a center value (mean or median) + and sigma value (standard deviation). + + #. If `fit_histogram` is set, compute a histogram from 4-sigma clipped + values and fit a Gaussian to it to refine the center and sigma values. + + #. Mask data more than 3 * sigma below the center as bad values. + + #. Mask data more than `n_sigma` * sigma above the center as signal + (not background). + +#. Iterate over each integration and group in the data, to fit and correct + for noise. + + #. Make a diff image (current group – previous group) to correct. + + #. Fit and remove a background level, using the scene mask to identify + background pixels. + + #. Clip the background data in the diff image to remove more outliers. + + #. If `background_method` = 'median', the background value is a simple + median of the remaining values. + + #. If `background_method` = 'model', the background data is fit with + a low-resolution model via the photutils + `Background2D `_ + utility. The resolution box size is set by `background_box_size`. + + #. Subtract the background level from the diff image and clip again + to `n_sigma` * sigma, with sigma recomputed from the + background-subtracted data in the remaining background pixels. + + #. Fit and remove the residual noise in the background-subtracted image. + + #. If `fit_method` = 'fft', the ``nsclean`` library is called to fit + and remove the noise in frequency space. + + #. If `fit_method` = 'median', the noise is fit with a simple median + along the appropriate detector axis and subtracted from the + background-subtracted image. + + If `fit_by_channel` = True, and the data is a NIR full-frame exposure, + the median value is computed and subtracted independently for each + detector channel. + + #. Restore the background level to the cleaned, background-subtracted + diff image. + + #. Add the cleaned diff back to a cleaned version of the previous + group image. + +References +========== + +The FFT cleaning algorithm implementation is based on NSClean, +developed by Bernard Rauscher. Details on the source of the correlated +noise and the algorithm used by the ``nsclean`` library to fit and +remove it can be found in +`Rauscher 2024 `__. + +The background fitting and median cleaning algorithm are based on +the image1overf algorithm, developed by Chris Willott, and available +on GitHub at `chriswillott/jwst `__. +The algorithm was adapted to the `clean_flicker_noise` step and is released +under the BSD license for the JWST calibration pipeline by permission +of the author. diff --git a/docs/jwst/clean_flicker_noise/reference_files.rst b/docs/jwst/clean_flicker_noise/reference_files.rst new file mode 100644 index 0000000000..552e1717a5 --- /dev/null +++ b/docs/jwst/clean_flicker_noise/reference_files.rst @@ -0,0 +1,3 @@ +Reference Files +=============== +The ``clean_flicker_noise`` step does not use any reference files. diff --git a/docs/jwst/nsclean/arguments.rst b/docs/jwst/nsclean/arguments.rst index c91bfaba3e..9772ce6497 100644 --- a/docs/jwst/nsclean/arguments.rst +++ b/docs/jwst/nsclean/arguments.rst @@ -6,19 +6,65 @@ Step Arguments The ``nsclean`` step has the following optional arguments to control the behavior of the processing. +``--fit_method`` (str, default='fft') + The noise fitting algorithm to use. Options are 'fft' and 'median'. + +``--fit_by_channel`` (boolean, default=False) + If set, flicker noise is fit independently for each detector channel. + Ignored for subarray data and for `fit_method` = 'fft'. + +``--background_method`` (str, default=None) + If 'median', the preliminary background to remove and restore + after fitting the noise is a simple median of the background data. + If 'model', the background data is fit with a low-resolution model + via `~photutils.background.Background2D`. + If None, the background value is set to 0.0. + +``--background_box_size`` (list of int, default=(32,32)) + Box size for the data grid used by `~photutils.background.Background2D` + when `background_method` = 'model'. For best results, use a + box size that evenly divides the input image shape. + ``--mask_spectral_regions`` (boolean, default=True) - Mask regions in IFU and MOS images that are within the bounding boxes - for each slice or slitlet defined in the WCS object of the image. + Mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. ``--n_sigma`` (float, default=5.0) The sigma-clipping threshold to use when searching for outliers - and illuminated pixels to be excluded from use in the fitting - process. + and illuminated pixels to be excluded from use in the background + and noise fitting processes. -``--save_mask`` (boolean, default=False) - A flag to indicate whether the mask constructed by the step - should be saved to a file. +``--fit_histogram`` (boolean, default=False) + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + +``--single_mask`` (boolean, default=False) + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. ``--user_mask`` (string, default=None) Path to a user-supplied mask file. If supplied, the mask is used - directly and the process of creating a mask in the step is skipped. + directly and the process of creating a scene mask in the step is + skipped. + + The mask file must contain either a `~jwst.datamodels.ImageModel` + or a `~jwst.datamodels.CubeModel`, with image dimensions matching + the input science data. If an ImageModel is provided, the same + mask will be used for all integrations. If a CubeModel is provided, + the number of slices must equal the number of integrations in + the input science data. + +``--save_mask`` (boolean, default=False) + If set, the mask constructed by the step will be saved to a file + with suffix 'mask'. + +``--save_background`` (boolean, default=False) + If set, the background fit to the group diff images will be saved + to a file with suffix 'flicker_bkg'. + +``--save_noise`` (boolean, default=False) + If set, the residual noise fit and removed from the input data + will be saved to a file with suffix 'flicker_noise'. diff --git a/docs/jwst/nsclean/main.rst b/docs/jwst/nsclean/main.rst index 161e84d9d1..40dcda7a69 100644 --- a/docs/jwst/nsclean/main.rst +++ b/docs/jwst/nsclean/main.rst @@ -4,152 +4,26 @@ Description :Class: `jwst.nsclean.NSCleanStep` :Alias: nsclean -Overview -======== -The ``nsclean`` step applies an algorithm for removing correlated read -noise from NIRSpec images. The noise often appears as faint vertical -banding and so-called "picture frame noise." The algorithm uses dark -(unilluminated) areas of an image to fit a background model in Fourier -space. When the fit is subtracted, it removes nearly all correlated noise. -Compared to simpler strategies, like subtracting a rolling median, this -algorithm is more thorough and uniform. It is also computationally -undemanding, typically requiring only a few seconds to clean a full-frame -image. - -The correction can be applied to any type of NIRSpec exposure, including -IFU, MOS, fixed slit, and Bright Object Time Series (BOTS), in both full-frame -and subarray readouts. Time series (3D) data are corrected one integration -at a time. - -Details on the source of the correlated noise and the algorithm used -in the ``nsclean`` step to fit and remove it can be found in -`Rauscher 2023 `_. - -Upon completion of the step, the step status keyword "S_NSCLEN" gets set -to "COMPLETE" in the output science data. - -Assumptions -=========== -As described below, the creation of a pixel mask depends on the presence -of a World Coordinate System (WCS) object for the image, which is -constructed by the :ref:`assign_wcs ` step. -In addition, creating a mask for IFU and MOS images depends on -the presence of DQ flags assigned by the -:ref:`msaflagopen ` step. -It is therefore required that those steps be run before attempting to -apply ``nsclean``. - -Creation of an image mask -========================= -One of the key components of the correction is knowing which pixels are -unilluminated and hence can be used in fitting the background noise. -The step builds a mask on the fly for each image, which is used to mark -useable and unuseable pixels. The mask is a 2D boolean array, having the same -size as the image, with pixels set to True interpreted as being OK to use. -The process of building the mask varies somewhat depending on the -observing mode of the image being processed. Some features are common -to all modes, while others are mode-specific. The following sections -describe each type of masking that can be applied and at the end there -is a summary of the types applied to each image mode. - -The user-settable step parameter `save_mask` can be used to save the -mask to a file, if desired (see :ref:`nsclean step arguments `). - -Note that a user may supply their own mask image as input to the step, -in which case the process of creating a mask is skipped. The step parameter -`user_mask` is used to specify an input mask. - -IFU Slices ----------- -For IFU images the majority of the mask is based on knowing which -pixels are contained within the footprints of the IFU slices. To do -this, the image's World Coordinate System (WCS) object is queried in -order to determine which pixels are contained within each of the 30 -slices. Pixels within each slice are set to False (do not use) in the -mask. +.. note:: -MOS/FS Slits ------------- -The footprints of each open MOS slitlet or fixed slit are flagged in -a similar way as IFU slices. For MOS and FS images, the WCS object is -queried to determine which pixels are contained within each open -slit/slitlet and they are set to False in the mask. + This step, which is intended to be called in the + :ref:`calwebb_spec2 ` pipeline on NIRSpec rate data, + is now deprecated. In future builds, it will be replaced by + the :ref:`clean_flicker_noise ` + step, called in the :ref:`calwebb_detector1 ` + pipeline on ramp data. -MSA Failed Open Shutters ------------------------- -Pixels affected by stuck open MSA shutters are masked, because they -may contain signal. This is accomplished by setting all pixels flagged by the -:ref:`msaflagopen ` step with DQ value "MSA_FAILED_OPEN" -to False in the mask. - -NaN Pixels ----------- -Any pixel in the input image that has a value of NaN is temporarily reset -to zero for input to the fitting routine and flagged as False in the mask. -Upon completion of the noise subtraction, this population of pixels is -set back to NaN again in the output (corrected) image. - -Fixed-Slit Region Pixels ------------------------- -Full-frame MOS and IFU images may contain signal from the always open -fixed slits, which appear in fixed region in the middle of each image. -The entire region containing the fixed slits is masked out when -processing MOS and IFU images. The masked region is currently hardwired -in the step to image indexes [1:2048, 923:1116], where the indexes are -in x, y order and in 1-indexed values. - -Note, however, that it is possible to plan one or more fixed slit targets -alongside MSA slitlets in MOS observations. In this situation, the fixed -slit region is not automatically masked. - -Left/Right Reference Pixel Columns ----------------------------------- -Full-frame images contain 4 columns of reference pixels on the left and -right edges of the image. These are not to be used in the fitting -algorithm and hence are set to False in the mask. - -Outliers --------- -Pixels in the unilluminated areas of the region can contain anomalous -signal, due to uncaught Cosmic Rays, hot pixels, etc. A sigma-clipping -routine is employed to find such outliers within the input image and set -them to False in the mask. All pixels with values greater than -:math:`median+n_sigma*sigma` are set to False in the mask. -Here `median` and `sigma` are computed -from the image using the astropy.stats `sigma_clipped_stats` routine, -using the image mask to exclude pixels that have already been flagged -and a clipping level of 5 sigma. `n_sigma` is a user-settable step -parameter, with a default value of 5.0 -(see :ref:`nsclean step arguments `). - -Mode-Specific Masking Steps ---------------------------- -The following table indicates which flavors of masking are applied to -images from each type of observing mode. - -.. |c| unicode:: U+2713 .. checkmark - -+--------------------------+-----+-----+-----+ -| | | Mode| | -+--------------------------+-----+-----+-----+ -| Masking | IFU | MOS | FS | -+==========================+=====+=====+=====+ -| IFU Slices\ :sup:`1` | |c| | | | -+--------------------------+-----+-----+-----+ -| Slits/Slitlets\ :sup:`1` | | |c| | |c| | -+--------------------------+-----+-----+-----+ -| MSA_FAILED_OPEN | |c| | |c| | |c| | -+--------------------------+-----+-----+-----+ -| NaN Pixels | |c| | |c| | |c| | -+--------------------------+-----+-----+-----+ -| FS Region | |c| | |c| | | -+--------------------------+-----+-----+-----+ -| Reference Pix | |c| | |c| | |c| | -+--------------------------+-----+-----+-----+ -| Outliers | |c| | |c| | |c| | -+--------------------------+-----+-----+-----+ - -:sup:`1`\ The application of these steps can be turned on and off via -the step parameter `mask_spectral_regions`. This parameter controls -whether the "IFU Slices" and "Slits/Slitlets" portions of the masking -are applied. +Overview +======== +This step currently runs a version of the +:ref:`clean_flicker_noise ` algorithm, +with slightly different parameters and default values, intended +to be backwards-compatible with the previous implementation of +the step developed specifically for NIRSpec data. The only +significant difference is that in this step, the cleaning is +performed directly on the rate data, rather than group images. + +See the :ref:`clean_flicker_noise ` +documentation for more information, or the +:ref:`nsclean step arguments ` for the default +values used for this step. diff --git a/docs/jwst/package_index.rst b/docs/jwst/package_index.rst index da12f11e51..4bd3b28ba1 100644 --- a/docs/jwst/package_index.rst +++ b/docs/jwst/package_index.rst @@ -16,6 +16,7 @@ Package Index badpix_selfcal/index.rst barshadow/index.rst charge_migration/index.rst + clean_flicker_noise/index.rst combine_1d/index.rst cube_build/index.rst dark_current/index.rst diff --git a/docs/jwst/pipeline/calwebb_detector1.rst b/docs/jwst/pipeline/calwebb_detector1.rst index e66d135afb..5083b0a618 100644 --- a/docs/jwst/pipeline/calwebb_detector1.rst +++ b/docs/jwst/pipeline/calwebb_detector1.rst @@ -45,52 +45,52 @@ on either the first group or frame zero pixel values. .. |check| unicode:: U+2713 .. checkmark -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| Near-IR | MIRI | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| Step | Non-TSO | TSO | Step | Non-TSO | TSO | -+======================================================================+=========+=========+=========================================+=========+=========+ -| :ref:`group_scale ` | |check| | |check| | :ref:`group_scale ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`dq_init ` | |check| | |check| | :ref:`dq_init ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| | | | :ref:`emicorr ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`saturation ` | |check| | |check| | :ref:`saturation ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`ipc ` [1]_ | | | :ref:`ipc ` | | | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`superbias ` | |check| | |check| | :ref:`firstframe ` | |check| | | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`refpix ` | |check| | |check| | :ref:`lastframe ` | |check| | | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| | | | :ref:`reset ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`linearity ` | |check| | |check| | :ref:`linearity ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`persistence ` [2]_ | |check| | | :ref:`rscd ` | |check| | | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`dark_current ` | |check| | |check| | :ref:`dark_current ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| | | | :ref:`refpix ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`charge_migration ` [3]_ | |check| | | | | | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`jump ` | |check| | |check| | :ref:`jump ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`ramp_fitting ` | |check| | |check| | :ref:`ramp_fitting ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ -| :ref:`gain_scale ` | |check| | |check| | :ref:`gain_scale ` | |check| | |check| | -+----------------------------------------------------------------------+---------+---------+-----------------------------------------+---------+---------+ - - -.. [1] By default, the parameter reference `pars-detector1pipeline` - retrieved from CRDS will skip the :ref:`ipc ` step for all instruments. ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| Near-IR | MIRI | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| Step | Non-TSO | TSO | Step | Non-TSO | TSO | ++======================================================================+=========+=========+======================================================+=========+=========+ +| :ref:`group_scale ` | |check| | |check| | :ref:`group_scale ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`dq_init ` | |check| | |check| | :ref:`dq_init ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| | | | :ref:`emicorr ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`saturation ` | |check| | |check| | :ref:`saturation ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`ipc ` [1]_ | | | :ref:`ipc ` | | | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`superbias ` | |check| | |check| | :ref:`firstframe ` | |check| | | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`refpix ` | |check| | |check| | :ref:`lastframe ` | |check| | | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| | | | :ref:`reset ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`linearity ` | |check| | |check| | :ref:`linearity ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`persistence ` [2]_ | |check| | | :ref:`rscd ` | |check| | | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`dark_current ` | |check| | |check| | :ref:`dark_current ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| | | | :ref:`refpix ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`charge_migration ` [1]_ | |check| | | | | | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`jump ` | |check| | |check| | :ref:`jump ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`clean_flicker_noise ` [1]_ | |check| | |check| | :ref:`clean_flicker_noise `| |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`ramp_fitting ` | |check| | |check| | :ref:`ramp_fitting ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ +| :ref:`gain_scale ` | |check| | |check| | :ref:`gain_scale ` | |check| | |check| | ++----------------------------------------------------------------------+---------+---------+------------------------------------------------------+---------+---------+ + + +.. [1] By default, this step is skipped in the ``calwebb_detector1`` pipeline + for all instruments and modes. .. [2] The :ref:`persistence ` step is currently hardwired to be skipped in the `Detector1Pipeline` module for all NIRSpec exposures. -.. [3] By default, the :ref:`charge_migration ` step is skipped in - the `Detector1Pipeline` module for all instruments. - + Arguments --------- diff --git a/jwst/clean_flicker_noise/__init__.py b/jwst/clean_flicker_noise/__init__.py new file mode 100644 index 0000000000..e9bd9a65dc --- /dev/null +++ b/jwst/clean_flicker_noise/__init__.py @@ -0,0 +1,3 @@ +from .clean_flicker_noise_step import CleanFlickerNoiseStep + +__all__ = ['CleanFlickerNoiseStep'] diff --git a/jwst/clean_flicker_noise/clean_flicker_noise.py b/jwst/clean_flicker_noise/clean_flicker_noise.py new file mode 100644 index 0000000000..2a41da373c --- /dev/null +++ b/jwst/clean_flicker_noise/clean_flicker_noise.py @@ -0,0 +1,1522 @@ +import logging +import warnings + +import gwcs +import numpy as np +from astropy.stats import sigma_clipped_stats, SigmaClip +from astropy.utils.exceptions import AstropyUserWarning +from photutils.background import Background2D, MedianBackground +from scipy.optimize import curve_fit +from stdatamodels.jwst.datamodels import dqflags + +from jwst import datamodels +from jwst.assign_wcs import nirspec, AssignWcsStep +from jwst.flatfield import FlatFieldStep +from jwst.clean_flicker_noise.lib import NSClean, NSCleanSubarray +from jwst.lib.basic_utils import LoggingContext +from jwst.msaflagopen import MSAFlagOpenStep +from jwst.ramp_fitting import RampFitStep + + +log = logging.getLogger(__name__) +log.setLevel(logging.DEBUG) + +# Fixed slit region to mask, for NIRSpec MOS and IFU data +# Values are y start and stop indices, for the edges of the +# region to mask. +NRS_FS_REGION = [922, 1116] + + +def make_rate(input_model, input_dir='', return_cube=False): + """ + Make a rate model from a ramp model. + + Parameters + ---------- + input_model : `~jwst.datamodel.RampModel` + Input ramp model. + + input_dir : str + Path to the input directory. Used by sub-steps (e.g. assign_wcs + for NIRSpec MOS data) to find auxiliary data. + + return_cube : bool, optional + If set, a CubeModel will be returned, with a separate + rate for each integration. Otherwise, an ImageModel is returned + with the combined rate for the full observation. + + Returns + ------- + rate_model : `~jwst.datamodel.ImageModel` or `~jwst.datamodel.CubeModel` + The rate or rateints model. + """ + # Call the ramp fit step on a copy of the input + # Use software default values for parameters + + log.info("Creating draft rate file for scene masking") + step = RampFitStep() + step.input_dir = input_dir + with LoggingContext(step.log, level=logging.WARNING): + # Note: the copy is currently needed because ramp fit + # closes the input model when it's done, and we need + # it to stay open. + rate, rateints = step.run(input_model.copy()) + + if return_cube: + output_model = rateints + rate.close() + del rate + else: + output_model = rate + rateints.close() + del rateints + + return output_model + + +def post_process_rate(input_model, input_dir='', assign_wcs=False, + msaflagopen=False, flat_dq=False): + """ + Post-process the input rate model, as needed. + + Parameters + ---------- + input_model : `~jwst.datamodel.ImageModel` or `~jwst.datamodel.CubeModel` + Input rate model. + + input_dir : str + Path to the input directory. Used by sub-steps (e.g. assign_wcs + for NIRSpec MOS data) to find auxiliary data. + + assign_wcs : bool, optional + If set and the input does not already have a WCS assigned, + the assign_wcs step will be called on the rate model. + + msaflagopen : bool, optional + If set, the msaflagopen step will be called on the rate model. + If a WCS is not already present, assign_wcs will be called first. + + flat_dq : bool, optional + If set, the flat_field step will be run on the input model. DQ + flags are retrieved from the output and added to the input + model's DQ array. The rate data is not modified. + + Returns + ------- + output_model : `~jwst.datamodel.ImageModel` or `~jwst.datamodel.CubeModel` + The updated model. + """ + output_model = input_model + + # If needed, assign a WCS + if (assign_wcs or msaflagopen) and not hasattr(output_model.meta, 'wcs'): + log.info("Assigning a WCS for scene masking") + step = AssignWcsStep() + step.input_dir = input_dir + with LoggingContext(step.log, level=logging.WARNING): + output_model = step.run(output_model) + + # If needed, flag open MSA shutters + if msaflagopen: + log.info("Flagging failed-open MSA shutters for scene masking") + step = MSAFlagOpenStep() + step.input_dir = input_dir + with LoggingContext(step.log, level=logging.WARNING): + output_model = step.run(output_model) + + # If needed, draft a flat correction to retrieve non-science areas + if flat_dq: + log.info("Retrieving flat DQ values for scene masking") + step = FlatFieldStep() + step.input_dir = input_dir + with LoggingContext(step.log, level=logging.WARNING): + flat_corrected_model = step.run(output_model) + + # Copy out the flat DQ plane, leave the data as is + output_model.dq |= flat_corrected_model.dq + flat_corrected_model.close() + del flat_corrected_model + + return output_model + + +def mask_ifu_slices(input_model, mask): + """ + Flag pixels within IFU slices. + + Find pixels located within IFU slices, according to the WCS, + and flag them in the mask, so that they do not get used. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + Science data model + + mask : array-like of bool + 2D input mask that will be updated. True indicates background + pixels to be used. IFU slice regions will be set to False. + + Returns + ------- + mask : array-like of bool + 2D output mask with additional flags for science pixels + """ + log.info("Finding slice pixels for an IFU image") + + # Initialize global DQ map to all zero (OK to use) + dqmap = np.zeros_like(input_model.dq) + + # Get the wcs objects for all IFU slices + list_of_wcs = nirspec.nrs_ifu_wcs(input_model) + + # Loop over the IFU slices, finding the valid region for each + for (k, ifu_wcs) in enumerate(list_of_wcs): + + # Construct array indexes for pixels in this slice + x, y = gwcs.wcstools.grid_from_bounding_box( + ifu_wcs.bounding_box, step=(1, 1), center=True) + + # Get the world coords for all pixels in this slice; + # all we actually need are wavelengths + coords = ifu_wcs(x, y) + dq = dqmap[..., y.astype(int), x.astype(int)] + wl = coords[2] + + # set non-NaN wavelength locations as do not use (one) + valid = ~np.isnan(wl) + dq = dq[..., valid] + x = x[..., valid] + y = y[..., valid] + dq[:] = 1 + + # Copy DQ for this slice into global DQ map + dqmap[..., y.astype(int), x.astype(int)] = dq + + # Now set all non-zero locations in the mask to False (do not use) + mask[dqmap == 1] = False + + return mask + + +def mask_slits(input_model, mask): + """ + Flag pixels within science regions. + + Find pixels located within MOS or fixed slit footprints + and flag them in the mask, so that they do not get used. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + Science data model. + + mask : array-like of bool + 2D input mask that will be updated. True indicates background + pixels to be used. Slit regions will be set to False. + + Returns + ------- + mask : array-like of bool + 2D output mask with additional flags for slit pixels + """ + + from jwst.extract_2d.nirspec import offset_wcs + + log.info("Finding slit/slitlet pixels") + + # Get the slit-to-msa frame transform from the WCS object + slit2msa = input_model.meta.wcs.get_transform('slit_frame', 'msa_frame') + + # Loop over the slits, marking all the pixels within each bounding + # box as False (do not use) in the mask. + # Note that for 3D masks (TSO mode), all planes will be set to the same value. + for slit in slit2msa.slits: + slit_wcs = nirspec.nrs_wcs_set_input(input_model, slit.name) + xlo, xhi, ylo, yhi = offset_wcs(slit_wcs) + mask[..., ylo:yhi, xlo:xhi] = False + + return mask + + +def clip_to_background(image, mask, sigma_lower=3.0, sigma_upper=2.0, + fit_histogram=False, lower_half_only=False, + verbose=False): + """ + Flag signal and bad pixels in the image mask. + + Given an image, estimate the background level and a sigma value for the + mean background. + + The center and sigma may be calculated from a simple sigma-clipped + median and standard deviation, or may be refined by fitting a Gaussian + to a histogram of the values. In that case, the center is the + Gaussian mean and the sigma is the Gaussian width. + + Pixels above the center + sigma_upper * sigma are assumed to + have signal; those below this level are assumed to be background pixels. + + Pixels less than center - sigma_lower * sigma are also excluded as bad values. + + The input mask is updated in place. + + Parameters + ---------- + image : array-like of float + 2D image containing signal and background values. + + mask : array-like of bool + 2D input mask to be updated. True indicates background + pixels to be used. Regions containing signal or outliers + will be set to False. + + sigma_lower : float, optional + The number of standard deviations to use as the lower bound + for the clipping limit. Values below this limit are marked + False in the mask. + + sigma_upper : float, optional + The number of standard deviations to use as the upper bound + for the clipping limit. Values above this limit are marked + False in the mask. + + fit_histogram : bool, optional + If set, the center value and standard deviation used with + `sigma_lower` and `sigma_upper` for clipping outliers is derived + from a Gaussian fit to a histogram of values. Otherwise, the + center and standard deviation are derived from a simple iterative + sigma clipping. + + lower_half_only : bool, optional + If set, the data used to compute the center and standard deviation + for clipping is the lower half of the distribution only. Values + below the median are mirrored around the median value to simulate + a symmetric distribution. This is intended to account for + asymmetrical value distributions, with long tails in the upper + half of the distribution, due to diffuse emission, for example. + + verbose : bool, optional + If set, DEBUG level messages are issued with details on the + computed statistics. + """ + # Sigma limit for basic stats + sigma_limit = 3.0 + + # Check mask for any valid data before proceeding + if not np.any(mask): + return + + # Initial iterative sigma clip + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", category=AstropyUserWarning) + mean, median, sigma = sigma_clipped_stats( + image, mask=~mask, sigma=sigma_limit) + if fit_histogram: + center = mean + else: + center = median + if verbose: + log.debug('From initial sigma clip:') + log.debug(f' center: {center:.5g}') + log.debug(f' sigma: {sigma:.5g}') + + # If desired, use only the lower half of the data distribution + if lower_half_only: + lower_half_idx = mask & (image <= center) + data_for_stats = np.concatenate( + ((image[lower_half_idx] - center), + (center - image[lower_half_idx]))) + center + + # Redo stats on lower half of distribution + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", category=AstropyUserWarning) + mean, median, sigma = sigma_clipped_stats( + data_for_stats, sigma=sigma_limit) + if fit_histogram: + center = mean + else: + center = median + if verbose: + log.debug('From lower half distribution:') + log.debug(f' center: {center:.5g}') + log.debug(f' sigma: {sigma:.5g}') + else: + data_for_stats = image[mask] + + # Refine sigma and center from a fit to a histogram, if desired + if fit_histogram: + try: + hist, edges = np.histogram( + data_for_stats, bins=2000, + range=(center - 4. * sigma, center + 4. * sigma)) + except ValueError: + log.error('Histogram failed; using clip center and sigma.') + hist, edges = None, None + + param_opt = None + if hist is not None: + values = (edges[1:] + edges[0:-1]) / 2. + ind = np.argmax(hist) + mode_estimate = values[ind] + + # Fit a Gaussian profile to the histogram + def gaussian(x, g_amp, g_mean, g_sigma): + return g_amp * np.exp(-0.5 * ((x - g_mean) / g_sigma) ** 2) + + param_start = (hist[ind], mode_estimate, sigma) + bounds = [(0, values[0], 0), + (np.inf, values[-1], values[-1] - values[0])] + try: + param_opt, _ = curve_fit(gaussian, values, hist, p0=param_start, + bounds=bounds) + except RuntimeError: + log.error('Gaussian fit failed; using clip center and sigma.') + param_opt = None + + if verbose: + log.debug('From histogram:') + log.debug(f' mode estimate: {mode_estimate:.5g}') + log.debug(f' range of values in histogram: ' + f'{values[0]:.5g} to {values[-1]:.5g}') + + if verbose: + log.debug('Gaussian fit results:') + if param_opt is None: + if verbose: + log.debug(' (fit failed)') + else: + if verbose: + log.debug(f' peak: {param_opt[0]:.5g}') + log.debug(f' center: {param_opt[1]:.5g}') + log.debug(f' sigma: {param_opt[2]:.5g}') + center = param_opt[1] + sigma = param_opt[2] + + # Set limits from center and sigma + background_lower_limit = center - sigma_lower * sigma + background_upper_limit = center + sigma_upper * sigma + if verbose: + log.debug(f'Mask limits: {background_lower_limit:.5g} ' + f'to {background_upper_limit:.5g}') + + # Clip bad values + bad_values = image < background_lower_limit + mask[bad_values] = False + + # Clip signal (> N sigma) + signal = image > background_upper_limit + mask[signal] = False + + +def create_mask(input_model, mask_science_regions=False, + n_sigma=2.0, fit_histogram=False, single_mask=False): + """ + Create a mask identifying background pixels. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + Science data model, containing rate data with all necessary + pre-processing already performed. + + mask_science_regions : bool, optional + For NIRSpec, mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. This requires the + `assign_wcs` and `msaflagopen` steps to have been run on the + input_model. + For MIRI imaging, mask regions of the detector not used for science. + This requires that DO_NOT_USE flags are set in the DQ array + for the input_model. + + n_sigma : float, optional + Sigma threshold for masking outliers. + + fit_histogram : bool, optional + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + + single_mask : bool, optional + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. + + Returns + ------- + mask : array-like of bool + 2D or 3D image mask + """ + exptype = input_model.meta.exposure.type.lower() + + # Initialize mask to all True. Subsequent operations will mask + # out pixels that contain signal. + mask = np.full(input_model.dq.shape, True) + + # If IFU, mask all pixels contained in the IFU slices + if exptype == 'nrs_ifu' and mask_science_regions: + mask = mask_ifu_slices(input_model, mask) + + # If MOS or FS, mask all pixels affected by open slitlets + if (exptype in ['nrs_fixedslit', 'nrs_brightobj', 'nrs_msaspec'] + and mask_science_regions): + mask = mask_slits(input_model, mask) + + # If IFU or MOS, mask pixels affected by failed-open shutters + if mask_science_regions and exptype in ['nrs_ifu', 'nrs_msaspec']: + open_pix = input_model.dq & dqflags.pixel['MSA_FAILED_OPEN'] + mask[open_pix > 0] = False + + # If MIRI imaging, mask the non-science regions: + # they contain irrelevant emission + if mask_science_regions and exptype in ['mir_image']: + non_science = (input_model.dq & dqflags.pixel['DO_NOT_USE']) > 0 + mask[non_science] = False + + # Mask any NaN pixels or exactly zero value pixels + no_data_pix = np.isnan(input_model.data) | (input_model.data == 0) + mask[no_data_pix] = False + + # If IFU or MOS, mask the fixed-slit area of the image; uses hardwired indexes + if mask_science_regions: + if exptype == 'nrs_ifu': + log.info("Masking the fixed slit region for IFU data.") + mask[..., NRS_FS_REGION[0]:NRS_FS_REGION[1], :] = False + elif exptype == 'nrs_msaspec': + # check for any slits defined in the fixed slit quadrant: + # if there is nothing there of interest, mask the whole FS region + try: + slit2msa = input_model.meta.wcs.get_transform('slit_frame', 'msa_frame') + is_fs = [s.quadrant == 5 for s in slit2msa.slits] + except (AttributeError, ValueError, TypeError): + log.warning('Slit to MSA transform not found.') + is_fs = [False] + if not any(is_fs): + log.info("Masking the fixed slit region for MOS data.") + mask[..., NRS_FS_REGION[0]:NRS_FS_REGION[1], :] = False + else: + log.info("Fixed slits found in MSA definition; " + "not masking the fixed slit region for MOS data.") + + # Mask outliers and signal using sigma clipping stats. + # For 3D data, loop over each integration separately. + if input_model.data.ndim == 3: + for i in range(input_model.data.shape[0]): + clip_to_background( + input_model.data[i], mask[i], + sigma_upper=n_sigma, fit_histogram=fit_histogram, verbose=True) + else: + clip_to_background( + input_model.data, mask, + sigma_upper=n_sigma, fit_histogram=fit_histogram, verbose=True) + + # Reduce the mask to a single plane if needed + if single_mask and mask.ndim == 3: + mask = np.all(mask, axis=0) + + # Return the mask + return mask + + +def background_level(image, mask, background_method='median', + background_box_size=None): + """ + Fit a low-resolution background level. + + Parameters + ---------- + image : array-like of float + The 2D image containing the background to fit. + + mask : array-like of bool + The mask that indicates which pixels are to be used in fitting. + True indicates a background pixel. + + background_method : {'median', 'model', None}, optional + If 'median', the preliminary background to remove and restore + is a simple median of the background data. If 'model', the + background data is fit with a low-resolution model via + `~photutils.background.Background2D`. If None, the background + value is 0.0. + + background_box_size : tuple of int, optional + Box size for the data grid used by `Background2D` when + `background_method` = 'model'. For best results, use a box size + that evenly divides the input image shape. Defaults to 32x32 if + not provided. + + Returns + ------- + background : float or array-like of float + The background level: a single value, if `background_method` + is 'median' or None, or an array matching the input image size + if `background_method` is 'model'. + """ + if background_method is None: + background = 0.0 + + else: + # Sigma limit for basic stats + sigma_limit = 3.0 + + # Flag more signal in the background subtracted image, + # with sigma set by the lower half of the distribution only + clip_to_background( + image, mask, sigma_lower=sigma_limit, sigma_upper=sigma_limit, + lower_half_only=True) + + if background_method == 'model': + sigma_clip_for_bkg = SigmaClip(sigma=sigma_limit, maxiters=5) + bkg_estimator = MedianBackground() + + if background_box_size is None: + background_box_size = [32, 32] + box_division_remainder = (image.shape[0] % background_box_size[0], + image.shape[1] % background_box_size[1]) + if not np.allclose(box_division_remainder, 0): + log.warning(f'Background box size {background_box_size} ' + f'does not divide evenly into the image ' + f'shape {image.shape}.') + + try: + with warnings.catch_warnings(): + warnings.filterwarnings( + action="ignore", category=AstropyUserWarning) + bkg = Background2D( + image, box_size=background_box_size, filter_size=(5, 5), + mask=~mask, sigma_clip=sigma_clip_for_bkg, + bkg_estimator=bkg_estimator) + background = bkg.background + except ValueError: + log.error('Background fit failed, using median value.') + background = np.nanmedian(image[mask]) + else: + background = np.nanmedian(image[mask]) + return background + + +def fft_clean_full_frame(image, mask, detector): + """ + Fit and remove background noise in frequency space for a full-frame image. + + Parameters + ---------- + image : array-like of float + The image to be cleaned. + + mask : array-like of bool + The mask that indicates which pixels are to be used in fitting. + + detector : str + The name of the detector from which the data originate. + + Returns + ------- + cleaned_image : array-like of float + The cleaned image. + """ + + # Instantiate the cleaner + cleaner = NSClean(detector, mask) + + # Clean the image + try: + cleaned_image = cleaner.clean(image, buff=True) + except np.linalg.LinAlgError: + log.warning("Error cleaning image; step will be skipped") + return None + + return cleaned_image + + +def fft_clean_subarray(image, mask, detector, npix_iter=512, + fc=(1061, 1211, 49943, 49957), + exclude_outliers=True, sigrej=4, minfrac=0.05): + """ + Fit and remove background noise in frequency space for a subarray image. + + Parameters + ---------- + image : array-like of float + The image to be cleaned. + + mask : array-like of bool + The mask that indicates which pixels are to be used in fitting. + + detector : str + The name of the detector from which the data originate. + + npix_iter : int + Number of pixels to process simultaneously. Default 512. Should + be at least a few hundred to access sub-kHz frequencies in areas + where most pixels are available for fitting. Previous default + behavior corresponds to npix_iter of infinity. + + fc : tuple + Apodizing filter definition. These parameters are tunable. The + defaults happen to work well for NIRSpec BOTS exposures. + 1) Unity gain for f < fc[0] + 2) Cosine roll-off from fc[0] to fc[1] + 3) Zero gain from fc[1] to fc[2] + 4) Cosine roll-on from fc[2] to fc[3] + Default (1061, 1211, 49943, 49957) + + exclude_outliers : bool + Find and mask outliers in the fit? Default True + + sigrej : float + Number of sigma to clip when identifying outliers. Default 4. + + minfrac : float + Minimum fraction of pixels locally available in the mask in + order to attempt a correction. Default 0.05 (i.e., 5%). + + Returns + ------- + cleaned_image : array-like of float + The cleaned image. + """ + # Flip the image to detector coords. NRS1 requires a transpose + # of the axes, while NRS2 requires a transpose and flip. + if detector == "NRS1": + image = image.transpose() + mask = mask.transpose() + else: + image = image.transpose()[::-1] + mask = mask.transpose()[::-1] + + # We must do the masking of discrepant pixels here: it just + # doesn't work if we wait and do it in the cleaner. This is + # basically copied from lib.py. Use a robust estimator for + # standard deviation, then exclude discrepant pixels and their + # four nearest neighbors from the fit. + + if exclude_outliers: + med = np.median(image[mask]) + std = 1.4825 * np.median(np.abs((image - med)[mask])) + outlier = mask & (np.abs(image - med) > sigrej * std) + + mask = mask & (~outlier) + + # also get four nearest neighbors of flagged pixels + mask[1:] = mask[1:] & (~outlier[:-1]) + mask[:-1] = mask[:-1] & (~outlier[1:]) + mask[:, 1:] = mask[:, 1:] & (~outlier[:, :-1]) + mask[:, :-1] = mask[:, :-1] & (~outlier[:, 1:]) + + # Used to determine the fitting intervals along the slow scan + # direction. Pre-pend a zero so that sum_mask[i] is equal + # to np.sum(mask[:i], axis=1). + + sum_mask = np.array([0] + list(np.cumsum(np.sum(mask, axis=1)))) + + # i1 will be the first row with a nonzero element in the mask + # imax will be the last row with a nonzero element in the mask + + nonzero_mask_element = np.sum(mask, axis=1) > 0 + + if np.sum(nonzero_mask_element) == 0: + log.warning("No good pixels in mask; step will be skipped") + return None + + i1 = np.amin(np.arange(mask.shape[0])[nonzero_mask_element]) + imax = np.amax(np.arange(mask.shape[0])[nonzero_mask_element]) + + i1_vals = [] + di_list = [] + models = [] + while i1 <= imax: + + # Want npix_iter available pixels in this section. If + # there are fewer than 1.5*npix_iter available pixels in + # the rest of the image, just go to the end. + k = 0 + for k in range(i1 + 1, imax + 2): + if (sum_mask[k] - sum_mask[i1] > npix_iter + and sum_mask[-1] - sum_mask[i1] > 1.5 * npix_iter): + break + + di = k - i1 + + i1_vals += [i1] + di_list += [di] + + # Fit this section only if at least minpct% of the pixels + # are available for finding the background. Don't flag + # outliers section-by-section; we have to do that earlier + # over the full array to get reliable values for the mean + # and standard deviation. + + if np.mean(mask[i1:i1 + di]) > minfrac: + cleaner = NSCleanSubarray(image[i1:i1 + di], mask[i1:i1 + di], + fc=fc, exclude_outliers=False) + try: + models += [cleaner.clean(return_model=True)] + except np.linalg.LinAlgError: + log.warning("Error cleaning image; step will be skipped") + return None + else: + log.warning("Insufficient reference pixels for NSClean around " + "row %d; no correction will be made here." % i1) + models += [np.zeros(image[i1:i1 + di].shape)] + + # If we have reached the end of the array, we are finished. + if k == imax + 1: + break + + # Step forward by half an interval so that we have + # overlapping fitting regions. + + i1 += max(int(np.round(di/2)), 1) + + model = np.zeros(image.shape) + tot_wgt = np.zeros(image.shape) + + # When we combine different corrections computed over + # different intervals, each one the highest weight towards the + # center of its interval and less weight towards the edge. + # Use nonzero weights everywhere so that if only one + # correction is available it gets unit weight when we + # normalize. + + for i in range(len(models)): + wgt = 1.001 - np.abs(np.linspace(-1, 1, di_list[i]))[:, np.newaxis] + model[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt*models[i] + tot_wgt[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt + + # don't divide by zero + tot_wgt[model == 0] = 1 + model /= tot_wgt + cleaned_image = image - model + + # Restore the cleaned image to the science frame + if detector == "NRS1": + cleaned_image = cleaned_image.transpose() + else: + cleaned_image = cleaned_image[::-1].transpose() + + return cleaned_image + + +def median_clean(image, mask, axis_to_correct, fit_by_channel=False): + """ + Fit and remove background noise via median values along one image axis. + + Parameters + ---------- + image : array-like of float + The image to be cleaned. + + mask : array-like of bool + The mask that indicates which pixels are to be used in fitting. + True indicates a background pixel. + + axis_to_correct : int + For NIR detectors, the axis to correct should be the detector slow + readout direction. Values expected are 1 or 2, following the JWST + datamodel definition (meta.subarray.slowaxis). For MIRI, flicker + noise appears along the vertical direction, so `axis_to_correct` + should be set to 1 (median along the y-axis). + + fit_by_channel : bool, optional + If set, flicker noise is fit independently for each detector channel. + Ignored for MIRI and for subarray data. + + Returns + ------- + cleaned_image : array-like of float + The cleaned image. + """ + # Masked median along slow axis + masked_image = np.ma.array(image, mask=~mask) + corrected_image = image.copy() + array_axis = axis_to_correct - 1 + + # If desired, take the median over each channel separately. + + # Full frame for the NIR detectors is 2048 x 2048, and there + # are 4 channels per detector, divided along the slow axis. + # The fit_by_channel option should only be set for full frame + # NIR data, but make sure the slow axis has the right size + # here anyway. + if fit_by_channel and image.shape[array_axis] == 2048: + n_output = 4 + channel_size = 512 + else: + # For MIRI data and subarrays, the whole image should + # be considered at once, as a single "channel" + n_output = 1 + channel_size = image.shape[array_axis] + + # Compute stripes from the median over the background data + # in the channel + cstart = 0 + cstop = channel_size + for channel in range(n_output): + if array_axis == 1: + channel_image = masked_image[:, cstart:cstop] + else: + channel_image = masked_image[cstart:cstop, :] + stripes = np.ma.median(channel_image, axis=array_axis, keepdims=True) + stripes = np.ma.filled(stripes, fill_value=0.0) + + # Remove median stripes + if array_axis == 1: + corrected_image[:, cstart:cstop] = image[:, cstart:cstop] - stripes + else: + corrected_image[cstart:cstop, :] = image[cstart:cstop, :] - stripes + + cstart += channel_size + cstop += channel_size + + return corrected_image + + +def _check_input(exp_type, fit_method): + """ + Check for valid input data and options. + + Parameters + ---------- + exp_type : str + Exposure type for the input. + fit_method : str + Noise fitting method. + + Returns + ------- + valid : bool + True if the input is valid. + """ + message = None + miri_allowed = ['MIR_IMAGE'] + if exp_type.startswith('MIR') and exp_type not in miri_allowed: + message = f"EXP_TYPE {exp_type} is not supported" + + nsclean_allowed = ['NRS_MSASPEC', 'NRS_IFU', 'NRS_FIXEDSLIT', 'NRS_BRIGHTOBJ'] + if fit_method == 'fft': + if exp_type not in nsclean_allowed: + message = f"Fit method 'fft' cannot be applied to exp_type {exp_type}" + + if message is not None: + log.warning(message) + log.warning("Step will be skipped") + return False + + return True + + +def _make_intermediate_model(input_model, intermediate_data): + """ + Make a data model to contain intermediate outputs. + + The output model type depends on the shape of the input + intermediate data. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + The input data. + intermediate_data : array-like + The intermediate data to save. + + Returns + ------- + intermediate_model : ~jwst.datamodel.JwstDataModel` + A model containing only the intermediate data and top-level + metadata matching the input. + """ + if intermediate_data.ndim == 4: + intermediate_model = datamodels.RampModel(data=intermediate_data) + elif intermediate_data.ndim == 3: + intermediate_model = datamodels.CubeModel(data=intermediate_data) + else: + intermediate_model = datamodels.ImageModel(data=intermediate_data) + + # Copy metadata from input model + intermediate_model.update(input_model) + return intermediate_model + + +def _standardize_parameters( + exp_type, subarray, slowaxis, background_method, fit_by_channel): + """ + Standardize input parameters. + + Check input parameters against input exposure type and assemble + values needed for subsequent correction. + + Parameters + ---------- + exp_type : str + Exposure type for the input data. + subarray : str + Subarray name for the input data. + slowaxis : int + Detector slow axis. + background_method : str + Input option for background method. + fit_by_channel : bool + Input option to fit noise by channel. + + Returns + ------- + axis_to_correct : int + Axis along which flicker noise appears for the input + exposure type. + background_method : str or None + Standardized parameter value for background correction. + fit_by_channel : bool + Standardized parameter value for the input subarray and + exposure type. + fc : tuple of int + Frequency cutoff values for use with FFT correction, + by input subarray. + """ + # Get axis to correct, by instrument + if exp_type.startswith('MIR'): + # MIRI doesn't have 1/f-noise, but it does have a vertical flickering. + # Set the axis for median correction to the y-axis. + axis_to_correct = 1 + else: + # For NIR detectors, the axis for median correction is the + # detector slowaxis. + axis_to_correct = abs(slowaxis) + + # Standardize background arguments + if str(background_method).lower() == 'none': + background_method = None + + # Check for fit_by_channel argument, and use only if data is full frame + if fit_by_channel and (subarray != 'FULL' or exp_type.startswith('MIR')): + log.warning("Fit by channel can only be used for full-frame NIR data.") + log.warning("Setting fit_by_channel to False.") + fit_by_channel = False + + # For a fft correction, ALLSLITS exposures need different + # ranges of 1/f frequencies. Be less aggressive with + # fitting higher frequencies in ALLSLITS mode. + if subarray == "ALLSLITS": + fc = (150, 200, 49943, 49957) + else: + fc = (1061, 1211, 49943, 49957) + + return axis_to_correct, background_method, fit_by_channel, fc + + +def _make_processed_rate_image( + input_model, single_mask, input_dir, exp_type, mask_science_regions): + """ + Make a draft rate image and postprocess if needed. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + The input data. + single_mask : bool + If set, a single scene mask is desired, so create + a single rate image. Otherwise, create a rateints + cube, with independent rate information for each + integration. + input_dir : str + Path to the input directory. Used by sub-steps to find auxiliary + data. + exp_type : str + Exposure type for the input data, used to determine + which kinds of postprocessing is necessary. + mask_science_regions : bool + If set, science regions should be masked, so run `assign_wcs` + and `msaflagopen` for NIRSpec and `flatfield` for MIRI + after creating the draft rate image. + + Returns + ------- + image_model : `~jwst.datamodel.JwstDataModel` + The processed rate image or cube. + """ + if isinstance(input_model, datamodels.RampModel): + image_model = make_rate(input_model, return_cube=(not single_mask), + input_dir=input_dir) + else: + # input is already a rate file + image_model = input_model + + # If needed, assign a WCS to the rate file, + # flag open MSA shutters, or retrieve flat DQ flags + assign_wcs = exp_type.startswith('NRS') + flag_open = (exp_type in ['NRS_IFU', 'NRS_MSASPEC']) + flat_dq = (exp_type in ['MIR_IMAGE']) + if mask_science_regions: + image_model = post_process_rate( + image_model, assign_wcs=assign_wcs, + msaflagopen=flag_open, flat_dq=flat_dq, + input_dir=input_dir) + + return image_model + + +def _make_scene_mask( + user_mask, image_model, mask_science_regions, + n_sigma, fit_histogram, single_mask, save_mask): + """ + Make a scene mask from user input or rate image. + + If provided, the user mask is opened as a datamodel and directly + returned. Otherwise, the mask is generated from the rate data in + `image_model`. + + Parameters + ---------- + user_mask : str or None + Path to user-supplied mask image. + image_model : `~jwst.datamodel.JwstDataModel` + A rate image or cube, processed as needed. + mask_science_regions: bool + For NIRSpec, mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. For MIRI imaging, mask + regions of the detector not used for science. + n_sigma : float + N-sigma rejection level for finding outliers. + fit_histogram : bool + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + single_mask : bool + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. + save_mask : bool + If set, a mask model is created and returned along with the mask + array. If not, the `mask_model` returned is None. + + Returns + ------- + background_mask : array-like of bool + Mask array, with True indicating background pixels, False + indicating source pixels. + mask_model : `~jwst.datamodel.JwstDataModel` or None + A datamodel containing the background mask, if `save_mask` + is True. + """ + # Check for a user-supplied mask image. If provided, use it. + if user_mask is not None: + mask_model = datamodels.open(user_mask) + background_mask = (mask_model.data.copy()).astype(np.bool_) + mask_model.close() + del mask_model + else: + # Create the pixel mask that will be used to indicate which pixels + # to include in the 1/f noise measurements. Basically, we're setting + # all illuminated pixels to False, so that they do not get used, and + # setting all unilluminated pixels to True (so they DO get used). + log.info("Creating mask") + background_mask = create_mask( + image_model, + mask_science_regions=mask_science_regions, + n_sigma=n_sigma, fit_histogram=fit_histogram, + single_mask=single_mask) + + # Store the mask image in a model, if requested + if save_mask: + mask_model = _make_intermediate_model(image_model, background_mask) + else: + mask_model = None + + return background_mask, mask_model + + +def _check_data_shapes(input_model, background_mask): + """ + Check data shape for input model and background mask. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + The input data model. + background_mask : array-like of bool + The background mask. + + Returns + ------- + mismatch : bool + If True, the background mask does not match the data + and the step should be skipped. + ndim : int + Number of dimensions in the input data. + nints : int + Number of integrations in the input data. + ngroups : int + Number of groups in the input data. + """ + mismatch = True + image_shape = input_model.data.shape[-2:] + ndim = input_model.data.ndim + if ndim == 2: + nints = 1 + ngroups = 1 + else: + nints = input_model.data.shape[0] + if ndim == 3: + ngroups = 1 + else: + ngroups = input_model.data.shape[1] - 1 + + # Check for 3D mask + if background_mask.ndim == 2: + if nints > 1: + log.info("Data has multiple integrations, but mask is 2D: " + "the same mask will be used for all integrations.") + elif background_mask.shape[0] != nints: + log.warning("Mask does not match data shape. Step will be skipped.") + return mismatch, ndim, nints, ngroups + + # Check that mask matches 2D data shape + if background_mask.shape[-2:] != image_shape: + log.warning("Mask does not match data shape. Step will be skipped.") + return mismatch, ndim, nints, ngroups + + return False, ndim, nints, ngroups + + +def _clean_one_image(image, mask, background_method, background_box_size, + n_sigma, fit_method, detector, fc, axis_to_correct, + fit_by_channel): + """ + Clean an image by fitting and removing background noise. + + Parameters + ---------- + image : array-like of float + The input image. + mask : array-like of bool + The scene mask. All non-background signal should be + marked as True. + background_method : str + The method for fitting the background. + background_box_size : tuple of int + Background box size, used with `background_method` + is 'model'. + n_sigma : float + N-sigma rejection level for outliers. + fit_method : str + The method for fitting the noise after background + signal is removed. + detector : str + The detector name. + fc : tuple of int + Frequency cutoff values, used for `fit_method` = 'fft'. + axis_to_correct : int + Axis along which noise appears, used for + `fit_method` = 'median'. + fit_by_channel : bool + Option to fit noise in detector channels separately, + used for `fit_method` = 'median' + + Returns + ------- + cleaned_image : array-like of float + The cleaned image. + background : array-like of float + The background fit and removed prior to cleaning. + Used for diagnostic purposes. + success : bool + True if cleaning proceeded as expected; False if + cleaning failed and the step should be skipped. + """ + success = True + + # Make sure data is float32 + image = image.astype(np.float32) + + # Find and replace/mask NaNs + nan_pix = np.isnan(image) + image[nan_pix] = 0.0 + mask[nan_pix] = False + + # If there's no good data remaining in the mask, + # skip this image + if not np.any(mask): + return None, None, success + + # Fit and remove a background level + if str(background_method).lower() == 'none': + background = 0.0 + bkg_sub = image + else: + background = background_level( + image, mask, background_method=background_method, + background_box_size=background_box_size) + log.debug(f'Background level: {np.nanmedian(background):.5g}') + bkg_sub = image - background + + # Flag more signal in the background subtracted image, + # with sigma set by the lower half of the distribution only + clip_to_background( + bkg_sub, mask, sigma_lower=n_sigma, + sigma_upper=n_sigma, lower_half_only=True) + + # Clean the noise + if fit_method == 'fft': + if bkg_sub.shape == (2048, 2048): + # Clean a full-frame image + cleaned_image = fft_clean_full_frame(bkg_sub, mask, detector) + else: + # Clean a subarray image + cleaned_image = fft_clean_subarray(bkg_sub, mask, detector, fc=fc) + else: + cleaned_image = median_clean(bkg_sub, mask, axis_to_correct, + fit_by_channel=fit_by_channel) + if cleaned_image is None: + return None, None, False + + # Restore the background level + cleaned_image += background + + # Restore NaNs in cleaned image + cleaned_image[nan_pix] = np.nan + + return cleaned_image, background, success + + +def _mask_unusable(mask, dq): + """ + Mask unusable data, according to DQ. + + Currently, JUMP and DO_NOT_USE flags are used. + The mask is updated in place + + Parameters + ---------- + mask : array-like of bool + Input mask, updated in place. + dq : array-like of int + DQ flag array matching the mask shape. + """ + dnu = (dq & dqflags.group['DO_NOT_USE']) > 0 + mask[dnu] = False + jump = (dq & dqflags.group['JUMP_DET']) > 0 + mask[jump] = False + + +def do_correction(input_model, input_dir=None, fit_method='median', + fit_by_channel=False, background_method='median', + background_box_size=None, + mask_science_regions=False, n_sigma=2.0, fit_histogram=False, + single_mask=True, user_mask=None, save_mask=False, + save_background=False, save_noise=False): + """ + Apply the 1/f noise correction. + + Parameters + ---------- + input_model : `~jwst.datamodel.JwstDataModel` + Science data to be corrected. + + input_dir : str + Path to the input directory. Used by sub-steps (e.g. assign_wcs + for NIRSpec MOS data) to find auxiliary data. + + fit_method : {'fft', 'median'}, optional + The algorithm to use to fit background noise. + + fit_by_channel : bool, optional + If set, flicker noise is fit independently for each detector channel. + Ignored for MIRI, for subarray data, and for `fit_method` = 'fft'. + + background_method : {'median', 'model', None}, optional + If 'median', the preliminary background to remove and restore + is a simple median of the background data. If 'model', the + background data is fit with a low-resolution model via + `~photutils.background.Background2D`. If None, the background + value is 0.0. + + background_box_size : tuple of int, optional + Box size for the data grid used by `Background2D` when + `background_method` = 'model'. For best results, use a box size + that evenly divides the input image shape. + + mask_science_regions : bool, optional + For NIRSpec, mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. For MIRI imaging, mask + regions of the detector not used for science. + + n_sigma : float, optional + N-sigma rejection level for finding outliers. + + fit_histogram : bool, optional + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + + single_mask : bool, optional + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. + + user_mask : str or None, optional + Path to user-supplied mask image. + + save_mask : bool, optional + Switch to indicate whether the mask should be saved. + + save_background : bool, optional + Switch to indicate whether the fit background should be saved. + + save_noise : bool, optional + Switch to indicate whether the fit noise should be saved. + + Returns + ------- + output_model : `~jwst.datamodel.JwstDataModel` + Corrected data. + + mask_model : `~jwst.datamodel.JwstDataModel` + Pixel mask to be saved or None. + + background_model : `~jwst.datamodel.JwstDataModel` + Background model to be saved or None. + + noise_model : `~jwst.datamodel.JwstDataModel` + Background model to be saved or None. + + status : {'COMPLETE', 'SKIPPED'} + Completion status. If errors were encountered, status = 'SKIPPED' + and the output data matches the input data. Otherwise, + status = 'COMPLETE'. + """ + # Track the completion status, for various failure conditions + status = 'SKIPPED' + + detector = input_model.meta.instrument.detector.upper() + subarray = input_model.meta.subarray.name.upper() + exp_type = input_model.meta.exposure.type + slowaxis = input_model.meta.subarray.slowaxis + log.info(f'Input exposure type is {exp_type}, detector={detector}') + + # Check for a valid input that we can work on + if not _check_input(exp_type, fit_method): + return input_model, None, None, None, status + + output_model = input_model.copy() + + # Get parameters needed for subsequent corrections, as appropriate + # to the input data + axis_to_correct, background_method, fit_by_channel, fc = _standardize_parameters( + exp_type, subarray, slowaxis, background_method, fit_by_channel) + + # Make a rate file if needed + if user_mask is None: + image_model = _make_processed_rate_image( + input_model, single_mask, input_dir, exp_type, mask_science_regions) + else: + image_model = input_model + + # Make a mask model from the user input or the rate data + background_mask, mask_model = _make_scene_mask( + user_mask, image_model, mask_science_regions, + n_sigma, fit_histogram, single_mask, save_mask) + + log.info(f"Cleaning image {input_model.meta.filename}") + + # Check data shapes for 2D, 3D, or 4D inputs + mismatch, ndim, nints, ngroups = _check_data_shapes( + input_model, background_mask) + if mismatch: + return output_model, None, None, None, status + + # Close the draft rate model if created - it is no longer needed. + if image_model is not input_model: + image_model.close() + del image_model + + # Make a background cube for saving, if desired + if save_background: + background_to_save = np.zeros_like(input_model.data) + else: + background_to_save = None + + # Loop over integrations and groups (even if there's only 1) + for i in range(nints): + log.debug(f"Working on integration {i + 1}") + for j in range(ngroups): + log.debug(f"Working on group {j + 1}") + + # Copy the scene mask, for further flagging + if background_mask.ndim == 3: + mask = background_mask[i].copy() + else: + mask = background_mask.copy() + + # Get the relevant image data + if ndim == 2: + image = input_model.data + elif ndim == 3: + image = input_model.data[i] + else: + # Ramp data input: + # subtract the current group from the next one + image = input_model.data[i, j+1] - input_model.data[i, j] + dq = input_model.groupdq[i, j+1] + + # Mask any DNU and JUMP pixels + _mask_unusable(mask, dq) + + # Clean the image + cleaned_image, background, success = _clean_one_image( + image, mask, background_method, background_box_size, n_sigma, + fit_method, detector, fc, axis_to_correct, fit_by_channel) + + if not success: + # Cleaning failed for internal reasons - probably the + # mask is not a good match to the data. + log.error(f'Cleaning failed for integration {i + 1}, group {j + 1}') + + # Restore input data to make sure any partial changes + # are thrown away + output_model.data = input_model.data.copy() + return output_model, None, None, None, status + + if cleaned_image is None: + # Cleaning did not proceed because the image is bad: + # leave it as is but continue correcting the rest + log.warning(f'No usable data in integration {i+1}, group {j+1}. ' + f'Skipping correction for this image.') + continue + + # Store the cleaned image in the output model + if ndim == 2: + output_model.data = cleaned_image + if save_background: + background_to_save[:] = background + elif ndim == 3: + output_model.data[i] = cleaned_image + if save_background: + background_to_save[i] = background + else: + # Add the cleaned data diff to the previously cleaned group, + # rather than the noisy input group + output_model.data[i, j+1] = output_model.data[i, j] + cleaned_image + if save_background: + background_to_save[i, j+1] = background + + # Store the background image in a model, if requested + if save_background: + background_model = _make_intermediate_model(output_model, background_to_save) + else: + background_model = None + + # Make a fit noise model for diagnostic purposes by + # diffing the input and output models + if save_noise: + noise_data = output_model.data - input_model.data + noise_model = _make_intermediate_model(output_model, noise_data) + else: + noise_model = None + + # Set completion status + status = 'COMPLETE' + + return output_model, mask_model, background_model, noise_model, status diff --git a/jwst/clean_flicker_noise/clean_flicker_noise_step.py b/jwst/clean_flicker_noise/clean_flicker_noise_step.py new file mode 100644 index 0000000000..a61c179bc8 --- /dev/null +++ b/jwst/clean_flicker_noise/clean_flicker_noise_step.py @@ -0,0 +1,146 @@ +from stdatamodels.jwst import datamodels + +from ..stpipe import Step +from . import clean_flicker_noise + +__all__ = ["CleanFlickerNoiseStep"] + + +class CleanFlickerNoiseStep(Step): + """ + CleanFlickerNoiseStep: This step performs flicker noise correction ("cleaning"). + + Input data is expected to be a ramp file (RampModel), in between + jump and ramp fitting steps, or a rate file (ImageModel or CubeModel). + + Correction algorithms implemented are: + - `fft`: Background noise is fit in frequency space. + Implementation is based on the NSClean algorithm, developed + by Bernard Rauscher. + - `median`: Background noise is characterized by a median + along the detector slow axis. Implementation is based on the + `image1overf` algorithm, developed by Chris Willott. + """ + + class_alias = "clean_flicker_noise" + + spec = """ + fit_method = option('fft', 'median', default='median') # Noise fitting algorithm + fit_by_channel = boolean(default=False) # Fit noise separately by amplifier (NIR only) + background_method = option('median', 'model', None, default='median') # Background fitting algorithm + background_box_size = int_list(min=2, max=2, default=None) # Background box size for modeled background + mask_science_regions = boolean(default=False) # Mask known science regions + n_sigma = float(default=2.0) # Clipping level for non-background signal + fit_histogram = boolean(default=False) # Fit a value histogram to derive sigma + single_mask = boolean(default=True) # Make a single mask for all integrations + user_mask = string(default=None) # Path to user-supplied mask + save_mask = boolean(default=False) # Save the created mask + save_background = boolean(default=False) # Save the fit background + save_noise = boolean(default=False) # Save the fit noise + skip = boolean(default=True) # By default, skip the step + """ + + def process(self, input): + """ + Fit and subtract 1/f background noise from a ramp data set. + + Parameters + ---------- + input : DataModel + Input datamodel to be corrected + + fit_method : str, optional + The noise fitting algorithm to use. Options are 'fft' and 'median'. + + fit_by_channel : bool, optional + If set, flicker noise is fit independently for each detector channel. + Ignored for MIRI, for subarray data, and for `fit_method` = 'fft' + + background_method : {'median', 'model', None} + If 'median', the preliminary background to remove and restore + is a simple median of the background data. If 'model', the + background data is fit with a low-resolution model via + `~photutils.background.Background2D`. If None, the background + value is 0.0. + + background_box_size : tuple of int, optional + Box size for the data grid used by `Background2D` when + `background_method` = 'model'. For best results, use a box size + that evenly divides the input image shape. + + mask_science_regions : bool, optional + For NIRSpec, mask regions of the image defined by WCS bounding + boxes for slits/slices, as well as any regions known to be + affected by failed-open MSA shutters. For MIRI imaging, mask + regions of the detector not used for science. + + n_sigma : float, optional + Sigma clipping threshold to be used in detecting outliers in the image. + + fit_histogram : bool, optional + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + + single_mask : bool, optional + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. + + user_mask : None, str, or `~jwst.datamodels.ImageModel` + Optional user-supplied mask image; path to file or opened datamodel. + + save_mask : bool, optional + Save the computed mask image. + + save_background : bool, optional + Save the computed background image. + + save_noise : bool, optional + Save the computed noise image. + + Returns + ------- + output_model : DataModel + The flicker noise corrected datamodel + """ + + # Open the input data model + with datamodels.open(input) as input_model: + + result = clean_flicker_noise.do_correction( + input_model, self.input_dir, self.fit_method, self.fit_by_channel, + self.background_method, self.background_box_size, + self.mask_science_regions, self.n_sigma, self.fit_histogram, + self.single_mask, self.user_mask, + self.save_mask, self.save_background, self.save_noise) + output_model, mask_model, background_model, noise_model, status = result + + # Save the mask, if requested + if self.save_mask and mask_model is not None: + mask_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='mask') + self.log.info(f"Saving mask file {mask_path}") + mask_model.save(mask_path) + mask_model.close() + + # Save the background, if requested + if self.save_background and background_model is not None: + bg_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='flicker_bkg') + self.log.info(f"Saving background file {bg_path}") + background_model.save(bg_path) + background_model.close() + + # Save the noise, if requested + if self.save_noise and noise_model is not None: + noise_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='flicker_noise') + self.log.info(f"Saving noise file {noise_path}") + noise_model.save(noise_path) + noise_model.close() + + # Set the step completion status + output_model.meta.cal_step.clean_flicker_noise = status + + return output_model diff --git a/jwst/nsclean/lib.py b/jwst/clean_flicker_noise/lib.py similarity index 99% rename from jwst/nsclean/lib.py rename to jwst/clean_flicker_noise/lib.py index d4bc05c0fd..0af311dd26 100644 --- a/jwst/nsclean/lib.py +++ b/jwst/clean_flicker_noise/lib.py @@ -517,7 +517,6 @@ def fit(self, return_fit=False, weight_fit=False): if return_fit: return(rfft) - def clean(self, weight_fit=True, return_model=False): """ Clean the data @@ -533,7 +532,7 @@ def clean(self, weight_fit=True, return_model=False): Returns ------- - data : 2D float array + data : array-like of float The cleaned data array. """ self.fit(weight_fit=weight_fit) # Fit the background model diff --git a/jwst/clean_flicker_noise/tests/__init__.py b/jwst/clean_flicker_noise/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py new file mode 100644 index 0000000000..7bc237bc94 --- /dev/null +++ b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py @@ -0,0 +1,844 @@ +import logging + +import gwcs +import numpy as np +import pytest +from stdatamodels.jwst import datamodels + +from jwst.assign_wcs.tests.test_nirspec import ( + create_nirspec_ifu_file, create_nirspec_fs_file) +from jwst.msaflagopen.tests.test_msa_open import make_nirspec_mos_model, get_file_path +from jwst.clean_flicker_noise import clean_flicker_noise as cfn +from jwst.tests.helpers import LogWatcher + + +def add_metadata(model, shape): + model.meta.instrument.name = 'MIRI' + model.meta.instrument.detector = 'MIRIMAGE' + model.meta.instrument.filter = 'F480M' + model.meta.observation.date = '2015-10-13' + model.meta.observation.time = "00:00:00" + model.meta.exposure.type = 'MIR_IMAGE' + model.meta.exposure.group_time = 1.0 + model.meta.subarray.name = 'FULL' + model.meta.subarray.xstart = 1 + model.meta.subarray.ystart = 1 + model.meta.subarray.xsize = shape[3] + model.meta.subarray.ysize = shape[2] + model.meta.exposure.frame_time = 1.0 + model.meta.exposure.ngroups = shape[1] + model.meta.exposure.group_time = 1.0 + model.meta.exposure.nints = shape[0] + model.meta.exposure.nframes = 1 + model.meta.exposure.groupgap = 0 + model.meta.exposure.readpatt = 'FASTR1' + model.meta.subarray.slowaxis = 2 + + +def make_small_ramp_model(shape=(3, 5, 10, 10)): + rampmodel = datamodels.RampModel(shape) + add_metadata(rampmodel, shape) + + # Make data with a constant rate + for group in range(shape[1]): + rampmodel.data[:, group, :, :] = group + + return rampmodel + + +def make_small_rate_model(shape=(3, 5, 10, 10)): + ratemodel = datamodels.ImageModel(shape[2:]) + add_metadata(ratemodel, shape) + ratemodel.data[:] = 1.0 + return ratemodel + + +def make_small_rateints_model(shape=(3, 5, 10, 10)): + ratemodel = datamodels.CubeModel((shape[0], shape[2], shape[3])) + add_metadata(ratemodel, shape) + ratemodel.data[:] = 1.0 + return ratemodel + + +def make_nirspec_ifu_model(shape=(2048, 2048)): + hdul = create_nirspec_ifu_file(grating='PRISM', filter='CLEAR', + gwa_xtil=0.35986012, gwa_ytil=0.13448857, + gwa_tilt=37.1) + hdul['SCI'].data = np.ones(shape, dtype=float) + rate_model = datamodels.IFUImageModel(hdul) + hdul.close() + return rate_model + + +def make_nirspec_mos_fs_model(): + mos_model = make_nirspec_mos_model() + mos_model.meta.instrument.msa_metadata_file = get_file_path( + 'msa_fs_configuration.fits') + return mos_model + + +def make_nirspec_fs_model(): + hdul = create_nirspec_fs_file(grating="G140M", filter="F100LP") + hdul['SCI'].data = np.ones((2048, 2048), dtype=float) + rate_model = datamodels.ImageModel(hdul) + hdul.close() + + # add the slow axis + rate_model.meta.subarray.slowaxis = 1 + return rate_model + + +class MockUpdate: + def __init__(self): + self.seen = False + + def __call__(self, input_model, mask): + self.seen = True + return mask + + +@pytest.fixture +def log_watcher(monkeypatch): + # Set a log watcher to check for a log message at any level + # in the clean_flicker_noise module + watcher = LogWatcher('') + logger = logging.getLogger('jwst.clean_flicker_noise.clean_flicker_noise') + for level in ['debug', 'info', 'warning', 'error']: + monkeypatch.setattr(logger, level, watcher) + return watcher + + +def test_make_rate(log_watcher): + shape = (3, 5, 10, 10) + ramp_model = make_small_ramp_model(shape) + + log_watcher.message = 'Creating draft rate file' + result = cfn.make_rate(ramp_model, return_cube=True) + assert isinstance(result, datamodels.CubeModel) + assert result.data.shape == (shape[0], shape[2], shape[3]) + assert np.all(result.data == 1.0) + + result = cfn.make_rate(ramp_model, return_cube=False) + assert isinstance(result, datamodels.ImageModel) + assert result.data.shape == shape[2:] + assert np.all(result.data == 1.0) + + ramp_model.close() + result.close() + + # Check for expected log message + log_watcher.assert_seen() + + +def test_postprocess_rate_nirspec(log_watcher): + rate_model = make_nirspec_mos_model() + + log_watcher.message = 'Assigning a WCS' + result = cfn.post_process_rate(rate_model, assign_wcs=True) + assert isinstance(result.meta.wcs, gwcs.WCS) + assert np.sum(result.dq & datamodels.dqflags.pixel['MSA_FAILED_OPEN']) == 0 + log_watcher.assert_seen() + + log_watcher.message = 'Flagging failed-open' + result = cfn.post_process_rate(result, msaflagopen=True) + assert np.sum(result.dq & datamodels.dqflags.pixel['MSA_FAILED_OPEN']) > 0 + log_watcher.assert_seen() + + rate_model.close() + result.close() + + +def test_postprocess_rate_miri(log_watcher): + rate_model = make_small_rate_model() + assert np.sum(rate_model.dq & datamodels.dqflags.pixel['DO_NOT_USE']) == 0 + + log_watcher.message = 'Retrieving flat DQ' + result = cfn.post_process_rate(rate_model, flat_dq=True) + log_watcher.assert_seen() + assert np.sum(result.dq & datamodels.dqflags.pixel['DO_NOT_USE']) > 0 + assert np.all(result.data == rate_model.data) + + rate_model.close() + result.close() + + +@pytest.mark.slow +def test_mask_ifu_slices(): + rate_model = make_nirspec_ifu_model() + + rate_model = cfn.post_process_rate(rate_model, assign_wcs=True) + mask = np.full_like(rate_model.data, True) + + # Mark IFU science regions as False: about 10% of the array + cfn.mask_ifu_slices(rate_model, mask) + assert np.allclose(np.sum(mask), 0.9 * mask.size, atol=0.01 * mask.size) + + rate_model.close() + + +@pytest.mark.parametrize('exptype,blocked', + [('mos', .012), ('mos_fs', .025), ('fs', .05)]) +def test_mask_slits(exptype, blocked): + if exptype == 'mos': + rate_model = make_nirspec_mos_model() + elif exptype == 'mos_fs': + rate_model = make_nirspec_mos_fs_model() + else: + rate_model = make_nirspec_fs_model() + + # Assign a WCS + rate_model = cfn.post_process_rate(rate_model, assign_wcs=True) + + # Mark slits as False + mask = np.full_like(rate_model.data, True) + cfn.mask_slits(rate_model, mask) + + # Check that the fraction of the array blocked is as expected + assert np.allclose(np.sum(mask) / mask.size, 1 - blocked, atol=0.001) + + rate_model.close() + + +@pytest.mark.parametrize('fit_histogram', [True, False]) +@pytest.mark.parametrize('lower_half_only', [True, False]) +def test_clip_to_background(log_watcher, fit_histogram, lower_half_only): + """Integrity checks for clipping a simple data array.""" + # Make an array with normal noise + shape = (100, 100) + rng = np.random.default_rng(seed=123) + image = rng.normal(0, 0.01, size=shape) + mask = np.full(shape, True) + + # Add a strong high outlier, strong low outlier + image[0, 0] += 1 + image[1, 1] += -1 + + # Center found is close to zero, printed when verbose=True + log_watcher.message = "center: 0.000" + cfn.clip_to_background( + image, mask, fit_histogram=fit_histogram, + lower_half_only=lower_half_only, verbose=True) + log_watcher.assert_seen() + + # All outliers clipped with defaults, as well as a small + # percent of the remaining data + assert not mask[0, 0] + assert not mask[1, 1] + assert np.allclose(np.sum(mask) / mask.size, 0.98, atol=0.019) + + # Upper outlier stays with large sigma_upper + mask = np.full(shape, True) + cfn.clip_to_background( + image, mask, fit_histogram=fit_histogram, + lower_half_only=lower_half_only, sigma_upper=1000) + assert mask[0, 0] + assert not mask[1, 1] + assert np.allclose(np.sum(mask) / mask.size, 0.98, atol=0.019) + + # Lower outlier stays with large sigma_lower + mask = np.full(shape, True) + cfn.clip_to_background( + image, mask, fit_histogram=fit_histogram, + lower_half_only=lower_half_only, sigma_lower=1000) + assert not mask[0, 0] + assert mask[1, 1] + assert np.allclose(np.sum(mask) / mask.size, 0.98, atol=0.019) + + +def test_clip_to_background_fit_fails(log_watcher): + shape = (10, 10) + + # histogram failure: all data NaN + log_watcher.message = "Histogram failed" + image = np.full(shape, np.nan) + mask = np.full(shape, True) + with pytest.warns(RuntimeWarning): + cfn.clip_to_background(image, mask, fit_histogram=True, verbose=True) + assert np.all(mask) + log_watcher.assert_seen() + + # if mask is all False, warning is avoided, mask is unchanged + mask[:] = False + cfn.clip_to_background(image, mask, fit_histogram=True, verbose=True) + assert not np.all(mask) + + # fit failure: data is not normal + log_watcher.message = "Gaussian fit failed" + image = np.full(shape, 0.0) + mask = np.full(shape, True) + image[5:, 5:] = 0.1 + cfn.clip_to_background(image, mask, fit_histogram=True, verbose=True) + assert np.all(mask) + log_watcher.assert_seen() + + +@pytest.mark.parametrize('exptype', ['mos', 'mos_fs', 'ifu']) +def test_create_mask_nirspec(monkeypatch, exptype): + # monkeypatch local functions for speed and check that they are called + # (actual behavior tested in separate unit tests) + mock = MockUpdate() + if exptype == 'mos': + # NIRSpec MOS data + monkeypatch.setattr(cfn, 'mask_slits', mock) + rate_model = make_nirspec_mos_model() + elif exptype == 'mos_fs': + monkeypatch.setattr(cfn, 'mask_slits', mock) + rate_model = make_nirspec_mos_fs_model() + + # also assign a WCS so the FS can be detected + rate_model = cfn.post_process_rate(rate_model, assign_wcs=True) + else: + # NIRSpec IFU data + monkeypatch.setattr(cfn, 'mask_ifu_slices', mock) + rate_model = make_nirspec_ifu_model((2048, 2038)) + + rate_model.data[:] = 1.0 + mask = cfn.create_mask(rate_model) + assert mask.shape == rate_model.data.shape + + # Nothing to mask in uniform data + assert np.all(mask) + + # Slit function not called if mask_science_regions not set + assert not mock.seen + + # Fixed slit region not blocked + assert np.all(mask[cfn.NRS_FS_REGION[0]:cfn.NRS_FS_REGION[1]]) + + # Call again but block science regions + mask = cfn.create_mask(rate_model, mask_science_regions=True) + assert mock.seen + if exptype == 'mos_fs': + # FS region still not blocked - may need correction + assert np.all(mask[cfn.NRS_FS_REGION[0]:cfn.NRS_FS_REGION[1]]) + else: + assert not np.any(mask[cfn.NRS_FS_REGION[0]:cfn.NRS_FS_REGION[1]]) + + +def test_create_mask_miri(): + # small MIRI imaging data + rate_model = make_small_rate_model() + rate_model.dq[5, 5] = datamodels.dqflags.pixel['DO_NOT_USE'] + + mask = cfn.create_mask(rate_model) + assert mask.shape == rate_model.data.shape + + # Nothing to mask in uniform data + assert np.all(mask) + + # Call again but block non-science regions + mask = cfn.create_mask(rate_model, mask_science_regions=True) + assert not mask[5, 5] + assert np.sum(mask) == mask.size - 1 + + rate_model.close() + + +def test_create_mask_from_rateints(): + # small rateints data + rate_model = make_small_rateints_model() + + # Add an outlier in each integration + for i in range(rate_model.data.shape[0]): + rate_model.data[i, i, i] += 100 + + mask = cfn.create_mask(rate_model) + assert mask.shape == rate_model.data.shape + assert mask.ndim == 3 + + # Outlier is masked in each integration + assert not mask[0, 0, 0] + assert not mask[1, 1, 1] + assert not mask[2, 2, 2] + assert np.sum(mask) == mask.size - 3 + + # Call again but make a single mask: + # bad data mask is or-ed across integrations + mask = cfn.create_mask(rate_model, single_mask=True) + assert mask.shape == rate_model.data.shape[1:] + assert mask.ndim == 2 + + assert not mask[0, 0] + assert not mask[1, 1] + assert not mask[2, 2] + assert np.sum(mask) == mask.size - 3 + + rate_model.close() + + +def test_background_level(log_watcher): + shape = (100, 100) + image = np.full(shape, 1.0) + mask = np.full(shape, True) + + # add an outlier to be clipped + image[50, 50] = 1000 + + # no background + background = cfn.background_level(image, mask, background_method=None) + assert background == 0.0 + + # median method + background = cfn.background_level(image, mask, background_method='median') + assert background == 1.0 + + # model method + background = cfn.background_level( + image, mask, background_method='model', background_box_size=(10, 10)) + assert background.shape == shape + assert np.all(background == 1.0) + + # model method with mismatched box size: + # warns, but completes successfully + log_watcher.message = "does not divide evenly" + background = cfn.background_level( + image, mask, background_method='model', background_box_size=None) + assert background.shape == shape + assert np.all(background == 1.0) + log_watcher.assert_seen() + + # make image mostly bad, one good region + image[:] = np.nan + image[20:25, 20:25] = 1.0 + + # background fit fails: falls back on simple median + log_watcher.message = "Background fit failed, using median" + background = cfn.background_level( + image, mask, background_method='model', background_box_size=(10, 10)) + assert background == 1.0 + log_watcher.assert_seen() + + +@pytest.mark.parametrize('array_type,fraction_good', [('full', 0.65), ('subarray', 0.37)]) +@pytest.mark.parametrize('detector', ['NRS1', 'NRS2']) +def test_fft_clean(array_type, fraction_good, detector): + if array_type == 'full': + clean_function = cfn.fft_clean_full_frame + else: + clean_function = cfn.fft_clean_subarray + + shape = (80, 80) + mask = np.full(shape, True) + + # zero image should still come out zero + image = np.full(shape, 0.0) + cleaned_image = clean_function(image.copy(), mask, detector) + assert np.allclose(cleaned_image, 0.0) + + # image with regular vertical pattern, centered on 0 + high = np.full((80, 16), 0.1) + low = np.full((80, 16), -0.1) + image = np.hstack([high, low, high, low, high]) + cleaned_image = clean_function(image.copy(), mask, detector) + + # results should be mostly close to zero, + # some artifacts at pattern and edge boundaries, + # edge boundaries are currently worse for subarray correction + good_correction = np.abs(cleaned_image) < 0.01 + assert np.allclose(np.sum(good_correction) / cleaned_image.size, + fraction_good, atol=0.1) + + +def test_fft_clean_error(monkeypatch, log_watcher): + def raise_error(*args, **kwargs): + raise np.linalg.LinAlgError('Linear algebra error') + + clean_function = cfn.fft_clean_full_frame + monkeypatch.setattr(cfn.NSClean, 'clean', raise_error) + + shape = (10, 10) + mask = np.full(shape, True) + image = np.full(shape, 1.0) + + log_watcher.message = "Error cleaning image" + cleaned_image = clean_function(image.copy(), mask, 'NRS1') + assert cleaned_image is None + log_watcher.assert_seen() + + +def test_fft_subarray_clean_error(monkeypatch, log_watcher): + shape = (10, 10) + image = np.full(shape, 1.0) + + # Mask is all bad: error message, returns None + mask = np.full(shape, False) + log_watcher.message = "No good pixels" + cleaned_image = cfn.fft_clean_subarray(image.copy(), mask, 'NRS1') + assert cleaned_image is None + log_watcher.assert_seen() + + # Mask is nearly all bad except a pixel at the edge: + # triggers a linear algebra error + mask[0, 0] = True + log_watcher.message = "Error cleaning image" + cleaned_image = cfn.fft_clean_subarray(image.copy(), mask, 'NRS1') + assert cleaned_image is None + log_watcher.assert_seen() + + # Mask is mostly bad: warns but continues + mask[5, 5] = True + log_watcher.message = "Insufficient reference pixels" + cleaned_image = cfn.fft_clean_subarray(image.copy(), mask, 'NRS1', minfrac=0.5) + assert np.allclose(cleaned_image, image) + log_watcher.assert_seen() + + +def test_median_clean(): + shape = (20, 20) + mask = np.full(shape, True) + + # zero image should still come out zero + image = np.full(shape, 0.0) + cleaned_image = cfn.median_clean(image, mask, 1) + assert np.allclose(cleaned_image, 0.0) + + # image with regular vertical pattern, centered on 0 + high = np.full((20, 4), 0.1) + low = np.full((20, 4), -0.1) + image = np.hstack([high, low, high, low, high]) + + # clean along vertical axis - + # image should be all zero + cleaned_image = cfn.median_clean(image, mask, 1) + assert np.allclose(cleaned_image, 0.0) + + # clean along horizontal axis - + # cleaning removes median value, from high stripes + cleaned_image = cfn.median_clean(image, mask, 2) + assert np.allclose(cleaned_image, image - 0.1) + + +def test_median_clean_by_channel(): + shape = (2048, 2048) + mask = np.full(shape, True) + + # zero image should still come out zero + image = np.full(shape, 0.0) + cleaned_image = cfn.median_clean(image, mask, 1, fit_by_channel=True) + assert np.allclose(cleaned_image, 0.0) + + # image with regular vertical pattern, centered on 0 + high = np.full((2048, 16), 0.1) + low = np.full((2048, 16), -0.1) + image = np.hstack([high, low] * 64) + + # add offset by vertical channel + offset = np.zeros_like(image) + offset[:512] += 0.2 + offset[512:1024] += 0.3 + offset[1024:1536] += 0.4 + offset[1536:] += 0.5 + image += offset + + # clean along vertical axis by channel - + # image should be all zero + cleaned_image = cfn.median_clean(image, mask, 1, fit_by_channel=True) + assert np.allclose(cleaned_image, 0.0) + + # clean along vertical axis for the whole image - + # vertical pattern is removed, offset by channel stays, + # but median value is subtracted + cleaned_image = cfn.median_clean(image, mask, 1, fit_by_channel=False) + assert np.allclose(cleaned_image, offset - 0.35) + + # clean along horizontal axis - + # cleaning removes offset value, leaves vertical pattern + cleaned_image = cfn.median_clean(image, mask, 2, fit_by_channel=True) + assert np.allclose(cleaned_image, image - offset) + + +@pytest.mark.parametrize('mask_science', [True, False]) +def test_do_correction_miri_imaging_ramp(mask_science): + ramp_model = make_small_ramp_model() + cleaned, mask, bg, noise, status = cfn.do_correction( + ramp_model, mask_science_regions=mask_science) + + # uniform data, correction has no effect + assert cleaned.data.shape == ramp_model.shape + assert np.allclose(cleaned.data, ramp_model.data) + assert status == 'COMPLETE' + + # extra models are not created + assert mask is None + assert bg is None + assert noise is None + + ramp_model.close() + cleaned.close() + + +@pytest.mark.parametrize('mask_science', [True, False]) +def test_do_correction_nirspec_rate(mask_science): + rate_model = make_nirspec_fs_model() + cleaned, mask, bg, noise, status = cfn.do_correction( + rate_model, mask_science_regions=mask_science) + + # uniform data, correction has no effect + assert cleaned.data.shape == rate_model.data.shape + assert np.allclose(cleaned.data, rate_model.data) + assert status == 'COMPLETE' + + # extra models are not created + assert mask is None + assert bg is None + assert noise is None + + rate_model.close() + cleaned.close() + + +@pytest.mark.parametrize('single_mask', [True, False]) +def test_do_correction_rateints(single_mask): + rate_model = make_small_rateints_model() + cleaned, mask, bg, noise, status = cfn.do_correction( + rate_model, single_mask=single_mask, save_mask=True) + + # uniform data, correction has no effect + assert cleaned.data.shape == rate_model.shape + assert np.allclose(cleaned.data, rate_model.data) + assert status == 'COMPLETE' + + if single_mask: + assert mask.shape == cleaned.data.shape[-2:] + else: + assert mask.shape == cleaned.data.shape + + rate_model.close() + cleaned.close() + mask.close() + + +def test_do_correction_unsupported(log_watcher): + ramp_model = make_small_ramp_model() + ramp_model.meta.exposure.type = 'MIR_MRS' + + log_watcher.message = "not supported" + cleaned, _, _, _, status = cfn.do_correction(ramp_model) + assert cleaned is ramp_model + assert status == 'SKIPPED' + log_watcher.assert_seen() + + ramp_model.close() + + +def test_do_correction_no_fit_by_channel(log_watcher): + ramp_model = make_small_ramp_model() + + # fit_by_channel is only used for NIR data - + # log a warning, but step still completes + log_watcher.message = "can only be used for full-frame NIR" + cleaned, _, _, _, status = cfn.do_correction(ramp_model, fit_by_channel=True) + assert status == 'COMPLETE' + log_watcher.assert_seen() + + ramp_model.close() + cleaned.close() + + +@pytest.mark.parametrize('exptype', ['MIR_IMAGE', 'NRC_IMAGE', 'NIS_IMAGE']) +def test_do_correction_fft_not_allowed(log_watcher, exptype): + ramp_model = make_small_ramp_model() + ramp_model.meta.exposure.type = exptype + + # not allowed for MIRI, NIRCAM, NIRISS + log_watcher.message = f"cannot be applied to exp_type {exptype}" + cleaned, _, _, _, status = cfn.do_correction(ramp_model, fit_method='fft') + assert cleaned is ramp_model + assert status == 'SKIPPED' + log_watcher.assert_seen() + + ramp_model.close() + + +@pytest.mark.parametrize('none_value', [None, 'none', 'None']) +def test_do_correction_no_background(none_value): + ramp_model = make_small_ramp_model() + + # Don't remove background before fitting noise + cleaned, _, _, _, _ = cfn.do_correction( + ramp_model, background_method=none_value) + + # Output data is all zero: uniform level is removed + assert np.allclose(cleaned.data, 0.0) + + ramp_model.close() + cleaned.close() + + +@pytest.mark.parametrize('ndim', [2, 3]) +def test_do_correction_user_mask(tmp_path, ndim): + ramp_model = make_small_ramp_model() + + if ndim == 3: + mask = np.full((ramp_model.shape[0], + ramp_model.shape[2], + ramp_model.shape[3]), True) + mask_model = datamodels.CubeModel(mask) + else: + mask = np.full(ramp_model.shape[-2:], True) + mask_model = datamodels.ImageModel(mask) + user_mask = str(tmp_path / 'mask.fits') + mask_model.save(user_mask) + mask_model.close() + + cleaned, output_mask, _, _, _ = cfn.do_correction( + ramp_model, user_mask=user_mask, save_mask=True) + + assert np.all(output_mask.data == mask_model.data) + assert np.allclose(cleaned.data, ramp_model.data) + + ramp_model.close() + cleaned.close() + output_mask.close() + + +@pytest.mark.parametrize('input_type', ['rate', 'rateints', 'ramp']) +def test_do_correction_user_mask_mismatch(tmp_path, input_type, log_watcher): + shape = (3, 5, 20, 20) + if input_type == 'rate': + model = make_small_rate_model(shape) + elif input_type == 'rateints': + model = make_small_rateints_model(shape) + else: + model = make_small_ramp_model(shape) + + mask = np.full((10, 10), True) + mask_model = datamodels.ImageModel(mask) + user_mask = str(tmp_path / 'mask.fits') + mask_model.save(user_mask) + mask_model.close() + + log_watcher.message = 'Mask does not match' + cleaned, output_mask, _, _, status = cfn.do_correction( + model, user_mask=user_mask, save_mask=True) + + log_watcher.assert_seen() + assert status == 'SKIPPED' + assert output_mask is None + + model.close() + cleaned.close() + + +def test_do_correction_user_mask_mismatch_integ(tmp_path, log_watcher): + shape = (3, 5, 20, 20) + model = make_small_rateints_model(shape) + + mask = np.full((2, 20, 20), True) + mask_model = datamodels.CubeModel(mask) + user_mask = str(tmp_path / 'mask.fits') + mask_model.save(user_mask) + mask_model.close() + + log_watcher.message = 'Mask does not match' + cleaned, output_mask, _, _, status = cfn.do_correction( + model, user_mask=user_mask, save_mask=True) + + log_watcher.assert_seen() + assert status == 'SKIPPED' + assert output_mask is None + + model.close() + cleaned.close() + + +@pytest.mark.parametrize('subarray', ['SUBS200A1', 'ALLSLITS']) +def test_do_correction_fft_subarray(subarray): + ramp_model = make_small_ramp_model() + ramp_model.meta.exposure.type = 'NRS_FIXEDSLIT' + ramp_model.meta.subarray.slowaxis = 1 + ramp_model.meta.subarray.name = subarray + + cleaned, _, _, noise, _ = cfn.do_correction( + ramp_model, fit_method='fft', save_noise=True) + + # uniform data, correction has no effect + assert cleaned.data.shape == ramp_model.data.shape + assert np.allclose(cleaned.data, ramp_model.data) + assert np.allclose(noise.data, 0.0) + + ramp_model.close() + cleaned.close() + noise.close() + + +def test_do_correction_fft_full(): + rate_model = make_nirspec_fs_model() + + cleaned, _, _, noise, _ = cfn.do_correction( + rate_model, fit_method='fft', save_noise=True) + + # uniform data, correction has no effect + assert cleaned.data.shape == rate_model.data.shape + assert np.allclose(cleaned.data, rate_model.data) + assert np.allclose(noise.data, 0.0) + + rate_model.close() + cleaned.close() + noise.close() + + +def test_do_correction_clean_fails(monkeypatch, log_watcher): + ramp_model = make_small_ramp_model() + ramp_model.meta.exposure.type = 'NRS_FIXEDSLIT' + ramp_model.meta.subarray.slowaxis = 1 + + # Add some noise so that input and output are + # expected to be different + rng = np.random.default_rng(seed=123) + ramp_model.data += rng.normal(0, 0.1, size=ramp_model.data.shape) + cleaned, _, _, _, status = cfn.do_correction( + ramp_model, fit_method='fft') + assert not np.allclose(cleaned.data, ramp_model.data) + assert status == 'COMPLETE' + + # Mock a None-value returned from cleaning function + monkeypatch.setattr(cfn, 'fft_clean_subarray', lambda *args, **kwargs: None) + + # Call again + log_watcher.message = "Cleaning failed" + cleaned, _, _, _, status = cfn.do_correction( + ramp_model, fit_method='fft') + + # Error message issued, status is skipped, + # output data is the same as input + log_watcher.assert_seen() + assert status == 'SKIPPED' + assert np.allclose(cleaned.data, ramp_model.data) + + ramp_model.close() + cleaned.close() + + +@pytest.mark.parametrize('save_type', ['noise', 'background']) +@pytest.mark.parametrize('input_type', ['rate', 'rateints', 'ramp']) +def test_do_correction_save_intermediate(save_type, input_type): + shape = (3, 5, 20, 20) + if input_type == 'rate': + model = make_small_rate_model(shape) + elif input_type == 'rateints': + model = make_small_rateints_model(shape) + else: + model = make_small_ramp_model(shape) + + save_bg = (save_type == 'background') + save_noise = (save_type == 'noise') + cleaned, _, background, noise, status = cfn.do_correction( + model, save_background=save_bg, save_noise=save_noise) + + # Output background model always matches input shape + # and datamodel type + if save_bg: + assert background.data.shape == model.data.shape + assert type(background) is type(model) + background.close() + else: + assert background is None + if save_noise: + assert noise.data.shape == model.data.shape + assert type(noise) is type(model) + noise.close() + else: + assert noise is None + + model.close() diff --git a/jwst/clean_flicker_noise/tests/test_clean_flicker_noise_step.py b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise_step.py new file mode 100644 index 0000000000..d8f2e1dcc6 --- /dev/null +++ b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise_step.py @@ -0,0 +1,82 @@ +import os + +import pytest +from stdatamodels.jwst import datamodels + +from jwst.clean_flicker_noise import CleanFlickerNoiseStep +from .test_clean_flicker_noise import make_small_ramp_model + + +@pytest.mark.parametrize('skip', [True, False]) +def test_output_type(skip): + input_model = make_small_ramp_model() + cleaned = CleanFlickerNoiseStep.call(input_model, skip=skip) + + # output is a ramp model either way + assert isinstance(cleaned, datamodels.RampModel) + + # status may be SKIPPED or COMPLETE + if skip: + assert cleaned.meta.cal_step.clean_flicker_noise == 'SKIPPED' + else: + assert cleaned.meta.cal_step.clean_flicker_noise == 'COMPLETE' + + input_model.close() + cleaned.close() + + +def test_save_mask(tmp_path): + input_model = make_small_ramp_model() + input_model.meta.filename = 'test_jump.fits' + + output_dir = str(tmp_path) + cleaned = CleanFlickerNoiseStep.call( + input_model, skip=False, save_mask=True, output_dir=output_dir) + + mask_file = str(tmp_path / 'test_mask.fits') + assert os.path.isfile(mask_file) + + mask_model = datamodels.open(mask_file) + assert isinstance(mask_model, datamodels.ImageModel) + + input_model.close() + cleaned.close() + mask_model.close() + + +def test_save_background(tmp_path): + input_model = make_small_ramp_model() + input_model.meta.filename = 'test_jump.fits' + + output_dir = str(tmp_path) + cleaned = CleanFlickerNoiseStep.call( + input_model, skip=False, save_background=True, output_dir=output_dir) + + bkg_file = str(tmp_path / 'test_flicker_bkg.fits') + assert os.path.isfile(bkg_file) + + bkg_model = datamodels.open(bkg_file) + assert isinstance(bkg_model, datamodels.RampModel) + + input_model.close() + cleaned.close() + bkg_model.close() + + +def test_save_noise(tmp_path): + input_model = make_small_ramp_model() + input_model.meta.filename = 'test_jump.fits' + + output_dir = str(tmp_path) + cleaned = CleanFlickerNoiseStep.call( + input_model, skip=False, save_noise=True, output_dir=output_dir) + + noise_file = str(tmp_path / 'test_flicker_noise.fits') + assert os.path.isfile(noise_file) + + noise_model = datamodels.open(noise_file) + assert isinstance(noise_model, datamodels.RampModel) + + input_model.close() + cleaned.close() + noise_model.close() diff --git a/jwst/lib/suffix.py b/jwst/lib/suffix.py index 3fff82ba9f..a7af86beb7 100644 --- a/jwst/lib/suffix.py +++ b/jwst/lib/suffix.py @@ -159,6 +159,8 @@ 'spec3pipeline', 'nsclean', 'nscleanstep', + 'clean_flicker_noise', + 'cleanflickernoisestep', 'outlierdetectionstep', 'groupscalestep', 'hlspstep', diff --git a/jwst/msaflagopen/msaflag_open.py b/jwst/msaflagopen/msaflag_open.py index b348ff4fb0..39a49e7b7c 100644 --- a/jwst/msaflagopen/msaflag_open.py +++ b/jwst/msaflagopen/msaflag_open.py @@ -84,10 +84,10 @@ def flag(input_datamodel, failed_slitlets, wcs_refnames): science data with DQ flags of affected modified """ - # # Use the machinery in assign_wcs to create a WCS object for the bad shutters pipeline = slitlets_wcs(input_datamodel, wcs_refnames, failed_slitlets) wcs = WCS(pipeline) + # Create output as a copy of the input science data model so we can overwrite # the wcs with the wcs for the failed open shutters # Have to make sure the EXP_TYPE is NRS_MSASPEC so that nrs_wcs_set_input works, @@ -98,31 +98,30 @@ def flag(input_datamodel, failed_slitlets, wcs_refnames): temporary_copy.meta.exposure.type = 'NRS_MSASPEC' dq_array = input_datamodel.dq for slitlet in failed_slitlets: - # # Pick the WCS for this slitlet from the WCS of the exposure thiswcs = nrs_wcs_set_input(temporary_copy, slitlet.name) - # + # Convert the bounding box for this slitlet to a set of indices to use as a slice xmin, xmax, ymin, ymax = boundingbox_to_indices(temporary_copy, thiswcs.bounding_box) - # + # Make a grid of points within the slice y_indices, x_indices = np.mgrid[ymin:ymax, xmin:xmax] - # + # Calculate the arrays of coordinates for each pixel in the slice coordinate_array = thiswcs(x_indices, y_indices) - # + # The coordinate_array is a tuple of arrays, one for each output coordinate # In this case there should be 3 arrays, one each for RA, Dec and Wavelength # For pixels outside the slitlet, the arrays have NaN - # + # Make a subarray from these coordinate arrays by setting pixels that aren't # NaN to FAILDOPENFLAG, the rest to 0 dq_subarray = wcs_to_dq(coordinate_array, FAILEDOPENFLAG) - # + # bitwise-or this subarray with the slice in the original exposure's DQ array dq_array = or_subarray_with_array(dq_array, dq_subarray, xmin, xmax, ymin, ymax) - # + # Set the dq array of the input datamodel to the corrected dq array input_datamodel.dq = dq_array return input_datamodel @@ -148,7 +147,7 @@ def boundingbox_to_indices(data_model, bounding_box): Range of indices of overlap between science data array and bounding box """ - nrows, ncols = data_model.data.shape + nrows, ncols = data_model.data.shape[-2:] ((x1, x2), (y1, y2)) = bounding_box xmin = int(min(x1, x2)) xmin = max(xmin, 0) @@ -243,5 +242,5 @@ def or_subarray_with_array(dq_array, dq_subarray, xmin, xmax, ymin, ymax): Bitwise-or the slice of the dq array with the section corresponding to the failed open shutter """ - dq_array[ymin:ymax, xmin:xmax] = np.bitwise_or(dq_array[ymin:ymax, xmin:xmax], dq_subarray) + dq_array[..., ymin:ymax, xmin:xmax] = np.bitwise_or(dq_array[..., ymin:ymax, xmin:xmax], dq_subarray) return dq_array diff --git a/jwst/msaflagopen/tests/test_msa_open.py b/jwst/msaflagopen/tests/test_msa_open.py index 131c5eba46..c94f7af81b 100644 --- a/jwst/msaflagopen/tests/test_msa_open.py +++ b/jwst/msaflagopen/tests/test_msa_open.py @@ -28,6 +28,55 @@ def get_file_path(filename): return os.path.join(data_path, filename) +def make_nirspec_mos_model(): + im = ImageModel((2048, 2048)) + im.meta.wcsinfo = { + 'dec_ref': -0.00601415671349804, + 'ra_ref': -0.02073605215697509, + 'roll_ref': -0.0, + 'v2_ref': -453.5134, + 'v3_ref': -373.4826, + 'v3yangle': 0.0, + 'vparity': -1} + + im.meta.instrument = { + 'detector': 'NRS1', + 'filter': 'F100LP', + 'grating': 'G140M', + 'name': 'NIRSPEC', + 'gwa_tilt': 37.0610, + 'gwa_xtilt': 0.0001, + 'gwa_ytilt': 0.0001, + 'msa_metadata_id': 12} + + im.meta.observation = { + 'program_number': '1234', + 'date': '2016-09-05', + 'time': '8:59:37'} + + im.meta.exposure = { + 'duration': 11.805952, + 'end_time': 58119.85416, + 'exposure_time': 11.776, + 'frame_time': 0.11776, + 'group_time': 0.11776, + 'groupgap': 0, + 'integration_time': 11.776, + 'nframes': 1, + 'ngroups': 100, + 'nints': 1, + 'nresets_between_ints': 0, + 'nsamples': 1, + 'readpatt': 'NRSRAPID', + 'sample_time': 10.0, + 'start_time': 58119.8333, + 'type': 'NRS_MSASPEC', + 'zero_frame': False} + im.meta.instrument.msa_metadata_file = get_file_path('msa_configuration.fits') + im.meta.dither.position_number = 1 + return im + + def test_or_subarray_with_array(): """test bitwise or with array and subarray.""" @@ -137,52 +186,7 @@ def test_boundingbox_from_indices(): def test_msaflagopen_step(): - im = ImageModel((2048, 2048)) - im.meta.wcsinfo = { - 'dec_ref': -0.00601415671349804, - 'ra_ref': -0.02073605215697509, - 'roll_ref': -0.0, - 'v2_ref': -453.5134, - 'v3_ref': -373.4826, - 'v3yangle': 0.0, - 'vparity': -1} - - im.meta.instrument = { - 'detector': 'NRS1', - 'filter': 'F100LP', - 'grating': 'G140M', - 'name': 'NIRSPEC', - 'gwa_tilt': 37.0610, - 'gwa_xtilt': 0.0001, - 'gwa_ytilt': 0.0001, - 'msa_metadata_id': 12} - - im.meta.observation = { - 'program_number': '1234', - 'date': '2016-09-05', - 'time': '8:59:37'} - - im.meta.exposure = { - 'duration': 11.805952, - 'end_time': 58119.85416, - 'exposure_time': 11.776, - 'frame_time': 0.11776, - 'group_time': 0.11776, - 'groupgap': 0, - 'integration_time': 11.776, - 'nframes': 1, - 'ngroups': 100, - 'nints': 1, - 'nresets_between_ints': 0, - 'nsamples': 1, - 'readpatt': 'NRSRAPID', - 'sample_time': 10.0, - 'start_time': 58119.8333, - 'type': 'NRS_MSASPEC', - 'zero_frame': False} - im.meta.instrument.msa_metadata_file = get_file_path('msa_configuration.fits') - im.meta.dither.position_number = 1 - + im = make_nirspec_mos_model() im = AssignWcsStep.call(im) result = MSAFlagOpenStep.call(im) diff --git a/jwst/nsclean/nsclean.py b/jwst/nsclean/nsclean.py deleted file mode 100644 index 27c6509548..0000000000 --- a/jwst/nsclean/nsclean.py +++ /dev/null @@ -1,508 +0,0 @@ -import logging -import numpy as np -import gwcs -from astropy.stats import sigma_clipped_stats - -from jwst import datamodels -from jwst.assign_wcs import nirspec -from stdatamodels.jwst.datamodels import dqflags - -from jwst.nsclean.lib import NSClean, NSCleanSubarray - -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -def mask_ifu_slices(input_model, mask): - - """Find pixels located within IFU slices and flag them in the - mask, so that they do not get used. - - Parameters - ---------- - input_model : data model object - science data - - mask : 2D bool array - input mask that will be updated - - Returns - ------- - mask : 2D bool array - output mask with additional flags for science pixels - """ - - log.info("Finding slice pixels for an IFU image") - - # Initialize global DQ map to all zero (OK to use) - dqmap = np.zeros_like(input_model.dq) - - # Get the wcs objects for all IFU slices - list_of_wcs = nirspec.nrs_ifu_wcs(input_model) - - # Loop over the IFU slices, finding the valid region for each - for (k, ifu_wcs) in enumerate(list_of_wcs): - - # Construct array indexes for pixels in this slice - x, y = gwcs.wcstools.grid_from_bounding_box(ifu_wcs.bounding_box, - step=(1, 1), - center=True) - # Get the world coords for all pixels in this slice; - # all we actually need are wavelengths - coords = ifu_wcs(x, y) - dq = dqmap[y.astype(int), x.astype(int)] - wl = coords[2] - # set non-NaN wavelength locations as do not use (one) - valid = ~np.isnan(wl) - dq = dq[valid] - x = x[valid] - y = y[valid] - dq[:] = 1 - - # Copy DQ for this slice into global DQ map - dqmap[y.astype(int), x.astype(int)] = dq - - # Now set all non-zero locations in the mask to False (do not use) - mask[dqmap==1] = False - - return mask - - -def mask_slits(input_model, mask): - - """Find pixels located within MOS or fixed slit footprints - and flag them in the mask, so that they do not get used. - - Parameters - ---------- - input_model : data model object - science data - - mask : 2D bool array - input mask that will be updated - - Returns - ------- - mask : 2D bool array - output mask with additional flags for slit pixels - """ - - from jwst.extract_2d.nirspec import offset_wcs - - log.info("Finding slit/slitlet pixels") - - # Get the slit-to-msa frame transform from the WCS object - slit2msa = input_model.meta.wcs.get_transform('slit_frame', 'msa_frame') - - # Loop over the slits, marking all the pixels within each bounding - # box as False (do not use) in the mask. - # Note that for 3D masks (TSO mode), all planes will be set to the same value. - for slit in slit2msa.slits: - slit_wcs = nirspec.nrs_wcs_set_input(input_model, slit.name) - xlo, xhi, ylo, yhi = offset_wcs(slit_wcs) - mask[..., ylo:yhi, xlo:xhi] = False - - return mask - - -def create_mask(input_model, mask_spectral_regions, n_sigma): - """Create the pixel mask needed for setting which pixels to use - for measuring 1/f noise. - - Parameters - ---------- - input_model : data model object - science data - - mask_spectral_regions : bool - mask slit/slice regions defined in WCS - - n_sigma : float - sigma threshold for masking outliers - - Returns - ------- - mask : 2D or 3D bool array - image mask - - nan_pix : array - indexes of image locations with NaN values - """ - exptype = input_model.meta.exposure.type.lower() - - # Initialize mask to all True. Subsequent operations will mask - # out pixels that contain signal. - # Note: mask will be 3D for BOTS mode data - mask = np.full(np.shape(input_model.dq), True) - - # Mask any reference pixels - ref_pix = input_model.dq & dqflags.pixel['REFERENCE_PIXEL'] - mask[ref_pix > 0] = False - - # If IFU, mask all pixels contained in the IFU slices - if exptype == 'nrs_ifu' and mask_spectral_regions: - mask = mask_ifu_slices(input_model, mask) - - # If MOS or FS, mask all pixels affected by open slitlets - if exptype in ['nrs_fixedslit', 'nrs_brightobj', 'nrs_msaspec'] and mask_spectral_regions: - mask = mask_slits(input_model, mask) - - # If IFU or MOS, mask pixels affected by failed-open shutters - if exptype in ['nrs_ifu', 'nrs_msaspec']: - open_pix = input_model.dq & dqflags.pixel['MSA_FAILED_OPEN'] - mask[open_pix > 0] = False - - # Temporarily reset NaN pixels and mask them. - # Save the list of NaN pixel coords, so that they can be reset at the end. - nan_pix = np.isnan(input_model.data) - input_model.data[nan_pix] = 0 - mask[nan_pix] = False - - # If IFU or MOS, mask the fixed-slit area of the image; uses hardwired indexes - if exptype == 'nrs_ifu': - log.info("Masking the fixed slit region for IFU data.") - mask[922:1116, :] = False - elif exptype == 'nrs_msaspec': - # check for any slits defined in the fixed slit quadrant: - # if there is nothing there of interest, mask the whole FS region - slit2msa = input_model.meta.wcs.get_transform('slit_frame', 'msa_frame') - is_fs = [s.quadrant == 5 for s in slit2msa.slits] - if not any(is_fs): - log.info("Masking the fixed slit region for MOS data.") - mask[922:1116, :] = False - else: - log.info("Fixed slits found in MSA definition; " - "not masking the fixed slit region for MOS data.") - - # Mask outliers using sigma clipping stats. - # For BOTS mode, which uses 3D data, loop over each integration separately. - if len(input_model.data.shape) == 3: - for i in range(input_model.data.shape[0]): - _, median, sigma = sigma_clipped_stats(input_model.data[i], mask=~mask[i], mask_value=0, sigma=5.0) - outliers = input_model.data[i] > (median + n_sigma * sigma) - mask[i][outliers] = False - else: - _, median, sigma = sigma_clipped_stats(input_model.data, mask=~mask, mask_value=0, sigma=5.0) - outliers = input_model.data > (median + n_sigma * sigma) - mask[outliers] = False - - - # Return the mask and the record of which pixels were NaN in the input; - # it'll be needed later - return mask, nan_pix - - -def clean_full_frame(detector, image, mask): - """Clean a full-frame (2048x2048) image. - - Parameters - ---------- - detector : str - The name of the detector from which the data originate. - - image : 2D float array - The image to be cleaned. - - mask : 2D bool array - The mask that indicates which pixels are to be used in fitting. - - Returns - ------- - cleaned_image : 2D float array - The cleaned image. - """ - - # Instantiate the cleaner - cleaner = NSClean(detector, mask) - - # Clean the image - try: - cleaned_image = cleaner.clean(image, buff=True) - except np.linalg.LinAlgError: - log.warning("Error cleaning image; step will be skipped") - return None - - return cleaned_image - - -def clean_subarray(detector, image, mask, npix_iter=512, - fc=(1061, 1211, 49943, 49957), - exclude_outliers=True, sigrej=4, minfrac=0.05): - """Clean a subarray image. - - Parameters - ---------- - detector : str - The name of the detector from which the data originate. - - image : 2D float array - The image to be cleaned. - - mask : 2D bool array - The mask that indicates which pixels are to be used in fitting. - - npix_iter : int - Number of pixels to process simultaneously. Default 512. Should - be at least a few hundred to access sub-kHz frequencies in areas - where most pixels are available for fitting. Previous default - behavior corresponds to npix_iter of infinity. - - fc : tuple - Apodizing filter definition. These parameters are tunable. The - defaults happen to work well for NIRSpec BOTS exposures. - 1) Unity gain for f < fc[0] - 2) Cosine roll-off from fc[0] to fc[1] - 3) Zero gain from fc[1] to fc[2] - 4) Cosine roll-on from fc[2] to fc[3] - Default (1061, 1211, 49943, 49957) - - exclude_outliers : bool - Find and mask outliers in the fit? Default True - - sigrej : float - Number of sigma to clip when identifying outliers. Default 4. - - minfrac : float - Minimum fraction of pixels locally available in the mask in - order to attempt a correction. Default 0.05 (i.e., 5%). - - Returns - ------- - cleaned_image : 2D float array - The cleaned image. - """ - - # Flip the image to detector coords. NRS1 requires a transpose - # of the axes, while NRS2 requires a transpose and flip. - if detector == "NRS1": - image = image.transpose() - mask = mask.transpose() - else: - image = image.transpose()[::-1] - mask = mask.transpose()[::-1] - - # We must do the masking of discrepant pixels here: it just - # doesn't work if we wait and do it in the cleaner. This is - # basically copied from lib.py. Use a robust estimator for - # standard deviation, then exclude discrepant pixels and their - # four nearest neighbors from the fit. - - if exclude_outliers: - med = np.median(image[mask]) - std = 1.4825 * np.median(np.abs((image - med)[mask])) - outlier = mask & (np.abs(image - med) > sigrej * std) - - mask = mask&(~outlier) - - # also get four nearest neighbors of flagged pixels - mask[1:] = mask[1:] & (~outlier[:-1]) - mask[:-1] = mask[:-1] & (~outlier[1:]) - mask[:, 1:] = mask[:, 1:] & (~outlier[:, :-1]) - mask[:, :-1] = mask[:, :-1] & (~outlier[:, 1:]) - - # Used to determine the fitting intervals along the slow scan - # direction. Pre-pend a zero so that sum_mask[i] is equal - # to np.sum(mask[:i], axis=1). - - sum_mask = np.array([0] + list(np.cumsum(np.sum(mask, axis=1)))) - - # i1 will be the first row with a nonzero element in the mask - # imax will be the last row with a nonzero element in the mask - - nonzero_mask_element = np.sum(mask, axis=1) > 0 - i1 = np.amin(np.arange(mask.shape[0])[nonzero_mask_element]) - imax = np.amax(np.arange(mask.shape[0])[nonzero_mask_element]) - - i1_vals = [] - di_list = [] - models = [] - while i1 <= imax: - - # Want npix_iter available pixels in this section. If - # there are fewer than 1.5*npix_iter available pixels in - # the rest of the image, just go to the end. - - for k in range(i1 + 1, imax + 2): - if (sum_mask[k] - sum_mask[i1] > npix_iter - and sum_mask[-1] - sum_mask[i1] > 1.5 * npix_iter): - break - - di = k - i1 - - i1_vals += [i1] - di_list += [di] - - # Fit this section only if at least minpct% of the pixels - # are available for finding the background. Don't flag - # outliers section-by-section; we have to do that earlier - # over the full array to get reliable values for the mean - # and standard deviation. - - if np.mean(mask[i1:i1 + di]) > minfrac: - cleaner = NSCleanSubarray(image[i1:i1 + di], mask[i1:i1 + di], - fc=fc, exclude_outliers=False) - models += [cleaner.clean(return_model=True)] - else: - log.warning("Insufficient reference pixels for NSClean around " - "row %d; no correction will be made here." % i1) - models += [np.zeros(image[i1:i1 + di].shape)] - - # If we have reached the end of the array, we are finished. - if k == imax + 1: - break - - # Step forward by half an interval so that we have - # overlapping fitting regions. - - i1 += max(int(np.round(di/2)), 1) - - model = np.zeros(image.shape) - tot_wgt = np.zeros(image.shape) - - # When we combine different corrections computed over - # different intervals, each one the highest weight towards the - # center of its interval and less weight towards the edge. - # Use nonzero weights everywhere so that if only one - # correction is available it gets unit weight when we - # normalize. - - for i in range(len(models)): - wgt = 1.001 - np.abs(np.linspace(-1, 1, di_list[i]))[:, np.newaxis] - model[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt*models[i] - tot_wgt[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt - - # don't divide by zero - tot_wgt[model == 0] = 1 - model /= tot_wgt - cleaned_image = image - model - - # Restore the cleaned image to the science frame - if detector == "NRS1": - cleaned_image = cleaned_image.transpose() - else: - cleaned_image = cleaned_image[::-1].transpose() - - return cleaned_image - - -def do_correction(input_model, mask_spectral_regions, n_sigma, save_mask, user_mask): - - """Apply the NSClean 1/f noise correction - - Parameters - ---------- - input_model : data model object - science data to be corrected - - mask_spectral_regions : bool - Mask slit/slice regions defined in WCS - - n_sigma : float - n-sigma rejection level for finding outliers - - save_mask : bool - switch to indicate whether the mask should be saved - - user_mask : str or None - Path to user-supplied mask image - - Returns - ------- - output_model : `~jwst.datamodel.JwstDataModel` - corrected data - - mask_model : `~jwst.datamodel.JwstDataModel` - pixel mask to be saved or None - """ - - detector = input_model.meta.instrument.detector.upper() - exp_type = input_model.meta.exposure.type - log.info(f'Input exposure type is {exp_type}, detector={detector}') - - output_model = input_model.copy() - - # Check for a user-supplied mask image. If so, use it. - if user_mask is not None: - mask_model = datamodels.open(user_mask) - Mask = (mask_model.data.copy()).astype(np.bool_) - - # Reset and save list of NaN pixels in the input image - nan_pix = np.isnan(input_model.data) - input_model.data[nan_pix] = 0 - Mask[nan_pix] = False - - else: - # Create the pixel mask that'll be used to indicate which pixels - # to include in the 1/f noise measurements. Basically, we're setting - # all illuminated pixels to False, so that they do not get used, and - # setting all unilluminated pixels to True (so they DO get used). - # For BOTS mode the mask will be 3D, to accommodate changes in masked - # pixels per integration. - log.info("Creating mask") - Mask, nan_pix = create_mask(input_model, mask_spectral_regions, n_sigma) - - # Store the mask image in a model, if requested - if save_mask: - if len(Mask.shape) == 3: - mask_model = datamodels.CubeModel(data=Mask) - else: - mask_model = datamodels.ImageModel(data=Mask) - else: - mask_model = None - - log.info(f"Cleaning image {input_model.meta.filename}") - - # Setup for handling 2D or 3D inputs - if len(input_model.data.shape) == 3: - nints = input_model.data.shape[0] - # Check for 3D mask - if len(Mask.shape) == 2: - log.warning("Data are 3D, but mask is 2D. Step will be skipped.") - output_model.meta.cal_step.nsclean = 'SKIPPED' - return output_model, None - else: - nints = 1 - - # Loop over integrations (even if there's only 1) - for i in range(nints): - log.debug(f" working on integration {i+1}") - if len(input_model.data.shape) == 3: - image = np.float32(input_model.data[i]) - mask = Mask[i] - else: - image = np.float32(input_model.data) - mask = Mask - - if input_model.data.shape[-2:] == (2048, 2048): - # Clean a full-frame image - cleaned_image = clean_full_frame(detector, image, mask) - else: - # BOTS and ALLSLITS exposures should be fitting different - # ranges of 1/f frequencies. Be less aggressive with - # fitting higher frequencies in ALLSLITS mode. - if input_model.meta.subarray.name.upper() == "ALLSLITS": - fc = (150, 200, 49943, 49957) - else: - fc = (1061, 1211, 49943, 49957) - - # Clean a subarray image - cleaned_image = clean_subarray(detector, image, mask, fc=fc) - - # Check for failure - if cleaned_image is None: - output_model.meta.cal_step.nsclean = 'SKIPPED' - break - else: - # Store the cleaned image in the output model - if len(output_model.data.shape) == 3: - output_model.data[i] = cleaned_image - else: - output_model.data = cleaned_image - - # Restore NaN's from original image - output_model.data[nan_pix] = np.nan - - # Set completion status - output_model.meta.cal_step.nsclean = 'COMPLETE' - - return output_model, mask_model diff --git a/jwst/nsclean/nsclean_step.py b/jwst/nsclean/nsclean_step.py index 79a61128e1..83c25f42fe 100644 --- a/jwst/nsclean/nsclean_step.py +++ b/jwst/nsclean/nsclean_step.py @@ -1,7 +1,7 @@ from stdatamodels.jwst import datamodels +from jwst.clean_flicker_noise import clean_flicker_noise from ..stpipe import Step -from . import nsclean __all__ = ["NSCleanStep"] @@ -10,15 +10,25 @@ class NSCleanStep(Step): """ NSCleanStep: This step performs 1/f noise correction ("cleaning") of NIRSpec images, using the "NSClean" method. + + NOTE: This step is a deprecated alias to the ``clean_flicker_noise`` step. """ class_alias = "nsclean" spec = """ - mask_spectral_regions = boolean(default=True) # Mask WCS-defined regions + fit_method = option('fft', 'median', default='fft') # Noise fitting algorithm + fit_by_channel = boolean(default=False) # Fit noise separately by amplifier + background_method = option('median', 'model', None, default=None) # Background fitting algorithm + background_box_size = int_list(min=2, max=2, default=None) # Background box size for modeled background + mask_spectral_regions = boolean(default=True) # Mask WCS-defined spectral regions n_sigma = float(default=5.0) # Clipping level for outliers - save_mask = boolean(default=False) # Save the created mask + fit_histogram = boolean(default=False) # Fit a value histogram to derive sigma + single_mask = boolean(default=False) # Make a single mask for all integrations user_mask = string(default=None) # Path to user-supplied mask + save_mask = boolean(default=False) # Save the created mask + save_background = boolean(default=False) # Save the fit background + save_noise = boolean(default=False) # Save the fit noise skip = boolean(default=True) # By default, skip the step """ @@ -29,39 +39,102 @@ def process(self, input): Parameters ---------- input : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel` - Input datamodel to be corrected + Input datamodel to be corrected. + + fit_method : str, optional + The background fit algorithm to use. Options are 'fft' and 'median'; + 'fft' performs the original NSClean implementation. + + fit_by_channel : bool, optional + If set, flicker noise is fit independently for each detector channel. + Ignored for subarray data and for `fit_method` = 'fft'. + + background_method : {'median', 'model', None} + If 'median', the preliminary background to remove and restore + is a simple median of the background data. If 'model', the + background data is fit with a low-resolution model via + `~photutils.background.Background2D`. If None, the background + value is 0.0. + + background_box_size : tuple of int, optional + Box size for the data grid used by `Background2D` when + `background_method` = 'model'. For best results, use a box size + that evenly divides the input image shape. + + mask_spectral_regions : bool, optional + Mask regions of the image defined by WCS bounding boxes for slits/slices. n_sigma : float, optional - Sigma clipping threshold to be used in detecting outliers in the image + Sigma clipping threshold to be used in detecting outliers in the image. - save_mask : bool, optional - Save the computed mask image + fit_histogram : bool, optional + If set, the 'sigma' used with `n_sigma` for clipping outliers + is derived from a Gaussian fit to a histogram of values. + Otherwise, a simple iterative sigma clipping is performed. + + single_mask : bool, optional + If set, a single mask will be created, regardless of + the number of input integrations. Otherwise, the mask will + be a 3D cube, with one plane for each integration. user_mask : None, str, or `~jwst.datamodels.ImageModel` - Optional user-supplied mask image; path to file or opened datamodel + Optional user-supplied mask image; path to file or opened datamodel. - mask_spectral_regions : bool, optional - Mask regions of the image defined by WCS bounding boxes for slits/slices + save_mask : bool, optional + Save the computed mask image. + + save_background : bool, optional + Save the computed background image. + + save_noise : bool, optional + Save the computed noise image. Returns ------- output_model : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel` - The 1/f corrected datamodel + The 1/f corrected datamodel. """ + message = ("The 'nsclean' step is a deprecated alias to 'clean_flicker_noise' " + "and will be removed in future builds.") + self.log.warning(message) # Open the input data model with datamodels.open(input) as input_model: # Do the NSClean correction - result = nsclean.do_correction(input_model, self.mask_spectral_regions, self.n_sigma, - self.save_mask, self.user_mask) - output_model, mask_model = result + result = clean_flicker_noise.do_correction( + input_model, self.input_dir, self.fit_method, self.fit_by_channel, + self.background_method, self.background_box_size, + self.mask_spectral_regions, self.n_sigma, self.fit_histogram, + self.single_mask, self.user_mask, + self.save_mask, self.save_background, self.save_noise) + output_model, mask_model, background_model, noise_model, status = result # Save the mask, if requested if self.save_mask and mask_model is not None: - mask_path = self.make_output_path(basepath=input_model.meta.filename, suffix='mask') + mask_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='mask') self.log.info(f"Saving mask file {mask_path}") mask_model.save(mask_path) mask_model.close() + # Save the background, if requested + if self.save_background and background_model is not None: + bg_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='flicker_bkg') + self.log.info(f"Saving background file {bg_path}") + background_model.save(bg_path) + background_model.close() + + # Save the noise, if requested + if self.save_noise and noise_model is not None: + noise_path = self.make_output_path( + basepath=input_model.meta.filename, suffix='flicker_noise') + self.log.info(f"Saving noise file {noise_path}") + noise_model.save(noise_path) + noise_model.close() + + # Set the step completion status + output_model.meta.cal_step.nsclean = status + return output_model diff --git a/jwst/pipeline/calwebb_detector1.py b/jwst/pipeline/calwebb_detector1.py index 16087ee73e..78699ff12f 100644 --- a/jwst/pipeline/calwebb_detector1.py +++ b/jwst/pipeline/calwebb_detector1.py @@ -22,6 +22,7 @@ from ..persistence import persistence_step from ..charge_migration import charge_migration_step from ..jump import jump_step +from ..clean_flicker_noise import clean_flicker_noise_step from ..ramp_fitting import ramp_fit_step from ..gain_scale import gain_scale_step @@ -64,6 +65,7 @@ class Detector1Pipeline(Pipeline): 'persistence': persistence_step.PersistenceStep, 'charge_migration': charge_migration_step.ChargeMigrationStep, 'jump': jump_step.JumpStep, + 'clean_flicker_noise': clean_flicker_noise_step.CleanFlickerNoiseStep, 'ramp_fit': ramp_fit_step.RampFitStep, 'gain_scale': gain_scale_step.GainScaleStep, } @@ -128,6 +130,9 @@ def process(self, input): # apply the jump step input = self.jump(input) + # apply the clean_flicker_noise step + input = self.clean_flicker_noise(input) + # save the corrected ramp data, if requested if self.save_calibrated_ramp: self.save_model(input, 'ramp') diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 37eafd0892..83b542ca51 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -249,11 +249,11 @@ def process_exposure_product( # Steps whose order is the same for all types of input: - # Self-calibrate to flag bad/warm pixels, and apply flags + # Self-calibrate to flag bad/warm pixels, and apply flags # to both background and science exposures. # skipped by default for all modes result = self.badpix_selfcal( - calibrated, members_by_type['selfcal'], members_by_type['background'], + calibrated, members_by_type['selfcal'], members_by_type['background'], ) if isinstance(result, datamodels.JwstDataModel): # if step is skipped, unchanged sci exposure is returned diff --git a/jwst/pipeline/clean_noise.cfg b/jwst/pipeline/clean_noise.cfg new file mode 100644 index 0000000000..f528229389 --- /dev/null +++ b/jwst/pipeline/clean_noise.cfg @@ -0,0 +1,2 @@ +name = "clean_flicker_noise" +class = "jwst.clean_flicker_noise.CleanFlickerNoiseStep" diff --git a/jwst/pipeline/tests/test_calwebb_spec2.py b/jwst/pipeline/tests/test_calwebb_spec2.py index d060aa9cd0..71a39232f3 100644 --- a/jwst/pipeline/tests/test_calwebb_spec2.py +++ b/jwst/pipeline/tests/test_calwebb_spec2.py @@ -155,4 +155,4 @@ def test_filenotfounderror_raised(capsys): # Verify the failure is printed to stderr captured = capsys.readouterr() - assert 'FileNotFoundError' in captured.err \ No newline at end of file + assert 'FileNotFoundError' in captured.err diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 5559daf121..192e6dcecb 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -47,6 +47,26 @@ def run_detector1_with_average_dark_current(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_with_clean_flicker_noise(rtdata_module): + """Run detector1 pipeline on MIRI imaging data with noise cleaning.""" + rtdata_module.get_data("miri/image/jw01024002001_02101_00001_mirimage_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files. + # Run optional clean_flicker_noise step, + # saving extra outputs and masking to science regions + args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--save_calibrated_ramp=True", + "--steps.clean_flicker_noise.skip=False", + "--steps.clean_flicker_noise.mask_science_regions=True", + "--steps.clean_flicker_noise.save_results=True", + "--steps.clean_flicker_noise.save_mask=True", + "--steps.clean_flicker_noise.save_background=True", + "--steps.clean_flicker_noise.save_noise=True", + ] + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_image2(run_detector1, rtdata_module): """Run image2 pipeline on the _rate file, saving intermediate products""" @@ -114,6 +134,29 @@ def test_miri_image_detector1_with_avg_dark_current(run_detector1_with_average_d assert diff.identical, diff.report() +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", + ["clean_flicker_noise", "mask", + "flicker_bkg", "flicker_noise", + "ramp", "rate", "rateints"]) +def test_miri_image_detector1_with_clean_flicker_noise( + run_detector1_with_clean_flicker_noise, + rtdata_module, fitsdiff_default_kwargs, suffix): + """Test detector1 pipeline for MIRI imaging data with noise cleaning.""" + rtdata = rtdata_module + rtdata.input = "jw01024002001_02101_00001_mirimage_uncal.fits" + output = f"jw01024002001_02101_00001_mirimage_{suffix}.fits" + rtdata.output = output + + rtdata.get_truth(f"truth/test_miri_image_clean_flicker_noise/{output}") + + # Set tolerances so the file comparisons work across architectures + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 1e-4 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["assign_wcs", "flat_field", "cal", "i2d"]) def test_miri_image_image2(run_image2, rtdata_module, fitsdiff_default_kwargs, suffix): diff --git a/jwst/regtest/test_nircam_image.py b/jwst/regtest/test_nircam_image.py index 11283baf01..1de07dbf93 100644 --- a/jwst/regtest/test_nircam_image.py +++ b/jwst/regtest/test_nircam_image.py @@ -30,6 +30,24 @@ def run_detector1pipeline(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_with_clean_flicker_noise(rtdata_module): + """Run detector1 pipeline on NIRCam imaging data with noise cleaning.""" + rtdata_module.get_data("nircam/image/jw01538046001_03105_00001_nrcalong_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files. + # Run optional clean_flicker_noise step, saving extra outputs + args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--save_calibrated_ramp=True", + "--steps.clean_flicker_noise.skip=False", + "--steps.clean_flicker_noise.save_results=True", + "--steps.clean_flicker_noise.save_mask=True", + "--steps.clean_flicker_noise.save_background=True", + "--steps.clean_flicker_noise.save_noise=True", + ] + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_image2pipeline(run_detector1pipeline, rtdata_module): """Run calwebb_image2 on NIRCam imaging long data""" @@ -225,3 +243,26 @@ def test_imaging_distortion(rtdata, fitsdiff_default_kwargs): assert_allclose(y, model.meta.wcsinfo.crpix2 - 1) assert_allclose(raout, ra) assert_allclose(decout, dec) + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", + ["clean_flicker_noise", "mask", + "flicker_bkg", "flicker_noise", + "ramp", "rate", "rateints"]) +def test_nircam_image_detector1_with_clean_flicker_noise( + run_detector1_with_clean_flicker_noise, + rtdata_module, fitsdiff_default_kwargs, suffix): + """Test detector1 pipeline for NIRCam imaging data with noise cleaning.""" + rtdata = rtdata_module + rtdata.input = "jw01538046001_03105_00001_nrcalong_uncal.fits" + output = f"jw01538046001_03105_00001_nrcalong_{suffix}.fits" + rtdata.output = output + + rtdata.get_truth(f"truth/test_nircam_image_clean_flicker_noise/{output}") + + # Set tolerances so the file comparisons work across architectures + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 1e-4 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 7e186986a2..62b268eb6b 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -36,14 +36,46 @@ def run_detector1(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_with_clean_flicker_noise(rtdata_module): + """Run detector1 pipeline on NIRISS imaging data with noise cleaning.""" + rtdata_module.get_data("niriss/imaging/jw01094001002_02107_00001_nis_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files. + # Run optional clean_flicker_noise step, + # saving extra outputs and masking to science regions + args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--save_calibrated_ramp=True", + "--steps.clean_flicker_noise.skip=False", + "--steps.clean_flicker_noise.save_results=True", + "--steps.clean_flicker_noise.save_mask=True", + "--steps.clean_flicker_noise.save_background=True", + "--steps.clean_flicker_noise.save_noise=True", + ] + Step.from_cmdline(args) + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["dq_init", "saturation", "superbias", "refpix", "linearity", "dark_current", "charge_migration", "jump", "rate", "rateints"]) def test_niriss_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwargs, suffix): + """Test detector1 pipeline for NIRISS imaging data with noise cleaning. + """ + truth_dir = 'test_niriss_image' + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir) + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["clean_flicker_noise", "mask", + "flicker_bkg", "flicker_noise", + "ramp", "rate", "rateints"]) +def test_niriss_image_detector1_with_clean_flicker_noise( + run_detector1_with_clean_flicker_noise, rtdata_module, fitsdiff_default_kwargs, suffix): """Regression test of detector1 pipeline performed on NIRISS imaging data. """ - _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) + truth_dir = 'test_niriss_image_clean_flicker_noise' + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir) @pytest.mark.bigdata @@ -77,14 +109,14 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): result.shelve(model, modify=False) -def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix): +def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir): """Assertion helper for the above tests""" rtdata = rtdata_module rtdata.input = "jw01094001002_02107_00001_nis_uncal.fits" output = f"jw01094001002_02107_00001_nis_{suffix}.fits" rtdata.output = output - rtdata.get_truth(f"truth/test_niriss_image/{output}") + rtdata.get_truth(f"truth/{truth_dir}/{output}") # Set tolerances so the crf, rscd and rateints file comparisons work across # architectures diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index 407a8bd2dc..8a8441e26c 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -30,6 +30,26 @@ def run_detector1pipeline(rtdata_module): ]) +@pytest.fixture(scope="module") +def run_detector1_with_clean_flicker_noise(rtdata_module): + """Run detector1 pipeline on NIRSpec IRS2 data with noise cleaning.""" + rtdata_module.get_data("nirspec/irs2/jw01335004001_03101_00002_nrs2_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files. + # Run optional clean_flicker_noise step, + # saving extra outputs and masking to science regions + args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--save_calibrated_ramp=True", + "--steps.clean_flicker_noise.skip=False", + "--steps.clean_flicker_noise.mask_science_regions=True", + "--steps.clean_flicker_noise.save_results=True", + "--steps.clean_flicker_noise.save_mask=True", + "--steps.clean_flicker_noise.save_background=True", + "--steps.clean_flicker_noise.save_noise=True", + ] + Step.from_cmdline(args) + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ['dq_init', 'saturation', 'superbias', 'refpix', 'linearity', 'dark_current', 'jump', @@ -51,3 +71,24 @@ def test_nirspec_irs2_detector1(run_detector1pipeline, rtdata_module, fitsdiff_default_kwargs["atol"] = 1e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["clean_flicker_noise", "mask", + "flicker_bkg", "flicker_noise", + "ramp", "rate", "rateints"]) +def test_nirspec_irs2_detector1_with_clean_flicker_noise( + run_detector1_with_clean_flicker_noise, rtdata_module, + fitsdiff_default_kwargs, suffix): + """Test detector1 pipeline for NIRSpec IRS2 data with noise cleaning.""" + rtdata = rtdata_module + + output_filename = f"jw01335004001_03101_00002_nrs2_{suffix}.fits" + rtdata.output = output_filename + rtdata.get_truth(f"truth/test_nirspec_irs2_clean_flicker_noise/{output_filename}") + + # Set tolerances so comparisons work across architectures + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 1e-4 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() diff --git a/jwst/step.py b/jwst/step.py index 1c9b67b58f..fe051a7b62 100644 --- a/jwst/step.py +++ b/jwst/step.py @@ -7,6 +7,7 @@ from .badpix_selfcal.badpix_selfcal_step import BadpixSelfcalStep from .barshadow.barshadow_step import BarShadowStep from .charge_migration.charge_migration_step import ChargeMigrationStep +from .clean_flicker_noise.clean_flicker_noise_step import CleanFlickerNoiseStep from .combine_1d.combine_1d_step import Combine1dStep from .coron.stack_refs_step import StackRefsStep from .coron.align_refs_step import AlignRefsStep @@ -76,6 +77,7 @@ "AlignRefsStep", "KlipStep", "HlspStep", + "CleanFlickerNoiseStep", "CubeBuildStep", "CubeSkyMatchStep", "DarkCurrentStep", diff --git a/jwst/stpipe/integration.py b/jwst/stpipe/integration.py index 597a6afe3e..7b787b47a7 100644 --- a/jwst/stpipe/integration.py +++ b/jwst/stpipe/integration.py @@ -44,6 +44,7 @@ def get_steps(): ("jwst.step.AlignRefsStep", 'align_refs', False), ("jwst.step.KlipStep", 'klip', False), ("jwst.step.HlspStep", 'hlsp', False), + ("jwst.step.CleanFlickerNoiseStep", 'clean_flicker_noise', False), ("jwst.step.CubeBuildStep", 'cube_build', False), ("jwst.step.CubeSkyMatchStep", 'cube_skymatch', False), ("jwst.step.DarkCurrentStep", 'dark_current', False), From 5b49c08db137ce751ef8b2518fbd79ce44a8e37a Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 17 Sep 2024 09:54:23 -0400 Subject: [PATCH 413/687] JP-3551: Exclude overlapping background candidates (#8744) Co-authored-by: Tyler Pauly --- CHANGES.rst | 4 + jwst/associations/lib/rules_level2_base.py | 261 +++++++++++------- .../tests/data/pool_024b_nirspec_fss_nods.csv | 20 ++ .../tests/data/pool_024c_nirspec_fss_nods.csv | 50 ++++ .../tests/test_level2_background.py | 41 +++ jwst/regtest/test_associations_sdp_pools.py | 5 + 6 files changed, 286 insertions(+), 95 deletions(-) create mode 100755 jwst/associations/tests/data/pool_024b_nirspec_fss_nods.csv create mode 100755 jwst/associations/tests/data/pool_024c_nirspec_fss_nods.csv diff --git a/CHANGES.rst b/CHANGES.rst index 51fdfe145b..d40d6fa86b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -36,6 +36,10 @@ associations - Remove ``MultilineLogger`` and no longer set it as the default logger. [#8781] +- Excluded nearby background candidates from NIRSpec fixed slit associations + for S1600A1 with 5 point dithers, to reduce overlap between background nods + and science exposure. [#8744] + badpix_selfcal -------------- diff --git a/jwst/associations/lib/rules_level2_base.py b/jwst/associations/lib/rules_level2_base.py index def32a8f2a..a152e1048b 100644 --- a/jwst/associations/lib/rules_level2_base.py +++ b/jwst/associations/lib/rules_level2_base.py @@ -26,7 +26,7 @@ IMAGE2_NONSCIENCE_EXP_TYPES, IMAGE2_SCIENCE_EXP_TYPES, PRODUCT_NAME_DEFAULT, - SPEC2_SCIENCE_EXP_TYPES, + SPEC2_SCIENCE_EXP_TYPES ) from jwst.associations.lib.member import Member from jwst.associations.lib.process_list import ListCategory @@ -391,98 +391,6 @@ def validate_candidates(self, member): # fail. return False - def make_nod_asns(self): - """Make background nod Associations - - For observing modes, such as NIRSpec MSA, exposures can be - nodded, such that the object is in a different position in the - slitlet. The association creation simply groups these all - together as a single association, all exposures marked as - `science`. When complete, this method will create separate - associations each exposure becoming the single science - exposure, and the other exposures then become `background`. - - Returns - ------- - associations : [association[, ...]] - List of new associations to be used in place of - the current one. - - """ - - for product in self['products']: - members = product['members'] - - # Split out the science vs. non-science - # The non-science exposures will get attached - # to every resulting association. - science_exps = [ - member - for member in members - if member['exptype'] == 'science' - ] - nonscience_exps = [ - member - for member in members - if member['exptype'] != 'science' - ] - - # Create new associations for each science, using - # the other science as background. - results = [] - for science_exp in science_exps: - asn = copy.deepcopy(self) - asn.data['products'] = None - - product_name = remove_suffix( - splitext(split(science_exp['expname'])[1])[0] - )[0] - asn.new_product(product_name) - new_members = asn.current_product['members'] - new_members.append(science_exp) - - for other_science in science_exps: - if other_science['expname'] != science_exp['expname']: - if science_exp.item['exp_type'] in ['nrs_fixedslit', 'nrs_msaspec']: - try: - sci_prim_dithpt = (int(science_exp.item['patt_num']) - 1) // \ - int(science_exp.item['subpxpts']) - other_prim_dithpt = (int(other_science.item['patt_num']) - 1) // \ - int(other_science.item['subpxpts']) - if sci_prim_dithpt != other_prim_dithpt: - now_background = Member(other_science) - now_background['exptype'] = 'background' - new_members.append(now_background) - # Catch missing values with KeyError, NULL values with ValueError - except (ValueError, KeyError): - try: - sci_prim_dithpt = (int(science_exp.item['patt_num']) - 1) // \ - int(science_exp.item['subpxpns']) - other_prim_dithpt = (int(other_science.item['patt_num']) - 1) // \ - int(other_science.item['subpxpns']) - if sci_prim_dithpt != other_prim_dithpt: - now_background = Member(other_science) - now_background['exptype'] = 'background' - new_members.append(now_background) - except (ValueError, KeyError, ZeroDivisionError): - if science_exp.item['exp_type'] == 'nrs_msaspec': - now_background = Member(other_science) - now_background['exptype'] = 'background' - new_members.append(now_background) - else: - pass - else: - now_background = Member(other_science) - now_background['exptype'] = 'background' - new_members.append(now_background) - - new_members += nonscience_exps - - if asn.is_valid: - results.append(asn) - - return results - def __repr__(self): try: file_name, json_repr = self.ioregistry['json'].dump(self) @@ -1060,7 +968,7 @@ def _init_hook(self, item): class AsnMixin_Lv2Nod: """Associations that need to create nodding associations - For some spectragraphic modes, background spectra are taken by + For some spectrographic modes, background spectra are taken by nodding between different slits, or internal slit positions. The main associations rules will collect all the exposures of all the nods into a single association. Then, on finalization, this one association @@ -1068,10 +976,173 @@ class AsnMixin_Lv2Nod: as science, and the other nods are treated as background. """ + @staticmethod + def nod_background_overlap(science_item, background_item): + """ + Check that a candidate background nod will not overlap with science. + + For NIRSpec fixed slit or MOS data, this returns True if the + background candidate shares a primary dither point with the + science. + + In addition, for NIRSpec fixed slit exposures taken with slit + S1600A1 in a 5-point nod pattern, this returns True when the + background candidate is in the next closest primary position + to the science. + + For any other data, this function returns False (no overlap). + """ + # Get exp_type, needed for any data: + # if not present, return False + try: + exptype = str(science_item['exp_type']).lower() + except KeyError: + return False + + # Return False for any non-FS or MOS data + if exptype not in ['nrs_fixedslit', 'nrs_msaspec']: + return False + + # Get pattern number values, needed for FS or MOS + try: + numdthpt = int(science_item['numdthpt']) + patt_num = int(science_item['patt_num']) + bkg_num = int(background_item['patt_num']) + except (KeyError, ValueError): + numdthpt = 0 + patt_num = None + bkg_num = None + + # Get subpxpts (or old alternate 'subpxpns') + try: + subpx = int(science_item['subpxpts']) + except (KeyError, ValueError): + try: + subpx = int(science_item['subpxpns']) + except (KeyError, ValueError): + subpx = None + try: + bkg_subpx = int(background_item['subpxpts']) + except (KeyError, ValueError): + try: + bkg_subpx = int(background_item['subpxpns']) + except (KeyError, ValueError): + bkg_subpx = None + + # If values not found, return False for MOS, True for FS + if (None in [patt_num, bkg_num, subpx, bkg_subpx] + or subpx == 0 or bkg_subpx == 0): + if exptype == 'nrs_fixedslit': + # These values are required for background + # candidates for FS: report an overlap. + return True + else: + # For MOS, it's okay to go ahead and use them. + # Report no overlap. + return False + + # Check for primary point overlap + sci_prim_dithpt = (patt_num - 1) // subpx + bkg_prim_dithpt = (bkg_num - 1) // bkg_subpx + if sci_prim_dithpt == bkg_prim_dithpt: + # Primary points are the same - overlap is present + return True + + # Primary points are not the same - + # no further check needed for MOS + if exptype == 'nrs_msaspec': + return False + + # Get slit name, needed only for FS S1600A1 check + try: + slit = str(science_item['fxd_slit']).lower() + except KeyError: + slit = None + + # Background is currently only expected to overlap severely for + # 5 point dithers with fixed slit S1600A1. Only the nearest + # dithers to the science observation need to be excluded. + if (exptype == 'nrs_fixedslit' + and slit == 's1600a1' + and numdthpt // subpx == 5 + and abs(sci_prim_dithpt - bkg_prim_dithpt) <= 1): + return True + + # For anything else, return False + return False + + def make_nod_asns(self): + """Make background nod Associations + + For observing modes, such as NIRSpec MSA, exposures can be + nodded, such that the object is in a different position in the + slitlet. The association creation simply groups these all + together as a single association, all exposures marked as + `science`. When complete, this method will create separate + associations each exposure becoming the single science + exposure, and the other exposures then become `background`. + + Returns + ------- + associations : [association[, ...]] + List of new associations to be used in place of + the current one. + + """ + + for product in self['products']: + members = product['members'] + + # Split out the science vs. non-science + # The non-science exposures will get attached + # to every resulting association. + science_exps = [ + member + for member in members + if member['exptype'] == 'science' + ] + nonscience_exps = [ + member + for member in members + if member['exptype'] != 'science' + ] + + # Create new associations for each science, using + # the other science as background. + results = [] + for science_exp in science_exps: + asn = copy.deepcopy(self) + asn.data['products'] = None + + product_name = remove_suffix( + splitext(split(science_exp['expname'])[1])[0] + )[0] + asn.new_product(product_name) + new_members = asn.current_product['members'] + new_members.append(science_exp) + + for other_science in science_exps: + if other_science['expname'] != science_exp['expname']: + # check for likely overlap between science + # and background candidate + overlap = self.nod_background_overlap( + science_exp.item, other_science.item) + if not overlap: + now_background = Member(other_science) + now_background['exptype'] = 'background' + new_members.append(now_background) + + new_members += nonscience_exps + + if asn.is_valid: + results.append(asn) + + return results + def finalize(self): """Finalize association - For some spectragraphic modes, background spectra and taken by + For some spectrographic modes, background spectra are taken by nodding between different slits, or internal slit positions. The main associations rules will collect all the exposures of all the nods into a single association. Then, on finalization, this one association diff --git a/jwst/associations/tests/data/pool_024b_nirspec_fss_nods.csv b/jwst/associations/tests/data/pool_024b_nirspec_fss_nods.csv new file mode 100755 index 0000000000..805a66a98a --- /dev/null +++ b/jwst/associations/tests/data/pool_024b_nirspec_fss_nods.csv @@ -0,0 +1,20 @@ +# NIRSpec Fixed-slit with various nods and dithers +# +# This example contains a 5 point dither for S1600A1, no subpixel dither +# +FILENAME|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|VISITYPE|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|GWA_XTIL|GWA_YTIL|ASN_CANDIDATE|EXPOSERR|IS_PSF|IS_IMPRT|BKGDTARG|TSOVISIT|MSAMETFL|SPAT_NUM|SPEC_NUM +# +# Initialize ACID. This is done with a dummy exposure that will not match any association +set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|VISITYPE|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|GWA_XTIL|GWA_YTIL|@!next(acid)|EXPOSERR|IS_PSF|IS_IMPRT|BKGDTARG|TSOVISIT|MSAMETFL|SPAT_NUM|SPEC_NUM +# +# Primary Dither=5, Sub-pixel Pattern=None, F290LP/G395H/S1600A1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!next(obsnum)|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|1|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|1|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|2|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|2|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|3|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|3|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|4|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|4|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|5|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|5|NULL|1|5|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|NULL diff --git a/jwst/associations/tests/data/pool_024c_nirspec_fss_nods.csv b/jwst/associations/tests/data/pool_024c_nirspec_fss_nods.csv new file mode 100755 index 0000000000..67dfcb8cd5 --- /dev/null +++ b/jwst/associations/tests/data/pool_024c_nirspec_fss_nods.csv @@ -0,0 +1,50 @@ +# NIRSpec Fixed-slit with various nods and dithers +# +# This example contains a 5 point dither for S1600A1, subpixel pattern both +# +FILENAME|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|VISITYPE|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|GWA_XTIL|GWA_YTIL|ASN_CANDIDATE|EXPOSERR|IS_PSF|IS_IMPRT|BKGDTARG|TSOVISIT|MSAMETFL|SPAT_NUM|SPEC_NUM +# +# Initialize ACID. This is done with a dummy exposure that will not match any association +set acid|OBS_ID|PROGRAM|OBS_NUM|VISIT|VISIT_ID|VISITGRP|VISITYPE|SEQ_ID|ACT_ID|EXPOSURE|EXP_TYPE|NEXPOSUR|EXPCOUNT|INSTRUME|DETECTOR|CHANNEL|TARGETID|TARGPROP|TARGNAME|TARGTYPE|TEMPLATE|PNTGTYPE|PNTG_SEQ|TARGORDN|EXPSPCIN|DITHPTIN|MOSTILNO|MODULE|FILTER|PUPIL|DITHERID|PATTTYPE|PATTSTRT|NUMDTHPT|PATTSIZE|SUBPXPNS|PATT_NUM|SUBPXNUM|SUBPIXEL|APERNAME|SDP_VER|SUBARRAY|GRATING|FXD_SLIT|BAND|GWA_XTIL|GWA_YTIL|@!next(acid)|EXPOSERR|IS_PSF|IS_IMPRT|BKGDTARG|TSOVISIT|MSAMETFL|SPAT_NUM|SPEC_NUM +# +# Primary Dither=5, Sub-pixel Pattern=Both, F290LP/G395H/S1600A1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!next(obsnum)|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|1|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|1|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|2|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|2 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|2|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|2 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|3|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|3 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|3|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|3 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|4|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|4|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|1|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|5|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|5|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|1 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|6|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|2 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|6|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|2 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|7|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|3 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|7|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|3 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|8|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|8|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|9|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|9|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|10|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|10|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|11|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|11|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|12|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|12|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|13|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|13|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|14|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|14|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|15|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|15|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|16|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|16|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|17|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|17|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|18|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|18|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|19|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|19|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS1|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|20|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 +@!fmt_fname(next(expnum))|V93125002001P0000000002106|93125|@!obsnum.value|1|93125002001|2|PRIME_TARGETED_FIXED|1|06|1|NRS_FIXEDSLIT|6|6|NIRSPEC|NRS2|NULL|1|TARGET-OBSERVATION-2|NULL|FIXED|NIRSpec Fixed-slit Spectroscopy|SCIENCE|6|1|1|1|1|NULL|F290LP|NULL|NULL|NULL|NULL|20|NULL|4|20|NULL|NULL|S200A1|2017_1|SUB200A1|G395H|S1600A1|NULL|0.2866025270000001|0.17855911|@!fmt_cand([(obsnum.value, 'OBSERVATION')])|NULL|NULL|F|F|F|F|2|4 diff --git a/jwst/associations/tests/test_level2_background.py b/jwst/associations/tests/test_level2_background.py index 4af3eb8685..8fb7ed2e1c 100644 --- a/jwst/associations/tests/test_level2_background.py +++ b/jwst/associations/tests/test_level2_background.py @@ -1,4 +1,6 @@ """Test Level2 background nods""" +import pytest + from jwst.associations.tests.helpers import ( combine_pools, registry_level2_only, @@ -15,6 +17,7 @@ '4': 4, # Both, 4-to-1 exposure count } + def test_nrs_msa_nod(): pool = combine_pools(t_path('data/pool_023_nirspec_msa_3nod.csv')) all_candidates = constrain_on_candidates(None) @@ -81,3 +84,41 @@ def test_nrs_fixedslit_nod_chop(): if asn['asn_id'].startswith('c'): nods += 1 assert len(asn['products'][0]['members']) == nods + + +@pytest.mark.parametrize('pool_name,n_asn', + [('pool_024b_nirspec_fss_nods', 10), + ('pool_024c_nirspec_fss_nods', 40)]) +def test_nrs_fixedslit_5point(pool_name, n_asn): + """Test NIRSpec Fixed-slit background nod S1600A1 5 point + subpixel""" + pool = combine_pools(t_path(f'data/{pool_name}.csv')) + constraint_all_candidates = constrain_on_candidates(None) + asns = generate(pool, registry_level2_only( + global_constraints=constraint_all_candidates) + ) + assert len(asns) == n_asn + for asn in asns: + n_dithers = int(asn.constraints['nods'].value) + n_spectral_dithers = int(asn.constraints['subpxpts'].value) + + sci_expnames = [] + for member in asn['products'][0]['members']: + if member['exptype'] == 'science': + sci_expnames.append(member['expname']) + assert len(sci_expnames) == 1 + + # Expect self + all exposures not at the same primary dither. + # Also expect nearest 2 dithers to be excluded, + # or 1 if it's the first or last primary dither. + first_last_files = list(range(1, n_spectral_dithers * 2 + 1)) + first_last_files += list(range(n_asn, + n_asn - n_spectral_dithers * 2, -1)) + first_last = [f'jw_000{i:02d}_rate.fits' for i in first_last_files] + + if sci_expnames[0] in first_last: + n_extra = 1 + else: + n_extra = 2 + n_members = n_dithers - (1 + n_extra) * n_spectral_dithers + 1 + + assert len(asn['products'][0]['members']) == n_members diff --git a/jwst/regtest/test_associations_sdp_pools.py b/jwst/regtest/test_associations_sdp_pools.py index 4444e40a3b..3c63554fda 100644 --- a/jwst/regtest/test_associations_sdp_pools.py +++ b/jwst/regtest/test_associations_sdp_pools.py @@ -132,6 +132,11 @@ 'xfail': None, 'slow': False, }, + 'jw01678_20240721t195707_pool': { + 'args': [], + 'xfail': None, + 'slow': False, + }, 'jw02064_20230302t112350_pool': { 'args': [], 'xfail': None, From f62b1a50fa577a34437a882e91b2bd3215417a35 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Fri, 13 Sep 2024 13:22:22 -0400 Subject: [PATCH 414/687] run nsclean on bkg and imprint members --- jwst/pipeline/calwebb_spec2.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 83b542ca51..94a3a53c1d 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -268,6 +268,15 @@ def process_exposure_product( # apply the "nsclean" 1/f correction to NIRSpec images calibrated = self.nsclean(calibrated) + + # Apply nsclean to NIRSpec imprint and background members + for i, imprint_file in enumerate(members_by_type['imprint']): + imprint_nsclean = self.nsclean(imprint_file) + members_by_type['imprint'][i] = imprint_nsclean + + for i, bkg_file in enumerate(members_by_type['background']): + bkg_nsclean = self.nsclean(bkg_file) + members_by_type['background'][i] = bkg_nsclean # Leakcal subtraction (imprint) occurs before background subtraction on a per-exposure basis. # If there is only one `imprint` member, this imprint exposure is subtracted from all the From 7ee7385f2a6726bfb64847659a57cdc066fd5183 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Fri, 13 Sep 2024 15:03:47 -0400 Subject: [PATCH 415/687] updated change log --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index d40d6fa86b..1ff5f45ded 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -172,6 +172,9 @@ pipeline - Updated `calwebb_image3` to use `ModelLibrary` instead of `ModelContainer`, added optional `on_disk` parameter to govern whether models in the library should be stored in memory or on disk. [#8683] + +- Updated ``calwebb_spec2`` to run ``nsclean`` on NIRSpec imprint and background + association members. [#8786] ramp_fitting ------------ From be36c547939b376f9028ff449049d9507039edf9 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 17 Sep 2024 08:11:24 -0700 Subject: [PATCH 416/687] Fixed filename conflicts in badpix_selfcal test (#8792) Co-authored-by: Tyler Pauly --- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 21 +++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index e8b1effb49..a65d0cbea4 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -4,17 +4,23 @@ import os +OUTSTEM_BKG = "result_bkgasn" +OUTSTEM_SELFCAL = "result_selfcalasn" + @pytest.fixture(scope="module") def run_pipeline_background(rtdata_module): + '''Run the pipeline with asn in which the background exposures are marked as `background`. + ''' rtdata = rtdata_module rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_asn.json") Step.from_cmdline(['calwebb_spec2', rtdata.input, + f"--steps.badpix_selfcal.output_file={OUTSTEM_BKG}", "--steps.badpix_selfcal.save_results=True", "--steps.badpix_selfcal.save_flagged_bkg=True", "--steps.badpix_selfcal.flagfrac_lower=0.0005", "--steps.badpix_selfcal.skip=False"]) - rtdata.output = "jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits" + rtdata.output = f"{OUTSTEM_BKG}_badpix_selfcal.fits" return rtdata @@ -25,10 +31,11 @@ def run_pipeline_selfcal(rtdata_module): rtdata = rtdata_module rtdata.get_asn("miri/mrs/jw01204-o021_20240127t024203_spec2_00010_selfcal_asn.json") Step.from_cmdline(['calwebb_spec2', rtdata.input, + f"--steps.badpix_selfcal.output_file={OUTSTEM_SELFCAL}", "--steps.badpix_selfcal.save_results=True", "--steps.badpix_selfcal.flagfrac_lower=0.0005", "--steps.badpix_selfcal.skip=False"]) - rtdata.output = "jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits" + rtdata.output = f"{OUTSTEM_SELFCAL}_badpix_selfcal.fits" return rtdata @@ -40,7 +47,7 @@ def test_miri_mrs_badpix_selfcal(run_pipeline_selfcal, fitsdiff_default_kwargs): rtdata = run_pipeline_selfcal # Get the truth file - rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") + rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{OUTSTEM_SELFCAL}_badpix_selfcal.fits") # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) @@ -48,7 +55,7 @@ def test_miri_mrs_badpix_selfcal(run_pipeline_selfcal, fitsdiff_default_kwargs): # check the bkg files in the background case, but not in the selfcal case for idx in range(4): - fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" + fname = f"{OUTSTEM_SELFCAL}_badpix_selfcal_bkg_{idx}.fits" assert not os.path.isfile(fname) @@ -59,7 +66,7 @@ def test_miri_mrs_badpix_selfcal_bkg(run_pipeline_background, fitsdiff_default_k rtdata = run_pipeline_background # Get the truth file - rtdata.get_truth("truth/test_miri_mrs_badpix_selfcal/jw01204021001_02101_00004_mirifulong_badpix_selfcal.fits") + rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{OUTSTEM_BKG}_badpix_selfcal.fits") # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) @@ -67,7 +74,7 @@ def test_miri_mrs_badpix_selfcal_bkg(run_pipeline_background, fitsdiff_default_k # check the bkg files in the background case, but not in the selfcal case for idx in range(4): - fname = f"jw01204021001_02101_00004_mirifulong_badpix_selfcal_bkg_{idx}.fits" + fname = f"{OUTSTEM_BKG}_badpix_selfcal_bkg_{idx}.fits" truth = rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{fname}") diff = FITSDiff(fname, truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() \ No newline at end of file + assert diff.identical, diff.report() From 120ee5ac5b34c0df19fb9e4e4575a1bfc60e12ba Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 17 Sep 2024 14:57:58 -0400 Subject: [PATCH 417/687] adding documentation --- docs/jwst/nsclean/main.rst | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docs/jwst/nsclean/main.rst b/docs/jwst/nsclean/main.rst index 40dcda7a69..79342f0885 100644 --- a/docs/jwst/nsclean/main.rst +++ b/docs/jwst/nsclean/main.rst @@ -27,3 +27,12 @@ See the :ref:`clean_flicker_noise ` documentation for more information, or the :ref:`nsclean step arguments ` for the default values used for this step. + +If this step is run as part of the +:ref:`calwebb_spec2 ` pipeline when processing an +association that includes background or imprint images, these +images will be processed using the same ``nsclean`` parameters +as the science image. If a user supplied mask is provided, it +will be used for all images in an association, otherwise, separate +masks will be calculated for each image using the same input +parameters. From 34517ec25bfea3b24bd6138f455a205bb8a178c9 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Tue, 17 Sep 2024 15:33:00 -0400 Subject: [PATCH 418/687] allow stdatamodels 2.1.0 (#8797) --- CHANGES.rst | 2 ++ pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index d40d6fa86b..cf15011996 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -101,6 +101,8 @@ general - Increase minimum required stpipe. [#8713] +- Increase minimum required stdatamodels. [#8797] + klip ---- diff --git a/pyproject.toml b/pyproject.toml index ff6b557472..7b259690ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ "scipy>=1.9.3", "spherical-geometry>=1.2.22", "stcal>=1.8.0,<1.9.0", - "stdatamodels>=2.0.0,<2.1.0", + "stdatamodels>=2.1.0,<2.2.0", "stpipe>=0.7.0,<0.8.0", "stsci.imagestats>=1.6.3", "synphot>=1.2", From b9137be1c27301ddfda880b0b071099757e120e5 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 20 Aug 2024 15:52:39 -0400 Subject: [PATCH 419/687] increase tolerance of leastsq in coron image registration --- jwst/coron/imageregistration.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 12fc185272..5c7d89d9ce 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -40,7 +40,8 @@ def align_fourierLSQ(reference, target, mask=None): init_pars = [0., 0., 1.] results, _ = optimize.leastsq(shift_subtract, init_pars, - args=(reference, target, mask)) + args=(reference, target, mask), + xtol=1E-15, ftol=1E-15) return results From c557425d9d4dc3ccf5fe5ef4e898b454760f62c1 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 22 Aug 2024 11:07:58 -0400 Subject: [PATCH 420/687] remove casts in coron image registration --- jwst/coron/imageregistration.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 5c7d89d9ce..97a01bc98e 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -41,7 +41,8 @@ def align_fourierLSQ(reference, target, mask=None): init_pars = [0., 0., 1.] results, _ = optimize.leastsq(shift_subtract, init_pars, args=(reference, target, mask), - xtol=1E-15, ftol=1E-15) + #xtol=1E-12, ftol=1E-12, + ) return results @@ -166,7 +167,7 @@ def align_array(reference, target, mask=None, return_aligned=True): elif len(target.shape) == 3: nslices = target.shape[0] - shifts = np.empty((nslices, 3), dtype=float) + shifts = np.empty((nslices, 3), dtype=np.float64) if return_aligned: aligned = np.empty_like(target) @@ -222,19 +223,19 @@ def align_models(reference, target, mask): # Compute the shifts of the PSF ("target") images relative to # the science ("reference") image in the first integration shifts = align_array( - reference.data[0].astype(np.float64), - target.data.astype(np.float64), + reference.data[0], + target.data, mask=mask.data, return_aligned=False) # Apply the shifts to the PSF images output_model.data = fourier_imshift( - target.data.astype(np.float64), + target.data, -shifts) # Apply the same shifts to the PSF error arrays, if they exist if target.err is not None: output_model.err = fourier_imshift( - target.err.astype(np.float64), + target.err, -shifts) # TODO: in the future we need to add shifts and other info (such as From 73edc90304f0b3b4308d5e76e5fe1194d31c4abb Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 22 Aug 2024 12:21:09 -0400 Subject: [PATCH 421/687] re-add tolerance --- jwst/coron/imageregistration.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/coron/imageregistration.py b/jwst/coron/imageregistration.py index 97a01bc98e..457b7664eb 100644 --- a/jwst/coron/imageregistration.py +++ b/jwst/coron/imageregistration.py @@ -41,7 +41,7 @@ def align_fourierLSQ(reference, target, mask=None): init_pars = [0., 0., 1.] results, _ = optimize.leastsq(shift_subtract, init_pars, args=(reference, target, mask), - #xtol=1E-12, ftol=1E-12, + xtol=1E-15, ftol=1E-15, ) return results From d450aa74033ff99d7968092a07eebcad4efc5743 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 22 Aug 2024 13:10:12 -0400 Subject: [PATCH 422/687] relax tolerances --- jwst/regtest/test_miri_coron3.py | 5 +++-- jwst/regtest/test_nircam_coron3.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_miri_coron3.py b/jwst/regtest/test_miri_coron3.py index d10af9e65d..ff7e6224d6 100644 --- a/jwst/regtest/test_miri_coron3.py +++ b/jwst/regtest/test_miri_coron3.py @@ -30,7 +30,7 @@ def test_miri_coron3_sci_exp(run_pipeline, suffix, exposure, fitsdiff_default_kw rtdata.output = output rtdata.get_truth("truth/test_miri_coron3/" + output) - fitsdiff_default_kwargs["atol"] = 1e-5 + fitsdiff_default_kwargs["atol"] = 1e-2 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -45,6 +45,7 @@ def test_miri_coron3_product(run_pipeline, suffix, fitsdiff_default_kwargs): rtdata.output = output rtdata.get_truth("truth/test_miri_coron3/" + output) - fitsdiff_default_kwargs['atol'] = 1e-5 + fitsdiff_default_kwargs['atol'] = 1e-4 + fitsdiff_default_kwargs['rtol'] = 1e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() diff --git a/jwst/regtest/test_nircam_coron3.py b/jwst/regtest/test_nircam_coron3.py index 9c4a77ba46..c69b11881c 100644 --- a/jwst/regtest/test_nircam_coron3.py +++ b/jwst/regtest/test_nircam_coron3.py @@ -32,7 +32,7 @@ def test_nircam_coron3_sci_exp(run_pipeline, suffix, obs, fitsdiff_default_kwarg rtdata.output = output rtdata.get_truth("truth/test_nircam_coron3/" + output) - fitsdiff_default_kwargs["atol"] = 1e-5 + fitsdiff_default_kwargs["atol"] = 1e-2 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -63,6 +63,7 @@ def test_nircam_coron3_product(run_pipeline, suffix, fitsdiff_default_kwargs): rtdata.output = output rtdata.get_truth("truth/test_nircam_coron3/" + output) - fitsdiff_default_kwargs['atol'] = 1e-5 + fitsdiff_default_kwargs['atol'] = 1e-4 + fitsdiff_default_kwargs['rtol'] = 1e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From 26f40c7979d80c9003cd0bc954823eb3a3c4840e Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 22 Aug 2024 13:32:18 -0400 Subject: [PATCH 423/687] update changelog --- CHANGES.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index cf15011996..dbbb1a5d5b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -46,6 +46,11 @@ badpix_selfcal - Subtract pedestal dark when constructing min array across selfcal exposures for MIRI MRS data. [#8771] +calwebb_coron3 +-------------- + +- Tighten tolerance of psf alignment. [#8717] + calwebb_detector1 ----------------- From 7c4ec623de676a291b526bd7281e066b146f3eb4 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 17 Sep 2024 16:18:37 -0400 Subject: [PATCH 424/687] minor docs change Co-authored-by: Melanie Clarke --- docs/jwst/nsclean/main.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/nsclean/main.rst b/docs/jwst/nsclean/main.rst index 79342f0885..c10b060c02 100644 --- a/docs/jwst/nsclean/main.rst +++ b/docs/jwst/nsclean/main.rst @@ -33,6 +33,6 @@ If this step is run as part of the association that includes background or imprint images, these images will be processed using the same ``nsclean`` parameters as the science image. If a user supplied mask is provided, it -will be used for all images in an association, otherwise, separate +will be used for all images in an association. Otherwise, separate masks will be calculated for each image using the same input parameters. From ce671eadce31fe9ee2c8f1ebc5e258d18124aa6a Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 17 Sep 2024 16:19:39 -0400 Subject: [PATCH 425/687] update nsclean output file for asn members Co-authored-by: Melanie Clarke --- jwst/pipeline/calwebb_spec2.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 94a3a53c1d..7a5f0b3ca3 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -271,10 +271,12 @@ def process_exposure_product( # Apply nsclean to NIRSpec imprint and background members for i, imprint_file in enumerate(members_by_type['imprint']): + self.nsclean.output_file = os.path.basename(imprint_file) imprint_nsclean = self.nsclean(imprint_file) members_by_type['imprint'][i] = imprint_nsclean for i, bkg_file in enumerate(members_by_type['background']): + self.nsclean.output_file = os.path.basename(bkg_file) bkg_nsclean = self.nsclean(bkg_file) members_by_type['background'][i] = bkg_nsclean From fee3779c8e3714106b9dce6797850248b534f1c2 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 18:39:44 -0700 Subject: [PATCH 426/687] remove setting pixel_replace.save to True --- jwst/pipeline/calwebb_spec3.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 7fd69e4373..da31f36823 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -100,14 +100,12 @@ def process(self, input): self.spectral_leak.save_results = self.save_results self.pixel_replace.suffix = 'pixel_replace' self.pixel_replace.output_use_model = True - self.pixel_replace.save_results = self.save_results # Overriding the Step.save_model method for the following steps. # These steps save intermediate files, resulting in meta.filename # being modified. This can affect the filenames of subsequent # steps. self.outlier_detection.save_model = invariant_filename(self.outlier_detection.save_model) - self.pixel_replace.save_model = invariant_filename(self.pixel_replace.save_model) # Retrieve the inputs: # could either be done via LoadAsAssociation and then manually From 4eff12d94213ca52c0deb7269740a24d33fa7c07 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 18:48:17 -0700 Subject: [PATCH 427/687] Update change log --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index cf15011996..df7337fdf4 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -181,6 +181,8 @@ ramp_fitting - Moved the read noise variance recalculation for CHARGELOSS flagging to the C implementation, when the algorithm is ``OLS_C``. [#8697, spacetelescope/stcal#278] +- Updated `calwebb_spec3` to not save the `pixel_replacement` output by default.[#8765] + resample -------- From 9aef4128dd7e3fd3dfc6aec75e8660e1e10060ba Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 10 Sep 2024 10:38:47 -0700 Subject: [PATCH 428/687] add back invariant_filename --- jwst/pipeline/calwebb_spec3.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index da31f36823..2644bee233 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -106,6 +106,7 @@ def process(self, input): # being modified. This can affect the filenames of subsequent # steps. self.outlier_detection.save_model = invariant_filename(self.outlier_detection.save_model) + self.pixel_replace.save_model = invariant_filename(self.pixel_replace.save_model) # Retrieve the inputs: # could either be done via LoadAsAssociation and then manually @@ -235,7 +236,6 @@ def process(self, input): # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. result = self.pixel_replace(result) - # Resample time. Dependent on whether the data is IFU or not. resample_complete = None if exptype in IFU_EXPTYPES: From 5783cfb9d7ebc8c2b662c7c9f32763e83aafc6f9 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 19 Sep 2024 10:42:59 -0400 Subject: [PATCH 429/687] Modify output file name to avoid collisions --- jwst/regtest/test_miri_image.py | 5 +++-- jwst/regtest/test_nircam_image.py | 5 +++-- jwst/regtest/test_niriss_image.py | 5 +++-- jwst/regtest/test_nirspec_irs2_detector1.py | 5 +++-- 4 files changed, 12 insertions(+), 8 deletions(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 192e6dcecb..9d204b89ee 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -56,6 +56,7 @@ def run_detector1_with_clean_flicker_noise(rtdata_module): # Run optional clean_flicker_noise step, # saving extra outputs and masking to science regions args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--output_file=jw01024002001_02101_00001_mirimage_cfn", "--save_calibrated_ramp=True", "--steps.clean_flicker_noise.skip=False", "--steps.clean_flicker_noise.mask_science_regions=True", @@ -136,9 +137,9 @@ def test_miri_image_detector1_with_avg_dark_current(run_detector1_with_average_d @pytest.mark.bigdata @pytest.mark.parametrize("suffix", - ["clean_flicker_noise", "mask", + ["cfn_clean_flicker_noise", "mask", "flicker_bkg", "flicker_noise", - "ramp", "rate", "rateints"]) + "cfn_ramp", "cfn_rate", "cfn_rateints"]) def test_miri_image_detector1_with_clean_flicker_noise( run_detector1_with_clean_flicker_noise, rtdata_module, fitsdiff_default_kwargs, suffix): diff --git a/jwst/regtest/test_nircam_image.py b/jwst/regtest/test_nircam_image.py index 1de07dbf93..398c3c720c 100644 --- a/jwst/regtest/test_nircam_image.py +++ b/jwst/regtest/test_nircam_image.py @@ -38,6 +38,7 @@ def run_detector1_with_clean_flicker_noise(rtdata_module): # Run detector1 pipeline only on one of the _uncal files. # Run optional clean_flicker_noise step, saving extra outputs args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--output_file=jw01538046001_03105_00001_nrcalong_cfn", "--save_calibrated_ramp=True", "--steps.clean_flicker_noise.skip=False", "--steps.clean_flicker_noise.save_results=True", @@ -247,9 +248,9 @@ def test_imaging_distortion(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata @pytest.mark.parametrize("suffix", - ["clean_flicker_noise", "mask", + ["cfn_clean_flicker_noise", "mask", "flicker_bkg", "flicker_noise", - "ramp", "rate", "rateints"]) + "cfn_ramp", "cfn_rate", "cfn_rateints"]) def test_nircam_image_detector1_with_clean_flicker_noise( run_detector1_with_clean_flicker_noise, rtdata_module, fitsdiff_default_kwargs, suffix): diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 62b268eb6b..cf07ae16df 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -45,6 +45,7 @@ def run_detector1_with_clean_flicker_noise(rtdata_module): # Run optional clean_flicker_noise step, # saving extra outputs and masking to science regions args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--output_file=jw01094001002_02107_00001_nis_cfn", "--save_calibrated_ramp=True", "--steps.clean_flicker_noise.skip=False", "--steps.clean_flicker_noise.save_results=True", @@ -67,9 +68,9 @@ def test_niriss_image_detector1(run_detector1, rtdata_module, fitsdiff_default_k @pytest.mark.bigdata -@pytest.mark.parametrize("suffix", ["clean_flicker_noise", "mask", +@pytest.mark.parametrize("suffix", ["cfn_clean_flicker_noise", "mask", "flicker_bkg", "flicker_noise", - "ramp", "rate", "rateints"]) + "cfn_ramp", "cfn_rate", "cfn_rateints"]) def test_niriss_image_detector1_with_clean_flicker_noise( run_detector1_with_clean_flicker_noise, rtdata_module, fitsdiff_default_kwargs, suffix): """Regression test of detector1 pipeline performed on NIRISS imaging data. diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index 8a8441e26c..1cb6756241 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -39,6 +39,7 @@ def run_detector1_with_clean_flicker_noise(rtdata_module): # Run optional clean_flicker_noise step, # saving extra outputs and masking to science regions args = ["jwst.pipeline.Detector1Pipeline", rtdata_module.input, + "--output_file=jw01335004001_03101_00002_nrs2_cfn", "--save_calibrated_ramp=True", "--steps.clean_flicker_noise.skip=False", "--steps.clean_flicker_noise.mask_science_regions=True", @@ -74,9 +75,9 @@ def test_nirspec_irs2_detector1(run_detector1pipeline, rtdata_module, @pytest.mark.bigdata -@pytest.mark.parametrize("suffix", ["clean_flicker_noise", "mask", +@pytest.mark.parametrize("suffix", ["cfn_clean_flicker_noise", "mask", "flicker_bkg", "flicker_noise", - "ramp", "rate", "rateints"]) + "cfn_ramp", "cfn_rate", "cfn_rateints"]) def test_nirspec_irs2_detector1_with_clean_flicker_noise( run_detector1_with_clean_flicker_noise, rtdata_module, fitsdiff_default_kwargs, suffix): From e39a989b4894e670a2436256673f96a072892132 Mon Sep 17 00:00:00 2001 From: Nadia Dencheva Date: Thu, 19 Sep 2024 12:33:01 -0400 Subject: [PATCH 430/687] update citations file --- CITATION.cff | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index f34b4c8c9a..6abd65c834 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -77,8 +77,23 @@ authors: - family-names: "Graham" given-names: "Brett" orcid: "https://orcid.org/0000-0001-6315-4507" +- family-names: "Molter" + given-names: "Ned" + orcid: "https://orcid.org/0000-0003-3799-9033" +- family-names: "Brandt" + given-names: "Timothy" + orcid: "https://orcid.org/0000-0003-2630-8073" +- family-names: "Hayes" + given-names: "Christian" + orcid: "https://orcid.org/0000-0003-2969-2445" +- family-names: "Cooper" + given-names: "Rachel" + orcid: "https://orcid.org/0000-0001-7864-308X" +- family-names: "Clarke" + given-names: "Melanie" + orcid: "https://orcid.org/0009-0002-3561-4347" title: "JWST Calibration Pipeline" -version: 1.15.1 +version: 1.16.0 doi: 10.5281/zenodo.7038885 -date-released: 2024-07-08 +date-released: 2024-09-20 url: "https://github.com/spacetelescope/jwst" From fcf96d74270137bdb2d6614bc59961604f6d71f5 Mon Sep 17 00:00:00 2001 From: Nadia Dencheva Date: Thu, 19 Sep 2024 15:57:43 -0400 Subject: [PATCH 431/687] Update CITATION.cff Co-authored-by: Melanie Clarke --- CITATION.cff | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CITATION.cff b/CITATION.cff index 6abd65c834..6d933fee0e 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -78,7 +78,7 @@ authors: given-names: "Brett" orcid: "https://orcid.org/0000-0001-6315-4507" - family-names: "Molter" - given-names: "Ned" + given-names: "Edward" orcid: "https://orcid.org/0000-0003-3799-9033" - family-names: "Brandt" given-names: "Timothy" From 132e4b248377152c80ed011d8e234606a927a06b Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Thu, 19 Sep 2024 16:25:48 -0400 Subject: [PATCH 432/687] pin `stcal>=1.9.0,<1.10.0` (#8808) --- CHANGES.rst | 2 ++ pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 4ab6e35ccf..bbd37047e5 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -111,6 +111,8 @@ general - Increase minimum required stdatamodels. [#8797] +- bump dependency to use ``stcal 1.9.0`` [#8808] + klip ---- diff --git a/pyproject.toml b/pyproject.toml index 7b259690ac..dd65909821 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal>=1.8.0,<1.9.0", + "stcal>=1.9.0,<1.10.0", "stdatamodels>=2.1.0,<2.2.0", "stpipe>=0.7.0,<0.8.0", "stsci.imagestats>=1.6.3", From f712e760a83e7c8e5151f596ed951ac1d3432fb9 Mon Sep 17 00:00:00 2001 From: Ken MacDonald <68608562+kmacdonald-stsci@users.noreply.github.com> Date: Thu, 19 Sep 2024 16:32:43 -0400 Subject: [PATCH 433/687] JP-3658: JWST Accommodation of the Ramp Fitting Likelihood Algorithm (#8631) Co-authored-by: Tyler Pauly --- CHANGES.rst | 7 +++++-- docs/jwst/ramp_fitting/description.rst | 5 +++++ jwst/ramp_fitting/ramp_fit_step.py | 20 +++++++++++++++----- 3 files changed, 25 insertions(+), 7 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index bbd37047e5..b79f9657f7 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -187,14 +187,17 @@ pipeline - Updated ``calwebb_spec2`` to run ``nsclean`` on NIRSpec imprint and background association members. [#8786] - + +- Updated `calwebb_spec3` to not save the `pixel_replacement` output by default.[#8765] + ramp_fitting ------------ - Moved the read noise variance recalculation for CHARGELOSS flagging to the C implementation, when the algorithm is ``OLS_C``. [#8697, spacetelescope/stcal#278] -- Updated `calwebb_spec3` to not save the `pixel_replacement` output by default.[#8765] +- Updated the flow of the detector 1 pipeline when selecting the ``LIKELY`` algorithm + for ramp fitting. The ramps must contain a minimum number of groups (4).[#8631] resample -------- diff --git a/docs/jwst/ramp_fitting/description.rst b/docs/jwst/ramp_fitting/description.rst index d4d63862a6..4d0e5329fc 100644 --- a/docs/jwst/ramp_fitting/description.rst +++ b/docs/jwst/ramp_fitting/description.rst @@ -54,6 +54,11 @@ JWST-specific features are described later within this document. Upon successful completion of this step, the status keyword S_RAMP will be set to "COMPLETE". +There is a new algorithm available for testing in ramp fitting, which is the +likelihood algorithm. It is selected by setting ``--ramp_fitting.algorithm=LIKELY``. +The details are in the ``stcal`` documentation at +:ref:`Likelihood Algorithm Details `. + Multiprocessing --------------- This step has the option of running in multiprocessing mode. In that mode it will diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index 1d4291730a..12c34b29df 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -5,6 +5,8 @@ from stcal.ramp_fitting import ramp_fit from stcal.ramp_fitting import utils +from stcal.ramp_fitting.likely_fit import LIKELY_MIN_NGROUPS + from stcal.ramp_fitting.utils import LARGE_VARIANCE from stcal.ramp_fitting.utils import LARGE_VARIANCE_THRESHOLD @@ -385,7 +387,7 @@ class RampFitStep(Step): class_alias = "ramp_fit" spec = """ - algorithm = option('OLS', 'OLS_C', default='OLS_C') # 'OLS' and 'OLS_C' use the same underlying algorithm, but OLS_C is implemented in C + algorithm = option('OLS', 'OLS_C', 'LIKELY', default='OLS_C') # 'OLS' and 'OLS_C' use the same underlying algorithm, but OLS_C is implemented in C int_name = string(default='') save_opt = boolean(default=False) # Save optional output opt_name = string(default='') @@ -414,8 +416,16 @@ def process(self, input): readnoise_filename = self.get_reference_file(input_model, 'readnoise') gain_filename = self.get_reference_file(input_model, 'gain') - log.info('Using READNOISE reference file: %s', readnoise_filename) - log.info('Using GAIN reference file: %s', gain_filename) + ngroups = input_model.data.shape[1] + if self.algorithm.upper() == "LIKELY" and ngroups < LIKELY_MIN_NGROUPS: + log.info(f"When selecting the LIKELY ramp fitting algorithm the" + f" ngroups needs to be a minimum of {LIKELY_MIN_NGROUPS}," + f" but ngroups = {ngroups}. Due to this, the ramp fitting algorithm" + f" is being changed to OLS_C") + self.algorithm = "OLS_C" + + log.info(f"Using READNOISE reference file: {readnoise_filename}") + log.info(f"Using GAIN reference file: {gain_filename}") with datamodels.ReadnoiseModel(readnoise_filename) as readnoise_model, \ datamodels.GainModel(gain_filename) as gain_model: @@ -432,8 +442,8 @@ def process(self, input): readnoise_2d, gain_2d = get_reference_file_subarrays( input_model, readnoise_model, gain_model, frames_per_group) - log.info('Using algorithm = %s' % self.algorithm) - log.info('Using weighting = %s' % self.weighting) + log.info(f"Using algorithm = {self.algorithm}") + log.info(f"Using weighting = {self.weighting}") buffsize = ramp_fit.BUFSIZE if self.algorithm == "GLS": From 8956a4be80c748e1d10b818b2d9cd24cd310b0c3 Mon Sep 17 00:00:00 2001 From: David Law Date: Thu, 19 Sep 2024 16:34:08 -0400 Subject: [PATCH 434/687] JP-3593: 2nd group saturation part 2 (#8731) Co-authored-by: Melanie Clarke Co-authored-by: Tyler Pauly --- CHANGES.rst | 6 ++ docs/jwst/saturation/arguments.rst | 5 ++ docs/jwst/saturation/description.rst | 5 +- jwst/saturation/saturation.py | 31 +++++-- jwst/saturation/saturation_step.py | 5 +- jwst/saturation/tests/test_saturation.py | 109 ++++++++++++++++++++--- 6 files changed, 141 insertions(+), 20 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b79f9657f7..0e56957067 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -236,6 +236,12 @@ resample_spec - Ensure that NaNs and DO_NOT_USE flags match up in all input data before resampling. [#8557] +saturation +---------- + +- Add option for using the readout pattern information to improve saturation flagging + in grouped data. [#8731] + scripts ------- diff --git a/docs/jwst/saturation/arguments.rst b/docs/jwst/saturation/arguments.rst index 272971127a..2df04a6565 100644 --- a/docs/jwst/saturation/arguments.rst +++ b/docs/jwst/saturation/arguments.rst @@ -6,3 +6,8 @@ The ``saturation`` step has one optional argument: The distance to use when growing saturation flag values to neighboring pixels, in order to account for charge migration (spilling). The total region size is 2*n_pix_grow_sat+1 pixels, centered on the primary pixel. + +``--use_readpatt`` (boolean, default=True) + Use information about the readout pattern for grouped data to perform additional + checks to find data that saturates mid-group. This can be important for + finding extremely bright cosmic rays that saturate in group 2. diff --git a/docs/jwst/saturation/description.rst b/docs/jwst/saturation/description.rst index 9a6e29cfcf..83dfd42f96 100644 --- a/docs/jwst/saturation/description.rst +++ b/docs/jwst/saturation/description.rst @@ -34,12 +34,13 @@ The "NO_SAT_CHECK" flag is propagated to the PIXELDQ array in the output science data to indicate which pixels fall into this category. -For grouped data, this step also looks for and flags data that saturated during +If the "use_readpatt" keyword is set, this step will use information about the +read pattern to find pixels that saturated in the middle of grouped data. This +can be particularly important for flagging data that saturated during the second group but did not trigger the normal saturation threshold due to the grouped data averaging. This requires that the third group be saturated, and the first group sufficiently low that the third group would not have been expected to saturate (i.e., flagging due to cosmic rays but not sources). -At present, this is only implemented for NIRSpec IRS2 data. .. _charge_migration: diff --git a/jwst/saturation/saturation.py b/jwst/saturation/saturation.py index f76b6fec66..f75abf0003 100644 --- a/jwst/saturation/saturation.py +++ b/jwst/saturation/saturation.py @@ -22,7 +22,7 @@ ATOD_LIMIT = 65535. # Hard DN limit of 16-bit A-to-D converter -def flag_saturation(input_model, ref_model, n_pix_grow_sat): +def flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): """ Short Summary ------------- @@ -41,6 +41,9 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat): as saturated (i.e '1' will flag the surrouding 8 pixels) to account for charge spilling. + use_readpatt : bool + Use grouped read pattern information to assist with flagging + Returns ------- output_model : `~jwst.datamodels.RampModel` @@ -49,6 +52,8 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat): """ data = input_model.data + ngroups = input_model.meta.exposure.ngroups + nframes = input_model.meta.exposure.nframes # Create the output model as a copy of the input output_model = input_model.copy() @@ -68,9 +73,16 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat): sat_dq = ref_sub_model.dq.copy() ref_sub_model.close() + # Enable use of read_pattern specific treatment if selected + if use_readpatt: + read_pattern = [[x + 1 + groupstart * nframes for x in range(nframes)] for groupstart in range(ngroups)] + log.info(f"Using read_pattern with nframes {nframes}") + else: + read_pattern=None + gdq_new, pdq_new, zframe = flag_saturated_pixels( data, gdq, pdq, sat_thresh, sat_dq, ATOD_LIMIT, dqflags.pixel, - n_pix_grow_sat=n_pix_grow_sat, zframe=zframe) + n_pix_grow_sat=n_pix_grow_sat, read_pattern=read_pattern, zframe=zframe) # Save the flags in the output GROUPDQ array output_model.groupdq = gdq_new @@ -84,7 +96,7 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat): return output_model -def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): +def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): """ Short Summary ------------- @@ -106,6 +118,9 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): as saturated (i.e '1' will flag the surrouding 8 pixels) to account for charge spilling. + use_readpatt : bool + Use grouped read pattern information to assist with flagging + Returns ------- output_model : `~jwst.datamodels.RampModel` @@ -118,7 +133,12 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): ngroups = data.shape[1] detector = input_model.meta.instrument.detector nframes = input_model.meta.exposure.nframes - read_pattern = [[x + 1 + groupstart * nframes for x in range(nframes)] for groupstart in range(ngroups)] + + if use_readpatt: + read_pattern = [[x + 1 + groupstart * nframes for x in range(nframes)] for groupstart in range(ngroups)] + log.info(f"Using read_pattern with nframes {nframes}") + else: + read_pattern=None # create a mask of the appropriate size irs2_mask = x_irs2.make_mask(input_model) @@ -162,7 +182,8 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat): irs2_mask, detector) # check for saturation flag_temp = np.where(sci_temp >= sat_thresh, SATURATED, 0) - if group == 2: + # Additional checks for group 2 saturation in grouped data + if ((group == 2) & (read_pattern is not None)): # Identify groups which we wouldn't expect to saturate by the third group, # on the basis of the first group scigp1 = x_irs2.from_irs2(data[ints, 0, :, :], irs2_mask, detector) diff --git a/jwst/saturation/saturation_step.py b/jwst/saturation/saturation_step.py index ad589b3408..849abadb5e 100755 --- a/jwst/saturation/saturation_step.py +++ b/jwst/saturation/saturation_step.py @@ -18,6 +18,7 @@ class SaturationStep(Step): spec = """ n_pix_grow_sat = integer(default=1) # number of layers adjacent pixels to flag + use_readpatt = boolean(default=True) # Use grouped read pattern information to assist with flagging """ reference_file_types = ['saturation'] @@ -44,9 +45,9 @@ def process(self, input): # Do the saturation check if pipe_utils.is_irs2(input_model): - sat = saturation.irs2_flag_saturation(input_model, ref_model, self.n_pix_grow_sat) + sat = saturation.irs2_flag_saturation(input_model, ref_model, self.n_pix_grow_sat, self.use_readpatt) else: - sat = saturation.flag_saturation(input_model, ref_model, self.n_pix_grow_sat) + sat = saturation.flag_saturation(input_model, ref_model, self.n_pix_grow_sat, self.use_readpatt) # Close the reference file and update the step status ref_model.close() diff --git a/jwst/saturation/tests/test_saturation.py b/jwst/saturation/tests/test_saturation.py index c39997107b..bb707e9e7f 100644 --- a/jwst/saturation/tests/test_saturation.py +++ b/jwst/saturation/tests/test_saturation.py @@ -36,7 +36,7 @@ def test_basic_saturation_flagging(setup_nrc_cube): satmap.data[5, 5] = satvalue # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure that groups with signal > saturation limit get flagged satindex = np.argmax(output.data[0, :, 4:6, 4:6] == satvalue) @@ -51,12 +51,53 @@ def test_nirspec_irs2_saturation_flagging(setup_nrs_irs2_cube): data.data[0, 4, pixx, pixy] = 67000 # Run saturation detection - output = irs2_flag_saturation(data, satmap, n_pix_grow_sat=1) + output = irs2_flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure that groups with signal > saturation limit get flagged assert np.all(output.groupdq[0, 3:, pixx - 1: pixx + 1, pixy - 1: pixy + 1] == dqflags.group['SATURATED']) +def test_nirspec_irs2_readpatt(setup_nrs_irs2_cube): + """ + Tests that the readpatt framework (saturation in grouped data) is working for IRS2 processing. + """ + data, satmap = setup_nrs_irs2_cube() + + pixx, pixy = 1000, 1000 + data.data[0, 0, pixx, pixy] = 500 # Low signal + data.data[0, 1, pixx, pixy] = 50000 # Suspiciously high signal + data.data[0, 2, pixx, pixy] = 65000 # Signal exceeds saturation limit of 60000 + data.data[0, 3, pixx, pixy] = 66000 + data.data[0, 4, pixx, pixy] = 67000 + + # Run saturation detection + output = irs2_flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=True) + + # Make sure that group 2 gets flagged + assert np.all(output.groupdq[0, 1, pixx, pixy] == dqflags.group['SATURATED']) + + +def test_nirspec_nrs_readpatt(setup_nrs_nrs_cube): + """ + Tests that the readpatt framework (saturation in grouped data) is working for regular grouped + data (code follows all instruments except NIRSpec IRS2) + """ + data, satmap = setup_nrs_nrs_cube() + + pixx, pixy = 1000, 1000 + data.data[0, 0, pixx, pixy] = 500 # Low signal + data.data[0, 1, pixx, pixy] = 50000 # Suspiciously high signal + data.data[0, 2, pixx, pixy] = 65000 # Signal exceeds saturation limit of 60000 + data.data[0, 3, pixx, pixy] = 66000 + data.data[0, 4, pixx, pixy] = 67000 + + # Run saturation detection + output = irs2_flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=True) + + # Make sure that group 2 gets flagged + assert np.all(output.groupdq[0, 1, pixx, pixy] == dqflags.group['SATURATED']) + + def test_irs2_zero_frame(setup_nrs_irs2_cube): """ Tests IRS2 ZEROFRAME processing. @@ -76,7 +117,7 @@ def test_irs2_zero_frame(setup_nrs_irs2_cube): ramp.zeroframe[nint, row, col] = 65000. ramp.zeroframe[nint, row + 1, col + 1] = -100. - output = irs2_flag_saturation(ramp, sat, n_pix_grow_sat=0) + output = irs2_flag_saturation(ramp, sat, n_pix_grow_sat=0, use_readpatt=False) check_zframe = np.ones(dims, dtype=ramp.data.dtype) * 1000. check_zframe[nint, row, col] = 0. @@ -111,7 +152,7 @@ def test_ad_floor_flagging(setup_nrc_cube): satmap.data[5, 5] = satvalue # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Check if the right frames are flagged as saturated assert np.all(output.groupdq[0, satindxs, 5, 5] @@ -146,7 +187,7 @@ def test_ad_floor_and_saturation_flagging(setup_nrc_cube): satmap.data[5, 5] = satvalue # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Check if the right frames are flagged as ad_floor assert np.all(output.groupdq[0, floorindxs, 5, 5] @@ -180,7 +221,7 @@ def test_signal_fluctuation_flagging(setup_nrc_cube): satmap.data[5, 5] = satvalue # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure that all groups after first saturated group are flagged satindex = np.argmax(output.data[0, :, 5, 5] == satvalue) @@ -210,7 +251,7 @@ def test_all_groups_saturated(setup_nrc_cube): satmap.data[5, 5] = satvalue # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure all groups are flagged assert np.all(np.bitwise_and(output.groupdq[0, :, 4:6, 4:6], @@ -237,7 +278,7 @@ def test_subarray_extraction(setup_miri_cube): satmap.dq[550, 100] = dqflags.pixel['NONLINEAR'] # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Check for DQ flag in PIXELDQ of subarray image assert output.pixeldq[76, 100] == dqflags.pixel['DO_NOT_USE'] @@ -267,7 +308,7 @@ def test_dq_propagation(setup_nrc_cube): satmap.dq[5, 5] = dqval2 # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure DQ values from data and reference file are added in the output assert output.pixeldq[5, 5] == dqval1 + dqval2 @@ -300,7 +341,7 @@ def test_no_sat_check(setup_nrc_cube): data.pixeldq[5, 5] = dqflags.pixel['RC'] # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Make sure output GROUPDQ does not get flagged as saturated # Make sure PIXELDQ is set to NO_SAT_CHECK and original flag @@ -332,7 +373,7 @@ def test_nans_in_mask(setup_nrc_cube): satmap.data[5, 5] = np.nan # Run the pipeline - output = flag_saturation(data, satmap, n_pix_grow_sat=1) + output = flag_saturation(data, satmap, n_pix_grow_sat=1, use_readpatt=False) # Check that output GROUPDQ is not flagged as saturated assert np.all(output.groupdq[0, :, 4:6, 4:6] != dqflags.group['SATURATED']) @@ -501,3 +542,49 @@ def _cube(): return data_model, saturation_model return _cube + +@pytest.fixture(scope='function') +def setup_nrs_nrs_cube(): + + def _cube(): + + # create a JWST datamodel for NIRSPEC NRS-mode data + data_model = RampModel((1, 5, 3200, 2048)) + data_model.data = np.ones(((1, 5, 3200, 2048))) + data_model.groupdq = np.zeros(((1, 5, 3200, 2048))) + data_model.pixeldq = np.zeros(((3200, 2048))) + data_model.meta.instrument.name = 'NIRSPEC' + data_model.meta.instrument.detector = 'NRS1' + data_model.meta.instrument.filter = 'F100LP' + data_model.meta.observation.date = '2019-07-19' + data_model.meta.observation.time = '23:23:30.912' + data_model.meta.exposure.type = 'NRS_LAMP' + data_model.meta.subarray.name = 'FULL' + data_model.meta.subarray.xstart = 1 + data_model.meta.subarray.xsize = 2048 + data_model.meta.subarray.ystart = 1 + data_model.meta.subarray.ysize = 2048 + data_model.meta.exposure.nrs_normal = 16 + data_model.meta.exposure.nrs_reference = 4 + data_model.meta.exposure.readpatt = 'NRS' + data_model.meta.exposure.nframes = 4 + data_model.meta.exposure.ngroups = 5 + + # create a saturation model for the saturation step + saturation_model = SaturationModel((2048, 2048)) + saturation_model.data = np.ones((2048, 2048)) * 60000 # saturation limit for every pixel is 60000 + saturation_model.meta.description = 'Fake data.' + saturation_model.meta.telescope = 'JWST' + saturation_model.meta.reftype = 'SaturationModel' + saturation_model.meta.useafter = '2015-10-01T00:00:00' + saturation_model.meta.instrument.name = 'NIRSPEC' + saturation_model.meta.instrument.detector = 'NRS1' + saturation_model.meta.author = 'David' + saturation_model.meta.pedigree = 'Dummy' + saturation_model.meta.subarray.xstart = 1 + saturation_model.meta.subarray.xsize = 2048 + saturation_model.meta.subarray.ystart = 1 + saturation_model.meta.subarray.ysize = 2048 + + return data_model, saturation_model + return _cube From d84ce8e9689a261470debb8f24381b55f66fa635 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Thu, 19 Sep 2024 16:34:36 -0400 Subject: [PATCH 435/687] JP-3305: New association candidate type to group mosaic tiles across targets (#8798) --- CHANGES.rst | 2 + jwst/associations/lib/rules_level3.py | 72 ++++++++++++++++++++- jwst/regtest/test_associations_sdp_pools.py | 6 ++ 3 files changed, 79 insertions(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 0e56957067..3069853c64 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -40,6 +40,8 @@ associations for S1600A1 with 5 point dithers, to reduce overlap between background nods and science exposure. [#8744] +- Added association rule for level 3 image mosaic candidates. [#8798] + badpix_selfcal -------------- diff --git a/jwst/associations/lib/rules_level3.py b/jwst/associations/lib/rules_level3.py index ac64e1128e..bd1b4d1f54 100644 --- a/jwst/associations/lib/rules_level3.py +++ b/jwst/associations/lib/rules_level3.py @@ -25,6 +25,7 @@ 'Asn_Lv3ACQ_Reprocess', 'Asn_Lv3AMI', 'Asn_Lv3Image', + 'Asn_Lv3ImageMosaic', 'Asn_Lv3ImageBackground', 'Asn_Lv3MIRCoron', 'Asn_Lv3MIRMRS', @@ -466,7 +467,7 @@ def __init__(self, *args, **kwargs): value = ('T') )], reduce=Constraint.notany - ), + ), DMSAttrConstraint( name='channel', sources=['channel'], @@ -1172,3 +1173,72 @@ def __init__(self, *args, **kwargs): @property def dms_product_name(self): return dms_product_name_sources(self) + + +@RegistryMarker.rule +class Asn_Lv3ImageMosaic(AsnMixin_Science): + """Level 3 Science Image Mosaic Association + + Characteristics: + - Association type: ``image3`` + - Pipeline: ``calwebb_image3`` + - Non-TSO + - Non-WFS&C + - Collect separate tiles of mosaic into one product + """ + + def __init__(self, *args, **kwargs): + + # Setup constraints + self.constraints = Constraint([ + Constraint_Optical_Path(), + Constraint_Image(), + DMSAttrConstraint( + name='wfsvisit', + sources=['visitype'], + value='((?!wfsc).)*', + required=False + ), + Constraint( + [ + DMSAttrConstraint( + name='bkgdtarg', + sources=['bkgdtarg'], + ), + Constraint_TSO() + ], + reduce=Constraint.notany + ), + ]) + + # Only valid if candidate type is 'mosaic'. + self.validity.update({ + 'is_type_mosaic': { + 'validated': False, + 'check': self._validate_candidates + } + }) + + # Now check and continue initialization. + super(Asn_Lv3ImageMosaic, self).__init__(*args, **kwargs) + + def _init_hook(self, item): + """Post-check and pre-add initialization""" + + self.data['asn_type'] = 'image3' + super(Asn_Lv3ImageMosaic, self)._init_hook(item) + + def _validate_candidates(self, item): + """Allow only mosaic asn candidates + + Returns + ------- + True if candidate type is mosaic. + False otherwise. + """ + + # If a group candidate, reject. + if self.acid.type.lower() != 'mosaic': + return False + + return True diff --git a/jwst/regtest/test_associations_sdp_pools.py b/jwst/regtest/test_associations_sdp_pools.py index 3c63554fda..3cbe4afe08 100644 --- a/jwst/regtest/test_associations_sdp_pools.py +++ b/jwst/regtest/test_associations_sdp_pools.py @@ -147,6 +147,12 @@ 'xfail': None, 'slow': False, }, + # This association tests the Asn_Lv3ImageMosaic rule + 'jw02739_20230710t150016_pool': { + 'args': ['-i', 'c1000'], + 'xfail': None, + 'slow': False, + }, 'jw80600_20171108T041522_pool': { 'args': [], 'xfail': 'PR #3450', From a46406d351bea2fb22833eeece6623ab7c0d1216 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 19 Sep 2024 16:15:47 -0400 Subject: [PATCH 436/687] Add safeguards for nsclean on background/imprint members --- jwst/pipeline/calwebb_spec2.py | 29 ++++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 7a5f0b3ca3..36e2993113 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -270,15 +270,26 @@ def process_exposure_product( calibrated = self.nsclean(calibrated) # Apply nsclean to NIRSpec imprint and background members - for i, imprint_file in enumerate(members_by_type['imprint']): - self.nsclean.output_file = os.path.basename(imprint_file) - imprint_nsclean = self.nsclean(imprint_file) - members_by_type['imprint'][i] = imprint_nsclean - - for i, bkg_file in enumerate(members_by_type['background']): - self.nsclean.output_file = os.path.basename(bkg_file) - bkg_nsclean = self.nsclean(bkg_file) - members_by_type['background'][i] = bkg_nsclean + if not self.nsclean.skip: + save_results = self.nsclean.save_results + + for i, imprint_file in enumerate(members_by_type['imprint']): + if save_results: + if isinstance(imprint_file, datamodels.JwstDataModel): + self.nsclean.output_file = imprint_file.meta.filename + else: + self.nsclean.output_file = os.path.basename(imprint_file) + imprint_nsclean = self.nsclean(imprint_file) + members_by_type['imprint'][i] = imprint_nsclean + + for i, bkg_file in enumerate(members_by_type['background']): + if save_results: + if isinstance(bkg_file, datamodels.JwstDataModel): + self.nsclean.output_file = bkg_file.meta.filename + else: + self.nsclean.output_file = os.path.basename(bkg_file) + bkg_nsclean = self.nsclean(bkg_file) + members_by_type['background'][i] = bkg_nsclean # Leakcal subtraction (imprint) occurs before background subtraction on a per-exposure basis. # If there is only one `imprint` member, this imprint exposure is subtracted from all the From 9c68fc3568528385a6f92635989007c76b33ce98 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 19 Sep 2024 17:02:30 -0400 Subject: [PATCH 437/687] Update change log --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3069853c64..8b97ac527a 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -188,7 +188,7 @@ pipeline in memory or on disk. [#8683] - Updated ``calwebb_spec2`` to run ``nsclean`` on NIRSpec imprint and background - association members. [#8786] + association members. [#8786, #8809] - Updated `calwebb_spec3` to not save the `pixel_replacement` output by default.[#8765] From 9fdf2c1fc6fc4c768cef3296b952dd9e550f74f9 Mon Sep 17 00:00:00 2001 From: Nadia Dencheva Date: Fri, 20 Sep 2024 07:49:19 -0400 Subject: [PATCH 438/687] Rebase nrs deepcopy (#8793) Co-authored-by: Timothy Brandt Co-authored-by: Timothy D Brandt Co-authored-by: Melanie Clarke --- CHANGES.rst | 34 ++++- jwst/assign_wcs/nirspec.py | 128 +++++++++++++++++- .../clean_flicker_noise.py | 46 ++++--- jwst/cube_build/blot_cube_build.py | 5 +- jwst/cube_build/cube_build_wcs_util.py | 5 +- jwst/cube_build/ifu_cube.py | 11 +- jwst/flatfield/flat_field.py | 9 +- jwst/master_background/expand_to_2d.py | 9 +- jwst/msaflagopen/msaflag_open.py | 15 +- jwst/pathloss/pathloss.py | 13 +- jwst/photom/photom.py | 9 +- jwst/pixel_replace/pixel_replace.py | 7 +- 12 files changed, 248 insertions(+), 43 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3069853c64..db515eadf3 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -26,12 +26,16 @@ assign_wcs - Moved `update_s_region_imaging`, `update_s_region_keyword`, and `wcs_from_footprints` into stcal. [#8624] +- Add helper functions to copy only the necessary parts of the WCS so that + these parts can be used within loops, avoiding copying the full WCS within + a loop [#8793] + associations ------------ - Restored slit name to level 3 product names for NIRSpec BOTS and background fixed slit targets. [#8699] - + - Update warning message about use of paths in associations. [#8752] - Remove ``MultilineLogger`` and no longer set it as the default logger. [#8781] @@ -70,12 +74,14 @@ cube_build - Removed direct setting of the ``self.skip`` attribute from within the step itself. [#8600] - + - Fixed a bug when ``cube_build`` was called from the ``mrs_imatch`` step. [#8728] - Ensure that NaNs and DO_NOT_USE flags match up in all input data before building a cube. [#8557] +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + datamodels ---------- @@ -102,6 +108,8 @@ flat_field - Ensure that NaNs and DO_NOT_USE flags match up in all science, error, variance, and DQ extensions for all modes. [#8557] +- Replaced deep copies of NIRSpec WCS objects within most loops [#8793] + general ------- @@ -127,6 +135,8 @@ master_background - Either of ``"background"`` or ``"bkg"`` in slit name now defines the slit as a background slit, instead of ``"bkg"`` only. [#8600] +- Replaced deep copies of NIRSpec WCS objects within most loops [#8793] + model_blender ------------- @@ -137,6 +147,11 @@ mrs_imatch - Added a deprecation warning and set the default to skip=True for the step. [#8728] +msaflagopen +----------- + +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + nsclean ------- @@ -149,6 +164,8 @@ nsclean can still be called from the ``calwebb_spec2`` pipeline on NIRSpec rate data, but it is now deprecated. [#8669] +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + outlier_detection ----------------- @@ -174,12 +191,16 @@ pathloss - Ensure that NaNs and DO_NOT_USE flags match up in all output science, error, variance, and DQ extensions. [#8557] +- Replaced deep copies of NIRSpec WCS objects within most loops [#8793] + photom ------ - Ensure that NaNs and DO_NOT_USE flags match up in all output science, error, variance, and DQ extensions. [#8557] +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + pipeline -------- @@ -192,6 +213,13 @@ pipeline - Updated `calwebb_spec3` to not save the `pixel_replacement` output by default.[#8765] +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + +pixel_replace +------------- + +- Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] + ramp_fitting ------------ @@ -522,6 +550,7 @@ master_background wavelength range instead of NaN to avoid NaN-ing out entire sets of science data when backgrounds are missing. [#8597] + master_background_mos --------------------- @@ -598,6 +627,7 @@ photom - Added a hook to bypass the ``photom`` step when the ``extract_1d`` step was bypassed for non-TSO NIRISS SOSS exposures. [#8575] + pipeline -------- diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index 740782a099..2168804bee 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -6,6 +6,7 @@ """ import logging import numpy as np +import copy from astropy.modeling import models from astropy.modeling.models import Mapping, Identity, Const1D, Scale, Tabular1D @@ -1696,6 +1697,131 @@ def gwa_to_ymsa(msa2gwa_model, lam_cen=None, slit=None, slit_y_range=None): return tab +def _get_transforms(input_model, slitnames, return_slits=False): + + """ + Return a WCS object with necessary transforms for all slits. + + This function enables the JWST pipeline to avoid excessive deep + copying of WCS objects in later steps. It is used internally in + the pipeline only and should not be used if any of the WCSs is + modified. + + Parameters + ---------- + input_model : `~jwst.datamodels.JwstDataModel` + A data model with a WCS object for the all open slitlets in + an observation. + slitnames : list of int or str + Slit.name of all open slits. + return_slits : bool, optional + Return the open slits + + Returns + ------- + wcsobj : `~gwcs.wcs.WCS` + WCS object deep copied from input_model.meta.wcs + sca2gwa : `~astropy.modeling.core.Model` + Transform from ``sca`` to ``gwa`` + gwa2slit : list of `~astropy.modeling.core.Model` + Transform from ``gwa`` to ``slit`` for each input slit + slit2slicer : list of `~astropy.modeling.core.Model` + Transform from ``slit_frame`` to ``slicer`` for each input slit + open_slits : list of `~stdatamodels.jwst.transforms.models.Slit` + open slits from wcs.get_transform('gwa', 'slit_frame').slits + Only returned if return_slits is True + """ + + wcs = copy.deepcopy(input_model.meta.wcs) + + sca2gwa = copy.deepcopy(wcs.pipeline[1].transform[1:]) + wcs.set_transform('sca', 'gwa', sca2gwa) + + gwa2slit = [copy.deepcopy(wcs.pipeline[2].transform.get_model(slit_name)) + for slit_name in slitnames] + + slit2slicer = [copy.deepcopy(wcs.pipeline[3].transform.get_model(slit_name)) + for slit_name in slitnames] + + if return_slits: + g2s = wcs.get_transform('gwa', 'slit_frame') + open_slits = g2s.slits + return wcs, sca2gwa, gwa2slit, slit2slicer, copy.deepcopy(open_slits) + else: + return wcs, sca2gwa, gwa2slit, slit2slicer + + +def _nrs_wcs_set_input_lite(input_model, input_wcs, slit_name, transforms, + wavelength_range=None, open_slits=None, + slit_y_low=None, slit_y_high=None): + + """ + Return a WCS object for a specific slit, slice or shutter + + The lite version of the routine is distinguished from the legacy + routine because it does not make a deep copy of the input WCS object. + + Parameters + ---------- + input_model : `~jwst.datamodels.JwstDataModel` + A WCS object for the all open slitlets in an observation. + input_wcs : `~gwcs.wcs.WCS` + A WCS object for the all open slitlets in an observation. This + will be modified and returned. + slit_name : int or str + Slit.name of an open slit. + transforms : list of `~astropy.modeling.core.Model` + Model transforms output from ``_get_transforms`` + wavelength_range: list + Wavelength range for the combination of filter and grating. Optional. + open_slits : list of slits + List of open slits. Optional. + + Returns + ------- + wcsobj : `~gwcs.wcs.WCS` + WCS object for this slit. + """ + + + def _get_y_range(input_model, open_slits): + if open_slits is None: + log_message = 'nrs_wcs_set_input_lite must be called with open_slits if not in ifu mode' + log.critical(log_message) + raise RuntimeError(log_message) + # Need the open slits to get the slit ymin,ymax + slit = [s for s in open_slits if s.name == slit_name][0] + return slit.ymin, slit.ymax + + if wavelength_range is None: + _, wavelength_range = spectral_order_wrange_from_model(input_model) + + slit_wcs = copy.copy(input_wcs) + + slit_wcs.set_transform('sca', 'gwa', transforms[0]) + slit_wcs.set_transform('gwa', 'slit_frame', transforms[1]) + + is_nirspec_ifu = is_nrs_ifu_lamp(input_model) or input_model.meta.exposure.type.lower() == 'nrs_ifu' + + if is_nirspec_ifu: + slit_wcs.set_transform('slit_frame', 'slicer', transforms[2] & Identity(1)) + else: + slit_wcs.set_transform('slit_frame', 'msa_frame', transforms[2] & Identity(1)) + + transform = slit_wcs.get_transform('detector', 'slit_frame') + + if is_nirspec_ifu: + bb = compute_bounding_box(transform, wavelength_range) + else: + if slit_y_low is None or slit_y_high is None: + slit_y_low, slit_y_high = _get_y_range(input_model, open_slits) + bb = compute_bounding_box(transform, wavelength_range, + slit_ymin=slit_y_low, slit_ymax=slit_y_high) + + slit_wcs.bounding_box = bb + return slit_wcs + + def _nrs_wcs_set_input(input_model, slit_name): """ Returns a WCS object for a specific slit, slice or shutter. @@ -1713,7 +1839,7 @@ def _nrs_wcs_set_input(input_model, slit_name): wcsobj : `~gwcs.wcs.WCS` WCS object for this slit. """ - import copy + wcsobj = input_model.meta.wcs slit_wcs = copy.deepcopy(wcsobj) diff --git a/jwst/clean_flicker_noise/clean_flicker_noise.py b/jwst/clean_flicker_noise/clean_flicker_noise.py index 2a41da373c..90b2e7c77f 100644 --- a/jwst/clean_flicker_noise/clean_flicker_noise.py +++ b/jwst/clean_flicker_noise/clean_flicker_noise.py @@ -2,6 +2,7 @@ import warnings import gwcs +from gwcs.utils import _toindex import numpy as np from astropy.stats import sigma_clipped_stats, SigmaClip from astropy.utils.exceptions import AstropyUserWarning @@ -166,11 +167,14 @@ def mask_ifu_slices(input_model, mask): # Initialize global DQ map to all zero (OK to use) dqmap = np.zeros_like(input_model.dq) - # Get the wcs objects for all IFU slices - list_of_wcs = nirspec.nrs_ifu_wcs(input_model) + # Note: 30 in the line below is hardcoded in nirspec.nrs.ifu_wcs, which + # the line below replaces. + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(input_model, np.arange(30)) # Loop over the IFU slices, finding the valid region for each - for (k, ifu_wcs) in enumerate(list_of_wcs): + for k in range(len(tr2)): + ifu_wcs = nirspec._nrs_wcs_set_input_lite(input_model, wcsobj, k, + [tr1, tr2[k], tr3[k]]) # Construct array indexes for pixels in this slice x, y = gwcs.wcstools.grid_from_bounding_box( @@ -220,8 +224,6 @@ def mask_slits(input_model, mask): 2D output mask with additional flags for slit pixels """ - from jwst.extract_2d.nirspec import offset_wcs - log.info("Finding slit/slitlet pixels") # Get the slit-to-msa frame transform from the WCS object @@ -230,9 +232,17 @@ def mask_slits(input_model, mask): # Loop over the slits, marking all the pixels within each bounding # box as False (do not use) in the mask. # Note that for 3D masks (TSO mode), all planes will be set to the same value. - for slit in slit2msa.slits: - slit_wcs = nirspec.nrs_wcs_set_input(input_model, slit.name) - xlo, xhi, ylo, yhi = offset_wcs(slit_wcs) + + slits = [s.name for s in slit2msa.slits] + wcsobj, tr1, tr2, tr3, open_slits = nirspec._get_transforms(input_model, slits, return_slits=True) + + for k in range(len(tr2)): + slit_wcs = nirspec._nrs_wcs_set_input_lite(input_model, wcsobj, slits[k], + [tr1, tr2[k], tr3[k]], + open_slits=open_slits) + + xlo, xhi = _toindex(slit_wcs.bounding_box[0]) + ylo, yhi = _toindex(slit_wcs.bounding_box[1]) mask[..., ylo:yhi, xlo:xhi] = False return mask @@ -691,14 +701,14 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, # basically copied from lib.py. Use a robust estimator for # standard deviation, then exclude discrepant pixels and their # four nearest neighbors from the fit. - + if exclude_outliers: med = np.median(image[mask]) std = 1.4825 * np.median(np.abs((image - med)[mask])) outlier = mask & (np.abs(image - med) > sigrej * std) - + mask = mask & (~outlier) - + # also get four nearest neighbors of flagged pixels mask[1:] = mask[1:] & (~outlier[:-1]) mask[:-1] = mask[:-1] & (~outlier[1:]) @@ -713,7 +723,7 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, # i1 will be the first row with a nonzero element in the mask # imax will be the last row with a nonzero element in the mask - + nonzero_mask_element = np.sum(mask, axis=1) > 0 if np.sum(nonzero_mask_element) == 0: @@ -722,7 +732,7 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, i1 = np.amin(np.arange(mask.shape[0])[nonzero_mask_element]) imax = np.amax(np.arange(mask.shape[0])[nonzero_mask_element]) - + i1_vals = [] di_list = [] models = [] @@ -736,7 +746,7 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, if (sum_mask[k] - sum_mask[i1] > npix_iter and sum_mask[-1] - sum_mask[i1] > 1.5 * npix_iter): break - + di = k - i1 i1_vals += [i1] @@ -747,7 +757,7 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, # outliers section-by-section; we have to do that earlier # over the full array to get reliable values for the mean # and standard deviation. - + if np.mean(mask[i1:i1 + di]) > minfrac: cleaner = NSCleanSubarray(image[i1:i1 + di], mask[i1:i1 + di], fc=fc, exclude_outliers=False) @@ -767,9 +777,9 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, # Step forward by half an interval so that we have # overlapping fitting regions. - + i1 += max(int(np.round(di/2)), 1) - + model = np.zeros(image.shape) tot_wgt = np.zeros(image.shape) @@ -779,7 +789,7 @@ def fft_clean_subarray(image, mask, detector, npix_iter=512, # Use nonzero weights everywhere so that if only one # correction is available it gets unit weight when we # normalize. - + for i in range(len(models)): wgt = 1.001 - np.abs(np.linspace(-1, 1, di_list[i]))[:, np.newaxis] model[i1_vals[i]:i1_vals[i] + di_list[i]] += wgt*models[i] diff --git a/jwst/cube_build/blot_cube_build.py b/jwst/cube_build/blot_cube_build.py index 27548a1378..09f12555bc 100644 --- a/jwst/cube_build/blot_cube_build.py +++ b/jwst/cube_build/blot_cube_build.py @@ -264,10 +264,13 @@ def blot_images_nirspec(self): log.info('Blotting 30 slices on NIRSPEC detector') roi_det = 1.0 # Just large enough that we don't get holes + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(model, np.arange(nslices)) + for ii in range(nslices): # for each slice pull out the blotted values that actually fall on the slice region # use the bounding box of each slice to determine the slice limits - slice_wcs = nirspec.nrs_wcs_set_input(model, ii) + slice_wcs = nirspec._nrs_wcs_set_input_lite(model, wcsobj, ii, + [tr1, tr2[ii], tr3[ii]]) slicer2world = slice_wcs.get_transform('slicer','world') detector2slicer = slice_wcs.get_transform('detector','slicer') diff --git a/jwst/cube_build/cube_build_wcs_util.py b/jwst/cube_build/cube_build_wcs_util.py index 0222dead53..86a1177ea0 100644 --- a/jwst/cube_build/cube_build_wcs_util.py +++ b/jwst/cube_build/cube_build_wcs_util.py @@ -155,8 +155,11 @@ def find_corners_NIRSPEC(input, instrument_info, coord_system): # for NIRSPEC there are 30 regions log.info('Looping over slices to determine cube size') + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(input, np.arange(nslices)) + for i in range(nslices): - slice_wcs = nirspec.nrs_wcs_set_input(input, i) + slice_wcs = nirspec._nrs_wcs_set_input_lite(input, wcsobj, i, + [tr1, tr2[i], tr3[i]]) x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box, step=(1, 1), center=True) if coord_system == 'internal_cal': diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index ed034949d1..eb66482dfa 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -747,9 +747,12 @@ def build_ifucube(self): 20, 9, 21, 8, 22, 7, 23, 6, 24, 5, 25, 4, 26, 3, 27, 2, 28, 1, 29, 0] + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(input_model, np.arange(nslices)) + for i in range(nslices): - slice_wcs = nirspec.nrs_wcs_set_input(input_model, i) + slice_wcs = nirspec._nrs_wcs_set_input_lite(input_model, wcsobj, i, + [tr1, tr2[i], tr3[i]]) x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box, step=(1, 1), center=True) detector2slicer = slice_wcs.get_transform('detector', 'slicer') @@ -1857,8 +1860,12 @@ def map_nirspec_pixel_to_sky(self, input_model): nslices = 30 log.info("Mapping each NIRSpec slice to sky for input file: %s", input_model.meta.filename) + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(input_model, np.arange(nslices)) + for ii in range(nslices): - slice_wcs = nirspec.nrs_wcs_set_input(input_model, ii) + slice_wcs = nirspec._nrs_wcs_set_input_lite(input_model, wcsobj, ii, + [tr1, tr2[ii], tr3[ii]]) + x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box) ra, dec, lam = slice_wcs(x, y) diff --git a/jwst/flatfield/flat_field.py b/jwst/flatfield/flat_field.py index 0ce1fcf805..264b2eebe7 100644 --- a/jwst/flatfield/flat_field.py +++ b/jwst/flatfield/flat_field.py @@ -1630,7 +1630,9 @@ def flat_for_nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, flat_err = np.zeros_like(output_model.data) * np.nan try: - list_of_wcs = nirspec.nrs_ifu_wcs(output_model) + # Note: the 30 is hardcoded in nirspec.nrs_ifu_wcs, which the line + # below replaced. + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(output_model, np.arange(30)) except (KeyError, AttributeError): if output_model.meta.cal_step.assign_wcs == 'COMPLETE': log.error("The input file does not appear to have WCS info.") @@ -1638,8 +1640,9 @@ def flat_for_nirspec_ifu(output_model, f_flat_model, s_flat_model, d_flat_model, else: log.error("This mode %s requires WCS information.", exposure_type) raise RuntimeError("The assign_wcs step has not been run.") - for (k, ifu_wcs) in enumerate(list_of_wcs): - + for k in range(len(tr2)): + ifu_wcs = nirspec._nrs_wcs_set_input_lite(output_model, wcsobj, k, + [tr1, tr2[k], tr3[k]]) # example: bounding_box = ((1600.5, 2048.5), # X # (1886.5, 1925.5)) # Y truncated = False diff --git a/jwst/master_background/expand_to_2d.py b/jwst/master_background/expand_to_2d.py index 4148569c67..0311c8fe6a 100644 --- a/jwst/master_background/expand_to_2d.py +++ b/jwst/master_background/expand_to_2d.py @@ -321,8 +321,13 @@ def bkg_for_ifu_image(input, tab_wavelength, tab_background): background.data[:, :] = 0. if input.meta.instrument.name.upper() == "NIRSPEC": - list_of_wcs = nirspec.nrs_ifu_wcs(input) - for ifu_wcs in list_of_wcs: + # Note: the 30 was hardcoded in nirpsec.nrs_ifu_wcs, which the line + # below replaces. + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(input, np.arange(30)) + for k in range(len(tr2)): + ifu_wcs = nirspec._nrs_wcs_set_input_lite(input, wcsobj, k, + [tr1, tr2[k], tr3[k]]) + x, y = grid_from_bounding_box(ifu_wcs.bounding_box) wl_array = ifu_wcs(x, y)[2] wl_array[np.isnan(wl_array)] = -1. diff --git a/jwst/msaflagopen/msaflag_open.py b/jwst/msaflagopen/msaflag_open.py index 39a49e7b7c..d0c55d9ee5 100644 --- a/jwst/msaflagopen/msaflag_open.py +++ b/jwst/msaflagopen/msaflag_open.py @@ -11,7 +11,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.transforms.models import Slit -from ..assign_wcs.nirspec import slitlets_wcs, nrs_wcs_set_input +from ..assign_wcs.nirspec import slitlets_wcs, _nrs_wcs_set_input_lite, _get_transforms log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -96,11 +96,18 @@ def flag(input_datamodel, failed_slitlets, wcs_refnames): temporary_copy = input_datamodel.copy() temporary_copy.meta.wcs = wcs temporary_copy.meta.exposure.type = 'NRS_MSASPEC' + + s = [slitlet.name for slitlet in failed_slitlets] + wcsobj, tr1, tr2, tr3, open_slits = _get_transforms(temporary_copy, s, return_slits=True) + dq_array = input_datamodel.dq - for slitlet in failed_slitlets: + for k in range(len(s)): + # # Pick the WCS for this slitlet from the WCS of the exposure - thiswcs = nrs_wcs_set_input(temporary_copy, slitlet.name) - + thiswcs = _nrs_wcs_set_input_lite(temporary_copy, wcsobj, s[k], + [tr1, tr2[k], tr3[k]], + open_slits=open_slits) + # # Convert the bounding box for this slitlet to a set of indices to use as a slice xmin, xmax, ymin, ymax = boundingbox_to_indices(temporary_copy, thiswcs.bounding_box) diff --git a/jwst/pathloss/pathloss.py b/jwst/pathloss/pathloss.py index c3aba3b324..75e3174058 100644 --- a/jwst/pathloss/pathloss.py +++ b/jwst/pathloss/pathloss.py @@ -993,9 +993,9 @@ def _corrections_for_fixedslit(slit, pathloss, exp_type, source_type): wavelength_array_uncorr, wavelength_uniformsource, pathloss_uniform_vector) - + pathloss_2d = pathloss_2d_ps - + else: wavelength_array = slit.wavelength @@ -1010,7 +1010,7 @@ def _corrections_for_fixedslit(slit, pathloss, exp_type, source_type): wavelength_array, wavelength_uniformsource, pathloss_uniform_vector) - + pathloss_2d = pathloss_2d_un # Save the corrections. The `data` portion is the correction used. @@ -1073,8 +1073,13 @@ def _corrections_for_ifu(data, pathloss, source_type): # Create the 2-d wavelength arrays, initialize with NaNs wavelength_array = np.zeros(data.shape, dtype=np.float32) wavelength_array.fill(np.nan) + + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(data, NIRSPEC_IFU_SLICES) + for slice in NIRSPEC_IFU_SLICES: - slice_wcs = nirspec.nrs_wcs_set_input(data, slice) + slice_wcs = nirspec._nrs_wcs_set_input_lite(data, wcsobj, slice, + [tr1, tr2[slice], tr3[slice]]) + x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box) ra, dec, wavelength = slice_wcs(x, y) valid = ~np.isnan(wavelength) diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py index f8ec165674..54ebfb7836 100644 --- a/jwst/photom/photom.py +++ b/jwst/photom/photom.py @@ -682,10 +682,15 @@ def calc_nrs_ifu_sens2d(self, area_data): dqmap = np.zeros_like(self.input.dq) + dqflags.pixel['NON_SCIENCE'] # Get the list of wcs's for the IFU slices - list_of_wcs = nirspec.nrs_ifu_wcs(self.input) + # Note: 30 in the line below is hardcoded in nirspec.nrs.ifu_wcs, which + # the line below replaces. + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(self.input, np.arange(30)) # Loop over the slices - for (k, ifu_wcs) in enumerate(list_of_wcs): + for k in range(len(tr2)): + + ifu_wcs = nirspec._nrs_wcs_set_input_lite(self.input, wcsobj, k, + [tr1, tr2[k], tr3[k]]) # Construct array indexes for pixels in this slice x, y = gwcs.wcstools.grid_from_bounding_box(ifu_wcs.bounding_box, diff --git a/jwst/pixel_replace/pixel_replace.py b/jwst/pixel_replace/pixel_replace.py index 75d78131aa..99a540d932 100644 --- a/jwst/pixel_replace/pixel_replace.py +++ b/jwst/pixel_replace/pixel_replace.py @@ -3,7 +3,7 @@ from stdatamodels.jwst import datamodels from scipy.optimize import minimize import warnings -from ..assign_wcs.nirspec import nrs_wcs_set_input +from ..assign_wcs import nirspec log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -137,10 +137,11 @@ def replace(self): self.output = new_model else: # fit_profile method - iterate over IFU slices + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(self.input, np.arange(30)) for i in range(30): - slice_wcs = nrs_wcs_set_input(self.input, i) + slice_wcs = nirspec._nrs_wcs_set_input_lite(self.input, wcsobj, i, + [tr1, tr2[i], tr3[i]]) _, _, wave = slice_wcs.transform('detector', 'slicer', yy, xx) - # Define a mask that is True where this trace is located trace_mask = (wave > 0) trace_model = self.input.copy() From 5c5bf7a6754ab204ad72b144a50070ceb5f81487 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 19 Aug 2024 12:56:46 -0700 Subject: [PATCH 439/687] first attempt --- jwst/cube_build/cube_build_step.py | 36 ++++++++++++++++++++++++++++++ jwst/cube_build/ifu_cube.py | 34 +++++++++++++++++++++++++--- 2 files changed, 67 insertions(+), 3 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index c675ae338f..e30b719721 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -9,6 +9,7 @@ from . import cube_build from . import ifu_cube from . import data_types +import asdf from ..assign_wcs.util import update_s_region_keyword from ..stpipe import Step, record_step_status @@ -64,6 +65,7 @@ class CubeBuildStep (Step): search_output_file = boolean(default=false) output_use_model = boolean(default=true) # Use filenames in the output models suffix = string(default='s3d') + offset_list = string(default=None) debug_spaxel = string(default='-1 -1 -1') # Default not used """ @@ -236,6 +238,17 @@ def process(self, input): self.pars_input['output_type'] = self.output_type self.log.info(f'Setting output type to: {self.output_type}') +# ________________________________________________________________________________ +# If an offset file is provided do some basic checks on the file and its contents + self.offsets = None + + if self.offset_list is not None: + offsets = self.check_offset_list() + + if offsets is not None: + print(offsets) + self.offsets = offsets +# ________________________________________________________________________________ # Read in Cube Parameter Reference file # identify what reference file has been associated with these input @@ -276,6 +289,7 @@ def process(self, input): 'roiw': self.roiw, 'wavemin': self.wavemin, 'wavemax': self.wavemax, + 'offsets':self.offsets, 'skip_dqflagging': self.skip_dqflagging, 'suffix': self.suffix, 'debug_spaxel': self.debug_spaxel} @@ -530,3 +544,25 @@ def read_user_input(self): # remove duplicates if needed self.pars_input['grating'] = list(set(self.pars_input['grating'])) # ________________________________________________________________________________ + + def check_offset_list(self): + # first check file is asdf + + check_asdf = asdf.util.get_file_type(asdf.generic_io.get_file(self.offset_list)) + if check_asdf == asdf.util.FileType.ASDF: + with asdf.open(self.offset_list) as af: + offsets = af.tree['offsets'] + + for model in self.input_models: + print(model.meta.filename) + file_check = model.meta.filename + if file_check in offsets['filename']: + #print('found file', file_check) + continue + else: + print('In file from association not found in offset list') + print(offsets['filename']) + return None + return offsets + + diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index eb66482dfa..ec606b2e1e 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -69,6 +69,7 @@ def __init__(self, self.cube_pa = pars_cube.get('cube_pa') self.nspax_x = pars_cube.get('nspax_x') self.nspax_y = pars_cube.get('nspax_y') + self.offsets = pars_cube.get('offsets') self.rois = pars_cube.get('rois') self.roiw = pars_cube.get('roiw') self.debug_spaxel = pars_cube.get('debug_spaxel') @@ -1515,9 +1516,12 @@ def map_detector_to_outputframe(self, this_par1, scalerad_det = None x_det = None y_det = None - + offsets = self.offsets + + if self.instrument == 'MIRI': - sky_result = self.map_miri_pixel_to_sky(input_model, this_par1, subtract_background) + sky_result = self.map_miri_pixel_to_sky(input_model, this_par1, subtract_background, + offsets) (x, y, ra, dec, wave_all, slice_no_all, dwave_all, corner_coord_all) = sky_result elif self.instrument == 'NIRSPEC': @@ -1692,7 +1696,8 @@ def map_detector_to_outputframe(self, this_par1, softrad_det, scalerad_det, x_det, y_det # ______________________________________________________________________ - def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background): + def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, + offsets): """Loop over a file and map the detector pixels to the output cube The output frame is on the SKY (ra-dec) @@ -1719,6 +1724,17 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background): dwave = None corner_coord = None + raoffset = 0.0 + decoffset = 0.0 + # pull out ra dec offset if it exists + if offsets is not None: + filename = input_model.meta.filename + index = offsets['filename'].index(filename) + raoffset = offsets['raoffset'][index]/3600.0 + decoffset = offsets['decoffset'][index]/3600.0 + + print('ra and dec offset to apply in degrees', raoffset, decoffset) + # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been # subtracted - subtract it. @@ -1754,6 +1770,8 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background): # if self.coord_system == 'skyalign' or self.coord_system == 'ifualign': ra, dec, wave = input_model.meta.wcs(x, y) + ra = ra + raoffset + dec = dec + decoffset valid1 = ~np.isnan(ra) ra = ra[valid1] dec = dec[valid1] @@ -1793,6 +1811,16 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background): input_model.meta.wcs.output_frame, alpha2, beta - dbeta * pixfrac / 2., wave) + ra1 = ra1 + raoffset + ra2 = ra2 + raoffset + ra3 = ra3 + raoffset + ra4 = ra4 + raoffset + + dec1 = dec1 + decoffset + dec2 = dec2 + decoffset + dec3 = dec3 + decoffset + dec4 = dec4 + decoffset + corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) From cbc2a50b832dc13f7bf184e4bee4f133ea2dba16 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 21 Aug 2024 13:20:55 -0700 Subject: [PATCH 440/687] First attempt at adding offsets --- jwst/cube_build/cube_build_step.py | 54 +++++++++++++++++++++--------- jwst/cube_build/ifu_cube.py | 45 +++++++++++++++++-------- 2 files changed, 70 insertions(+), 29 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index e30b719721..5726c4af6c 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -239,14 +239,15 @@ def process(self, input): self.log.info(f'Setting output type to: {self.output_type}') # ________________________________________________________________________________ -# If an offset file is provided do some basic checks on the file and its contents +# If an offset file is provided do some basic checks on the file and its contents. +# The offset list contains a matching list to the files in the association +# used in calspec3 (or offline cube building). +# Each row in the offset list contain a filename, ra offset and dec offset. +# The offset list is an asdf file. self.offsets = None - if self.offset_list is not None: offsets = self.check_offset_list() - if offsets is not None: - print(offsets) self.offsets = offsets # ________________________________________________________________________________ # Read in Cube Parameter Reference file @@ -289,7 +290,7 @@ def process(self, input): 'roiw': self.roiw, 'wavemin': self.wavemin, 'wavemax': self.wavemax, - 'offsets':self.offsets, + 'offsets': self.offsets, 'skip_dqflagging': self.skip_dqflagging, 'suffix': self.suffix, 'debug_spaxel': self.debug_spaxel} @@ -546,23 +547,46 @@ def read_user_input(self): # ________________________________________________________________________________ def check_offset_list(self): - # first check file is asdf - + """Read in an optional ra and dec offsets for each file. + + Summary + ---------- + Check that is file is asdf file. + check the file has the correct format: + For each file in the input assocation check that there is a corresponding + file in the offset file. + Also check that each file in the offset list contain a ra offset and dec offset. + + """ + check_asdf = asdf.util.get_file_type(asdf.generic_io.get_file(self.offset_list)) if check_asdf == asdf.util.FileType.ASDF: with asdf.open(self.offset_list) as af: offsets = af.tree['offsets'] - + + format_failure = False + # Currently the offset list has to have the following keys: filename, raoffset, decoffset + if 'filename' not in offsets.keys(): + self.log.warning('Filename is not listed in the offset list') + format_failure = True + if 'raoffset' not in offsets.keys(): + self.log.warning('raoffset is not listed in the offset list') + format_failure = True + if 'decoffset' not in offsets.keys(): + self.log.warning('decoffset is not listed in the offset list') + format_failure = True + if format_failure: + self.log.warning('Offset list does not have the correct format') + self.log.warning('No offsets are applied') + return None + for model in self.input_models: - print(model.meta.filename) file_check = model.meta.filename if file_check in offsets['filename']: - #print('found file', file_check) + ra = offsets['raoffset'] + dec = offsets['decoffset'] continue else: - print('In file from association not found in offset list') - print(offsets['filename']) + self.log.info('File in assocation is not found in offset list list %s', file_check) return None - return offsets - - + return offsets diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index ec606b2e1e..144804472c 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1316,7 +1316,21 @@ def setup_ifucube_wcs(self): input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] input_model = datamodels.open(input_file) - +# ________________________________________________________________________________ + # If offsets are provided. Pull in ra and dec offsets. + raoffset = 0.0 + decoffset = 0.0 + # pull out ra dec offset if it exists + if self.offsets is not None: + filename = input_model.meta.filename + index = self.offsets['filename'].index(filename) + raoffset = self.offsets['raoffset'][index] + decoffset = self.offsets['decoffset'][index] + log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", + raoffset, decoffset, filename) + raoffset = raoffset/3600.0 # convert to degress + decoffset = decoffset/3600.0 +# ________________________________________________________________________________ # Find the footprint of the image spectral_found = hasattr(input_model.meta.wcsinfo, 'spectral_region') spatial_found = hasattr(input_model.meta.wcsinfo, 's_region') @@ -1372,15 +1386,15 @@ def setup_ifucube_wcs(self): ca1, cb1, ca2, cb2, ca3, cb3, ca4, cb4, lmin, lmax = ch_corners # now append this model spatial and spectral corner - corner_a.append(ca1) - corner_a.append(ca2) - corner_a.append(ca3) - corner_a.append(ca4) + corner_a.append(ca1 + raoffset) + corner_a.append(ca2 + raoffset) + corner_a.append(ca3 + raoffset) + corner_a.append(ca4 + raoffset) - corner_b.append(cb1) - corner_b.append(cb2) - corner_b.append(cb3) - corner_b.append(cb4) + corner_b.append(cb1 + decoffset) + corner_b.append(cb2 + decoffset) + corner_b.append(cb3 + decoffset) + corner_b.append(cb4 + decoffset) lambda_min.append(lmin) lambda_max.append(lmax) @@ -1518,7 +1532,6 @@ def map_detector_to_outputframe(self, this_par1, y_det = None offsets = self.offsets - if self.instrument == 'MIRI': sky_result = self.map_miri_pixel_to_sky(input_model, this_par1, subtract_background, offsets) @@ -1713,6 +1726,8 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, needed for MIRI data input: datamodel input data model + offsets: dictionary + optional dictionary of ra and dec offsets to apply Returns ------- @@ -1730,10 +1745,12 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, if offsets is not None: filename = input_model.meta.filename index = offsets['filename'].index(filename) - raoffset = offsets['raoffset'][index]/3600.0 - decoffset = offsets['decoffset'][index]/3600.0 - - print('ra and dec offset to apply in degrees', raoffset, decoffset) + raoffset = offsets['raoffset'][index] + decoffset = offsets['decoffset'][index] + log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", + raoffset, decoffset, filename) + raoffset = raoffset/3600.0 # convert to degress + decoffset = decoffset/3600.0 # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been From 7d17e04fc82ba5b893fc845d9a4ba1f9d9f5253d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 27 Aug 2024 09:15:30 -0700 Subject: [PATCH 441/687] updates --- docs/jwst/cube_build/arguments.rst | 32 +++++++++++++++++++++++++++ jwst/cube_build/cube_build_step.py | 1 + jwst/cube_build/ifu_cube.py | 35 +++++++++++++++++++++--------- 3 files changed, 58 insertions(+), 10 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index a30a181b6c..af13f1e596 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -91,6 +91,13 @@ The following arguments control the size and sampling characteristics of the out ``nspax_y`` The odd integer number of spaxels to use in the y dimension of the tangent plane. +``offset_list = [string]`` + The string contains the name of the file holding ra and dec offsets to apply to each input file. This file + must be an asdf file and the it has a specific format. It is assumed the user has determined the ra and dec + offsets to apply to the data. For details on how to construct the format of the offset file see creating + :ref:`offsets` files. + + ``coord_system [string]`` The default IFU cubes are built on the ra-dec coordinate system (``coord_system=skyalign``). In these cubes north is up and east is left. There are two other coordinate systems an IFU cube can be built on: @@ -122,3 +129,28 @@ A parameter only used for investigating which detector pixels contributed to a c The string is the x,y,z value of the cube spaxel that is being investigated. The numbering starts counting at 0. To print information to the screeen about the x = 10, y = 20, z = 35 spaxel the parameter string value is '10 20 35'. + +.. _offsets: +The offset file is an ASDF formated file : `_ stands for "Advanced Scientific Data. For each input file in the spec3 assocation used to build the IFU cubes an ra and dec offset is provided. The offsets are in arc seconds. Below is an example of how to make an offset file. It is assumed the user has determined the +offsets to apply for each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary are `filenames`, `raoffset` and `decoffset`. The cube_building code is expects this dictionary to hold the information +for storing the file names and the associated ra and dec offsets. + +It is assumed there exists a list of files, ra and dec offsets that are feed to this routine. The ra and dec offsets +provided in arcseconds. The cube_building code will apply the ra offsets after multiplying by the +`num` is the number of files. + +import asdf +offsets = {} +offsets['filename'] = [] +offsets['raoffset'] = [] +offsets['decoffset'] = [] +for i in range(num): + + offsets['filename'].append(file[i]) + offsets['raoffset'].append(ra_center1[i]) + offsets['decoffset'].append(dec_center1[i]) + +af = asdf.AsdfFile({'offsets':offsets}) +af.write_to('offsets.asdf') + +The offset asdf filename can be any name, but it must have the `asdf` extension. diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 5726c4af6c..702b496090 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -582,6 +582,7 @@ def check_offset_list(self): for model in self.input_models: file_check = model.meta.filename + print(file_check) if file_check in offsets['filename']: ra = offsets['raoffset'] dec = offsets['decoffset'] diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 144804472c..a88129657e 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1861,6 +1861,21 @@ def map_nirspec_pixel_to_sky(self, input_model): x, y, ra, dec, lambda, slice_no """ + + # check if we have an ra and dec offset file + raoffset = 0.0 + decoffset = 0.0 + # pull out ra dec offset if it exists + if offsets is not None: + filename = input_model.meta.filename + index = offsets['filename'].index(filename) + raoffset = offsets['raoffset'][index] + decoffset = offsets['decoffset'][index] + log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", + raoffset, decoffset, filename) + raoffset = raoffset/3600.0 # convert to degress + decoffset = decoffset/3600.0 + # initialize the ra,dec, and wavelength arrays # we will loop over slice_nos and fill in values # the flag_det will be set when a slice_no pixel is filled in @@ -2009,19 +2024,19 @@ def map_nirspec_pixel_to_sky(self, input_model): valid_data = np.where(flag_det == 1) y, x = valid_data - ra = ra_det[valid_data] - dec = dec_det[valid_data] + ra = ra_det[valid_data] + raoffset + dec = dec_det[valid_data] + decoffset wave = lam_det[valid_data] slice_no = slice_det[valid_data] dwave = dwave_det[valid_data] - ra1 = ra1_det[valid_data] - ra2 = ra2_det[valid_data] - ra3 = ra3_det[valid_data] - ra4 = ra4_det[valid_data] - dec1 = dec1_det[valid_data] - dec2 = dec2_det[valid_data] - dec3 = dec3_det[valid_data] - dec4 = dec4_det[valid_data] + ra1 = ra1_det[valid_data] + raoffset + ra2 = ra2_det[valid_data] + raoffset + ra3 = ra3_det[valid_data] + raoffset + ra4 = ra4_det[valid_data] + raoffset + dec1 = dec1_det[valid_data] + decoffset + dec2 = dec2_det[valid_data] + decoffset + dec3 = dec3_det[valid_data] + decoffset + dec4 = dec4_det[valid_data] + decoffset corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) From aa5d546f820478fb1f84c550dc6e64f47ab19be5 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 27 Aug 2024 09:25:56 -0700 Subject: [PATCH 442/687] updates --- docs/jwst/cube_build/arguments.rst | 9 ++++++--- jwst/cube_build/cube_build_step.py | 1 - 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index af13f1e596..6d718358b1 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -131,14 +131,17 @@ A parameter only used for investigating which detector pixels contributed to a c To print information to the screeen about the x = 10, y = 20, z = 35 spaxel the parameter string value is '10 20 35'. .. _offsets: -The offset file is an ASDF formated file : `_ stands for "Advanced Scientific Data. For each input file in the spec3 assocation used to build the IFU cubes an ra and dec offset is provided. The offsets are in arc seconds. Below is an example of how to make an offset file. It is assumed the user has determined the +The offset file is an ASDF formated file : `_ stands for "Advanced Scientific Data. For each +input file in the spec3 assocation used to build the IFU cubes, an ra and dec offset, in arc seconds, is provided. +Below is an example of how to make an ASDF offset file. It is assumed the user has determined the offsets to apply for each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary are `filenames`, `raoffset` and `decoffset`. The cube_building code is expects this dictionary to hold the information for storing the file names and the associated ra and dec offsets. It is assumed there exists a list of files, ra and dec offsets that are feed to this routine. The ra and dec offsets -provided in arcseconds. The cube_building code will apply the ra offsets after multiplying by the +provided in arcseconds. The cube_building code will apply the ra offsets after multiplying by the cos(crval2), where crval2 is the +declination center of the IFU cube. `num` is the number of files. - +y import asdf offsets = {} offsets['filename'] = [] diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 702b496090..5726c4af6c 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -582,7 +582,6 @@ def check_offset_list(self): for model in self.input_models: file_check = model.meta.filename - print(file_check) if file_check in offsets['filename']: ra = offsets['raoffset'] dec = offsets['decoffset'] From c121c1fc8ac54ed79c94fc61cff6ed8abbae726c Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 28 Aug 2024 17:15:05 -0700 Subject: [PATCH 443/687] update for cos(dec) --- docs/jwst/cube_build/arguments.rst | 19 ++++++++++--------- jwst/cube_build/ifu_cube.py | 26 +++++++++++++++++++++++++- 2 files changed, 35 insertions(+), 10 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 6d718358b1..ab329aa6ee 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -131,24 +131,25 @@ A parameter only used for investigating which detector pixels contributed to a c To print information to the screeen about the x = 10, y = 20, z = 35 spaxel the parameter string value is '10 20 35'. .. _offsets: -The offset file is an ASDF formated file : `_ stands for "Advanced Scientific Data. For each -input file in the spec3 assocation used to build the IFU cubes, an ra and dec offset, in arc seconds, is provided. +The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each +input file in the spec3 assocation used to build the IFU cubes, there is a corresponding right ascension and declination offset, +given arc seconds. Below is an example of how to make an ASDF offset file. It is assumed the user has determined the -offsets to apply for each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary are `filenames`, `raoffset` and `decoffset`. The cube_building code is expects this dictionary to hold the information -for storing the file names and the associated ra and dec offsets. +offsets to apply the data in each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary +are `filenames`, `raoffset` and `decoffset`. The IFU cube building code expects this dictionary to hold the information +for storing the file names and the associated ra and dec offsets. The file names should not contain the directory path. -It is assumed there exists a list of files, ra and dec offsets that are feed to this routine. The ra and dec offsets -provided in arcseconds. The cube_building code will apply the ra offsets after multiplying by the cos(crval2), where crval2 is the +It is assumed there exists a list of files, ra and dec offsets that are feed to this method. The ra and dec offsets +provided in arcseconds. The cube building code will apply the ra offsets after dividing by cos(crval2), where crval2 is the declination center of the IFU cube. -`num` is the number of files. -y +Below `num` is the number of files. + import asdf offsets = {} offsets['filename'] = [] offsets['raoffset'] = [] offsets['decoffset'] = [] for i in range(num): - offsets['filename'].append(file[i]) offsets['raoffset'].append(ra_center1[i]) offsets['decoffset'].append(dec_center1[i]) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index a88129657e..6755e9e2e4 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -114,7 +114,8 @@ def __init__(self, self.naxis3 = None self.cdelt3_normal = None self.rot_angle = None # rotation angle between Ra-Dec and IFU local instrument plane - + self.median_dec = None + self.a_min = 0 self.a_max = 0 self.b_min = 0 @@ -1234,6 +1235,7 @@ def setup_ifucube_wcs(self): # _____________________________________________________________________________ self.cdelt1 = self.spatial_size self.cdelt2 = self.spatial_size + deg2rad = math.pi / 180.0 if self.linear_wavelength: self.cdelt3 = self.spectral_size @@ -1310,6 +1312,25 @@ def setup_ifucube_wcs(self): log.debug(f'Working on data from {this_a}, {this_b}') n = len(self.master_table.FileMap[self.instrument][this_a][this_b]) log.debug('number of files %d', n) + + # find the median center declination if we have an offset file + if self.offsets is not None: + decs = [] + for k in range(n): + input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] + input_model = datamodels.open(input_file) + spatial_box = input_model.meta.wcsinfo.s_region + s = spatial_box.split(' ') + cb1 = float(s[4]) + cb2 = float(s[6]) + cb3 = float(s[8]) + cb4 = float(s[10]) + m = (cb1 + cb2 + cb3 + cb4)/4 + decs.append(m) + + self.median_dec = np.nanmedian(decs) + print('Median declination ', self.median_dec) + for k in range(n): lmin = 0.0 lmax = 0.0 @@ -1329,6 +1350,7 @@ def setup_ifucube_wcs(self): log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", raoffset, decoffset, filename) raoffset = raoffset/3600.0 # convert to degress + raoffset = raoffset /np.cos(self.median_dec *deg2rad) decoffset = decoffset/3600.0 # ________________________________________________________________________________ # Find the footprint of the image @@ -1738,6 +1760,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, slice_no = None # Slice number dwave = None corner_coord = None + deg2rad = math.pi / 180.0 raoffset = 0.0 decoffset = 0.0 @@ -1750,6 +1773,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", raoffset, decoffset, filename) raoffset = raoffset/3600.0 # convert to degress + raoffset = raoffset /np.cos(self.median_dec *deg2rad) decoffset = decoffset/3600.0 # check if background sky matching as been done in mrs_imatch step From 44f416a68eab891ea989b6f542f2a3b633a6671c Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 29 Aug 2024 09:47:44 -0700 Subject: [PATCH 444/687] update --- docs/jwst/cube_build/arguments.rst | 30 ++++++++++++++++++------------ 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index ab329aa6ee..71810e5b76 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -131,6 +131,10 @@ A parameter only used for investigating which detector pixels contributed to a c To print information to the screeen about the x = 10, y = 20, z = 35 spaxel the parameter string value is '10 20 35'. .. _offsets: + +Creating an offset file +----------------------- + The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each input file in the spec3 assocation used to build the IFU cubes, there is a corresponding right ascension and declination offset, given arc seconds. @@ -144,17 +148,19 @@ provided in arcseconds. The cube building code will apply the ra offsets after d declination center of the IFU cube. Below `num` is the number of files. -import asdf -offsets = {} -offsets['filename'] = [] -offsets['raoffset'] = [] -offsets['decoffset'] = [] -for i in range(num): - offsets['filename'].append(file[i]) - offsets['raoffset'].append(ra_center1[i]) - offsets['decoffset'].append(dec_center1[i]) - -af = asdf.AsdfFile({'offsets':offsets}) -af.write_to('offsets.asdf') + +.. code-block:: python + + import asdf + offsets = {} + offsets['filename'] = [] + offsets['raoffset'] = [] + offsets['decoffset'] = [] + for i in range(num): + offsets['filename'].append(file[i]) + offsets['raoffset'].append(ra_center1[i]) + offsets['decoffset'].append(dec_center1[i]) + af = asdf.AsdfFile({'offsets':offsets}) + af.write_to('offsets.asdf') The offset asdf filename can be any name, but it must have the `asdf` extension. From e1fcac64db1352c0cb9d72c0d00f8886ce0ab80b Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 29 Aug 2024 10:03:26 -0700 Subject: [PATCH 445/687] fix docs --- docs/jwst/cube_build/arguments.rst | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 71810e5b76..3f318941db 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -91,11 +91,11 @@ The following arguments control the size and sampling characteristics of the out ``nspax_y`` The odd integer number of spaxels to use in the y dimension of the tangent plane. -``offset_list = [string]`` +``offset_list [string]`` The string contains the name of the file holding ra and dec offsets to apply to each input file. This file must be an asdf file and the it has a specific format. It is assumed the user has determined the ra and dec - offsets to apply to the data. For details on how to construct the format of the offset file see creating - :ref:`offsets` files. + offsets to apply to the data. For details on how to construct the format of the offset file see + :ref:`offsets`. ``coord_system [string]`` @@ -136,15 +136,14 @@ Creating an offset file ----------------------- The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each -input file in the spec3 assocation used to build the IFU cubes, there is a corresponding right ascension and declination offset, -given arc seconds. +input file in the spec3 assocation used to build the IFU cubes, the offset files needs to have a corresponding right ascension and declination offset given arc seconds. Below is an example of how to make an ASDF offset file. It is assumed the user has determined the -offsets to apply the data in each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary +offsets to apply to the data in each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary are `filenames`, `raoffset` and `decoffset`. The IFU cube building code expects this dictionary to hold the information for storing the file names and the associated ra and dec offsets. The file names should not contain the directory path. -It is assumed there exists a list of files, ra and dec offsets that are feed to this method. The ra and dec offsets -provided in arcseconds. The cube building code will apply the ra offsets after dividing by cos(crval2), where crval2 is the +It is assumed there exists a list of files, ra and dec offsets that are feed to this method. The ra and dec offsets need to be +in arcseconds. The cube building code will apply the ra offsets after dividing by cos(crval2), where crval2 is the declination center of the IFU cube. Below `num` is the number of files. From 984c0c7a2c39c33847890e90d71e995bba6b3d2d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 29 Aug 2024 10:05:12 -0700 Subject: [PATCH 446/687] update docs --- docs/jwst/cube_build/arguments.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 3f318941db..c7c06b0746 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -144,7 +144,7 @@ for storing the file names and the associated ra and dec offsets. The file names It is assumed there exists a list of files, ra and dec offsets that are feed to this method. The ra and dec offsets need to be in arcseconds. The cube building code will apply the ra offsets after dividing by cos(crval2), where crval2 is the -declination center of the IFU cube. +declination center of the IFU cube. The offset asdf filename can be any name, but it must have the `asdf` extension. Below `num` is the number of files. @@ -162,4 +162,4 @@ Below `num` is the number of files. af = asdf.AsdfFile({'offsets':offsets}) af.write_to('offsets.asdf') -The offset asdf filename can be any name, but it must have the `asdf` extension. + From afec305c902697c474f4004c981abd03fdd670b4 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 5 Sep 2024 06:48:53 -0700 Subject: [PATCH 447/687] added schema to cube_build --- jwst/cube_build/cube_build_step.py | 17 ++++++++------- jwst/cube_build/ifu_cube.py | 25 +++++++++++----------- jwst/cube_build/ifuoffset.schema.yaml | 30 +++++++++++++++++++++++++++ 3 files changed, 52 insertions(+), 20 deletions(-) create mode 100644 jwst/cube_build/ifuoffset.schema.yaml diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 5726c4af6c..3af0d8d6ea 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -41,7 +41,7 @@ class CubeBuildStep (Step): spec = """ channel = option('1','2','3','4','all',default='all') # Channel - band = option('short','medium','long','short-medium','short-long','medium-short', \ + band = option('short','medium','long','short-medium','short-long','medium-short', 'medium-long', 'long-short', 'long-medium','all',default='all') # Band grating = option('prism','g140m','g140h','g235m','g235h','g395m','g395h','all',default='all') # Grating filter = option('clear','f100lp','f070lp','f170lp','f290lp','all',default='all') # Filter @@ -65,7 +65,7 @@ class CubeBuildStep (Step): search_output_file = boolean(default=false) output_use_model = boolean(default=true) # Use filenames in the output models suffix = string(default='s3d') - offset_list = string(default=None) + offset_file = string(default=None) debug_spaxel = string(default='-1 -1 -1') # Default not used """ @@ -245,8 +245,8 @@ def process(self, input): # Each row in the offset list contain a filename, ra offset and dec offset. # The offset list is an asdf file. self.offsets = None - if self.offset_list is not None: - offsets = self.check_offset_list() + if self.offset_file is not None: + offsets = self.check_offset_file() if offsets is not None: self.offsets = offsets # ________________________________________________________________________________ @@ -546,7 +546,8 @@ def read_user_input(self): self.pars_input['grating'] = list(set(self.pars_input['grating'])) # ________________________________________________________________________________ - def check_offset_list(self): + + def check_offset_file(self): """Read in an optional ra and dec offsets for each file. Summary @@ -559,9 +560,11 @@ def check_offset_list(self): """ - check_asdf = asdf.util.get_file_type(asdf.generic_io.get_file(self.offset_list)) + af = asdf.open(self.offset_file, custom_schema = 'ifuoffset_schema.yaml') + + check_asdf = asdf.util.get_file_type(asdf.generic_io.get_file(self.offset_file)) if check_asdf == asdf.util.FileType.ASDF: - with asdf.open(self.offset_list) as af: + with asdf.open(self.offset_file) as af: offsets = af.tree['offsets'] format_failure = False diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 6755e9e2e4..ad27bad270 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1329,7 +1329,15 @@ def setup_ifucube_wcs(self): decs.append(m) self.median_dec = np.nanmedian(decs) - print('Median declination ', self.median_dec) + # fold in the median_dec information into ra offset + noffsets = len(self.offsets['raoffset']) + # convert ra and dec offsets to degrees and adjust ra offset for cos(dec) + for im in range(noffsets): + self.offsets['raoffset'][im] = (self.offsets['raoffset'][im]/3600.0)/np.cos(self.median_dec*deg2rad) + self.offsets['decoffset'][im] = self.offsets['decoffset'][im]/3600.0 + + print(self.offsets['raoffset']) + print(self.offsets['decoffset']) for k in range(n): lmin = 0.0 @@ -1347,11 +1355,6 @@ def setup_ifucube_wcs(self): index = self.offsets['filename'].index(filename) raoffset = self.offsets['raoffset'][index] decoffset = self.offsets['decoffset'][index] - log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", - raoffset, decoffset, filename) - raoffset = raoffset/3600.0 # convert to degress - raoffset = raoffset /np.cos(self.median_dec *deg2rad) - decoffset = decoffset/3600.0 # ________________________________________________________________________________ # Find the footprint of the image spectral_found = hasattr(input_model.meta.wcsinfo, 'spectral_region') @@ -1770,11 +1773,9 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, index = offsets['filename'].index(filename) raoffset = offsets['raoffset'][index] decoffset = offsets['decoffset'][index] - log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", - raoffset, decoffset, filename) - raoffset = raoffset/3600.0 # convert to degress - raoffset = raoffset /np.cos(self.median_dec *deg2rad) - decoffset = decoffset/3600.0 + log.info("Ra and dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", + raoffset*3600.0*np.cos(self.median_dec*deg2rad), + decoffset*3600.0, filename) # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been @@ -1897,8 +1898,6 @@ def map_nirspec_pixel_to_sky(self, input_model): decoffset = offsets['decoffset'][index] log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", raoffset, decoffset, filename) - raoffset = raoffset/3600.0 # convert to degress - decoffset = decoffset/3600.0 # initialize the ra,dec, and wavelength arrays # we will loop over slice_nos and fill in values diff --git a/jwst/cube_build/ifuoffset.schema.yaml b/jwst/cube_build/ifuoffset.schema.yaml new file mode 100644 index 0000000000..c003afcb64 --- /dev/null +++ b/jwst/cube_build/ifuoffset.schema.yaml @@ -0,0 +1,30 @@ +%YAML 1.1 +--- +$schema: "http://stsci.edu/schemas/asdf/asdf-schema-1.0.0" +id: "http://stsci.edu/schemas/yaml-schema/ifuoffset.schema" + +title: IFUoffset reference file model +type: object +properties: + meta: + type: object + properties: + units: + description: Units of the ra and dec offset values. + anyOf: + - type: string + - $ref: http://stsci.edu/schemas/asdf/unit/unit-1.0.0 + offsets: + description: dictionary defining offsets values + type: object + items: + type: object + properties: + filename: + type: string + raoffset: + type: number + decoffset: + type: number + +required: [meta, offsets] From 8e464b24f211599200914c63160dab0fa4fb1010 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 5 Sep 2024 07:13:21 -0700 Subject: [PATCH 448/687] trying to include offset yaml file --- jwst/cube_build/cube_build_step.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 3af0d8d6ea..79d69c765e 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -7,6 +7,7 @@ from jwst.lib.pipe_utils import match_nans_and_flags from . import cube_build +from . import ifuoffset.schema.yaml from . import ifu_cube from . import data_types import asdf @@ -41,7 +42,7 @@ class CubeBuildStep (Step): spec = """ channel = option('1','2','3','4','all',default='all') # Channel - band = option('short','medium','long','short-medium','short-long','medium-short', + band = option('short','medium','long','short-medium','short-long','medium-short', \ 'medium-long', 'long-short', 'long-medium','all',default='all') # Band grating = option('prism','g140m','g140h','g235m','g235h','g395m','g395h','all',default='all') # Grating filter = option('clear','f100lp','f070lp','f170lp','f290lp','all',default='all') # Filter From bb351f8fbb4c8110343ebb7288a35db9b42b0247 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Sun, 8 Sep 2024 13:46:47 -0700 Subject: [PATCH 449/687] corrected using a local schema file --- docs/jwst/cube_build/arguments.rst | 24 ++++++++----- jwst/cube_build/cube_build_step.py | 49 ++++++++++++--------------- jwst/cube_build/ifu_cube.py | 3 -- jwst/cube_build/ifuoffset.schema.yaml | 43 ++++++++++++----------- 4 files changed, 57 insertions(+), 62 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index c7c06b0746..4cfcc95ed1 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -151,15 +151,21 @@ Below `num` is the number of files. .. code-block:: python import asdf - offsets = {} - offsets['filename'] = [] - offsets['raoffset'] = [] - offsets['decoffset'] = [] + filename = [] + raoffset = [] + decoffset = [] for i in range(num): - offsets['filename'].append(file[i]) - offsets['raoffset'].append(ra_center1[i]) - offsets['decoffset'].append(dec_center1[i]) - af = asdf.AsdfFile({'offsets':offsets}) - af.write_to('offsets.asdf') + filename.append(file[i]) + raoffset.append(ra_center1[i]) + decoffset.append(dec_center1[i]) + + tree = { + "units": str(u.arcsec), + "filename": filename, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + af.write_to(input_dir + 'offsets.asdf') diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 79d69c765e..a9e121efc5 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -2,17 +2,16 @@ """ import time - from jwst.datamodels import ModelContainer from jwst.lib.pipe_utils import match_nans_and_flags from . import cube_build -from . import ifuoffset.schema.yaml from . import ifu_cube from . import data_types import asdf from ..assign_wcs.util import update_s_region_keyword from ..stpipe import Step, record_step_status +from pathlib import Path __all__ = ["CubeBuildStep"] @@ -547,7 +546,6 @@ def read_user_input(self): self.pars_input['grating'] = list(set(self.pars_input['grating'])) # ________________________________________________________________________________ - def check_offset_file(self): """Read in an optional ra and dec offsets for each file. @@ -561,36 +559,31 @@ def check_offset_file(self): """ - af = asdf.open(self.offset_file, custom_schema = 'ifuoffset_schema.yaml') - - check_asdf = asdf.util.get_file_type(asdf.generic_io.get_file(self.offset_file)) - if check_asdf == asdf.util.FileType.ASDF: - with asdf.open(self.offset_file) as af: - offsets = af.tree['offsets'] - - format_failure = False - # Currently the offset list has to have the following keys: filename, raoffset, decoffset - if 'filename' not in offsets.keys(): - self.log.warning('Filename is not listed in the offset list') - format_failure = True - if 'raoffset' not in offsets.keys(): - self.log.warning('raoffset is not listed in the offset list') - format_failure = True - if 'decoffset' not in offsets.keys(): - self.log.warning('decoffset is not listed in the offset list') - format_failure = True - if format_failure: - self.log.warning('Offset list does not have the correct format') - self.log.warning('No offsets are applied') + # validate the offset file using the schema file + DATA_PATH = Path(__file__).parent + af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') + + offset_filename = af['filename'] + offset_ra = af['raoffset'] + offset_dec = af['decoffset'] + offset_unit = af['units'] + + if offset_unit != 'arcsec': + self.log.error('Provide the offset units in units of arcsec ') + self.log.error('Turning off adjusting by offsets ') return None for model in self.input_models: file_check = model.meta.filename - if file_check in offsets['filename']: - ra = offsets['raoffset'] - dec = offsets['decoffset'] + if file_check in offset_filename: continue else: - self.log.info('File in assocation is not found in offset list list %s', file_check) + self.log.error('File in assocation is not found in offset list list %s', file_check) + self.log.error('Turning off adjusting by offsets') return None + offsets = {} + offsets['filename'] = offset_filename + offsets['raoffset'] = offset_ra + offsets['decoffset'] = offset_dec + return offsets diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index ad27bad270..1033c7a26c 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1335,9 +1335,6 @@ def setup_ifucube_wcs(self): for im in range(noffsets): self.offsets['raoffset'][im] = (self.offsets['raoffset'][im]/3600.0)/np.cos(self.median_dec*deg2rad) self.offsets['decoffset'][im] = self.offsets['decoffset'][im]/3600.0 - - print(self.offsets['raoffset']) - print(self.offsets['decoffset']) for k in range(n): lmin = 0.0 diff --git a/jwst/cube_build/ifuoffset.schema.yaml b/jwst/cube_build/ifuoffset.schema.yaml index c003afcb64..c2c7d40c6a 100644 --- a/jwst/cube_build/ifuoffset.schema.yaml +++ b/jwst/cube_build/ifuoffset.schema.yaml @@ -2,29 +2,28 @@ --- $schema: "http://stsci.edu/schemas/asdf/asdf-schema-1.0.0" id: "http://stsci.edu/schemas/yaml-schema/ifuoffset.schema" - title: IFUoffset reference file model type: object properties: - meta: - type: object - properties: - units: - description: Units of the ra and dec offset values. - anyOf: - - type: string - - $ref: http://stsci.edu/schemas/asdf/unit/unit-1.0.0 - offsets: - description: dictionary defining offsets values - type: object - items: - type: object - properties: - filename: - type: string - raoffset: - type: number - decoffset: - type: number + units: + description: Units of the ra and dec offset values. + anyOf: + - type: string + - $ref: http://stsci.edu/schemas/asdf/unit/unit-1.0.0 + filename: + description: list of filenames + type: array + items: + type: string + raoffset: + description: list of ra offsets + type: array + items: + type: number + decoffset: + descrition: list of dec offsets + type: array + items: + type: number -required: [meta, offsets] +required: [filename, raoffset, decoffset, units] From e20bd6a4adf62734d209a6037a49d8db07ea96bc Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 07:00:56 -0700 Subject: [PATCH 450/687] fix conflict --- jwst/cube_build/cube_build_step.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index a9e121efc5..4b2d6133c3 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -4,7 +4,6 @@ import time from jwst.datamodels import ModelContainer from jwst.lib.pipe_utils import match_nans_and_flags - from . import cube_build from . import ifu_cube from . import data_types @@ -237,7 +236,6 @@ def process(self, input): self.output_type = 'channel' self.pars_input['output_type'] = self.output_type self.log.info(f'Setting output type to: {self.output_type}') - # ________________________________________________________________________________ # If an offset file is provided do some basic checks on the file and its contents. # The offset list contains a matching list to the files in the association From 798a5f71c8dfb30abb24d5d6830cdb2c319464bc Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 11:15:44 -0700 Subject: [PATCH 451/687] added more checks for offset file --- jwst/cube_build/cube_build_step.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 4b2d6133c3..069af6bdbc 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -559,7 +559,12 @@ def check_offset_file(self): # validate the offset file using the schema file DATA_PATH = Path(__file__).parent - af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') + try: + af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') + except + self.log.error('Validation Error for offset file') + self.log.error('Turning off adjusting by offsets') + return None offset_filename = af['filename'] offset_ra = af['raoffset'] From a503c29af04e7782f4eaa17fb7469230abef0721 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 12:53:52 -0700 Subject: [PATCH 452/687] fix typo --- jwst/cube_build/cube_build_step.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 069af6bdbc..d5f36cdefb 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -561,7 +561,7 @@ def check_offset_file(self): DATA_PATH = Path(__file__).parent try: af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') - except + except: self.log.error('Validation Error for offset file') self.log.error('Turning off adjusting by offsets') return None From 0e7cc1b7882f1848229d7d7b7aa4bc9188b893d2 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 9 Sep 2024 14:43:17 -0700 Subject: [PATCH 453/687] attempt on unit test --- jwst/cube_build/tests/test_configuration.py | 78 ++++++++++++++++++++- 1 file changed, 77 insertions(+), 1 deletion(-) diff --git a/jwst/cube_build/tests/test_configuration.py b/jwst/cube_build/tests/test_configuration.py index e3783c1ca8..9619219e28 100644 --- a/jwst/cube_build/tests/test_configuration.py +++ b/jwst/cube_build/tests/test_configuration.py @@ -3,12 +3,15 @@ """ import pytest - +import asdf from stdatamodels.jwst import datamodels +import astropy.units as u +from jwst.cube_build import CubeBuildStep from jwst.cube_build import cube_build from jwst.cube_build import file_table + wcsinfo = { 'dec_ref': -0.00244536159612126, 'ra_ref': -0.00205553321270217, @@ -100,6 +103,23 @@ } +@pytest.fixture(scope='function') +def offset_file(): + """ Generate a offset file """ + + filename = ['test1.fits', 'test2.fits'] + raoffset = [0.0, 0.1] + decoffset = [0.0, 0.15] + tree = { + "units": str(u.arcsec), + "filename": filename, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + return af + + @pytest.fixture(scope='function') def miri_ifushort_short(): """ Generate a IFU image """ @@ -113,6 +133,30 @@ def miri_ifushort_short(): return input_model +@pytest.fixture(scope='function') +def miri_ifushort_short_2files(): + """ Generate a IFU image """ + + input_model1 = datamodels.IFUImageModel() + input_model1.meta.wcsinfo._instance.update(wcsinfo) + input_model1.meta.instrument._instance.update(mirifushort_short) + input_model1.meta.observation._instance.update(observation) + input_model1.meta.subarray._instance.update(subarray) + input_model1.meta.cal_step.assign_wcs = 'COMPLETE' + + input_model2 = datamodels.IFUImageModel() + input_model2.meta.wcsinfo._instance.update(wcsinfo) + input_model2.meta.instrument._instance.update(mirifushort_short) + input_model2.meta.observation._instance.update(observation) + input_model2.meta.subarray._instance.update(subarray) + input_model2.meta.cal_step.assign_wcs = 'COMPLETE' + + input_models = [] + input_models.append(input_model1) + input_models.append(input_model1) + return input_models + + @pytest.fixture(scope='function') def miri_full_coverage(): """ Generate a IFU images SHORT, LONG for all three bands """ @@ -467,3 +511,35 @@ def test_calspec3_config_nirspec_multi(tmp_cwd, nirspec_medium_coverage): assert cube_pars['1']['par1'] == ['g140m', 'g235m'] assert cube_pars['1']['par2'] == ['f100lp', 'f170lp'] + + +def test_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): + """ Test validation of the offset configuration""" + + pars_input = {} + pars_input['channel'] = [] + pars_input['subchannel'] = [] + pars_input['filter'] = [] + pars_input['grating'] = [] + output_type = 'band' + weighting = 'drizzle' + par_filename = 'None' + + pars = { + 'channel': pars_input['channel'], + 'subchannel': pars_input['subchannel'], + 'grating': pars_input['grating'], + 'filter': pars_input['filter'], + 'weighting': weighting, + 'output_type': output_type} + + cubeinfo = cube_build.CubeData( + miri_ifushort_short_2files, + par_filename, + **pars) + + offsets = CubeBuildStep.check_offset_file(cubeinfo.input_models) + + + # want to test that offsets is None with it fails or Dictionary when it works + From 9446cadfcaff919842ec5801feedea1462555302 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 11 Sep 2024 19:05:52 -0700 Subject: [PATCH 454/687] added a unit test --- jwst/cube_build/cube_build_step.py | 2 + jwst/cube_build/tests/test_configuration.py | 87 ++++++++++++++------- 2 files changed, 62 insertions(+), 27 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index d5f36cdefb..7a977d15b0 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -559,6 +559,7 @@ def check_offset_file(self): # validate the offset file using the schema file DATA_PATH = Path(__file__).parent + try: af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') except: @@ -589,4 +590,5 @@ def check_offset_file(self): offsets['raoffset'] = offset_ra offsets['decoffset'] = offset_dec + af.close() return offsets diff --git a/jwst/cube_build/tests/test_configuration.py b/jwst/cube_build/tests/test_configuration.py index 9619219e28..d155b7ac82 100644 --- a/jwst/cube_build/tests/test_configuration.py +++ b/jwst/cube_build/tests/test_configuration.py @@ -103,21 +103,46 @@ } -@pytest.fixture(scope='function') -def offset_file(): +@pytest.fixture(scope='module') +def offset_file(tmp_path_factory): """ Generate a offset file """ - filename = ['test1.fits', 'test2.fits'] + filename = tmp_path_factory.mktemp('offset') + filename = filename / 'offset.asdf' + + testfile = ['test1.fits', 'test2.fits'] raoffset = [0.0, 0.1] decoffset = [0.0, 0.15] tree = { "units": str(u.arcsec), - "filename": filename, + "filename": testfile, "raoffset": raoffset, "decoffset": decoffset } af = asdf.AsdfFile(tree) - return af + af.write_to(filename) + return filename + + +@pytest.fixture(scope='module') +def offset_file_arcmin(tmp_path_factory): + """ Generate a offset file with units = arcmin """ + + filename = tmp_path_factory.mktemp('offset_arcmin') + filename = filename / 'offset_arcmin.asdf' + + testfile = ['test1.fits', 'test2.fits'] + raoffset = [0.0, 0.1] + decoffset = [0.0, 0.15] + tree = { + "units": str(u.arcmin), + "filename": testfile, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + af.write_to(filename) + return filename @pytest.fixture(scope='function') @@ -143,6 +168,7 @@ def miri_ifushort_short_2files(): input_model1.meta.observation._instance.update(observation) input_model1.meta.subarray._instance.update(subarray) input_model1.meta.cal_step.assign_wcs = 'COMPLETE' + input_model1.meta.filename = 'test1.fits' input_model2 = datamodels.IFUImageModel() input_model2.meta.wcsinfo._instance.update(wcsinfo) @@ -150,10 +176,11 @@ def miri_ifushort_short_2files(): input_model2.meta.observation._instance.update(observation) input_model2.meta.subarray._instance.update(subarray) input_model2.meta.cal_step.assign_wcs = 'COMPLETE' + input_model2.meta.filename = 'test2.fits' input_models = [] input_models.append(input_model1) - input_models.append(input_model1) + input_models.append(input_model2) return input_models @@ -516,30 +543,36 @@ def test_calspec3_config_nirspec_multi(tmp_cwd, nirspec_medium_coverage): def test_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): """ Test validation of the offset configuration""" - pars_input = {} - pars_input['channel'] = [] - pars_input['subchannel'] = [] - pars_input['filter'] = [] - pars_input['grating'] = [] - output_type = 'band' - weighting = 'drizzle' - par_filename = 'None' + # first test that it is a valid asdf file and has what is needed + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + step.offset_file = offset_file + offsets = step.check_offset_file() + assert isinstance(offsets, dict) - pars = { - 'channel': pars_input['channel'], - 'subchannel': pars_input['subchannel'], - 'grating': pars_input['grating'], - 'filter': pars_input['filter'], - 'weighting': weighting, - 'output_type': output_type} +def test2_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): + """ Test validation of the offset configuration""" - cubeinfo = cube_build.CubeData( - miri_ifushort_short_2files, - par_filename, - **pars) + # Test changing one of the filenames so it is not in the list given + # in the offset_file + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + miri_ifushort_short_2files[0].meta.filename = 'test3.fits' + step.offset_file = offset_file + offsets = step.check_offset_file() + assert offsets is None - offsets = CubeBuildStep.check_offset_file(cubeinfo.input_models) +def test_offset_file_config2(tmp_cwd, miri_ifushort_short_2files, offset_file_arcmin): + """ Test validation of the offset configuration""" - # want to test that offsets is None with it fails or Dictionary when it works + # test is the if the user set the units to arcmins + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + step.offset_file = offset_file_arcmin + offsets = step.check_offset_file() + assert offsets is None From 0fd9af7c641021624a9e5ece29ef9789c7045343 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 12 Sep 2024 06:29:54 -0700 Subject: [PATCH 455/687] fix api for nirspec --- jwst/cube_build/ifu_cube.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 1033c7a26c..9545cf4451 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1866,7 +1866,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, return sky_result # ______________________________________________________________________ - def map_nirspec_pixel_to_sky(self, input_model): + def map_nirspec_pixel_to_sky(self, input_model, offsets): """Loop over a file and map the detector pixels to the output cube From 407c21653420fb595ef4dc41e75f43b2c196dd4d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 12 Sep 2024 10:35:35 -0700 Subject: [PATCH 456/687] added closing asdf file --- jwst/cube_build/cube_build_step.py | 3 +++ jwst/cube_build/tests/test_configuration.py | 3 ++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 7a977d15b0..08f00d2d52 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -567,6 +567,7 @@ def check_offset_file(self): self.log.error('Turning off adjusting by offsets') return None + offset_filename = af['filename'] offset_ra = af['raoffset'] offset_dec = af['decoffset'] @@ -575,6 +576,7 @@ def check_offset_file(self): if offset_unit != 'arcsec': self.log.error('Provide the offset units in units of arcsec ') self.log.error('Turning off adjusting by offsets ') + af.close() return None for model in self.input_models: @@ -584,6 +586,7 @@ def check_offset_file(self): else: self.log.error('File in assocation is not found in offset list list %s', file_check) self.log.error('Turning off adjusting by offsets') + af.close() return None offsets = {} offsets['filename'] = offset_filename diff --git a/jwst/cube_build/tests/test_configuration.py b/jwst/cube_build/tests/test_configuration.py index d155b7ac82..0ef5ab1e2e 100644 --- a/jwst/cube_build/tests/test_configuration.py +++ b/jwst/cube_build/tests/test_configuration.py @@ -121,6 +121,7 @@ def offset_file(tmp_path_factory): } af = asdf.AsdfFile(tree) af.write_to(filename) + af.close() return filename @@ -128,7 +129,7 @@ def offset_file(tmp_path_factory): def offset_file_arcmin(tmp_path_factory): """ Generate a offset file with units = arcmin """ - filename = tmp_path_factory.mktemp('offset_arcmin') + filename = tmp_path_factory.mktemp('offset') filename = filename / 'offset_arcmin.asdf' testfile = ['test1.fits', 'test2.fits'] From a27cdaaeb2e1a4ab5c49d6a6b2a94a0d8e757caf Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 12 Sep 2024 13:12:39 -0700 Subject: [PATCH 457/687] Update Change Log --- CHANGES.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index db515eadf3..32b9ab706d 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -82,6 +82,8 @@ cube_build - Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] +- Allow the user to provide ra and dec shifts to apply for each file to fine tune the WCS. [#JP-3364] + datamodels ---------- From c3dbb3de750fae436919652b58d47bfb598b8295 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 12 Sep 2024 17:28:36 -0700 Subject: [PATCH 458/687] updates to docs --- CHANGES.rst | 3 ++- docs/jwst/cube_build/arguments.rst | 17 +++++++++-------- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 32b9ab706d..4e8212be57 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -82,7 +82,8 @@ cube_build - Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] -- Allow the user to provide ra and dec shifts to apply for each file to fine tune the WCS. [#JP-3364] +- Allow the user to provide ra and dec shifts to apply for each file to fine + tune the WCS. [#JP-3364] datamodels ---------- diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 4cfcc95ed1..633cdd68f8 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -91,7 +91,7 @@ The following arguments control the size and sampling characteristics of the out ``nspax_y`` The odd integer number of spaxels to use in the y dimension of the tangent plane. -``offset_list [string]`` +``offset_file [string]`` The string contains the name of the file holding ra and dec offsets to apply to each input file. This file must be an asdf file and the it has a specific format. It is assumed the user has determined the ra and dec offsets to apply to the data. For details on how to construct the format of the offset file see @@ -138,19 +138,19 @@ Creating an offset file The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each input file in the spec3 assocation used to build the IFU cubes, the offset files needs to have a corresponding right ascension and declination offset given arc seconds. Below is an example of how to make an ASDF offset file. It is assumed the user has determined the -offsets to apply to the data in each file. The offsets are stored in a python dictionary, `offsets`. The items of this dictionary -are `filenames`, `raoffset` and `decoffset`. The IFU cube building code expects this dictionary to hold the information -for storing the file names and the associated ra and dec offsets. The file names should not contain the directory path. +offsets to apply to the data in each file. The offsets information is stored in three lists: + `filenames`, `raoffset` and `decoffset`. The units of the ra and dec offsets + are required to be in the offset set file and only the unit, `arcsec`, is allowed. The file names should +not contain the directory path. The offset asdf filename can be any name, but it must have the `asdf` extension. -It is assumed there exists a list of files, ra and dec offsets that are feed to this method. The ra and dec offsets need to be -in arcseconds. The cube building code will apply the ra offsets after dividing by cos(crval2), where crval2 is the -declination center of the IFU cube. The offset asdf filename can be any name, but it must have the `asdf` extension. Below `num` is the number of files. .. code-block:: python import asdf + import astropy.units as u + filename = [] raoffset = [] decoffset = [] @@ -166,6 +166,7 @@ Below `num` is the number of files. "decoffset": decoffset } af = asdf.AsdfFile(tree) - af.write_to(input_dir + 'offsets.asdf') + af.write_to(input_dir + 'offsets.asdf') + af.close() From d9183228d259fa9628f240c589a7e166b714fcf5 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 13 Sep 2024 08:28:59 -0700 Subject: [PATCH 459/687] update docs --- docs/jwst/cube_build/arguments.rst | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 633cdd68f8..abd08926b1 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -136,13 +136,12 @@ Creating an offset file ----------------------- The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each -input file in the spec3 assocation used to build the IFU cubes, the offset files needs to have a corresponding right ascension and declination offset given arc seconds. +input file in the spec3 assocation used to build the IFU cubes, the offset files needs to have a corresponding right ascension and declination offset given arc seconds. Below is an example of how to make an ASDF offset file. It is assumed the user has determined the offsets to apply to the data in each file. The offsets information is stored in three lists: - `filenames`, `raoffset` and `decoffset`. The units of the ra and dec offsets - are required to be in the offset set file and only the unit, `arcsec`, is allowed. The file names should +`filenames`, `raoffset` and `decoffset`. The units of the ra and dec offsets +are required to be in the offset file and only the unit, `arcsec`, is allowed. The file names should not contain the directory path. The offset asdf filename can be any name, but it must have the `asdf` extension. - Below `num` is the number of files. From 7cba5e4fc4ac84cbdd9fc116e58f7881449e4c6d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 13 Sep 2024 08:56:26 -0700 Subject: [PATCH 460/687] fixed nirspec offset issue --- jwst/cube_build/ifu_cube.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 9545cf4451..56e5b72f4c 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1560,7 +1560,7 @@ def map_detector_to_outputframe(self, this_par1, (x, y, ra, dec, wave_all, slice_no_all, dwave_all, corner_coord_all) = sky_result elif self.instrument == 'NIRSPEC': - sky_result = self.map_nirspec_pixel_to_sky(input_model) + sky_result = self.map_nirspec_pixel_to_sky(input_model, offsets) (x, y, ra, dec, wave_all, slice_no_all, dwave_all, corner_coord_all) = sky_result # ______________________________________________________________________________ From add9f8b31f0942ead094b217962d4ca291bdefcd Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 13 Sep 2024 09:12:39 -0700 Subject: [PATCH 461/687] CHANGES.rst --- CHANGES.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.rst b/CHANGES.rst index 4e8212be57..bc92289e1e 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -83,7 +83,7 @@ cube_build - Replaced deep copies of NIRSpec WCS objects within most loops. [#8793] - Allow the user to provide ra and dec shifts to apply for each file to fine - tune the WCS. [#JP-3364] + tune the WCS. [#8720] datamodels ---------- From 30370f6d5a8ca18cec74206ca7b239d0f11f6d31 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 13 Sep 2024 20:18:51 -0700 Subject: [PATCH 462/687] update docs --- docs/jwst/cube_build/arguments.rst | 40 ++++++++++++++++++++++++------ 1 file changed, 32 insertions(+), 8 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index abd08926b1..88e0e2c8dd 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -92,9 +92,8 @@ The following arguments control the size and sampling characteristics of the out The odd integer number of spaxels to use in the y dimension of the tangent plane. ``offset_file [string]`` - The string contains the name of the file holding ra and dec offsets to apply to each input file. This file - must be an asdf file and the it has a specific format. It is assumed the user has determined the ra and dec - offsets to apply to the data. For details on how to construct the format of the offset file see + The string contains the name of the file holding RA and Dec offsets to apply to each input file. This file + must be an asdf file with a specific format. For details on how to construct the offset file see :ref:`offsets`. @@ -135,13 +134,15 @@ A parameter only used for investigating which detector pixels contributed to a c Creating an offset file ----------------------- -The offset file is an ASDF formated file :``_ stands for "Advanced Scientific Data. For each -input file in the spec3 assocation used to build the IFU cubes, the offset files needs to have a corresponding right ascension and declination offset given arc seconds. +The offset file is an ASDF formatted file :``_ stands for "Advanced Scientific Data. +For each input file in the spec3 association used to build the IFU cubes, the offset file needs to have a +corresponding right ascension and declination offset given in arc seconds. + Below is an example of how to make an ASDF offset file. It is assumed the user has determined the offsets to apply to the data in each file. The offsets information is stored in three lists: -`filenames`, `raoffset` and `decoffset`. The units of the ra and dec offsets +`filenames`, `raoffset` and `decoffset`. The units of the Ra and Dec offsets are required to be in the offset file and only the unit, `arcsec`, is allowed. The file names should -not contain the directory path. The offset asdf filename can be any name, but it must have the `asdf` extension. +not contain the directory path. The offset file can have any name, but it must have the `asdf` extension. Below `num` is the number of files. @@ -165,7 +166,30 @@ Below `num` is the number of files. "decoffset": decoffset } af = asdf.AsdfFile(tree) - af.write_to(input_dir + 'offsets.asdf') + af.write_to('offsets.asdf') af.close() +Or lets say there a small number of files in the assocations. The filename, raoffset and decoffset can be set +in the code. For example, if there are three files in the assocation the offset file can be created as follows: + +.. code-block:: python + + import asdf + import astropy.units as u + + filename = ['file1.fits', 'file2.fits', 'file3.fits'] + raoffset = [0.0, -1.0, 1.0] + decoffset = [0.0, 1.0, -1.0] + + tree = { + "units": str(u.arcsec), + "filename": filename, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + af.write_to('offsets.asdf') + af.close() + + From c356158df3e11bbc480f3b4c62c0ef2e1c8802c1 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 13 Sep 2024 20:22:36 -0700 Subject: [PATCH 463/687] update docs --- docs/jwst/cube_build/arguments.rst | 31 +++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 88e0e2c8dd..b66b53b419 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -143,21 +143,16 @@ offsets to apply to the data in each file. The offsets information is stored in `filenames`, `raoffset` and `decoffset`. The units of the Ra and Dec offsets are required to be in the offset file and only the unit, `arcsec`, is allowed. The file names should not contain the directory path. The offset file can have any name, but it must have the `asdf` extension. -Below `num` is the number of files. - +An example of making an offset file for an association containing three files is: .. code-block:: python import asdf import astropy.units as u - filename = [] - raoffset = [] - decoffset = [] - for i in range(num): - filename.append(file[i]) - raoffset.append(ra_center1[i]) - decoffset.append(dec_center1[i]) + filename = ['file1.fits', 'file2.fits', 'file3.fits'] + raoffset = [0.0, -1.0, 1.0] + decoffset = [0.0, 1.0, -1.0] tree = { "units": str(u.arcsec), @@ -168,19 +163,25 @@ Below `num` is the number of files. af = asdf.AsdfFile(tree) af.write_to('offsets.asdf') af.close() + + -Or lets say there a small number of files in the assocations. The filename, raoffset and decoffset can be set -in the code. For example, if there are three files in the assocation the offset file can be created as follows: +An exmaple of making an offset file for `num` files is + .. code-block:: python import asdf import astropy.units as u - filename = ['file1.fits', 'file2.fits', 'file3.fits'] - raoffset = [0.0, -1.0, 1.0] - decoffset = [0.0, 1.0, -1.0] + filename = [] + raoffset = [] + decoffset = [] + for i in range(num): + filename.append(file[i]) + raoffset.append(ra_center1[i]) + decoffset.append(dec_center1[i]) tree = { "units": str(u.arcsec), @@ -191,5 +192,5 @@ in the code. For example, if there are three files in the assocation the offset af = asdf.AsdfFile(tree) af.write_to('offsets.asdf') af.close() - + From cc66ae9e2fb3303c77e4325fd85e16f9a0d87e8d Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 16 Sep 2024 15:37:46 -0700 Subject: [PATCH 464/687] changed how ra offset values are determined --- docs/jwst/cube_build/arguments.rst | 48 ++-- jwst/cube_build/cube_build_step.py | 21 +- jwst/cube_build/ifu_cube.py | 158 +++++++---- jwst/cube_build/tests/test_configuration.py | 109 -------- jwst/cube_build/tests/test_offset.py | 290 ++++++++++++++++++++ 5 files changed, 450 insertions(+), 176 deletions(-) create mode 100644 jwst/cube_build/tests/test_offset.py diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index b66b53b419..74a1f72e46 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -167,30 +167,44 @@ An example of making an offset file for an association containing three files is -An exmaple of making an offset file for `num` files is +An example of making an offset file for `num` files +where the user has set up list called `file` containing the `num` filenames. +The cooresponding Ra and Dec offsets, both containing num values, are stored in lists called, +`ra_offset` and `dec_offset` .. code-block:: python import asdf import astropy.units as u - - filename = [] - raoffset = [] - decoffset = [] - for i in range(num): - filename.append(file[i]) - raoffset.append(ra_center1[i]) - decoffset.append(dec_center1[i]) - - tree = { - "units": str(u.arcsec), - "filename": filename, - "raoffset": raoffset, - "decoffset": decoffset + def create_offset_asdf(files, ra_offset, dec_offset): + + filename = [] + raoffset = [] + decoffset = [] + num = len(files) + for i in range(num): + filename.append(files[i]) + raoffset.append(ra_offset[i]) + decoffset.append(dec_offset[i]) + + tree = { + "units": str(u.arcsec), + "filename": filename, + "raoffset": raoffset, + "decoffset": decoffset } af = asdf.AsdfFile(tree) - af.write_to('offsets.asdf') - af.close() + af.write_to( 'offsets.asdf') +Set up the lists and call the above function: + +.. code-block:: python + + files = ['test1.fits', 'test2.fits', 'test3.fits'] + ra_offset = [0.1, 0.12, 0.13] + dec_offset = [0.14, 0.15, 0.16] + create_offset_asdf(files, ra_offset, dec_offset) + + diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 08f00d2d52..3341b6ba18 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -243,6 +243,7 @@ def process(self, input): # Each row in the offset list contain a filename, ra offset and dec offset. # The offset list is an asdf file. self.offsets = None + if self.offset_file is not None: offsets = self.check_offset_file() if offsets is not None: @@ -578,7 +579,8 @@ def check_offset_file(self): self.log.error('Turning off adjusting by offsets ') af.close() return None - + + # check that all the file names in input_model are in the offset filename for model in self.input_models: file_check = model.meta.filename if file_check in offset_filename: @@ -588,10 +590,25 @@ def check_offset_file(self): self.log.error('Turning off adjusting by offsets') af.close() return None + # check that all the lists have the same length + len_file = len(offset_filename) + len_ra = len(offset_ra) + len_dec = len(offset_dec) + if (len_file != len_ra or len_ra != len_dec or len_file != len_dec): + self.log.error('The offset file does not have the same number of values for filename, offset_ra, offset_dec') + self.log.error('Turning off adjusting by offsets') + af.close() + return None + + # The offset file has passed tests so set the offset dictionary offsets = {} offsets['filename'] = offset_filename offsets['raoffset'] = offset_ra offsets['decoffset'] = offset_dec - + n = len(offsets['raoffset']) + # convert to degrees + for i in range(n): + offsets['raoffset'][i] = offsets['raoffset'][i]/3600.0 + offsets['decoffset'][i] = offsets['decoffset'][i]/3600.0 af.close() return offsets diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 56e5b72f4c..e325424407 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -14,6 +14,8 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags from stdatamodels.jwst.transforms.models import _toindex +from astropy import units +from astropy.coordinates import SkyCoord from ..model_blender import blendmeta from ..assign_wcs import pointing @@ -602,7 +604,8 @@ def build_ifucube(self): # ________________________________________________________________________________ # loop over the files that cover the spectral range the cube is for - input_model = datamodels.open(input) + #input_model = datamodels.open(input) + input_model = input self.input_models_this_cube.append(input_model.copy()) # set up input_model to be first file used to copy in basic header info # to ifucube meta data @@ -1235,7 +1238,6 @@ def setup_ifucube_wcs(self): # _____________________________________________________________________________ self.cdelt1 = self.spatial_size self.cdelt2 = self.spatial_size - deg2rad = math.pi / 180.0 if self.linear_wavelength: self.cdelt3 = self.spectral_size @@ -1315,7 +1317,6 @@ def setup_ifucube_wcs(self): # find the median center declination if we have an offset file if self.offsets is not None: - decs = [] for k in range(n): input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] input_model = datamodels.open(input_file) @@ -1325,17 +1326,7 @@ def setup_ifucube_wcs(self): cb2 = float(s[6]) cb3 = float(s[8]) cb4 = float(s[10]) - m = (cb1 + cb2 + cb3 + cb4)/4 - decs.append(m) - - self.median_dec = np.nanmedian(decs) - # fold in the median_dec information into ra offset - noffsets = len(self.offsets['raoffset']) - # convert ra and dec offsets to degrees and adjust ra offset for cos(dec) - for im in range(noffsets): - self.offsets['raoffset'][im] = (self.offsets['raoffset'][im]/3600.0)/np.cos(self.median_dec*deg2rad) - self.offsets['decoffset'][im] = self.offsets['decoffset'][im]/3600.0 - + for k in range(n): lmin = 0.0 lmax = 0.0 @@ -1408,15 +1399,38 @@ def setup_ifucube_wcs(self): ca1, cb1, ca2, cb2, ca3, cb3, ca4, cb4, lmin, lmax = ch_corners # now append this model spatial and spectral corner - corner_a.append(ca1 + raoffset) - corner_a.append(ca2 + raoffset) - corner_a.append(ca3 + raoffset) - corner_a.append(ca4 + raoffset) - - corner_b.append(cb1 + decoffset) - corner_b.append(cb2 + decoffset) - corner_b.append(cb3 + decoffset) - corner_b.append(cb4 + decoffset) + if self.offsets is not None: + c1 = SkyCoord(ca1, cb1, unit='deg') + c2 = SkyCoord(ca2, cb2, unit='deg') + c3 = SkyCoord(ca3, cb3, unit='deg') + c4 = SkyCoord(ca4, cb4, unit='deg') + raoffset = raoffset* units.deg + decoffset = decoffset* units.deg + + c1_new = c1.spherical_offsets_by(raoffset, decoffset) + c2_new = c2.spherical_offsets_by(raoffset, decoffset) + c3_new = c3.spherical_offsets_by(raoffset, decoffset) + c4_new = c4.spherical_offsets_by(raoffset, decoffset) + ca1 = c1_new.ra.value + cb1 = c1_new.dec.value + + ca2 = c2_new.ra.value + cb2 = c2_new.dec.value + + ca3 = c3_new.ra.value + cb3 = c3_new.dec.value + + ca4 = c4_new.ra.value + cb4 = c4_new.dec.value + corner_a.append(ca1) + corner_a.append(ca2) + corner_a.append(ca3) + corner_a.append(ca4) + + corner_b.append(cb1) + corner_b.append(cb2) + corner_b.append(cb3) + corner_b.append(cb4) lambda_min.append(lmin) lambda_max.append(lmax) @@ -1760,7 +1774,6 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, slice_no = None # Slice number dwave = None corner_coord = None - deg2rad = math.pi / 180.0 raoffset = 0.0 decoffset = 0.0 @@ -1771,9 +1784,10 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, raoffset = offsets['raoffset'][index] decoffset = offsets['decoffset'][index] log.info("Ra and dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", - raoffset*3600.0*np.cos(self.median_dec*deg2rad), + raoffset*3600.0, decoffset*3600.0, filename) - + raoffset = raoffset* units.deg + decoffset = decoffset* units.deg # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been # subtracted - subtract it. @@ -1809,8 +1823,13 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, # if self.coord_system == 'skyalign' or self.coord_system == 'ifualign': ra, dec, wave = input_model.meta.wcs(x, y) - ra = ra + raoffset - dec = dec + decoffset + + if offsets is not None: + c1 = SkyCoord(ra, dec, unit='deg') + c1_new = c1.spherical_offsets_by(raoffset, decoffset) + ra = c1_new.ra.value + dec = c1_new.dec.value + valid1 = ~np.isnan(ra) ra = ra[valid1] dec = dec[valid1] @@ -1850,15 +1869,28 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, input_model.meta.wcs.output_frame, alpha2, beta - dbeta * pixfrac / 2., wave) - ra1 = ra1 + raoffset - ra2 = ra2 + raoffset - ra3 = ra3 + raoffset - ra4 = ra4 + raoffset + if offsets is not None: + c1 = SkyCoord(ra1, dec1, unit='deg') + c2 = SkyCoord(ra2, dec2, unit='deg') + c3 = SkyCoord(ra3, dec3, unit='deg') + c4 = SkyCoord(ra4, dec4, unit='deg') + + c1_new = c1.spherical_offsets_by(raoffset, decoffset) + c2_new = c2.spherical_offsets_by(raoffset, decoffset) + c3_new = c3.spherical_offsets_by(raoffset, decoffset) + c4_new = c4.spherical_offsets_by(raoffset, decoffset) + ra1 = c1_new.ra.value + dec1 = c1_new.dec.value + + ra2 = c2_new.ra.value + dec2 = c2_new.dec.value - dec1 = dec1 + decoffset - dec2 = dec2 + decoffset - dec3 = dec3 + decoffset - dec4 = dec4 + decoffset + ra3 = c3_new.ra.value + dec3 = c3_new.dec.value + + ra4 = c4_new.ra.value + dec4 = c4_new.dec.value + corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] @@ -1895,7 +1927,8 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): decoffset = offsets['decoffset'][index] log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", raoffset, decoffset, filename) - + raoffset = raoffset* units.deg + decoffset = decoffset* units.deg # initialize the ra,dec, and wavelength arrays # we will loop over slice_nos and fill in values # the flag_det will be set when a slice_no pixel is filled in @@ -2043,21 +2076,50 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): valid_data = np.where(flag_det == 1) y, x = valid_data - - ra = ra_det[valid_data] + raoffset - dec = dec_det[valid_data] + decoffset + wave = lam_det[valid_data] slice_no = slice_det[valid_data] dwave = dwave_det[valid_data] - ra1 = ra1_det[valid_data] + raoffset - ra2 = ra2_det[valid_data] + raoffset - ra3 = ra3_det[valid_data] + raoffset - ra4 = ra4_det[valid_data] + raoffset - dec1 = dec1_det[valid_data] + decoffset - dec2 = dec2_det[valid_data] + decoffset - dec3 = dec3_det[valid_data] + decoffset - dec4 = dec4_det[valid_data] + decoffset + + ra = ra_det[valid_data] + dec = dec_det[valid_data] + ra1 = ra1_det[valid_data] + ra2 = ra2_det[valid_data] + ra3 = ra3_det[valid_data] + ra4 = ra4_det[valid_data] + dec1 = dec1_det[valid_data] + dec2 = dec2_det[valid_data] + dec3 = dec3_det[valid_data] + dec4 = dec4_det[valid_data] + + if offsets is not None: + c1 = SkyCoord(ra, dec, unit='deg') + + c1_new = c1.spherical_offsets_by(raoffset, decoffset) + ra = c1_new.ra.value + dec = c1_new.dec.value + + c1 = SkyCoord(ra1, dec1, unit='deg') + c2 = SkyCoord(ra2, dec2, unit='deg') + c3 = SkyCoord(ra3, dec3, unit='deg') + c4 = SkyCoord(ra4, dec4, unit='deg') + c1_new = c1.spherical_offsets_by(raoffset, decoffset) + c2_new = c2.spherical_offsets_by(raoffset, decoffset) + c3_new = c3.spherical_offsets_by(raoffset, decoffset) + c4_new = c4.spherical_offsets_by(raoffset, decoffset) + ra1 = c1_new.ra.value + dec1 = c1_new.dec.value + + ra2= c2_new.ra.value + dec2 = c2_new.dec.value + + ra3 = c3_new.ra.value + dec3 = c3_new.dec.value + + ra4= c4_new.ra.value + dec4 = c4_new.dec.value + corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) return sky_result diff --git a/jwst/cube_build/tests/test_configuration.py b/jwst/cube_build/tests/test_configuration.py index 0ef5ab1e2e..9a9eee11db 100644 --- a/jwst/cube_build/tests/test_configuration.py +++ b/jwst/cube_build/tests/test_configuration.py @@ -102,50 +102,6 @@ 'ystart': 1 } - -@pytest.fixture(scope='module') -def offset_file(tmp_path_factory): - """ Generate a offset file """ - - filename = tmp_path_factory.mktemp('offset') - filename = filename / 'offset.asdf' - - testfile = ['test1.fits', 'test2.fits'] - raoffset = [0.0, 0.1] - decoffset = [0.0, 0.15] - tree = { - "units": str(u.arcsec), - "filename": testfile, - "raoffset": raoffset, - "decoffset": decoffset - } - af = asdf.AsdfFile(tree) - af.write_to(filename) - af.close() - return filename - - -@pytest.fixture(scope='module') -def offset_file_arcmin(tmp_path_factory): - """ Generate a offset file with units = arcmin """ - - filename = tmp_path_factory.mktemp('offset') - filename = filename / 'offset_arcmin.asdf' - - testfile = ['test1.fits', 'test2.fits'] - raoffset = [0.0, 0.1] - decoffset = [0.0, 0.15] - tree = { - "units": str(u.arcmin), - "filename": testfile, - "raoffset": raoffset, - "decoffset": decoffset - } - af = asdf.AsdfFile(tree) - af.write_to(filename) - return filename - - @pytest.fixture(scope='function') def miri_ifushort_short(): """ Generate a IFU image """ @@ -158,33 +114,6 @@ def miri_ifushort_short(): input_model.meta.cal_step.assign_wcs = 'COMPLETE' return input_model - -@pytest.fixture(scope='function') -def miri_ifushort_short_2files(): - """ Generate a IFU image """ - - input_model1 = datamodels.IFUImageModel() - input_model1.meta.wcsinfo._instance.update(wcsinfo) - input_model1.meta.instrument._instance.update(mirifushort_short) - input_model1.meta.observation._instance.update(observation) - input_model1.meta.subarray._instance.update(subarray) - input_model1.meta.cal_step.assign_wcs = 'COMPLETE' - input_model1.meta.filename = 'test1.fits' - - input_model2 = datamodels.IFUImageModel() - input_model2.meta.wcsinfo._instance.update(wcsinfo) - input_model2.meta.instrument._instance.update(mirifushort_short) - input_model2.meta.observation._instance.update(observation) - input_model2.meta.subarray._instance.update(subarray) - input_model2.meta.cal_step.assign_wcs = 'COMPLETE' - input_model2.meta.filename = 'test2.fits' - - input_models = [] - input_models.append(input_model1) - input_models.append(input_model2) - return input_models - - @pytest.fixture(scope='function') def miri_full_coverage(): """ Generate a IFU images SHORT, LONG for all three bands """ @@ -539,41 +468,3 @@ def test_calspec3_config_nirspec_multi(tmp_cwd, nirspec_medium_coverage): assert cube_pars['1']['par1'] == ['g140m', 'g235m'] assert cube_pars['1']['par2'] == ['f100lp', 'f170lp'] - - -def test_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): - """ Test validation of the offset configuration""" - - # first test that it is a valid asdf file and has what is needed - step = CubeBuildStep() - step.input_models = miri_ifushort_short_2files - - step.offset_file = offset_file - offsets = step.check_offset_file() - assert isinstance(offsets, dict) - -def test2_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): - """ Test validation of the offset configuration""" - - # Test changing one of the filenames so it is not in the list given - # in the offset_file - step = CubeBuildStep() - step.input_models = miri_ifushort_short_2files - - miri_ifushort_short_2files[0].meta.filename = 'test3.fits' - step.offset_file = offset_file - offsets = step.check_offset_file() - assert offsets is None - - -def test_offset_file_config2(tmp_cwd, miri_ifushort_short_2files, offset_file_arcmin): - """ Test validation of the offset configuration""" - - # test is the if the user set the units to arcmins - step = CubeBuildStep() - step.input_models = miri_ifushort_short_2files - - step.offset_file = offset_file_arcmin - offsets = step.check_offset_file() - assert offsets is None - diff --git a/jwst/cube_build/tests/test_offset.py b/jwst/cube_build/tests/test_offset.py new file mode 100644 index 0000000000..275764bba2 --- /dev/null +++ b/jwst/cube_build/tests/test_offset.py @@ -0,0 +1,290 @@ +""" +Unit test for Cube Build testing setting up configuration +""" + +import pytest +import sys +import math +import asdf +from stdatamodels.jwst import datamodels +import astropy.units as u +from gwcs import WCS +import numpy as np +from jwst.cube_build import CubeBuildStep +from jwst.cube_build import cube_build +from jwst.cube_build import ifu_cube +from jwst.cube_build import file_table +from jwst.cube_build import instrument_defaults +from jwst import assign_wcs + + +@pytest.fixture(scope='module') +def offset_file(tmp_path_factory): + """ Generate a offset file """ + + filename = tmp_path_factory.mktemp('offset') + filename = filename / 'offset.asdf' + + testfile = ['test1.fits', 'test2.fits'] + raoffset = [0.0, 0.1] + decoffset = [0.0, 0.15] + tree = { + "units": str(u.arcsec), + "filename": testfile, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + af.write_to(filename) + af.close() + return filename + + +@pytest.fixture(scope='module') +def offset_file_arcmin(tmp_path_factory): + """ Generate a offset file with units = arcmin """ + + filename = tmp_path_factory.mktemp('offset') + filename = filename / 'offset_arcmin.asdf' + + testfile = ['test1.fits', 'test2.fits'] + raoffset = [0.0, 0.1] + decoffset = [0.0, 0.15] + tree = { + "units": str(u.arcmin), + "filename": testfile, + "raoffset": raoffset, + "decoffset": decoffset + } + af = asdf.AsdfFile(tree) + af.write_to(filename) + return filename + +@pytest.fixture(scope='function') +def miri_ifushort_short_2files(): + """ Generate input model with 2 IFU images """ + + observation = { + 'date': '2019-01-01', + 'time': '17:00:00'} + + subarray = { + 'fastaxis': 1, + 'name': 'FULL', + 'slowaxis': 2, + 'xsize': 1032, + 'xstart': 1, + 'ysize': 1024, + 'ystart': 1 + } + + wcsinfo = { + 'dec_ref': 39.05036271706514, + 'ra_ref': 339.8149235604264 , + 'roll_ref': 217.25027556008598 , + 'v2_ref': -503.378, + 'v3_ref': -318.9992, + 'v3yangle': 0.0, + 'vparity': -1, + 's_region': 'POLYGON ICRS 339.813915797 39.049575409 339.816080118 39.049575409 339.816080118 39.051260090 339.813915797 39.051260090', + 'spectral_region': ([4.889451133245338, 8.781164838427532]) + } + + mirifushort_short = { + 'detector': 'MIRIFUSHORT', + 'channel': '12', + 'band': 'SHORT', + 'name': 'MIRI' + } + + input_model1 = datamodels.IFUImageModel() + input_model1.meta.exposure.type = 'MIR_MRS' + input_model1.meta.wcsinfo._instance.update(wcsinfo) + input_model1.meta.instrument._instance.update(mirifushort_short) + input_model1.meta.observation._instance.update(observation) + input_model1.meta.subarray._instance.update(subarray) + input_model1.meta.cal_step.assign_wcs = 'COMPLETE' + input_model1.meta.filename = 'test1.fits' + input_model1.data = np.random.random((1024, 1032)) + + input_model2 = datamodels.IFUImageModel() + input_model2.meta.exposure.type = 'MIR_MRS' + input_model2.meta.wcsinfo._instance.update(wcsinfo) + input_model2.meta.instrument._instance.update(mirifushort_short) + input_model2.meta.observation._instance.update(observation) + input_model2.meta.subarray._instance.update(subarray) + input_model2.meta.cal_step.assign_wcs = 'COMPLETE' + input_model2.meta.filename = 'test2.fits' + input_model2.data = np.random.random((1024, 1032)) + + input_models = [] + + step = assign_wcs.assign_wcs_step.AssignWcsStep() + refs = {} + for reftype in assign_wcs.assign_wcs_step.AssignWcsStep.reference_file_types: + refs[reftype] = step.get_reference_file(input_model1, reftype) + pipe = assign_wcs.miri.create_pipeline(input_model1, refs) + input_model1.meta.wcs = WCS(pipe) + + for reftype in assign_wcs.assign_wcs_step.AssignWcsStep.reference_file_types: + refs[reftype] = step.get_reference_file(input_model2, reftype) + pipe = assign_wcs.miri.create_pipeline(input_model2, refs) + input_model2.meta.wcs = WCS(pipe) + + input_models.append(input_model1) + input_models.append(input_model2) + return input_models + + +def test_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): + """ Test validation of the offset configuration""" + + # first test that it is a valid asdf file and has what is needed + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + step.offset_file = offset_file + offsets = step.check_offset_file() + assert isinstance(offsets, dict) + + +def test2_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): + """ Test validation of the offset configuration""" + + # Test changing one of the filenames so it is not in the list given + # in the offset_file + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + miri_ifushort_short_2files[0].meta.filename = 'test3.fits' + step.offset_file = offset_file + offsets = step.check_offset_file() + assert offsets is None + + +def test_offset_file_units(tmp_cwd, miri_ifushort_short_2files, offset_file_arcmin): + """ Test offsets are not used when units are arc minutes""" + + # test is the if the user set the units to arcmins + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + + step.offset_file = offset_file_arcmin + offsets = step.check_offset_file() + assert offsets is None + + +def test_read_offset_file(miri_ifushort_short_2files, offset_file): + """ Test offset file has been read in correctly""" + + step = CubeBuildStep() + step.input_models = miri_ifushort_short_2files + step.offset_file = offset_file + offsets = step.check_offset_file() + # Test that the offset file is read in and is a dictionary + assert isinstance(offsets, dict) + + pars_input = {} + pars_input['channel'] = [] + pars_input['subchannel'] = [] + pars_input['filter'] = [] + pars_input['grating'] = [] + weighting = 'drizzle' + output_type = 'multi' + single = False + par_filename = 'None' + + # set up pars needed for CubeData class + pars = { + 'channel': pars_input['channel'], + 'subchannel': pars_input['subchannel'], + 'grating': pars_input['grating'], + 'filter': pars_input['filter'], + 'weighting': weighting, + 'single': single, + 'output_type': output_type, + 'offset_file': offset_file} + + cubeinfo = cube_build.CubeData( + miri_ifushort_short_2files, + par_filename, + **pars) + + master_table = file_table.FileTable() + this_instrument = master_table.set_file_table( + cubeinfo.input_models) + + cubeinfo.instrument = this_instrument + cubeinfo.determine_band_coverage(master_table) + num_cubes, cube_pars = cubeinfo.number_cubes() + # test with output_type = mulit we get 1 cube + # test that cube info sets up the correct channels and band for data + assert num_cubes == 1 + assert cube_pars['1']['par1'] == ['1','2'] + assert cube_pars['1']['par2'] == ['short','short'] + + wave_min = 4.88 + wave_max = 8.78 + + # set up par for IFUCubeData CLASS + pars_cube = { + 'scalexy': 0.0, + 'scalew': 0.0, + 'interpolation': 'drizzle', + 'weighting': 'drizzle', + 'weight_power':None, + 'coord_system': 'skyalign', + 'ra_center': None, + 'dec_center': None, + 'cube_pa': None, + 'nspax_x': None, + 'nspax_y': None, + 'rois': None, + 'riow': None, + 'wavemin': wave_min, + 'wavemax': wave_max, + 'skip_dqflagging': False, + 'offsets': offsets, + 'debug_spaxel': '0 0 0'} + + pipeline = 3 + list_par1 = ['1','2'] + list_par2 = ['short','short'] + output_name_base = 'TEMP' + + instrument_info = instrument_defaults.InstrumentInfo() + + thiscube = ifu_cube.IFUCubeData( + pipeline, + miri_ifushort_short_2files, + output_name_base, + output_type, + this_instrument, + list_par1, + list_par2, + instrument_info, + master_table, + **pars_cube) + + thiscube.linear_wavelength = True + thiscube.spatial_size = 0.13 + thiscube.spectral_size = 0.001 + thiscube.setup_ifucube_wcs() + + # test the offset file was read in correctly + filename = ['test1.fits', 'test2.fits'] + raoffset = [0.0, 0.1] + decoffset = [0.0, 0.15] + + ravalues = thiscube.offsets['raoffset'] + decvalues = thiscube.offsets['decoffset'] + + assert thiscube.offsets['filename'] == filename + + assert math.isclose(ravalues[0]*3600.0, raoffset[0], abs_tol=0.0001) + assert math.isclose(ravalues[1]*3600.0, raoffset[1], abs_tol=0.0001) + assert math.isclose(decvalues[0]*3600.0, decoffset[0], abs_tol=0.0001) + assert math.isclose(decvalues[1]*3600.0, decoffset[1], abs_tol=0.0001) + + + From 9c0aaca6503351453d03e7ba0e69f3ff080aa0d9 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 17 Sep 2024 14:03:22 -0700 Subject: [PATCH 465/687] fix doc error --- docs/jwst/cube_build/arguments.rst | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 74a1f72e46..ca749d5d21 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -145,6 +145,7 @@ are required to be in the offset file and only the unit, `arcsec`, is allowed. T not contain the directory path. The offset file can have any name, but it must have the `asdf` extension. An example of making an offset file for an association containing three files is: + .. code-block:: python import asdf @@ -167,10 +168,10 @@ An example of making an offset file for an association containing three files is -An example of making an offset file for `num` files -where the user has set up list called `file` containing the `num` filenames. -The cooresponding Ra and Dec offsets, both containing num values, are stored in lists called, -`ra_offset` and `dec_offset` +Below is an example of making an offset file for `num` files. +The user has set up the `file` list containing the `num` filenames and the +cooresponding Ra and Dec offsets lists, both containing num values. In this example +these list are called `ra_offset` and `dec_offset` .. code-block:: python @@ -202,9 +203,9 @@ Set up the lists and call the above function: .. code-block:: python - files = ['test1.fits', 'test2.fits', 'test3.fits'] - ra_offset = [0.1, 0.12, 0.13] - dec_offset = [0.14, 0.15, 0.16] + files = ['test1.fits', 'test2.fits', 'test3.fits', 'test4,fits', 'test5.fits'] + ra_offset = [0.1, 0.12, 0.13, 0.11, 0.12] + dec_offset = [0.14, 0.15, 0.16, 0.01, 0.1] create_offset_asdf(files, ra_offset, dec_offset) From d5c6c7d86e150e21935747cbdbfd1dd94f864569 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 17 Sep 2024 15:10:22 -0700 Subject: [PATCH 466/687] update comments and doc --- docs/jwst/cube_build/arguments.rst | 1 - jwst/cube_build/ifu_cube.py | 29 +++++++++++++++-------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index ca749d5d21..90b3901bc8 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -167,7 +167,6 @@ An example of making an offset file for an association containing three files is - Below is an example of making an offset file for `num` files. The user has set up the `file` list containing the `num` filenames and the cooresponding Ra and Dec offsets lists, both containing num values. In this example diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index e325424407..54270a7481 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1316,16 +1316,16 @@ def setup_ifucube_wcs(self): log.debug('number of files %d', n) # find the median center declination if we have an offset file - if self.offsets is not None: - for k in range(n): - input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] - input_model = datamodels.open(input_file) - spatial_box = input_model.meta.wcsinfo.s_region - s = spatial_box.split(' ') - cb1 = float(s[4]) - cb2 = float(s[6]) - cb3 = float(s[8]) - cb4 = float(s[10]) + #if self.offsets is not None: + # for k in range(n): + # input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] + # input_model = datamodels.open(input_file) + # spatial_box = input_model.meta.wcsinfo.s_region + # s = spatial_box.split(' ') + # cb1 = float(s[4]) + # cb2 = float(s[6]) + # cb3 = float(s[8]) + # cb4 = float(s[10]) for k in range(n): lmin = 0.0 @@ -1824,6 +1824,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, # if self.coord_system == 'skyalign' or self.coord_system == 'ifualign': ra, dec, wave = input_model.meta.wcs(x, y) + # offset the central pixel if offsets is not None: c1 = SkyCoord(ra, dec, unit='deg') c1_new = c1.spherical_offsets_by(raoffset, decoffset) @@ -1869,6 +1870,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, input_model.meta.wcs.output_frame, alpha2, beta - dbeta * pixfrac / 2., wave) + # now offset the pixel corners if offsets is not None: c1 = SkyCoord(ra1, dec1, unit='deg') c2 = SkyCoord(ra2, dec2, unit='deg') @@ -1891,7 +1893,6 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, ra4 = c4_new.ra.value dec4 = c4_new.dec.value - corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) @@ -2081,7 +2082,6 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): slice_no = slice_det[valid_data] dwave = dwave_det[valid_data] - ra = ra_det[valid_data] dec = dec_det[valid_data] ra1 = ra1_det[valid_data] @@ -2093,13 +2093,14 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): dec3 = dec3_det[valid_data] dec4 = dec4_det[valid_data] - if offsets is not None: + if offsets is not None: + # central pixel c1 = SkyCoord(ra, dec, unit='deg') - c1_new = c1.spherical_offsets_by(raoffset, decoffset) ra = c1_new.ra.value dec = c1_new.dec.value + # pixel corners c1 = SkyCoord(ra1, dec1, unit='deg') c2 = SkyCoord(ra2, dec2, unit='deg') c3 = SkyCoord(ra3, dec3, unit='deg') From 78f702c3fde3143df7b9c87beff7c4755e015d61 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 17 Sep 2024 15:32:00 -0700 Subject: [PATCH 467/687] update docs and update log message --- docs/jwst/cube_build/arguments.rst | 8 ++++---- jwst/cube_build/ifu_cube.py | 7 ++++--- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 90b3901bc8..479a0253f6 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -168,10 +168,10 @@ An example of making an offset file for an association containing three files is Below is an example of making an offset file for `num` files. -The user has set up the `file` list containing the `num` filenames and the -cooresponding Ra and Dec offsets lists, both containing num values. In this example -these list are called `ra_offset` and `dec_offset` - +The user has set up three lists `file`, `ra_offset` and `dec_offset`. The `file` list +contains the filenames and the `ra_offset` and `dec_offset` contain the Ra and Dec offsets respectively. +In this example, all the list have five values. The units of the of Ra and Dec offsets are given in +the `units` value. .. code-block:: python diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 54270a7481..d2e73e44cf 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1783,7 +1783,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, index = offsets['filename'].index(filename) raoffset = offsets['raoffset'][index] decoffset = offsets['decoffset'][index] - log.info("Ra and dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", + log.info("Ra and Dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", raoffset*3600.0, decoffset*3600.0, filename) raoffset = raoffset* units.deg @@ -1926,8 +1926,9 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): index = offsets['filename'].index(filename) raoffset = offsets['raoffset'][index] decoffset = offsets['decoffset'][index] - log.info("Ra and dec offset (arc seconds) applied to file :%5.2f, %5.2f, %s", - raoffset, decoffset, filename) + log.info("Ra and Dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", + raoffset*3600.0, decoffset*3600.0, filename) + raoffset = raoffset* units.deg decoffset = decoffset* units.deg # initialize the ra,dec, and wavelength arrays From 32b4044a4968ebe59521ee3c8e825c16e6f15d89 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 17 Sep 2024 15:48:42 -0700 Subject: [PATCH 468/687] docs update --- docs/jwst/cube_build/arguments.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 479a0253f6..717095d38d 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -140,7 +140,7 @@ corresponding right ascension and declination offset given in arc seconds. Below is an example of how to make an ASDF offset file. It is assumed the user has determined the offsets to apply to the data in each file. The offsets information is stored in three lists: -`filenames`, `raoffset` and `decoffset`. The units of the Ra and Dec offsets +`filename`, `raoffset` and `decoffset`. The units of the Ra and Dec offsets are required to be in the offset file and only the unit, `arcsec`, is allowed. The file names should not contain the directory path. The offset file can have any name, but it must have the `asdf` extension. @@ -171,7 +171,7 @@ Below is an example of making an offset file for `num` files. The user has set up three lists `file`, `ra_offset` and `dec_offset`. The `file` list contains the filenames and the `ra_offset` and `dec_offset` contain the Ra and Dec offsets respectively. In this example, all the list have five values. The units of the of Ra and Dec offsets are given in -the `units` value. +the `units` value and this value must be arc seconds. .. code-block:: python From 4e665cb885c5e28ce47a371fb888dae030981823 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 18 Sep 2024 07:38:50 -0700 Subject: [PATCH 469/687] removed openning and closing model --- jwst/cube_build/ifu_cube.py | 22 ++-------------------- 1 file changed, 2 insertions(+), 20 deletions(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index d2e73e44cf..5d3802d1ae 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -600,12 +600,10 @@ def build_ifucube(self): for ib in range(number_bands): this_par1 = self.list_par1[ib] this_par2 = self.list_par2[ib] - for input in self.master_table.FileMap[self.instrument][this_par1][this_par2]: + for input_model in self.master_table.FileMap[self.instrument][this_par1][this_par2]: # ________________________________________________________________________________ # loop over the files that cover the spectral range the cube is for - #input_model = datamodels.open(input) - input_model = input self.input_models_this_cube.append(input_model.copy()) # set up input_model to be first file used to copy in basic header info # to ifucube meta data @@ -777,8 +775,6 @@ def build_ifucube(self): result = None del spaxel_flux, spaxel_weight, spaxel_var, spaxel_iflux, result k = k + 1 - input_model.close() - del input_model # _______________________________________________________________________ # done looping over files @@ -1315,24 +1311,11 @@ def setup_ifucube_wcs(self): n = len(self.master_table.FileMap[self.instrument][this_a][this_b]) log.debug('number of files %d', n) - # find the median center declination if we have an offset file - #if self.offsets is not None: - # for k in range(n): - # input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] - # input_model = datamodels.open(input_file) - # spatial_box = input_model.meta.wcsinfo.s_region - # s = spatial_box.split(' ') - # cb1 = float(s[4]) - # cb2 = float(s[6]) - # cb3 = float(s[8]) - # cb4 = float(s[10]) - for k in range(n): lmin = 0.0 lmax = 0.0 + input_model = self.master_table.FileMap[self.instrument][this_a][this_b][k] - input_file = self.master_table.FileMap[self.instrument][this_a][this_b][k] - input_model = datamodels.open(input_file) # ________________________________________________________________________________ # If offsets are provided. Pull in ra and dec offsets. raoffset = 0.0 @@ -1434,7 +1417,6 @@ def setup_ifucube_wcs(self): lambda_min.append(lmin) lambda_max.append(lmax) - input_model.close() # ________________________________________________________________________________ # done looping over files determine final size of cube corner_a = np.array(corner_a) From 299b68caa1a180a9a99efeaf25d3719d878cabc4 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 18 Sep 2024 16:14:00 -0700 Subject: [PATCH 470/687] various updates from review --- docs/jwst/cube_build/arguments.rst | 41 -------- jwst/cube_build/cube_build_step.py | 34 +++---- jwst/cube_build/ifu_cube.py | 138 +++++++++----------------- jwst/cube_build/ifuoffset.schema.yaml | 5 +- jwst/cube_build/tests/test_offset.py | 20 ++-- 5 files changed, 69 insertions(+), 169 deletions(-) diff --git a/docs/jwst/cube_build/arguments.rst b/docs/jwst/cube_build/arguments.rst index 717095d38d..8d2bfa3053 100644 --- a/docs/jwst/cube_build/arguments.rst +++ b/docs/jwst/cube_build/arguments.rst @@ -167,44 +167,3 @@ An example of making an offset file for an association containing three files is -Below is an example of making an offset file for `num` files. -The user has set up three lists `file`, `ra_offset` and `dec_offset`. The `file` list -contains the filenames and the `ra_offset` and `dec_offset` contain the Ra and Dec offsets respectively. -In this example, all the list have five values. The units of the of Ra and Dec offsets are given in -the `units` value and this value must be arc seconds. - -.. code-block:: python - - import asdf - import astropy.units as u - def create_offset_asdf(files, ra_offset, dec_offset): - - filename = [] - raoffset = [] - decoffset = [] - num = len(files) - for i in range(num): - filename.append(files[i]) - raoffset.append(ra_offset[i]) - decoffset.append(dec_offset[i]) - - tree = { - "units": str(u.arcsec), - "filename": filename, - "raoffset": raoffset, - "decoffset": decoffset - } - af = asdf.AsdfFile(tree) - af.write_to( 'offsets.asdf') - - -Set up the lists and call the above function: - -.. code-block:: python - - files = ['test1.fits', 'test2.fits', 'test3.fits', 'test4,fits', 'test5.fits'] - ra_offset = [0.1, 0.12, 0.13, 0.11, 0.12] - dec_offset = [0.14, 0.15, 0.16, 0.01, 0.1] - create_offset_asdf(files, ra_offset, dec_offset) - - diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 3341b6ba18..30da89f019 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -11,7 +11,7 @@ from ..assign_wcs.util import update_s_region_keyword from ..stpipe import Step, record_step_status from pathlib import Path - +from astropy import units __all__ = ["CubeBuildStep"] @@ -564,51 +564,41 @@ def check_offset_file(self): try: af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') except: - self.log.error('Validation Error for offset file') - self.log.error('Turning off adjusting by offsets') - return None + schema_message = ('Validation Error for offset file. Fix the offset file. \n' + \ + 'The offset file needs to have the same number of elements the filename, raoffset and decoffset lists.\n' +\ + 'The units need to provided and only arcsec is allowed.') + raise Exception(schema_message) offset_filename = af['filename'] offset_ra = af['raoffset'] offset_dec = af['decoffset'] - offset_unit = af['units'] - if offset_unit != 'arcsec': - self.log.error('Provide the offset units in units of arcsec ') - self.log.error('Turning off adjusting by offsets ') - af.close() - return None - # check that all the file names in input_model are in the offset filename for model in self.input_models: file_check = model.meta.filename if file_check in offset_filename: continue else: - self.log.error('File in assocation is not found in offset list list %s', file_check) - self.log.error('Turning off adjusting by offsets') af.close() - return None + raise Exception('Error in offset file. A file in assocation is not found in offset list list %s', file_check) + # check that all the lists have the same length len_file = len(offset_filename) len_ra = len(offset_ra) len_dec = len(offset_dec) if (len_file != len_ra or len_ra != len_dec or len_file != len_dec): - self.log.error('The offset file does not have the same number of values for filename, offset_ra, offset_dec') - self.log.error('Turning off adjusting by offsets') af.close() - return None + raise Exception('Offset file error. The offset file does not have the same number of values for filename, offset_ra, offset_dec') + + offset_ra = offset_ra* units.arcsec + offset_dec = offset_dec* units.arcsec # The offset file has passed tests so set the offset dictionary offsets = {} offsets['filename'] = offset_filename offsets['raoffset'] = offset_ra offsets['decoffset'] = offset_dec - n = len(offsets['raoffset']) - # convert to degrees - for i in range(n): - offsets['raoffset'][i] = offsets['raoffset'][i]/3600.0 - offsets['decoffset'][i] = offsets['decoffset'][i]/3600.0 + af.close() return offsets diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 5d3802d1ae..c94b41fa69 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -8,14 +8,13 @@ import math from astropy.stats import circmean -from astropy import units as u from gwcs import wcstools from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags from stdatamodels.jwst.transforms.models import _toindex -from astropy import units from astropy.coordinates import SkyCoord +from astropy import units as u from ..model_blender import blendmeta from ..assign_wcs import pointing @@ -116,7 +115,6 @@ def __init__(self, self.naxis3 = None self.cdelt3_normal = None self.rot_angle = None # rotation angle between Ra-Dec and IFU local instrument plane - self.median_dec = None self.a_min = 0 self.a_max = 0 @@ -1322,10 +1320,7 @@ def setup_ifucube_wcs(self): decoffset = 0.0 # pull out ra dec offset if it exists if self.offsets is not None: - filename = input_model.meta.filename - index = self.offsets['filename'].index(filename) - raoffset = self.offsets['raoffset'][index] - decoffset = self.offsets['decoffset'][index] + raoffset, decoffset = self.find_ra_dec_offset(input_model.meta.filename) # ________________________________________________________________________________ # Find the footprint of the image spectral_found = hasattr(input_model.meta.wcsinfo, 'spectral_region') @@ -1383,28 +1378,11 @@ def setup_ifucube_wcs(self): # now append this model spatial and spectral corner if self.offsets is not None: - c1 = SkyCoord(ca1, cb1, unit='deg') - c2 = SkyCoord(ca2, cb2, unit='deg') - c3 = SkyCoord(ca3, cb3, unit='deg') - c4 = SkyCoord(ca4, cb4, unit='deg') - raoffset = raoffset* units.deg - decoffset = decoffset* units.deg - - c1_new = c1.spherical_offsets_by(raoffset, decoffset) - c2_new = c2.spherical_offsets_by(raoffset, decoffset) - c3_new = c3.spherical_offsets_by(raoffset, decoffset) - c4_new = c4.spherical_offsets_by(raoffset, decoffset) - ca1 = c1_new.ra.value - cb1 = c1_new.dec.value - - ca2 = c2_new.ra.value - cb2 = c2_new.dec.value - - ca3 = c3_new.ra.value - cb3 = c3_new.dec.value - - ca4 = c4_new.ra.value - cb4 = c4_new.dec.value + ca1, cb1 = self.offset_coord(ca1, cb1, raoffset, decoffset) + ca2, cb2 = self.offset_coord(ca2, cb2, raoffset, decoffset) + ca3, cb3 = self.offset_coord(ca3, cb3, raoffset, decoffset) + ca4, cb4 = self.offset_coord(ca4, cb4, raoffset, decoffset) + corner_a.append(ca1) corner_a.append(ca2) corner_a.append(ca3) @@ -1761,15 +1739,11 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, decoffset = 0.0 # pull out ra dec offset if it exists if offsets is not None: - filename = input_model.meta.filename - index = offsets['filename'].index(filename) - raoffset = offsets['raoffset'][index] - decoffset = offsets['decoffset'][index] + raoffset, decoffset = self.find_ra_dec_offset(input_model.meta.filename) log.info("Ra and Dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", - raoffset*3600.0, - decoffset*3600.0, filename) - raoffset = raoffset* units.deg - decoffset = decoffset* units.deg + raoffset.value, + decoffset.value, input_model.meta.filename) + # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been # subtracted - subtract it. @@ -1808,10 +1782,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, # offset the central pixel if offsets is not None: - c1 = SkyCoord(ra, dec, unit='deg') - c1_new = c1.spherical_offsets_by(raoffset, decoffset) - ra = c1_new.ra.value - dec = c1_new.dec.value + ra, dec = self.offset_coord(ra, dec, raoffset, decoffset) valid1 = ~np.isnan(ra) ra = ra[valid1] @@ -1854,27 +1825,11 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, # now offset the pixel corners if offsets is not None: - c1 = SkyCoord(ra1, dec1, unit='deg') - c2 = SkyCoord(ra2, dec2, unit='deg') - c3 = SkyCoord(ra3, dec3, unit='deg') - c4 = SkyCoord(ra4, dec4, unit='deg') - - c1_new = c1.spherical_offsets_by(raoffset, decoffset) - c2_new = c2.spherical_offsets_by(raoffset, decoffset) - c3_new = c3.spherical_offsets_by(raoffset, decoffset) - c4_new = c4.spherical_offsets_by(raoffset, decoffset) - ra1 = c1_new.ra.value - dec1 = c1_new.dec.value - - ra2 = c2_new.ra.value - dec2 = c2_new.dec.value - - ra3 = c3_new.ra.value - dec3 = c3_new.dec.value - - ra4 = c4_new.ra.value - dec4 = c4_new.dec.value - + ra1, dec1 = self.offset_coord(ra1, dec1, raoffset, decoffset) + ra2, dec2 = self.offset_coord(ra2, dec2, raoffset, decoffset) + ra3, dec3 = self.offset_coord(ra3, dec3, raoffset, decoffset) + ra4, dec4 = self.offset_coord(ra4, dec4, raoffset, decoffset) + corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) @@ -1904,15 +1859,10 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): decoffset = 0.0 # pull out ra dec offset if it exists if offsets is not None: - filename = input_model.meta.filename - index = offsets['filename'].index(filename) - raoffset = offsets['raoffset'][index] - decoffset = offsets['decoffset'][index] + raoffset, decoffset = self.find_ra_dec_offset(input_model.meta.filename) log.info("Ra and Dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", - raoffset*3600.0, decoffset*3600.0, filename) + raoffset.value, decoffset.value, input_model.meta.filename) - raoffset = raoffset* units.deg - decoffset = decoffset* units.deg # initialize the ra,dec, and wavelength arrays # we will loop over slice_nos and fill in values # the flag_det will be set when a slice_no pixel is filled in @@ -2078,32 +2028,14 @@ def map_nirspec_pixel_to_sky(self, input_model, offsets): if offsets is not None: # central pixel - c1 = SkyCoord(ra, dec, unit='deg') - c1_new = c1.spherical_offsets_by(raoffset, decoffset) - ra = c1_new.ra.value - dec = c1_new.dec.value + ra, dec = self.offset_coord(ra, dec, raoffset, decoffset) # pixel corners - c1 = SkyCoord(ra1, dec1, unit='deg') - c2 = SkyCoord(ra2, dec2, unit='deg') - c3 = SkyCoord(ra3, dec3, unit='deg') - c4 = SkyCoord(ra4, dec4, unit='deg') - c1_new = c1.spherical_offsets_by(raoffset, decoffset) - c2_new = c2.spherical_offsets_by(raoffset, decoffset) - c3_new = c3.spherical_offsets_by(raoffset, decoffset) - c4_new = c4.spherical_offsets_by(raoffset, decoffset) - ra1 = c1_new.ra.value - dec1 = c1_new.dec.value - - ra2= c2_new.ra.value - dec2 = c2_new.dec.value - - ra3 = c3_new.ra.value - dec3 = c3_new.dec.value - - ra4= c4_new.ra.value - dec4 = c4_new.dec.value - + ra1, dec1 = self.offset_coord(ra1, dec1, raoffset, decoffset) + ra2, dec2 = self.offset_coord(ra2, dec2, raoffset, decoffset) + ra3, dec3 = self.offset_coord(ra3, dec3, raoffset, decoffset) + ra4, dec4 = self.offset_coord(ra4, dec4, raoffset, decoffset) + corner_coord = [ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4] sky_result = (x, y, ra, dec, wave, slice_no, dwave, corner_coord) return sky_result @@ -2504,7 +2436,27 @@ def blend_output_metadata(self, IFUCube): ], ) + # ******************************************************************************** + def find_ra_dec_offset(self, filename): + """ Match the filename in the offset list with input_model.meta.filename and return + the corresponding Ra and Dec offset + """ + + index = self.offsets['filename'].index(filename) + raoffset = self.offsets['raoffset'][index] + decoffset = self.offsets['decoffset'][index] + return raoffset, decoffset + + # ******************************************************************************** + def offset_coord(self, ra, dec, raoffset, decoffset): + coord = SkyCoord(ra, dec, unit='deg') + coord_new = coord.spherical_offsets_by(raoffset, decoffset) + + ra_new = coord_new.ra.value + dec_new = coord_new.dec.value + return ra_new, dec_new + class IncorrectInput(Exception): """ Raises an exception if input parameter, Interpolation, is set to area when more than one file is used to build the cube. diff --git a/jwst/cube_build/ifuoffset.schema.yaml b/jwst/cube_build/ifuoffset.schema.yaml index c2c7d40c6a..cd9d2737a4 100644 --- a/jwst/cube_build/ifuoffset.schema.yaml +++ b/jwst/cube_build/ifuoffset.schema.yaml @@ -7,9 +7,8 @@ type: object properties: units: description: Units of the ra and dec offset values. - anyOf: - - type: string - - $ref: http://stsci.edu/schemas/asdf/unit/unit-1.0.0 + type: string + enum: ['arcsec'] filename: description: list of filenames type: array diff --git a/jwst/cube_build/tests/test_offset.py b/jwst/cube_build/tests/test_offset.py index 275764bba2..58149a5bd5 100644 --- a/jwst/cube_build/tests/test_offset.py +++ b/jwst/cube_build/tests/test_offset.py @@ -158,8 +158,9 @@ def test2_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): miri_ifushort_short_2files[0].meta.filename = 'test3.fits' step.offset_file = offset_file - offsets = step.check_offset_file() - assert offsets is None + + with pytest.raises(Exception): + offsets = step.check_offset_file() def test_offset_file_units(tmp_cwd, miri_ifushort_short_2files, offset_file_arcmin): @@ -170,9 +171,8 @@ def test_offset_file_units(tmp_cwd, miri_ifushort_short_2files, offset_file_arcm step.input_models = miri_ifushort_short_2files step.offset_file = offset_file_arcmin - offsets = step.check_offset_file() - assert offsets is None - + with pytest.raises(Exception): + offsets = step.check_offset_file() def test_read_offset_file(miri_ifushort_short_2files, offset_file): """ Test offset file has been read in correctly""" @@ -276,15 +276,15 @@ def test_read_offset_file(miri_ifushort_short_2files, offset_file): raoffset = [0.0, 0.1] decoffset = [0.0, 0.15] - ravalues = thiscube.offsets['raoffset'] + ravalues = thiscube.offsets['raoffset'] decvalues = thiscube.offsets['decoffset'] assert thiscube.offsets['filename'] == filename - assert math.isclose(ravalues[0]*3600.0, raoffset[0], abs_tol=0.0001) - assert math.isclose(ravalues[1]*3600.0, raoffset[1], abs_tol=0.0001) - assert math.isclose(decvalues[0]*3600.0, decoffset[0], abs_tol=0.0001) - assert math.isclose(decvalues[1]*3600.0, decoffset[1], abs_tol=0.0001) + assert math.isclose(ravalues[0].value, raoffset[0], abs_tol=0.0001) + assert math.isclose(ravalues[1].value, raoffset[1], abs_tol=0.0001) + assert math.isclose(decvalues[0].value, decoffset[0], abs_tol=0.0001) + assert math.isclose(decvalues[1].value, decoffset[1], abs_tol=0.0001) From 3c51fca28cd7b24983c2e71ead92a7d66c489916 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 19 Sep 2024 09:39:21 -0700 Subject: [PATCH 471/687] update comments --- jwst/cube_build/cube_build_step.py | 17 ++++++++++------- jwst/cube_build/ifu_cube.py | 7 +++++-- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 30da89f019..33a349eb8c 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -64,7 +64,7 @@ class CubeBuildStep (Step): search_output_file = boolean(default=false) output_use_model = boolean(default=true) # Use filenames in the output models suffix = string(default='s3d') - offset_file = string(default=None) + offset_file = string(default=None) # Filename containing a list of Ra and Dec offsets to apply to files. debug_spaxel = string(default='-1 -1 -1') # Default not used """ @@ -239,7 +239,7 @@ def process(self, input): # ________________________________________________________________________________ # If an offset file is provided do some basic checks on the file and its contents. # The offset list contains a matching list to the files in the association -# used in calspec3 (or offline cube building). +# used in calspec3 (for offline cube building). # Each row in the offset list contain a filename, ra offset and dec offset. # The offset list is an asdf file. self.offsets = None @@ -546,15 +546,15 @@ def read_user_input(self): # ________________________________________________________________________________ def check_offset_file(self): - """Read in an optional ra and dec offsets for each file. + """Read in an optional ra and dec offset for each file. Summary ---------- Check that is file is asdf file. - check the file has the correct format: + Check the file has the correct format using an local schema file. + The schema file, ifuoffset.schema.yaml, is located in the jwst/cube_build directory. For each file in the input assocation check that there is a corresponding file in the offset file. - Also check that each file in the offset list contain a ra offset and dec offset. """ @@ -565,7 +565,8 @@ def check_offset_file(self): af = asdf.open(self.offset_file, custom_schema=DATA_PATH/'ifuoffset.schema.yaml') except: schema_message = ('Validation Error for offset file. Fix the offset file. \n' + \ - 'The offset file needs to have the same number of elements the filename, raoffset and decoffset lists.\n' +\ + 'The offset file needs to have the same number of elements ' + \ + 'in the three lists: filename, raoffset and decoffset.\n' +\ 'The units need to provided and only arcsec is allowed.') raise Exception(schema_message) @@ -573,7 +574,9 @@ def check_offset_file(self): offset_filename = af['filename'] offset_ra = af['raoffset'] offset_dec = af['decoffset'] - + # Note: + # af['units'] is checked by the schema validation. It must be arcsec or a validation error occurs. + # check that all the file names in input_model are in the offset filename for model in self.input_models: file_check = model.meta.filename diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index c94b41fa69..3512e03542 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -1741,8 +1741,7 @@ def map_miri_pixel_to_sky(self, input_model, this_par1, subtract_background, if offsets is not None: raoffset, decoffset = self.find_ra_dec_offset(input_model.meta.filename) log.info("Ra and Dec offset (arc seconds) applied to file :%8.6f, %8.6f, %s", - raoffset.value, - decoffset.value, input_model.meta.filename) + raoffset.value, decoffset.value, input_model.meta.filename) # check if background sky matching as been done in mrs_imatch step # If it has not been subtracted and the background has not been @@ -2449,6 +2448,10 @@ def find_ra_dec_offset(self, filename): # ******************************************************************************** def offset_coord(self, ra, dec, raoffset, decoffset): + """ Given an ra,dec and ra offset and dec offset, use astropy SkyCoord functions + to apply the offsets + """ + coord = SkyCoord(ra, dec, unit='deg') coord_new = coord.spherical_offsets_by(raoffset, decoffset) From 45d15f1f009d6f44dc46c7131943f23b2d6f9cec Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 19 Sep 2024 10:00:46 -0700 Subject: [PATCH 472/687] fix comment --- jwst/cube_build/cube_build_step.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 33a349eb8c..6ee50d3f1b 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -240,7 +240,6 @@ def process(self, input): # If an offset file is provided do some basic checks on the file and its contents. # The offset list contains a matching list to the files in the association # used in calspec3 (for offline cube building). -# Each row in the offset list contain a filename, ra offset and dec offset. # The offset list is an asdf file. self.offsets = None From 04cbf6846b7fecb0d15155e55b69e8d9c3c45109 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 19 Sep 2024 10:12:36 -0700 Subject: [PATCH 473/687] cube_build_step.py --- jwst/cube_build/cube_build_step.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/cube_build/cube_build_step.py b/jwst/cube_build/cube_build_step.py index 6ee50d3f1b..d9678cc7a4 100755 --- a/jwst/cube_build/cube_build_step.py +++ b/jwst/cube_build/cube_build_step.py @@ -583,7 +583,7 @@ def check_offset_file(self): continue else: af.close() - raise Exception('Error in offset file. A file in assocation is not found in offset list list %s', file_check) + raise ValueError('Error in offset file. A file in the assocation is not found in offset list %s', file_check) # check that all the lists have the same length len_file = len(offset_filename) @@ -591,7 +591,7 @@ def check_offset_file(self): len_dec = len(offset_dec) if (len_file != len_ra or len_ra != len_dec or len_file != len_dec): af.close() - raise Exception('Offset file error. The offset file does not have the same number of values for filename, offset_ra, offset_dec') + raise ValueError('The offset file does not have the same number of values for filename, raoffset, decoffset') offset_ra = offset_ra* units.arcsec offset_dec = offset_dec* units.arcsec From 8e8540eef554818134021356b1b7596b989479b5 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 19 Sep 2024 14:21:31 -0700 Subject: [PATCH 474/687] fix test test_offset.py ruff issue --- jwst/cube_build/tests/test_offset.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/jwst/cube_build/tests/test_offset.py b/jwst/cube_build/tests/test_offset.py index 58149a5bd5..e14da0fdf3 100644 --- a/jwst/cube_build/tests/test_offset.py +++ b/jwst/cube_build/tests/test_offset.py @@ -3,7 +3,6 @@ """ import pytest -import sys import math import asdf from stdatamodels.jwst import datamodels @@ -159,8 +158,8 @@ def test2_offset_file_config(tmp_cwd, miri_ifushort_short_2files, offset_file): miri_ifushort_short_2files[0].meta.filename = 'test3.fits' step.offset_file = offset_file - with pytest.raises(Exception): - offsets = step.check_offset_file() + with pytest.raises(ValueError): + step.check_offset_file() def test_offset_file_units(tmp_cwd, miri_ifushort_short_2files, offset_file_arcmin): @@ -172,7 +171,7 @@ def test_offset_file_units(tmp_cwd, miri_ifushort_short_2files, offset_file_arcm step.offset_file = offset_file_arcmin with pytest.raises(Exception): - offsets = step.check_offset_file() + step.check_offset_file() def test_read_offset_file(miri_ifushort_short_2files, offset_file): """ Test offset file has been read in correctly""" From dfa83a7ae0056922067e6f258220b52985834114 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 19 Sep 2024 14:25:11 -0700 Subject: [PATCH 475/687] fix test --- jwst/cube_build/tests/test_configuration.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/jwst/cube_build/tests/test_configuration.py b/jwst/cube_build/tests/test_configuration.py index 9a9eee11db..b98fa754fb 100644 --- a/jwst/cube_build/tests/test_configuration.py +++ b/jwst/cube_build/tests/test_configuration.py @@ -3,11 +3,8 @@ """ import pytest -import asdf from stdatamodels.jwst import datamodels -import astropy.units as u -from jwst.cube_build import CubeBuildStep from jwst.cube_build import cube_build from jwst.cube_build import file_table From 4d60e7a08b69edf3d01db9afcbbc5d0377dc1404 Mon Sep 17 00:00:00 2001 From: Maria Date: Fri, 20 Sep 2024 13:48:45 -0400 Subject: [PATCH 476/687] JP-3695: Clean up unnecessary copies (#8676) Co-authored-by: Tyler Pauly --- CHANGES.rst | 88 ++++++++++- docs/jwst/emicorr/arguments.rst | 3 - jwst/charge_migration/charge_migration.py | 11 +- .../charge_migration/charge_migration_step.py | 19 +-- jwst/dark_current/dark_current_step.py | 38 +++-- jwst/dq_init/dq_init_step.py | 27 ++-- jwst/dq_init/dq_initialization.py | 15 +- jwst/emicorr/emicorr.py | 66 ++++---- jwst/emicorr/emicorr_step.py | 32 ++-- jwst/emicorr/tests/test_emicorr.py | 8 +- jwst/firstframe/firstframe_step.py | 25 +-- jwst/firstframe/firstframe_sub.py | 12 +- jwst/firstframe/tests/test_firstframe.py | 5 +- jwst/gain_scale/gain_scale.py | 7 +- jwst/gain_scale/gain_scale_step.py | 17 +- jwst/gain_scale/tests/test_gain_scale.py | 3 +- jwst/group_scale/group_scale.py | 2 + jwst/group_scale/group_scale_step.py | 33 ++-- jwst/ipc/ipc_corr.py | 33 ++-- jwst/ipc/ipc_step.py | 23 +-- jwst/jump/jump.py | 33 ++-- jwst/jump/jump_step.py | 28 ++-- jwst/lastframe/lastframe_step.py | 24 +-- jwst/lastframe/lastframe_sub.py | 9 +- jwst/lastframe/tests/test_lastframe.py | 5 +- jwst/linearity/linearity.py | 15 +- jwst/linearity/linearity_step.py | 23 +-- jwst/persistence/persistence_step.py | 146 +++++++++--------- jwst/pipeline/calwebb_detector1.py | 2 +- jwst/ramp_fitting/ramp_fit_step.py | 47 +++--- jwst/refpix/irs2_subtract_reference.py | 21 ++- jwst/refpix/refpix_step.py | 42 ++--- jwst/regtest/test_miri_image.py | 35 +++++ jwst/reset/reset_step.py | 53 ++++--- jwst/reset/reset_sub.py | 21 ++- jwst/reset/tests/test_reset_sub.py | 5 +- jwst/rscd/rscd_step.py | 47 +++--- jwst/rscd/rscd_sub.py | 55 +++---- jwst/rscd/tests/test_rscd.py | 8 +- jwst/saturation/saturation.py | 48 +++--- jwst/saturation/saturation_step.py | 26 ++-- jwst/stpipe/core.py | 1 + jwst/superbias/bias_sub.py | 13 +- jwst/superbias/superbias_step.py | 22 +-- jwst/superbias/tests/test_bias_sub.py | 12 +- 45 files changed, 672 insertions(+), 536 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index f269d7a904..1a61f13086 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -63,6 +63,11 @@ calwebb_detector1 - Added the optional ``clean_flicker_noise`` step between ``jump`` and ``ramp_fit``. [#8669] +change_migration +---------------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + clean_flicker_noise ------------------- @@ -91,6 +96,11 @@ datamodels - Added `ModelLibrary` class to allow passing on-disk models between steps in the image3 pipeline. [#8683] +dark_current +------------ + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + documentation ------------- @@ -99,12 +109,24 @@ documentation - Updated description of association keyword `expname`: including path information in addition to the filename is discouraged, but allowed. [#8789] +dq_init +-------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + emicorr ------- - Fixed a bug where MIRI EMI correction step would return NaNs when it was unable to compute a correction. [#8675] +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +first_frame +----------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + flat_field ---------- @@ -113,6 +135,11 @@ flat_field - Replaced deep copies of NIRSpec WCS objects within most loops [#8793] +gain_scale +---------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + general ------- @@ -126,12 +153,37 @@ general - bump dependency to use ``stcal 1.9.0`` [#8808] +group_scale +----------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +ipc +--- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +jump +---- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + klip ---- - Allowed klip to ingest a single shifted 3-D PSF model instead of a 4-D structure containing one shifted PSF per science integration. [#8747] +lastframe +--------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +linearity +--------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + master_background ----------------- @@ -196,6 +248,11 @@ pathloss - Replaced deep copies of NIRSpec WCS objects within most loops [#8793] +persistence +----------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + photom ------ @@ -232,6 +289,18 @@ ramp_fitting - Updated the flow of the detector 1 pipeline when selecting the ``LIKELY`` algorithm for ramp fitting. The ramps must contain a minimum number of groups (4).[#8631] +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +refpix +------ + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + +regtest +------- + +- Added memory usage test for Detector1 pipeline. [#8676] + resample -------- @@ -245,6 +314,11 @@ resample - Ensure that NaNs and DO_NOT_USE flags match up in all input data before resampling. [#8557] +reset +----- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + resample_spec ------------- @@ -269,12 +343,19 @@ resample_spec - Ensure that NaNs and DO_NOT_USE flags match up in all input data before resampling. [#8557] +rscd +---- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + saturation ---------- - Add option for using the readout pattern information to improve saturation flagging in grouped data. [#8731] - + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + scripts ------- @@ -301,6 +382,11 @@ stpipe - Log jwst version at end of `Step.run`. [#8769] +superbias +--------- + +- Removed unnecessary copies, and created a single copy at step.py level. [#8676] + tso_photometry -------------- diff --git a/docs/jwst/emicorr/arguments.rst b/docs/jwst/emicorr/arguments.rst index bb0510fb14..b4000e8997 100644 --- a/docs/jwst/emicorr/arguments.rst +++ b/docs/jwst/emicorr/arguments.rst @@ -12,9 +12,6 @@ The ``emicorr`` step has the following step-specific arguments. If True, the reference wavelength will be scaled to the data's phase amplitude. -``--user_supplied_reffile`` (boolean, default=None) - This is to specify an ASDF-format user-created reference file. - ``--save_intermediate_results`` (string, default=False) This is a boolean flag to specify whether to write a step output file with the EMI correction, and a reference file with all the diff --git a/jwst/charge_migration/charge_migration.py b/jwst/charge_migration/charge_migration.py index ccfcc38366..aa5e909468 100644 --- a/jwst/charge_migration/charge_migration.py +++ b/jwst/charge_migration/charge_migration.py @@ -16,13 +16,13 @@ CHLO_DNU = CHLO + DNU -def charge_migration(input_model, signal_threshold): +def charge_migration(output_model, signal_threshold): """ Correct for chargemigration Parameters ---------- - input_model : `~jwst.datamodels.RampModel` + output_model : `~jwst.datamodels.RampModel` The input science data to be corrected signal_threshold : float @@ -35,11 +35,8 @@ def charge_migration(input_model, signal_threshold): DO_NOT_USE flags to groups exceeding signal_threshold """ - data = input_model.data - gdq = input_model.groupdq - - # Create the output model as a copy of the input - output_model = input_model.copy() + data = output_model.data + gdq = output_model.groupdq log.info('Using signal_threshold: %.2f', signal_threshold) diff --git a/jwst/charge_migration/charge_migration_step.py b/jwst/charge_migration/charge_migration_step.py index e788e4a182..a110e4610b 100755 --- a/jwst/charge_migration/charge_migration_step.py +++ b/jwst/charge_migration/charge_migration_step.py @@ -1,9 +1,8 @@ #! /usr/bin/env python import logging -from ..stpipe import Step - from stdatamodels.jwst import datamodels +from ..stpipe import Step from . import charge_migration @@ -25,22 +24,24 @@ class ChargeMigrationStep(Step): skip = boolean(default=True) """ - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: + if (input_model.data.shape[1] < 3): # skip step if only 1 or 2 groups/integration log.info('Too few groups per integration; skipping charge_migration') - - result = input_model - result.meta.cal_step.charge_migration = 'SKIPPED' - return result + input_model.meta.cal_step.charge_migration = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() # Retrieve the parameter value(s) signal_threshold = self.signal_threshold - result = charge_migration.charge_migration(input_model, signal_threshold) + result = charge_migration.charge_migration(result, signal_threshold) result.meta.cal_step.charge_migration = 'COMPLETE' return result diff --git a/jwst/dark_current/dark_current_step.py b/jwst/dark_current/dark_current_step.py index a7ae3c2761..0bf01cfdb8 100755 --- a/jwst/dark_current/dark_current_step.py +++ b/jwst/dark_current/dark_current_step.py @@ -23,10 +23,10 @@ class DarkCurrentStep(Step): reference_file_types = ['dark'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: # Get the name of the dark reference file to use self.dark_name = self.get_reference_file(input_model, 'dark') @@ -36,9 +36,11 @@ def process(self, input): if self.dark_name == 'N/A': self.log.warning('No DARK reference file found') self.log.warning('Dark current step will be skipped') - result = input_model.copy() - result.meta.cal_step.dark = 'SKIPPED' - return result + input_model.meta.cal_step.dark = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() # Create name for the intermediate dark, if desired. dark_output = self.dark_output @@ -49,7 +51,7 @@ def process(self, input): ) # Open the dark ref file data model - based on Instrument - instrument = input_model.meta.instrument.name + instrument = result.meta.instrument.name if instrument == 'MIRI': dark_model = datamodels.DarkMIRIModel(self.dark_name) else: @@ -58,20 +60,23 @@ def process(self, input): # Store user-defined average_dark_current in model, if provided # A user-defined value will take precedence over any value present # in dark reference file - self.set_average_dark_current(input_model, dark_model) + self.set_average_dark_current(result, dark_model) # Do the dark correction - result = dark_sub.do_correction( - input_model, dark_model, dark_output + correction = dark_sub.do_correction( + result, dark_model, dark_output ) - out_data, dark_data = result + out_data, dark_data = correction if dark_data is not None and dark_data.save: save_dark_data_as_dark_model(dark_data, dark_model, instrument) - dark_model.close() - out_ramp = dark_output_data_2_ramp_model(out_data, input_model) + out_ramp = dark_output_data_2_ramp_model(out_data, result) + + # Cleanup + del dark_model + del result return out_ramp @@ -140,7 +145,7 @@ def save_dark_data_as_dark_model(dark_data, dark_model, instrument): out_dark_model.close() -def dark_output_data_2_ramp_model(out_data, input_model): +def dark_output_data_2_ramp_model(out_data, out_model): """ Convert computed output data from the dark step to a RampModel. @@ -149,7 +154,7 @@ def dark_output_data_2_ramp_model(out_data, input_model): out_data: ScienceData Computed science data from the dark current step. - input_model: RampModel + out_model: RampModel The input ramp model from which to subtract the dark current. Return @@ -161,10 +166,9 @@ def dark_output_data_2_ramp_model(out_data, input_model): if out_data.cal_step == "SKIPPED": # If processing was skipped in the lower-level routines, # just return the unmodified input model - input_model.meta.cal_step.dark_sub = "SKIPPED" - return input_model + out_model.meta.cal_step.dark_sub = "SKIPPED" + return out_model else: - out_model = input_model.copy() out_model.meta.cal_step.dark_sub = out_data.cal_step out_model.data = out_data.data out_model.groupdq = out_data.groupdq diff --git a/jwst/dq_init/dq_init_step.py b/jwst/dq_init/dq_init_step.py index ead63fda7c..1f62970454 100644 --- a/jwst/dq_init/dq_init_step.py +++ b/jwst/dq_init/dq_init_step.py @@ -21,9 +21,11 @@ class DQInitStep(Step): class_alias = "dq_init" + spec = """ + """ reference_file_types = ['mask'] - def process(self, input): + def process(self, step_input): """Perform the dq_init calibration step Parameters @@ -39,20 +41,19 @@ def process(self, input): # Try to open the input as a regular RampModel try: - input_model = datamodels.RampModel(input) - + input_model = datamodels.RampModel(step_input) # Check to see if it's Guider raw data if input_model.meta.exposure.type in dq_initialization.guider_list: # Reopen as a GuiderRawModel input_model.close() - input_model = datamodels.GuiderRawModel(input) + input_model = datamodels.GuiderRawModel(step_input) self.log.info("Input opened as GuiderRawModel") except (TypeError, ValueError): # If the initial open attempt fails, # try to open as a GuiderRawModel try: - input_model = datamodels.GuiderRawModel(input) + input_model = datamodels.GuiderRawModel(step_input) self.log.info("Input opened as GuiderRawModel") except (TypeError, ValueError): self.log.error("Unexpected or unknown input model type") @@ -68,18 +69,20 @@ def process(self, input): if self.mask_filename == 'N/A': self.log.warning('No MASK reference file found') self.log.warning('DQ initialization step will be skipped') - result = input_model.copy() - result.meta.cal_step.dq_init = 'SKIPPED' - return result + input_model.meta.cal_step.dq_init = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() # Load the reference file mask_model = datamodels.MaskModel(self.mask_filename) # Apply the step - result = dq_initialization.correct_model(input_model, mask_model) + result = dq_initialization.correct_model(result, mask_model) - # Close the data models for the input and ref file - input_model.close() - mask_model.close() + # Cleanup + del mask_model + del input_model return result diff --git a/jwst/dq_init/dq_initialization.py b/jwst/dq_init/dq_initialization.py index d07c7e6794..9c67d16a35 100644 --- a/jwst/dq_init/dq_initialization.py +++ b/jwst/dq_init/dq_initialization.py @@ -38,12 +38,12 @@ def correct_model(input_model, mask_model): return output_model -def do_dqinit(input_model, mask_model): +def do_dqinit(output_model, mask_model): """Perform the dq_init step on a JWST datamodel Parameters ---------- - input_model : input JWST datamodel + output_model : input JWST datamodel The jwst datamodel to be corrected mask_model : mask datamodel @@ -56,10 +56,7 @@ def do_dqinit(input_model, mask_model): """ # Inflate empty DQ array, if necessary - check_dimensions(input_model) - - # Create output model as copy of input - output_model = input_model.copy() + check_dimensions(output_model) # Extract subarray from reference data, if necessary if reffile_utils.ref_matches_sci(output_model, mask_model): @@ -72,11 +69,11 @@ def do_dqinit(input_model, mask_model): mask_sub_model.close() # Set model-specific data quality in output - if input_model.meta.exposure.type in guider_list: - dq = np.bitwise_or(input_model.dq, mask_array) + if output_model.meta.exposure.type in guider_list: + dq = np.bitwise_or(output_model.dq, mask_array) output_model.dq = dq else: - dq = np.bitwise_or(input_model.pixeldq, mask_array) + dq = np.bitwise_or(output_model.pixeldq, mask_array) output_model.pixeldq = dq # Additionally, propagate mask DO_NOT_USE flags to groupdq to # ensure no ramps are fit to bad pixels. diff --git a/jwst/emicorr/emicorr.py b/jwst/emicorr/emicorr.py index 099d5e908f..3c714a1e7d 100644 --- a/jwst/emicorr/emicorr.py +++ b/jwst/emicorr/emicorr.py @@ -79,7 +79,6 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): will operate. Valid parameters include: save_intermediate_results - saves the output into a file and the reference file (if created on-the-fly) - user_supplied_reffile - reference file supplied by the user Returns ------- @@ -88,7 +87,6 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): """ save_intermediate_results = pars['save_intermediate_results'] - user_supplied_reffile = pars['user_supplied_reffile'] nints_to_phase = pars['nints_to_phase'] nbins = pars['nbins'] scale_reference = pars['scale_reference'] @@ -98,7 +96,6 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): output_model = apply_emicorr(input_model, emicorr_model, onthefly_corr_freq, save_onthefly_reffile, save_intermediate_results=save_intermediate_results, - user_supplied_reffile=user_supplied_reffile, nints_to_phase=nints_to_phase, nbins_all=nbins, scale_reference=scale_reference, @@ -108,11 +105,10 @@ def do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars): return output_model -def apply_emicorr(input_model, emicorr_model, +def apply_emicorr(output_model, emicorr_model, onthefly_corr_freq, save_onthefly_reffile, - save_intermediate_results=False, user_supplied_reffile=None, - nints_to_phase=None, nbins_all=None, scale_reference=True, - use_n_cycles=3): + save_intermediate_results=False, nints_to_phase=None, + nbins_all=None, scale_reference=True, use_n_cycles=3): """ -> NOTE: This is translated from IDL code fix_miri_emi.pro @@ -141,7 +137,7 @@ def apply_emicorr(input_model, emicorr_model, Parameters ---------- - input_model : `~jwst.datamodels.JwstDataModel` + output_model : `~jwst.datamodels.JwstDataModel` input science data model to be emi-corrected emicorr_model : `~jwst.datamodels.EmiModel` @@ -156,9 +152,6 @@ def apply_emicorr(input_model, emicorr_model, save_intermediate_results : bool Saves the output into a file and the reference file (if created on-the-fly) - user_supplied_reffile : str - Reference file supplied by the user - nints_to_phase : int Number of integrations to phase @@ -178,11 +171,13 @@ def apply_emicorr(input_model, emicorr_model, input science data model which has been emi-corrected """ # get the subarray case and other info - detector = input_model.meta.instrument.detector - subarray = input_model.meta.subarray.name - readpatt = input_model.meta.exposure.readpatt - xsize = input_model.meta.subarray.xsize # SUBSIZE1 keyword - xstart = input_model.meta.subarray.xstart # SUBSTRT1 keyword + detector = output_model.meta.instrument.detector + subarray = output_model.meta.subarray.name + readpatt = output_model.meta.exposure.readpatt + xsize = output_model.meta.subarray.xsize # SUBSIZE1 keyword + xstart = output_model.meta.subarray.xstart # SUBSTRT1 keyword + # get the number of samples, 10us sample times per pixel (1 for fastmode, 9 for slowmode) + nsamples = output_model.meta.exposure.nsamples # get the subarray case from either the ref file or set default values freqs_numbers = [] @@ -216,11 +211,7 @@ def apply_emicorr(input_model, emicorr_model, # no subarray or read pattern match found, print to log and skip correction return subname - # get the number of samples, 10us sample times per pixel (1 for fastmode, 9 for slowmode) - nsamples = input_model.meta.exposure.nsamples - # Initialize the output model as a copy of the input - output_model = input_model.copy() nints, ngroups, ny, nx = np.shape(output_model.data) # create the dictionary to store the frequencies and corresponding phase amplitudes @@ -235,8 +226,7 @@ def apply_emicorr(input_model, emicorr_model, log.info('Correcting for frequency: {} Hz ({} out of {})'.format(frequency, fi+1, len(freqs2correct))) # Read image data and set up some variables - orig_data = output_model.data - data = orig_data.copy() + data = output_model.data.copy() # Correspondance of array order in IDL # sz[0] = 4 in idl @@ -246,7 +236,7 @@ def apply_emicorr(input_model, emicorr_model, # sz[4] = nints nx4 = int(nx/4) - dd_all = np.ones((nints, ngroups, ny, nx4)) + dd_all = np.zeros((nints, ngroups, ny, nx4)) log.info('Subtracting self-superbias from each group of each integration and') # Calculate times of all pixels in the input integration, then use that to calculate @@ -287,7 +277,7 @@ def apply_emicorr(input_model, emicorr_model, # this number comes from the subarray definition (see subarray_cases dict above), but # calculate it from the input image header here just in case the subarray definitions # are not available to this routine. - colstop = int( xsize/4 + xstart - 1 ) + colstop = int(xsize/4 + xstart - 1) log.info('doing phase calculation per integration') for ninti in range(nints): @@ -301,7 +291,7 @@ def apply_emicorr(input_model, emicorr_model, # subtract source+sky from each frame of this ramp for ngroupi in range(ngroups): - data[ninti, ngroupi, ...] = orig_data[ninti, ngroupi, ...] - (s0 * ngroupi) + data[ninti, ngroupi, ...] = output_model.data[ninti, ngroupi, ...] - (s0 * ngroupi) # make a self-superbias m0 = minmed(data[ninti, 1:ngroups-1, :, :]) @@ -384,12 +374,12 @@ def apply_emicorr(input_model, emicorr_model, nb_over_nbins = [nb/nbins for nb in range(nbins)] nbp1_over_nbins = [(nb + 1)/nbins for nb in range(nbins)] # Construct a phase map and dd map for only the nints_to_phase - phase_temp = phaseall[0:nints_to_phase,:,:,:] - dd_temp = dd_all[0:nints_to_phase,:,:,:] + phase_temp = phaseall[0: nints_to_phase, :, :, :] + dd_temp = dd_all[0: nints_to_phase, :, :, :] for nb in range(nbins): u = np.where((phase_temp > nb_over_nbins[nb]) & (phase_temp <= nbp1_over_nbins[nb])) # calculate the sigma-clipped mean - dmean,_,_ = scs(dd_temp[u]) + dmean, _, _ = scs(dd_temp[u]) pa[nb] = dmean # amplitude in this bin pa -= np.median(pa) @@ -441,32 +431,32 @@ def apply_emicorr(input_model, emicorr_model, lut = lut_reference if save_intermediate_results and save_onthefly_reffile is not None: - freq_pa_dict['frequencies'][frequency_name] = {'frequency' : frequency, - 'phase_amplitudes' : pa} + freq_pa_dict['frequencies'][frequency_name] = {'frequency': frequency, + 'phase_amplitudes': pa} log.info('Creating phased-matched noise model to subtract from data') # This is the phase matched noise model to subtract from each pixel of the input image dd_noise = lut[(phaseall * period_in_pixels).astype(int)] # Interleave (straight copy) into 4 amps - noise = np.ones((nints, ngroups, ny, nx)) # same size as input data + noise = np.zeros((nints, ngroups, ny, nx)) # same size as input data noise_x = np.arange(nx4) * 4 for k in range(4): noise[:, :, :, noise_x + k] = dd_noise # Safety catch; anywhere the noise value is not finite, set it to zero - noise[~np.isfinite(noise)] = 0 + noise[~np.isfinite(noise)] = 0.0 # Subtract EMI noise from the input data log.info('Subtracting EMI noise from data') - corr_data = orig_data - noise - output_model.data = corr_data + output_model.data = output_model.data - noise # clean up del data del dd_all del times_this_int del phaseall + del noise if save_intermediate_results and save_onthefly_reffile is not None: if 'FAST' in readpatt: @@ -477,7 +467,8 @@ def apply_emicorr(input_model, emicorr_model, on_the_fly_subarr_case[subarray] = { 'rowclocks': rowclocks, 'frameclocks': frameclocks, - 'freqs': freqs_dict } + 'freqs': freqs_dict + } freq_pa_dict['subarray_cases'] = on_the_fly_subarr_case mk_reffile(freq_pa_dict, save_onthefly_reffile) @@ -628,7 +619,7 @@ def get_subarcase(subarray_cases, subarray, readpatt, detector): else: if "SLOW" in readpatt and "SLOW" in item and detector in item: frequencies.append(val) - elif "FAST" in readpatt and "FAST" in item: + elif "FAST" in readpatt and "FAST" in item: frequencies.append(val) if subname is not None and rowclocks is not None and frameclocks is not None and frequencies is not None: break @@ -670,6 +661,7 @@ def get_frequency_info(freqs_names_vals, frequency_name): break return freq_number, phase_amplitudes + def rebin(arr, newshape): """Rebin an array to a new shape. @@ -718,5 +710,3 @@ def mk_reffile(freq_pa_dict, emicorr_ref_filename): emicorr_model.save(emicorr_ref_filename) emicorr_model.close() log.info('On-the-fly reference file written as: %s', emicorr_ref_filename) - - diff --git a/jwst/emicorr/emicorr_step.py b/jwst/emicorr/emicorr_step.py index 27d4966098..5f24c099f2 100755 --- a/jwst/emicorr/emicorr_step.py +++ b/jwst/emicorr/emicorr_step.py @@ -1,6 +1,5 @@ #! /usr/bin/env python - from stdatamodels.jwst import datamodels from ..stpipe import Step from . import emicorr @@ -29,9 +28,10 @@ class EmiCorrStep(Step): reference_file_types = ['emicorr'] - def process(self, input): + def process(self, step_input): + # Open the input data model - with datamodels.open(input) as input_model: + with datamodels.open(step_input) as input_model: # Catch the cases to skip instrument = input_model.meta.instrument.name @@ -47,10 +47,12 @@ def process(self, input): input_model.meta.cal_step.emicorr = 'SKIPPED' return input_model + # Work on a copy + result = input_model.copy() + # Setup parameters pars = { 'save_intermediate_results': self.save_intermediate_results, - 'user_supplied_reffile': self.user_supplied_reffile, 'nints_to_phase': self.nints_to_phase, 'nbins': self.nbins, 'scale_reference': self.scale_reference, @@ -65,13 +67,13 @@ def process(self, input): self.log.info('Correcting with reference file created on-the-fly.') elif self.user_supplied_reffile is None: - emicorr_ref_filename = self.get_reference_file(input_model, 'emicorr') + emicorr_ref_filename = self.get_reference_file(result, 'emicorr') # Skip the spep if no reference file is found if emicorr_ref_filename == 'N/A': self.log.warning('No reference file found.') self.log.warning('EMICORR step will be skipped') - input_model.meta.cal_step.emicorr = 'SKIPPED' - return input_model + result.meta.cal_step.emicorr = 'SKIPPED' + return result else: self.log.info('Using CRDS reference file: {}'.format(emicorr_ref_filename)) emicorr_model = datamodels.EmiModel(emicorr_ref_filename) @@ -88,15 +90,17 @@ def process(self, input): save_onthefly_reffile = emicorr_ref_filename else: save_onthefly_reffile = None - output_model = emicorr.do_correction(input_model, emicorr_model, save_onthefly_reffile, **pars) - if isinstance(output_model, str) or output_model is None: + result = emicorr.do_correction(result, emicorr_model, save_onthefly_reffile, **pars) + if isinstance(result, str) or result is None: # in this case output_model=subarray_readpatt configuration self.log.warning('No correction match for this configuration') self.log.warning('Step skipped') - input_model.meta.cal_step.emicorr = 'SKIPPED' - return input_model + result.meta.cal_step.emicorr = 'SKIPPED' + return result + + result.meta.cal_step.emicorr = 'COMPLETE' - # close and remove the reference file created on-the-fly - output_model.meta.cal_step.emicorr = 'COMPLETE' + # Cleanup + del emicorr_model - return output_model + return result diff --git a/jwst/emicorr/tests/test_emicorr.py b/jwst/emicorr/tests/test_emicorr.py index f7678ea332..412e6033e6 100644 --- a/jwst/emicorr/tests/test_emicorr.py +++ b/jwst/emicorr/tests/test_emicorr.py @@ -65,7 +65,6 @@ def test_do_correction(): input_model = mk_data_mdl(data, 'MASK1550', 'FAST', 'MIRIMAGE') pars = { 'save_intermediate_results': False, - 'user_supplied_reffile': None, 'nints_to_phase': None, 'nbins': None, 'scale_reference': True, @@ -82,9 +81,8 @@ def test_apply_emicorr(): data = np.ones((1, 5, 20, 20)) input_model = mk_data_mdl(data, 'MASK1550', 'FAST', 'MIRIMAGE') emicorr_model, onthefly_corr_freq, save_onthefly_reffile = None, [218.3], None - outmdl = emicorr.apply_emicorr(input_model, emicorr_model, - onthefly_corr_freq, save_onthefly_reffile, - save_intermediate_results=False, user_supplied_reffile=None, + outmdl = emicorr.apply_emicorr(input_model, emicorr_model, onthefly_corr_freq, + save_onthefly_reffile, save_intermediate_results=False, nints_to_phase=None, nbins_all=None, scale_reference=True) assert outmdl is not None @@ -135,6 +133,6 @@ def test_rebin(): data[9] = 2.0 rebinned_data = emicorr.rebin(data, [7]) - compare_arr = np.array([1., 0.55, 1. , 1., 1.55, 1., 1.]) + compare_arr = np.array([1., 0.55, 1., 1., 1.55, 1., 1.]) assert compare_arr.all() == rebinned_data.all() diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 47df912d48..47e9fcb72b 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -1,7 +1,6 @@ -from stdatamodels.jwst import datamodels - from ..stpipe import Step from . import firstframe_sub +from stdatamodels.jwst import datamodels __all__ = ["FirstFrameStep"] @@ -16,20 +15,26 @@ class FirstFrameStep(Step): class_alias = "firstframe" - def process(self, input): + spec = """ + """ + + def process(self, step_input): # Open the input data model - with datamodels.open(input) as input_model: + with datamodels.open(step_input) as input_model: # check the data is MIRI data detector = input_model.meta.instrument.detector.upper() - if detector[:3] == 'MIR': - # Do the firstframe correction subtraction - result = firstframe_sub.do_correction(input_model) - else: + if detector[:3] != 'MIR': self.log.warning('First Frame Correction is only for MIRI data') self.log.warning('First frame step will be skipped') - result = input_model.copy() - result.meta.cal_step.firstframe = 'SKIPPED' + input_model.meta.cal_step.firstframe = 'SKIPPED' + return input_model + + # Cork on a copy + result = input_model.copy() + + # Do the firstframe correction subtraction + result = firstframe_sub.do_correction(result) return result diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 0086b3cc0d..a535fb203e 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -1,7 +1,6 @@ # # Module for the firstframe correction for MIRI science data sets # - import numpy as np import logging @@ -11,7 +10,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model): +def do_correction(output): """ Short Summary ------------- @@ -21,7 +20,7 @@ def do_correction(input_model): Parameters ---------- - input_model: data model object + output: data model object science data to be corrected Returns @@ -32,12 +31,9 @@ def do_correction(input_model): """ # Save some data params for easy use later - sci_ngroups = input_model.data.shape[1] - - # Create output as a copy of the input science data model - output = input_model.copy() + sci_ngroups = output.data.shape[1] - # Update the step status, and if ngroups > 3, set all of the GROUPDQ in + # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' if sci_ngroups > 3: output.groupdq[:, 0, :, :] = \ diff --git a/jwst/firstframe/tests/test_firstframe.py b/jwst/firstframe/tests/test_firstframe.py index e99c76da56..7d48d5bfce 100644 --- a/jwst/firstframe/tests/test_firstframe.py +++ b/jwst/firstframe/tests/test_firstframe.py @@ -25,8 +25,9 @@ def test_firstframe_set_groupdq(): # create a JWST datamodel for MIRI data dm_ramp = RampModel(data=data, groupdq=groupdq) - # run the first frame correction step - dm_ramp_firstframe = do_correction(dm_ramp) + # run the first frame correction step on a copy (the detection to make the copy or + # not would have happened at _step.py) + dm_ramp_firstframe = do_correction(dm_ramp.copy()) # check that the difference in the groupdq flags is equal to # the 'do_not_use' flag diff --git a/jwst/gain_scale/gain_scale.py b/jwst/gain_scale/gain_scale.py index 78ce9a2701..2de676ce7d 100644 --- a/jwst/gain_scale/gain_scale.py +++ b/jwst/gain_scale/gain_scale.py @@ -5,7 +5,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model, gain_factor): +def do_correction(output_model, gain_factor): """ Short Summary ------------- @@ -15,7 +15,7 @@ def do_correction(input_model, gain_factor): Parameters ---------- - input_model : `~jwst.datamodels.JwstDataModel` + output_model : `~jwst.datamodels.JwstDataModel` Input datamodel to be corrected Returns @@ -25,9 +25,6 @@ def do_correction(input_model, gain_factor): """ - # Create output as a copy of the input science data model - output_model = input_model.copy() - # Apply the gain factor to the SCI and ERR arrays log.info('Rescaling by {0}'.format(gain_factor)) output_model.data *= gain_factor diff --git a/jwst/gain_scale/gain_scale_step.py b/jwst/gain_scale/gain_scale_step.py index 82b06ddcf5..97d66b6ab7 100755 --- a/jwst/gain_scale/gain_scale_step.py +++ b/jwst/gain_scale/gain_scale_step.py @@ -1,9 +1,7 @@ from stdatamodels.jwst import datamodels - from ..stpipe import Step from . import gain_scale - __all__ = ["GainScaleStep"] @@ -16,12 +14,14 @@ class GainScaleStep(Step): class_alias = "gain_scale" + spec = """ + """ reference_file_types = ['gain'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.open(input) as input_model: + with datamodels.open(step_input) as input_model: # Is the gain_factor already populated in the input model? if input_model.meta.exposure.gain_factor is None: @@ -35,16 +35,19 @@ def process(self, input): self.log.info('GAINFACT not found in gain reference file') self.log.info('Step will be skipped') input_model.meta.cal_step.gain_scale = 'SKIPPED' - gain_model.close() + del gain_model return input_model else: gain_factor = gain_model.meta.exposure.gain_factor - gain_model.close() + del gain_model else: gain_factor = input_model.meta.exposure.gain_factor + # Work on a copy + result = input_model.copy() + # Do the scaling - result = gain_scale.do_correction(input_model, gain_factor) + result = gain_scale.do_correction(result, gain_factor) return result diff --git a/jwst/gain_scale/tests/test_gain_scale.py b/jwst/gain_scale/tests/test_gain_scale.py index bea0d5f651..ee507b0a4f 100644 --- a/jwst/gain_scale/tests/test_gain_scale.py +++ b/jwst/gain_scale/tests/test_gain_scale.py @@ -15,7 +15,8 @@ def test_correction(make_cubemodel): """ datmod = make_cubemodel(2, 50, 50) gf = datmod.meta.exposure.gain_factor - output = do_correction(datmod, gain_factor=gf) + # run on a copy (the detection to make the copy or not would have happened at _step.py) + output = do_correction(datmod.copy(), gain_factor=gf) assert output.meta.cal_step.gain_scale == 'COMPLETE' assert np.all(output.data == datmod.data * gf) diff --git a/jwst/group_scale/group_scale.py b/jwst/group_scale/group_scale.py index 811cb15f1a..354e8ad76c 100644 --- a/jwst/group_scale/group_scale.py +++ b/jwst/group_scale/group_scale.py @@ -40,6 +40,8 @@ def do_correction(model): # Apply the rescaling to the entire data array scale = float(frame_divisor) / nframes + if not isinstance(type(model.data), float): + model.data = (model.data).astype(float) model.data *= scale model.meta.cal_step.group_scale = 'COMPLETE' diff --git a/jwst/group_scale/group_scale_step.py b/jwst/group_scale/group_scale_step.py index 3d3166697d..9e69e77a6b 100755 --- a/jwst/group_scale/group_scale_step.py +++ b/jwst/group_scale/group_scale_step.py @@ -1,5 +1,4 @@ from stdatamodels.jwst import datamodels - from ..stpipe import Step from . import group_scale @@ -16,26 +15,26 @@ class GroupScaleStep(Step): class_alias = "group_scale" - def process(self, input): + spec = """ + """ - # Open the input data model - with datamodels.RampModel(input) as input_model: + def process(self, step_input): - # Always work on a copy - result = input_model.copy() + # Open the input data model + with datamodels.RampModel(step_input) as input_model: # Try to get values of NFRAMES and FRMDIVSR to see # if we need to do any rescaling - nframes = result.meta.exposure.nframes - frame_divisor = result.meta.exposure.frame_divisor + nframes = input_model.meta.exposure.nframes + frame_divisor = input_model.meta.exposure.frame_divisor # If we didn't find NFRAMES, we don't have enough info # to continue. Skip the step. if nframes is None: self.log.warning('NFRAMES value not found') self.log.warning('Step will be skipped') - result.meta.cal_step.group_scale = 'SKIPPED' - return result + input_model.meta.cal_step.group_scale = 'SKIPPED' + return input_model # If we didn't find FRMDIVSR, then check to see if NFRAMES # is a power of 2. If it is, rescaling isn't needed. @@ -43,18 +42,24 @@ def process(self, input): if (nframes & (nframes - 1) == 0): self.log.info('NFRAMES={} is a power of 2; correction not needed'.format(nframes)) self.log.info('Step will be skipped') - result.meta.cal_step.group_scale = 'SKIPPED' - return result + input_model.meta.cal_step.group_scale = 'SKIPPED' + return input_model # Compare NFRAMES and FRMDIVSR. If they're equal, # rescaling isn't needed. elif nframes == frame_divisor: self.log.info('NFRAMES and FRMDIVSR are equal; correction not needed') self.log.info('Step will be skipped') - result.meta.cal_step.group_scale = 'SKIPPED' - return result + input_model.meta.cal_step.group_scale = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() # Do the scaling group_scale.do_correction(result) + # Cleanup + del input_model + return result diff --git a/jwst/ipc/ipc_corr.py b/jwst/ipc/ipc_corr.py index 059e23282b..d5ff99dff1 100644 --- a/jwst/ipc/ipc_corr.py +++ b/jwst/ipc/ipc_corr.py @@ -50,12 +50,12 @@ def do_correction(input_model, ipc_model): return output_model -def ipc_correction(input_model, ipc_model): +def ipc_correction(output, ipc_model): """Apply the IPC correction to the science arrays. Parameters ---------- - input_model : data model object + output : data model object The input science data. ipc_model : IPCModel object @@ -69,32 +69,29 @@ def ipc_correction(input_model, ipc_model): """ log.debug("ipc_correction: nints=%d, ngroups=%d, size=%d,%d", - input_model.meta.exposure.nints, - input_model.meta.exposure.ngroups, - input_model.data.shape[-1], - input_model.data.shape[-2]) - - # Create output as a copy of the input science data model. - output = input_model.copy() + output.meta.exposure.nints, + output.meta.exposure.ngroups, + output.data.shape[-1], + output.data.shape[-2]) # Was IRS2 readout used? - is_irs2_format = pipe_utils.is_irs2(input_model) + is_irs2_format = pipe_utils.is_irs2(output) if is_irs2_format: - irs2_mask = x_irs2.make_mask(input_model) + irs2_mask = x_irs2.make_mask(output) - detector = input_model.meta.instrument.detector + detector = output.meta.instrument.detector # The number of reference pixels along the bottom edge, top edge, # left edge, and right edge. - nref = get_num_ref_pixels(input_model) + nref = get_num_ref_pixels(output) # Get the data for the IPC kernel. This can be a slice, if input_model # is a subarray. - kernel = get_ipc_slice(input_model, ipc_model) + kernel = get_ipc_slice(output, ipc_model) log.debug("substrt1 = %d, subsize1 = %d, substrt2 = %d, subsize2 = %d" % - (input_model.meta.subarray.xstart, input_model.meta.subarray.xsize, - input_model.meta.subarray.ystart, input_model.meta.subarray.ysize)) + (output.meta.subarray.xstart, output.meta.subarray.xsize, + output.meta.subarray.ystart, output.meta.subarray.ysize)) log.debug('Number of reference pixels: bottom, top, left, right =' ' %d, %d, %d, %d' % (nref.bottom_rows, nref.top_rows, @@ -102,8 +99,8 @@ def ipc_correction(input_model, ipc_model): log.debug("Shape of ipc image = %s" % repr(ipc_model.data.shape)) # Loop over all integrations and groups in input science data. - for i in range(input_model.data.shape[0]): # integrations - for j in range(input_model.data.shape[1]): # groups + for i in range(output.data.shape[0]): # integrations + for j in range(output.data.shape[1]): # groups # Convolve the current group in-place with the IPC kernel. if is_irs2_format: # Extract normal data from input IRS2-format data. diff --git a/jwst/ipc/ipc_step.py b/jwst/ipc/ipc_step.py index 97a59f24d2..bb1fcf359f 100755 --- a/jwst/ipc/ipc_step.py +++ b/jwst/ipc/ipc_step.py @@ -14,9 +14,12 @@ class IPCStep(Step): class_alias = "ipc" + spec = """ + """ + reference_file_types = ['ipc'] - def process(self, input): + def process(self, step_input): """Apply the IPC correction. Parameters @@ -31,7 +34,7 @@ def process(self, input): """ # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: # Get the name of the ipc reference file to use self.ipc_name = self.get_reference_file(input_model, 'ipc') @@ -41,18 +44,20 @@ def process(self, input): if self.ipc_name == 'N/A': self.log.warning('No IPC reference file found') self.log.warning('IPC step will be skipped') - result = input_model.copy() - result.meta.cal_step.ipc = 'SKIPPED' - return result + input_model.meta.cal_step.ipc = 'SKIPPED' + return input_model # Open the ipc reference file data model ipc_model = datamodels.IPCModel(self.ipc_name) - # Do the ipc correction - result = ipc_corr.do_correction(input_model, ipc_model) + # Work on a copy + result = input_model.copy() - # Close the reference file and update the step status - ipc_model.close() + # Do the ipc correction + result = ipc_corr.do_correction(result, ipc_model) result.meta.cal_step.ipc = 'COMPLETE' + # Cleanup + del ipc_model + return result diff --git a/jwst/jump/jump.py b/jwst/jump/jump.py index 1658ce8ebd..92f70de2fb 100644 --- a/jwst/jump/jump.py +++ b/jwst/jump/jump.py @@ -9,7 +9,7 @@ log.setLevel(logging.DEBUG) -def run_detect_jumps(input_model, gain_model, readnoise_model, +def run_detect_jumps(output_model, gain_model, readnoise_model, rejection_thresh, three_grp_thresh, four_grp_thresh, max_cores, max_jump_to_flag_neighbors, min_jump_to_flag_neighbors, flag_4_neighbors, @@ -34,35 +34,31 @@ def run_detect_jumps(input_model, gain_model, readnoise_model, # Runs `detect_jumps` in stcal # extract data and info from input_model to pass to detect_jumps - frames_per_group = input_model.meta.exposure.nframes - data = input_model.data - gdq = input_model.groupdq - pdq = input_model.pixeldq - err = input_model.err - output_model = input_model.copy() + frames_per_group = output_model.meta.exposure.nframes # determine the number of groups that correspond to the after_jump times # needed because the group time is not passed to detect_jumps - gtime = input_model.meta.exposure.group_time + gtime = output_model.meta.exposure.group_time after_jump_flag_n1 = int(after_jump_flag_time1 // gtime) after_jump_flag_n2 = int(after_jump_flag_time2 // gtime) grps_masked_after_shower = int(time_masked_after_shower // gtime) snowball_grps_masked_next_int = int(snowball_time_masked_next_int // gtime) # Get 2D gain and read noise values from their respective models - if reffile_utils.ref_matches_sci(input_model, gain_model): + if reffile_utils.ref_matches_sci(output_model, gain_model): gain_2d = gain_model.data else: log.info('Extracting gain subarray to match science data') - gain_2d = reffile_utils.get_subarray_data(input_model, gain_model) + gain_2d = reffile_utils.get_subarray_data(output_model, gain_model) - if reffile_utils.ref_matches_sci(input_model, readnoise_model): + if reffile_utils.ref_matches_sci(output_model, readnoise_model): readnoise_2d = readnoise_model.data else: log.info('Extracting readnoise subarray to match science data') - readnoise_2d = reffile_utils.get_subarray_data(input_model, + readnoise_2d = reffile_utils.get_subarray_data(output_model, readnoise_model) new_gdq, new_pdq, number_crs, number_extended_events, stddev\ - = detect_jumps(frames_per_group, data, gdq, pdq, err, + = detect_jumps(frames_per_group, output_model.data, output_model.groupdq, + output_model.pixeldq, output_model.err, gain_2d, readnoise_2d, rejection_thresh, three_grp_thresh, four_grp_thresh, max_cores, @@ -99,14 +95,15 @@ def run_detect_jumps(input_model, gain_model, readnoise_model, # determine the number of groups with all pixels set to DO_NOT_USE dnu_flag = 1 num_flagged_grps = 0 - for integ in range(data.shape[0]): - for grp in range(data.shape[1]): - if np.all(np.bitwise_and(gdq[integ, grp, :, :], dnu_flag)): + datashape = np.shape(output_model.data) + for integ in range(datashape[0]): + for grp in range(datashape[1]): + if np.all(np.bitwise_and(output_model.groupdq[integ, grp, :, :], dnu_flag)): num_flagged_grps += 1 - total_groups = data.shape[0] * data.shape[1] - num_flagged_grps - data.shape[0] + total_groups = datashape[0] * datashape[1] - num_flagged_grps - datashape[0] if total_groups >= 1: total_time = output_model.meta.exposure.group_time * total_groups - total_pixels = data.shape[2] * data.shape[3] + total_pixels = datashape[2] * datashape[3] output_model.meta.exposure.primary_cosmic_rays = 1000 * number_crs / (total_time * total_pixels) output_model.meta.exposure.extended_emission_events = 1e6 * number_extended_events /\ (total_time * total_pixels) diff --git a/jwst/jump/jump_step.py b/jwst/jump/jump_step.py index 8670a44d31..86f2b33f23 100755 --- a/jwst/jump/jump_step.py +++ b/jwst/jump/jump_step.py @@ -55,18 +55,22 @@ class JumpStep(Step): class_alias = 'jump' - def process(self, input): + def process(self, step_input): + + # Open the input data model + with datamodels.RampModel(step_input) as input_model: - with datamodels.RampModel(input) as input_model: tstart = time.time() # Check for an input model with NGROUPS<=2 ngroups = input_model.data.shape[1] if ngroups <= 2: self.log.warning('Cannot apply jump detection when NGROUPS<=2;') self.log.warning('Jump step will be skipped') - result = input_model.copy() - result.meta.cal_step.jump = 'SKIPPED' - return result + input_model.meta.cal_step.jump = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() # Retrieve the parameter values rej_thresh = self.rejection_threshold @@ -91,18 +95,18 @@ def process(self, input): self.log.info('Maximum cores to use = %s', max_cores) # Get the gain and readnoise reference files - gain_filename = self.get_reference_file(input_model, 'gain') + gain_filename = self.get_reference_file(result, 'gain') self.log.info('Using GAIN reference file: %s', gain_filename) gain_model = datamodels.GainModel(gain_filename) - readnoise_filename = self.get_reference_file(input_model, + readnoise_filename = self.get_reference_file(result, 'readnoise') self.log.info('Using READNOISE reference file: %s', readnoise_filename) readnoise_model = datamodels.ReadnoiseModel(readnoise_filename) # Call the jump detection routine - result = run_detect_jumps(input_model, gain_model, readnoise_model, + result = run_detect_jumps(result, gain_model, readnoise_model, rej_thresh, three_grp_rej_thresh, four_grp_rej_thresh, max_cores, max_jump_to_flag_neighbors, min_jump_to_flag_neighbors, flag_4_neighbors, @@ -131,11 +135,13 @@ def process(self, input): ) - gain_model.close() - readnoise_model.close() tstop = time.time() self.log.info('The execution time in seconds: %f', tstop - tstart) - result.meta.cal_step.jump = 'COMPLETE' + result.meta.cal_step.jump = 'COMPLETE' + + # Cleanup + del gain_model + del readnoise_model return result diff --git a/jwst/lastframe/lastframe_step.py b/jwst/lastframe/lastframe_step.py index 27ff6cc172..9c79370396 100755 --- a/jwst/lastframe/lastframe_step.py +++ b/jwst/lastframe/lastframe_step.py @@ -1,5 +1,4 @@ from stdatamodels.jwst import datamodels - from ..stpipe import Step from . import lastframe_sub @@ -15,20 +14,27 @@ class LastFrameStep(Step): class_alias = "lastframe" - def process(self, input): + spec = """ + """ + + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: # check the data is MIRI data detector = input_model.meta.instrument.detector - if detector[:3] == 'MIR': - # Do the lastframe correction subtraction - result = lastframe_sub.do_correction(input_model) - else: + + if detector[:3] != 'MIR': self.log.warning('Last Frame Correction is only for MIRI data') self.log.warning('Last frame step will be skipped') - result = input_model.copy() - result.meta.cal_step.lastframe = 'SKIPPED' + input_model.meta.cal_step.lastframe = 'SKIPPED' + return input_model + + # Work on a copy + result = input_model.copy() + + # Do the lastframe correction subtraction + result = lastframe_sub.do_correction(result) return result diff --git a/jwst/lastframe/lastframe_sub.py b/jwst/lastframe/lastframe_sub.py index 4a6dacd2af..d507261bb1 100644 --- a/jwst/lastframe/lastframe_sub.py +++ b/jwst/lastframe/lastframe_sub.py @@ -10,7 +10,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model): +def do_correction(output): """ Short Summary ------------- @@ -19,7 +19,7 @@ def do_correction(input_model): Parameters ---------- - input_model: data model object + output: data model object science data to be corrected Returns @@ -30,10 +30,7 @@ def do_correction(input_model): """ # Save some data params for easy use later - sci_ngroups = input_model.data.shape[1] - - # Create output as a copy of the input science data model - output = input_model.copy() + sci_ngroups = output.data.shape[1] # Update the step status, and if ngroups > 2, set all of the GROUPDQ in # the final group to 'DO_NOT_USE' diff --git a/jwst/lastframe/tests/test_lastframe.py b/jwst/lastframe/tests/test_lastframe.py index 590fc73885..29d0090ad4 100755 --- a/jwst/lastframe/tests/test_lastframe.py +++ b/jwst/lastframe/tests/test_lastframe.py @@ -25,8 +25,9 @@ def test_lastframe_set_groupdq(): # create a JWST datamodel for MIRI data dm_ramp = RampModel(data=data, groupdq=groupdq) - # run the last frame correction step - dm_ramp_lastframe = do_correction(dm_ramp) + # run the last frame correction step on a copy of the input datamodel (the detection to make the + # copy or not would have happened at _step.py + dm_ramp_lastframe = do_correction(dm_ramp.copy()) # check that the difference in the groupdq flags is equal to # the 'do_not_use' flag diff --git a/jwst/linearity/linearity.py b/jwst/linearity/linearity.py index 21c3fc9813..b5af9ebf21 100644 --- a/jwst/linearity/linearity.py +++ b/jwst/linearity/linearity.py @@ -10,27 +10,26 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model, lin_model): +def do_correction(output_model, lin_model): # Create the output model as a copy of the input - output_model = input_model.copy() zframe = None if output_model.meta.exposure.zero_frame: zframe = output_model.zeroframe # Get dq arrays - pdq = input_model.pixeldq - gdq = input_model.groupdq + pdq = output_model.pixeldq + gdq = output_model.groupdq # If the input data does not have an expanded DQ array, create one - if len(input_model.groupdq) == 0: - gdq = (input_model.data * 0).astype(np.uint32) + if len(output_model.groupdq) == 0: + gdq = (output_model.data * 0).astype(np.uint32) # Obtain linearity coefficients and dq array from reference file - if reffile_utils.ref_matches_sci(input_model, lin_model): + if reffile_utils.ref_matches_sci(output_model, lin_model): lin_coeffs = lin_model.coeffs lin_dq = lin_model.dq else: - sub_lin_model = reffile_utils.get_subarray_model(input_model, lin_model) + sub_lin_model = reffile_utils.get_subarray_model(output_model, lin_model) lin_coeffs = sub_lin_model.coeffs.copy() lin_dq = sub_lin_model.dq.copy() sub_lin_model.close() diff --git a/jwst/linearity/linearity_step.py b/jwst/linearity/linearity_step.py index 29b6c94b45..a4934a1ec6 100644 --- a/jwst/linearity/linearity_step.py +++ b/jwst/linearity/linearity_step.py @@ -14,12 +14,15 @@ class LinearityStep(Step): class_alias = "linearity" + spec = """ + """ + reference_file_types = ['linearity'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: # Get the name of the linearity reference file to use self.lin_name = self.get_reference_file(input_model, 'linearity') @@ -29,18 +32,20 @@ def process(self, input): if self.lin_name == 'N/A': self.log.warning('No Linearity reference file found') self.log.warning('Linearity step will be skipped') - result = input_model.copy() - result.meta.cal_step.linearity = 'SKIPPED' - return result + input_model.meta.cal_step.linearity = 'SKIPPED' + return input_model # Open the linearity reference file data model lin_model = datamodels.LinearityModel(self.lin_name) - # Do the linearity correction - result = linearity.do_correction(input_model, lin_model) + # Work on a copy + result = input_model.copy() - # Close the reference file and update the step status - lin_model.close() + # Do the linearity correction + result = linearity.do_correction(result, lin_model) result.meta.cal_step.linearity = 'COMPLETE' + # Cleanup + del lin_model + return result diff --git a/jwst/persistence/persistence_step.py b/jwst/persistence/persistence_step.py index a69090973b..daa006fb0a 100644 --- a/jwst/persistence/persistence_step.py +++ b/jwst/persistence/persistence_step.py @@ -19,85 +19,89 @@ class PersistenceStep(Step): flag_pers_cutoff = float(default=40.) # Pixels with persistence correction >= this value in DN will be flagged in the DQ save_persistence = boolean(default=False) # Save subtracted persistence to an output file with suffix '_output_pers' save_trapsfilled = boolean(default=True) # Save updated trapsfilled file with suffix '_trapsfilled' + modify_input = boolean(default=False) """ reference_file_types = ["trapdensity", "trappars", "persat"] - def process(self, input): + def process(self, step_input): if self.input_trapsfilled is not None: if (self.input_trapsfilled == "None" or len(self.input_trapsfilled) == 0): self.input_trapsfilled = None - output_obj = datamodels.RampModel(input).copy() - - self.trap_density_filename = self.get_reference_file(output_obj, - "trapdensity") - self.trappars_filename = self.get_reference_file(output_obj, - "trappars") - self.persat_filename = self.get_reference_file(output_obj, "persat") - - # Is any reference file missing? - missing = False - missing_reftypes = [] - if self.persat_filename == "N/A": - missing = True - missing_reftypes.append("PERSAT") - if self.trap_density_filename == "N/A": - missing = True - missing_reftypes.append("TRAPDENSITY") - if self.trappars_filename == "N/A": - missing = True - missing_reftypes.append("TRAPPARS") - if missing: - if len(missing_reftypes) == 1: - msg = "Missing reference file type: " + missing_reftypes[0] + with datamodels.RampModel(step_input) as input_model: + + self.trap_density_filename = self.get_reference_file(input_model, + "trapdensity") + self.trappars_filename = self.get_reference_file(input_model, + "trappars") + self.persat_filename = self.get_reference_file(input_model, "persat") + + # Is any reference file missing? + missing = False + missing_reftypes = [] + if self.persat_filename == "N/A": + missing = True + missing_reftypes.append("PERSAT") + if self.trap_density_filename == "N/A": + missing = True + missing_reftypes.append("TRAPDENSITY") + if self.trappars_filename == "N/A": + missing = True + missing_reftypes.append("TRAPPARS") + if missing: + if len(missing_reftypes) == 1: + msg = "Missing reference file type: " + missing_reftypes[0] + else: + msg = "Missing reference file types: " + for name in missing_reftypes: + msg += (" " + name) + self.log.warning("%s", msg) + input_model.meta.cal_step.persistence = "SKIPPED" + return input_model + + # Work on a copy + result = input_model.copy() + + if self.input_trapsfilled is None: + traps_filled_model = None else: - msg = "Missing reference file types: " - for name in missing_reftypes: - msg += (" " + name) - self.log.warning("%s", msg) - output_obj.meta.cal_step.persistence = "SKIPPED" - return output_obj - - if self.input_trapsfilled is None: - traps_filled_model = None - else: - traps_filled_model = datamodels.TrapsFilledModel( - self.input_trapsfilled) - trap_density_model = datamodels.TrapDensityModel( - self.trap_density_filename) - trappars_model = datamodels.TrapParsModel(self.trappars_filename) - persat_model = datamodels.PersistenceSatModel(self.persat_filename) - - pers_a = persistence.DataSet(output_obj, traps_filled_model, - self.flag_pers_cutoff, - self.save_persistence, - trap_density_model, trappars_model, - persat_model) - (output_obj, traps_filled, output_pers, skipped) = pers_a.do_all() - if skipped: - output_obj.meta.cal_step.persistence = 'SKIPPED' - else: - output_obj.meta.cal_step.persistence = 'COMPLETE' - - if traps_filled_model is not None: # input traps_filled - traps_filled_model.close() - if traps_filled is not None: # output traps_filled - # Save the traps_filled image with suffix 'trapsfilled'. - self.save_model( - traps_filled, suffix='trapsfilled', force=self.save_trapsfilled - ) - traps_filled.close() - - if output_pers is not None: # output file of persistence - self.save_model(output_pers, suffix='output_pers') - output_pers.close() - - # Close reference files. - trap_density_model.close() - trappars_model.close() - persat_model.close() - - return output_obj + traps_filled_model = datamodels.TrapsFilledModel( + self.input_trapsfilled) + trap_density_model = datamodels.TrapDensityModel( + self.trap_density_filename) + trappars_model = datamodels.TrapParsModel(self.trappars_filename) + persat_model = datamodels.PersistenceSatModel(self.persat_filename) + + pers_a = persistence.DataSet(result, traps_filled_model, + self.flag_pers_cutoff, + self.save_persistence, + trap_density_model, trappars_model, + persat_model) + (result, traps_filled, output_pers, skipped) = pers_a.do_all() + if skipped: + result.meta.cal_step.persistence = 'SKIPPED' + else: + result.meta.cal_step.persistence = 'COMPLETE' + + if traps_filled_model is not None: # input traps_filled + del traps_filled_model + if traps_filled is not None: # output traps_filled + # Save the traps_filled image with suffix 'trapsfilled'. + self.save_model( + traps_filled, suffix='trapsfilled', force=self.save_trapsfilled + ) + del traps_filled + + if output_pers is not None: # output file of persistence + self.save_model(output_pers, suffix='output_pers') + output_pers.close() + + # Cleanup + del trap_density_model + del trappars_model + del persat_model + + return result diff --git a/jwst/pipeline/calwebb_detector1.py b/jwst/pipeline/calwebb_detector1.py index 78699ff12f..afc3bc5ab3 100644 --- a/jwst/pipeline/calwebb_detector1.py +++ b/jwst/pipeline/calwebb_detector1.py @@ -176,4 +176,4 @@ def setup_output(self, input): if input.meta.cal_step.ramp_fit == 'COMPLETE': self.suffix = 'rate' else: - self.suffix = 'ramp' + self.suffix = 'ramp' \ No newline at end of file diff --git a/jwst/ramp_fitting/ramp_fit_step.py b/jwst/ramp_fitting/ramp_fit_step.py index 12c34b29df..a374e53663 100644 --- a/jwst/ramp_fitting/ramp_fit_step.py +++ b/jwst/ramp_fitting/ramp_fit_step.py @@ -1,5 +1,4 @@ #! /usr/bin/env python - import numpy as np from stcal.ramp_fitting import ramp_fit @@ -18,7 +17,6 @@ from ..lib import reffile_utils import logging -import copy import warnings log = logging.getLogger(__name__) @@ -65,7 +63,7 @@ def get_reference_file_subarrays(model, readnoise_model, gain_model, nframes): gain_2d = reffile_utils.get_subarray_data(model, gain_model) if reffile_utils.ref_matches_sci(model, readnoise_model): - readnoise_2d = readnoise_model.data.copy() + readnoise_2d = readnoise_model.data else: log.info('Extracting readnoise subarray to match science data') readnoise_2d = reffile_utils.get_subarray_data(model, readnoise_model) @@ -409,12 +407,17 @@ class RampFitStep(Step): reference_file_types = ['readnoise', 'gain'] - def process(self, input): + def process(self, step_input): + + # Open the input data model + with datamodels.RampModel(step_input) as input_model: + + # Cork on a copy + result = input_model.copy() - with datamodels.RampModel(input) as input_model: max_cores = self.maximum_cores - readnoise_filename = self.get_reference_file(input_model, 'readnoise') - gain_filename = self.get_reference_file(input_model, 'gain') + readnoise_filename = self.get_reference_file(result, 'readnoise') + gain_filename = self.get_reference_file(result, 'gain') ngroups = input_model.data.shape[1] if self.algorithm.upper() == "LIKELY" and ngroups < LIKELY_MIN_NGROUPS: @@ -435,12 +438,12 @@ def process(self, input): # available later in the gain_scale step, which avoids having to # load the gain ref file again in that step. if gain_model.meta.exposure.gain_factor is not None: - input_model.meta.exposure.gain_factor = gain_model.meta.exposure.gain_factor + result.meta.exposure.gain_factor = gain_model.meta.exposure.gain_factor # Get gain arrays, subarrays if desired. - frames_per_group = input_model.meta.exposure.nframes + frames_per_group = result.meta.exposure.nframes readnoise_2d, gain_2d = get_reference_file_subarrays( - input_model, readnoise_model, gain_model, frames_per_group) + result, readnoise_model, gain_model, frames_per_group) log.info(f"Using algorithm = {self.algorithm}") log.info(f"Using weighting = {self.weighting}") @@ -449,16 +452,17 @@ def process(self, input): if self.algorithm == "GLS": buffsize //= 10 - int_times = input_model.int_times + int_times = result.int_times # Before the ramp_fit() call, copy the input model ("_W" for weighting) # for later reconstruction of the fitting array tuples. - input_model_W = copy.copy(input_model) + input_model_W = result.copy() + # Run ramp_fit(), ignoring all DO_NOT_USE groups, and return the # ramp fitting arrays for the ImageModel, the CubeModel, and the # RampFitOutputModel. image_info, integ_info, opt_info, gls_opt_model = ramp_fit.ramp_fit( - input_model, buffsize, self.save_opt, readnoise_2d, gain_2d, + result, buffsize, self.save_opt, readnoise_2d, gain_2d, self.algorithm, self.weighting, max_cores, dqflags.pixel, suppress_one_group=self.suppress_one_group) @@ -478,7 +482,7 @@ def process(self, input): gdq[where_sat] = np.bitwise_or(gdq[where_sat], dqflags.group['DO_NOT_USE']) # Get group_time for readnoise variance calculation - group_time = input_model.meta.exposure.group_time + group_time = result.meta.exposure.group_time # Using the modified GROUPDQ array, create new readnoise variance arrays image_var_RN, integ_var_RN, opt_var_RN = \ @@ -511,7 +515,7 @@ def process(self, input): # Save the OLS optional fit product, if it exists. if opt_info is not None: - opt_model = create_optional_results_model(input_model, opt_info) + opt_model = create_optional_results_model(result, opt_info) self.save_model(opt_model, 'fitopt', output_file=self.opt_name) ''' # GLS removed from code, since it's not implemented right now. @@ -525,19 +529,22 @@ def process(self, input): out_model, int_model = None, None # Create models from possibly updated info if image_info is not None and integ_info is not None: - out_model = create_image_model(input_model, image_info) + out_model = create_image_model(result, image_info) out_model.meta.bunit_data = 'DN/s' out_model.meta.bunit_err = 'DN/s' out_model.meta.cal_step.ramp_fit = 'COMPLETE' - if ((input_model.meta.exposure.type in ['NRS_IFU', 'MIR_MRS']) or - (input_model.meta.exposure.type in ['NRS_AUTOWAVE', 'NRS_LAMP'] and - input_model.meta.instrument.lamp_mode == 'IFU')): + if ((result.meta.exposure.type in ['NRS_IFU', 'MIR_MRS']) or + (result.meta.exposure.type in ['NRS_AUTOWAVE', 'NRS_LAMP'] and + result.meta.instrument.lamp_mode == 'IFU')): out_model = datamodels.IFUImageModel(out_model) - int_model = create_integration_model(input_model, integ_info, int_times) + int_model = create_integration_model(result, integ_info, int_times) int_model.meta.bunit_data = 'DN/s' int_model.meta.bunit_err = 'DN/s' int_model.meta.cal_step.ramp_fit = 'COMPLETE' + # Cleanup + del result + return out_model, int_model diff --git a/jwst/refpix/irs2_subtract_reference.py b/jwst/refpix/irs2_subtract_reference.py index 7277a3cfce..b05261538e 100644 --- a/jwst/refpix/irs2_subtract_reference.py +++ b/jwst/refpix/irs2_subtract_reference.py @@ -10,13 +10,13 @@ log.setLevel(logging.DEBUG) -def correct_model(input_model, irs2_model, scipix_n_default=16, refpix_r_default=4, +def correct_model(output_model, irs2_model, scipix_n_default=16, refpix_r_default=4, pad=8, preserve_refpix=False): """Correct an input NIRSpec IRS2 datamodel using reference pixels. Parameters ---------- - input_model: ramp model + output_model: ramp model The input science data model. irs2_model: IRS2 model @@ -76,9 +76,9 @@ def correct_model(input_model, irs2_model, scipix_n_default=16, refpix_r_default # Copy in SCI and PIXELDQ arrays for now; that's all we need. The rest # of the input model will be copied to output at the end of the step. - data = input_model.data.copy() - pixeldq = input_model.pixeldq.copy() - input_model.meta.cal_step.refpix = 'not specified yet' + data = output_model.data.copy() + pixeldq = output_model.pixeldq.copy() + output_model.meta.cal_step.refpix = 'not specified yet' # Load the reference file data. # The reference file data are complex, but they're stored as float, with @@ -90,8 +90,8 @@ def correct_model(input_model, irs2_model, scipix_n_default=16, refpix_r_default if nrows != expected_nrows: log.error("Number of rows in reference file = {}," " but it should be {}.".format(nrows, expected_nrows)) - input_model.meta.cal_step.refpix = 'SKIPPED' - return input_model + output_model.meta.cal_step.refpix = 'SKIPPED' + return output_model alpha = np.ones((4, nrows // 2), dtype=np.complex64) beta = np.zeros((4, nrows // 2), dtype=np.complex64) @@ -105,20 +105,20 @@ def correct_model(input_model, irs2_model, scipix_n_default=16, refpix_r_default beta[2, :] = float_to_complex(irs2_model.irs2_table.field("beta_2")) beta[3, :] = float_to_complex(irs2_model.irs2_table.field("beta_3")) - scipix_n = input_model.meta.exposure.nrs_normal + scipix_n = output_model.meta.exposure.nrs_normal if scipix_n is None: log.warning("Keyword NRS_NORM not found; using default value %d" % scipix_n_default) scipix_n = scipix_n_default - refpix_r = input_model.meta.exposure.nrs_reference + refpix_r = output_model.meta.exposure.nrs_reference if refpix_r is None: log.warning("Keyword NRS_REF not found; using default value %d" % refpix_r_default) refpix_r = refpix_r_default # Convert from sky (DMS) orientation to detector orientation. - detector = input_model.meta.instrument.detector + detector = output_model.meta.instrument.detector if detector == "NRS1": data = np.swapaxes(data, 2, 3) pixeldq = np.swapaxes(pixeldq, 0, 1) @@ -176,7 +176,6 @@ def correct_model(input_model, irs2_model, scipix_n_default=16, refpix_r_default data[integ, :, :, :] = data0 # Convert corrected data back to sky orientation - output_model = input_model.copy() if not preserve_refpix: temp_data = data[:, :, :, nx - ny:] else: diff --git a/jwst/refpix/refpix_step.py b/jwst/refpix/refpix_step.py index 1e1df78328..e7000ba610 100644 --- a/jwst/refpix/refpix_step.py +++ b/jwst/refpix/refpix_step.py @@ -29,17 +29,19 @@ class RefPixStep(Step): reference_file_types = ['refpix'] - def process(self, input): + def process(self, step_input): - # Load the input science data - with datamodels.RampModel(input) as input_model: + # Open the input data model + with datamodels.RampModel(step_input) as input_model: - if pipe_utils.is_irs2(input_model): + # Work on a copy + result = input_model.copy() + + if pipe_utils.is_irs2(result): # Flag bad reference pixels first - datamodel = input_model.copy() irs2_subtract_reference.flag_bad_refpix( - datamodel, n_sigma=self.ovr_corr_mitigation_ftr, flag_only=True) + result, n_sigma=self.ovr_corr_mitigation_ftr, flag_only=True) # If desired, do the normal refpix correction before IRS2, without # side pixel handling @@ -48,39 +50,38 @@ def process(self, input): self.log.info('Turning off side pixel correction for IRS2') self.use_side_ref_pixels = False reference_pixels.correct_model( - datamodel, self.odd_even_columns, self.use_side_ref_pixels, + result, self.odd_even_columns, self.use_side_ref_pixels, self.side_smoothing_length, self.side_gain, self.odd_even_rows) # Now that values are updated, replace bad reference pixels - irs2_subtract_reference.flag_bad_refpix(datamodel, replace_only=True) + irs2_subtract_reference.flag_bad_refpix(result, replace_only=True) # Get the necessary refpix reference file for IRS2 correction - self.irs2_name = self.get_reference_file(datamodel, 'refpix') + self.irs2_name = self.get_reference_file(result, 'refpix') self.log.info(f'Using refpix reference file: {self.irs2_name}') # Check for a valid reference file if self.irs2_name == 'N/A': self.log.warning('No refpix reference file found') self.log.warning('RefPix step will be skipped') - datamodel.meta.cal_step.refpix = 'SKIPPED' - return datamodel + result.meta.cal_step.refpix = 'SKIPPED' + return result # Load the reference file into a datamodel irs2_model = datamodels.IRS2Model(self.irs2_name) # Apply the IRS2 correction scheme result = irs2_subtract_reference.correct_model( - datamodel, irs2_model, preserve_refpix=self.preserve_irs2_refpix) + result, irs2_model, preserve_refpix=self.preserve_irs2_refpix) if result.meta.cal_step.refpix != 'SKIPPED': result.meta.cal_step.refpix = 'COMPLETE' - irs2_model.close() + del irs2_model return result else: # Not an NRS IRS2 exposure. Do the normal refpix correction. - datamodel = input_model.copy() - status = reference_pixels.correct_model(datamodel, + status = reference_pixels.correct_model(result, self.odd_even_columns, self.use_side_ref_pixels, self.side_smoothing_length, @@ -88,13 +89,14 @@ def process(self, input): self.odd_even_rows) if status == reference_pixels.REFPIX_OK: - datamodel.meta.cal_step.refpix = 'COMPLETE' + result.meta.cal_step.refpix = 'COMPLETE' elif status == reference_pixels.SUBARRAY_DOESNTFIT: self.log.warning("Subarray doesn't fit in full-sized array") - datamodel.meta.cal_step.refpix = 'SKIPPED' + result.meta.cal_step.refpix = 'SKIPPED' elif status == reference_pixels.BAD_REFERENCE_PIXELS: self.log.warning("No valid reference pixels, refpix step skipped") - datamodel.meta.cal_step.refpix = 'SKIPPED' + result.meta.cal_step.refpix = 'SKIPPED' elif status == reference_pixels.SUBARRAY_SKIPPED: - datamodel.meta.cal_step.refpix = 'SKIPPED' - return datamodel + result.meta.cal_step.refpix = 'SKIPPED' + + return result diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 9d204b89ee..2d6f46061f 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -2,6 +2,8 @@ from astropy.io.fits.diff import FITSDiff from numpy.testing import assert_allclose from gwcs.wcstools import grid_from_bounding_box +import tracemalloc +import numpy as np from stdatamodels.jwst import datamodels @@ -113,6 +115,39 @@ def test_miri_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwa _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) +@pytest.mark.bigdata +def test_detector1_mem_usage(rtdata_module): + """Determine the memory usage for Detector 1""" + rtdata = rtdata_module + rtdata.get_data("miri/image/jw01024001001_04101_00001_mirimage_uncal.fits") + args = ["jwst.pipeline.Detector1Pipeline", rtdata.input] + + # starting the monitoring + tracemalloc.start() + + # run Detector1 + Step.from_cmdline(args) + + # displaying the memory + current_mem, peak_mem = tracemalloc.get_traced_memory() + # convert bytes to GB + peak_mem *= 1e-9 + peak_mem = np.round(peak_mem, decimals=1) + + # stopping the monitoring + tracemalloc.stop() + + # set comparison values in GB + mem_threshold = 16.0 # average user's available memory + mem_benchmark = 11.0 # benchmark run with build 1.15.1 + 1 additional GB + + # test that max memory is less than threshold + assert peak_mem < mem_threshold, "Max memory used is greater than 16 GB!" + + # test that max memory is less or equal to benchmark + assert peak_mem <= mem_benchmark, "Max memory used is greater than 11 GB" + + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["dark_current", "ramp", "rate", "rateints"]) diff --git a/jwst/reset/reset_step.py b/jwst/reset/reset_step.py index 12457482aa..4ad03e7d52 100755 --- a/jwst/reset/reset_step.py +++ b/jwst/reset/reset_step.py @@ -14,43 +14,46 @@ class ResetStep(Step): class_alias = "reset" + spec = """ + """ + reference_file_types = ['reset'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.open(input) as input_model: + with datamodels.open(step_input) as input_model: # check the data is MIRI data detector = input_model.meta.instrument.detector - if detector.startswith('MIR'): + if not detector.startswith('MIR'): + self.log.warning('Reset Correction is only for MIRI data') + self.log.warning('Reset step will be skipped') + input_model.meta.cal_step.reset = 'SKIPPED' + return input_model - # Get the name of the reset reference file to use - self.reset_name = self.get_reference_file(input_model, 'reset') - self.log.info('Using RESET reference file %s', self.reset_name) + # Get the name of the reset reference file to use + self.reset_name = self.get_reference_file(input_model, 'reset') + self.log.info('Using RESET reference file %s', self.reset_name) - # Check for a valid reference file - if self.reset_name == 'N/A': - self.log.warning('No RESET reference file found') - self.log.warning('Reset step will be skipped') - result = input_model.copy() - result.meta.cal_step.reset = 'SKIPPED' - return result + # Check for a valid reference file + if self.reset_name == 'N/A': + self.log.warning('No RESET reference file found') + self.log.warning('Reset step will be skipped') + input_model.meta.cal_step.reset = 'SKIPPED' + return input_model - # Open the reset ref file data model - reset_model = datamodels.ResetModel(self.reset_name) + # Open the reset ref file data model + reset_model = datamodels.ResetModel(self.reset_name) - # Do the reset correction subtraction - result = reset_sub.do_correction(input_model, reset_model) + # Work on a copy + result = input_model.copy() - # Close the reference file and update the step status - reset_model.close() - result.meta.cal_step.reset = 'COMPLETE' + # Do the reset correction subtraction + result = reset_sub.do_correction(result, reset_model) + result.meta.cal_step.reset = 'COMPLETE' - else: - self.log.warning('Reset Correction is only for MIRI data') - self.log.warning('Reset step will be skipped') - result = input_model.copy() - result.meta.cal_step.reset = 'SKIPPED' + # Cleanup + del reset_model return result diff --git a/jwst/reset/reset_sub.py b/jwst/reset/reset_sub.py index cc2ffae834..4dba18c5d3 100644 --- a/jwst/reset/reset_sub.py +++ b/jwst/reset/reset_sub.py @@ -8,7 +8,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model, reset_model): +def do_correction(output_model, reset_model): """ Short Summary ------------- @@ -21,7 +21,7 @@ def do_correction(input_model, reset_model): Parameters ---------- - input_model: data model object + output_model: data model object science data to be corrected reset_model: reset model object @@ -36,10 +36,10 @@ def do_correction(input_model, reset_model): """ # Save some data params for easy use later - sci_nints = input_model.meta.exposure.nints # num ints in input data - sci_ngroups = input_model.meta.exposure.ngroups # num groups in input data - sci_integration_start = input_model.meta.exposure.integration_start - sci_integration_end = input_model.meta.exposure.integration_end + sci_nints = output_model.meta.exposure.nints # num ints in input data + sci_ngroups = output_model.meta.exposure.ngroups # num groups in input data + sci_integration_start = output_model.meta.exposure.integration_start + sci_integration_end = output_model.meta.exposure.integration_end istart = 0 iend = sci_nints @@ -55,9 +55,6 @@ def do_correction(input_model, reset_model): reset_model.data[np.isnan(reset_model.data)] = 0.0 log.debug("Reset Sub using: nints = {}, ngroups = {}".format(sci_nints, sci_ngroups)) - # Create output as a copy of the input science data model - output = input_model.copy() - # find out how many groups we are correcting # the maximum number of groups to correct is reset_ngroups igroup = sci_ngroups @@ -72,15 +69,15 @@ def do_correction(input_model, reset_model): ir = reset_nints - 1 # combine the science and reset DQ arrays - output.pixeldq = np.bitwise_or(input_model.pixeldq, reset_model.dq) + output_model.pixeldq = np.bitwise_or(output_model.pixeldq, reset_model.dq) # we are only correcting the first reset_ngroups for j in range(igroup): - output.data[i - istart, j] -= reset_model.data[ir, j] + output_model.data[i - istart, j] -= reset_model.data[ir, j] # combine the ERR arrays in quadrature # NOTE: currently stubbed out until ERR handling is decided # output.err[i,j] = np.sqrt( # output.err[i,j]**2 + reset.err[j]**2) - return output + return output_model diff --git a/jwst/reset/tests/test_reset_sub.py b/jwst/reset/tests/test_reset_sub.py index 74d2e6e86e..212c6fba65 100644 --- a/jwst/reset/tests/test_reset_sub.py +++ b/jwst/reset/tests/test_reset_sub.py @@ -154,8 +154,9 @@ def test_2_int(make_rampmodel, make_resetmodel): reset.data[0, i] = i * 0.1 reset.data[1, i] = i * 0.2 - # run correction - outfile = resetcorr(dm_ramp, reset) + # run correction on a copy of the input datamodel (the detection to make the copy + # or not would have happened at _step.py + outfile = resetcorr(dm_ramp.copy(), reset) # check that the reset file is subtracted frame by frame from the science data diff = dm_ramp.data[0] - reset.data[0, :ngroups] diff --git a/jwst/rscd/rscd_step.py b/jwst/rscd/rscd_step.py index 43eb9ddd66..98dcf3a02e 100755 --- a/jwst/rscd/rscd_step.py +++ b/jwst/rscd/rscd_step.py @@ -27,39 +27,40 @@ class RscdStep(Step): reference_file_types = ['rscd'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.RampModel(step_input) as input_model: # check the data is MIRI data detector = input_model.meta.instrument.detector - if detector.startswith('MIR'): + if not detector.startswith('MIR'): + self.log.warning('RSCD correction is only for MIRI data') + self.log.warning('RSCD step will be skipped') + input_model.meta.cal_step.rscd = 'SKIPPED' + return input_model - # Get the name of the rscd reference file to use - self.rscd_name = self.get_reference_file(input_model, 'rscd') - self.log.info('Using RSCD reference file %s', self.rscd_name) + # Get the name of the rscd reference file to use + self.rscd_name = self.get_reference_file(input_model, 'rscd') + self.log.info('Using RSCD reference file %s', self.rscd_name) - # Check for a valid reference file - if self.rscd_name == 'N/A': - self.log.warning('No RSCD reference file found') - self.log.warning('RSCD step will be skipped') - input_model.meta.cal_step.rscd = 'SKIPPED' - return input_model + # Check for a valid reference file + if self.rscd_name == 'N/A': + self.log.warning('No RSCD reference file found') + self.log.warning('RSCD step will be skipped') + input_model.meta.cal_step.rscd = 'SKIPPED' + return input_model - # Load the rscd ref file data model - rscd_model = datamodels.RSCDModel(self.rscd_name) + # Load the rscd ref file data model + rscd_model = datamodels.RSCDModel(self.rscd_name) - # Do the rscd correction - result = rscd_sub.do_correction(input_model, rscd_model, self.type) + # Work on a copy + result = input_model.copy() - # Close the reference file - rscd_model.close() + # Do the rscd correction + result = rscd_sub.do_correction(result, rscd_model, self.type) - else: - self.log.warning('RSCD correction is only for MIRI data') - self.log.warning('RSCD step will be skipped') - result = input_model.copy() - result.meta.cal_step.rscd = 'SKIPPED' + # Cleanup + del rscd_model return result diff --git a/jwst/rscd/rscd_sub.py b/jwst/rscd/rscd_sub.py index 47ecee38f1..305c55f320 100644 --- a/jwst/rscd/rscd_sub.py +++ b/jwst/rscd/rscd_sub.py @@ -11,7 +11,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model, rscd_model, type): +def do_correction(output_model, rscd_model, type): """ Short Summary ------------- @@ -21,7 +21,7 @@ def do_correction(input_model, rscd_model, type): Parameters ---------- - input_model: ~jwst.datamodels.RampModel + output_model: ~jwst.datamodels.RampModel science data to be corrected rscd_model: ~jwst.datamodels.RSCDModel @@ -38,28 +38,28 @@ def do_correction(input_model, rscd_model, type): """ # Retrieve the reference parameters for this exposure type - param = get_rscd_parameters(input_model, rscd_model) + param = get_rscd_parameters(output_model, rscd_model) if not bool(param): # empty dictionary log.warning('READPATT, SUBARRAY combination not found in ref file: RSCD correction will be skipped') - input_model.meta.cal_step.rscd = 'SKIPPED' - return input_model + output_model.meta.cal_step.rscd = 'SKIPPED' + return output_model if type == 'baseline': group_skip = param['skip'] - output = correction_skip_groups(input_model, group_skip) + output_model = correction_skip_groups(output_model, group_skip) else: # enhanced algorithm is not enabled yet (updated code and validation needed) log.warning('Enhanced algorithm not support yet: RSCD correction will be skipped') - input_model.meta.cal_step.rscd = 'SKIPPED' - return input_model + output_model.meta.cal_step.rscd = 'SKIPPED' + return output_model # decay function algorithm update needed - # output = correction_decay_function(input_model, param) + # output_model = correction_decay_function(input_model, param) - return output + return output_model -def correction_skip_groups(input_model, group_skip): +def correction_skip_groups(output, group_skip): """ Short Summary ------------- @@ -68,7 +68,7 @@ def correction_skip_groups(input_model, group_skip): Parameters ---------- - input_model: ~jwst.datamodels.RampModel + output: ~jwst.datamodels.RampModel science data to be corrected group_skip: int @@ -81,15 +81,12 @@ def correction_skip_groups(input_model, group_skip): """ # Save some data params for easy use later - sci_nints = input_model.data.shape[0] # number of integrations - sci_ngroups = input_model.data.shape[1] # number of groups + sci_nints = output.data.shape[0] # number of integrations + sci_ngroups = output.data.shape[1] # number of groups log.debug("RSCD correction using: nints=%d, ngroups=%d" % (sci_nints, sci_ngroups)) - # Create output as a copy of the input science data model - output = input_model.copy() - # If ngroups <= group_skip+3, skip the flagging # the +3 is to ensure there is a slope to be fit including the flagging for # the last frame correction @@ -108,7 +105,7 @@ def correction_skip_groups(input_model, group_skip): return output -def correction_decay_function(input_model, param): +def correction_decay_function(output, param): """ Short Summary ------------- @@ -133,7 +130,7 @@ def correction_decay_function(input_model, param): Parameters ---------- - input_model: ~jwst.datamodels.RampModel + output: ~jwst.datamodels.RampModel science data to be corrected param: dict @@ -147,15 +144,12 @@ def correction_decay_function(input_model, param): """ # Save some data params for easy use later - sci_nints = input_model.data.shape[0] # number of integrations - sci_ngroups = input_model.data.shape[1] # number of groups + sci_nints = output.data.shape[0] # number of integrations + sci_ngroups = output.data.shape[1] # number of groups log.debug("RSCD correction using: nints=%d, ngroups=%d" % (sci_nints, sci_ngroups)) - # Create output as a copy of the input science data model - output = input_model.copy() - # Check for valid parameters if sci_ngroups < 2: log.warning('RSCD correction requires > 1 group per integration') @@ -208,17 +202,15 @@ def correction_decay_function(input_model, param): log.info(' Working on integration %d', i + 1) sat, dn_last23, dn_lastfit = \ - get_DNaccumulated_last_int(input_model, i, sci_ngroups) + get_DNaccumulated_last_int(output, i, sci_ngroups) lastframe_even = dn_last23[1::2, :] lastframe_odd = dn_last23[0::2, :] - correction_even = lastframe_even.copy() * 0.0 - correction_odd = lastframe_odd.copy() * 0.0 factor2_even = lastframe_even.copy() * 0.0 factor2_odd = lastframe_odd.copy() * 0.0 - a1_even = lastframe_even.copy() * 0.0 - a1_odd = lastframe_odd.copy() * 0.0 + # these will be created in the loop: correction_even, correction_odd, + # a1_even, and a1_odd counts2_even = lastframe_even - crossopt_even counts2_odd = lastframe_odd - crossopt_odd @@ -434,13 +426,8 @@ def get_DNaccumulated_last_int(input_model, i, sci_ngroups): dn_lastframe_fit: extrapolated last frame using the fit to the entire ramp """ - nrows = input_model.data.shape[2] - ncols = input_model.data.shape[3] dn_lastframe2 = input_model.data[i - 1][sci_ngroups - 2] dn_lastframe3 = input_model.data[i - 1][sci_ngroups - 3] - dn_lastframe23 = dn_lastframe2.copy() * 0.0 - dn_lastframe_fit = dn_lastframe2.copy() * 0.0 - saturated = np.full((nrows, ncols), False) diff = dn_lastframe2 - dn_lastframe3 dn_lastframe23 = dn_lastframe2 + diff diff --git a/jwst/rscd/tests/test_rscd.py b/jwst/rscd/tests/test_rscd.py index 9ef0011b89..eddec0c1a8 100644 --- a/jwst/rscd/tests/test_rscd.py +++ b/jwst/rscd/tests/test_rscd.py @@ -28,8 +28,8 @@ def test_rscd_baseline_set_groupdq(): # get the number of groups to flag nflag = 3 - # run the RSCD baseline correction step - dm_ramp_rscd = correction_skip_groups(dm_ramp, nflag) + # run the RSCD baseline correction step on a copy (the copy is created at the step script) + dm_ramp_rscd = correction_skip_groups(dm_ramp.copy(), nflag) # check that the difference in the groupdq flags is equal to # the 'do_not_use' flag for the 2nd integration @@ -81,14 +81,14 @@ def test_rscd_baseline_too_few_groups(): data = np.full(csize, 1.0, dtype=np.float32) groupdq = np.zeros(csize, dtype=np.uint8) - # create a JWST datamodel for MIRI data + # create a JWST datamodel for MIRI data on a copy (the copy is created at the step script) dm_ramp = RampModel(data=data, groupdq=groupdq) # get the number of groups to flag nflag = 3 # run the RSCD baseline correction step - dm_ramp_rscd = correction_skip_groups(dm_ramp, nflag) + dm_ramp_rscd = correction_skip_groups(dm_ramp.copy(), nflag) # test that the groupdq flags are not changed for any integration dq_diff = (dm_ramp_rscd.groupdq[:, :, :, :] diff --git a/jwst/saturation/saturation.py b/jwst/saturation/saturation.py index f75abf0003..8918d42dd9 100644 --- a/jwst/saturation/saturation.py +++ b/jwst/saturation/saturation.py @@ -22,7 +22,7 @@ ATOD_LIMIT = 65535. # Hard DN limit of 16-bit A-to-D converter -def flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): +def flag_saturation(output_model, ref_model, n_pix_grow_sat, use_readpatt): """ Short Summary ------------- @@ -30,7 +30,7 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): Parameters ---------- - input_model : `~jwst.datamodels.RampModel` + output_model : `~jwst.datamodels.RampModel` The input science data to be corrected ref_model : `~jwst.datamodels.SaturationModel` @@ -51,24 +51,22 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): the GROUPDQ array """ - data = input_model.data - ngroups = input_model.meta.exposure.ngroups - nframes = input_model.meta.exposure.nframes + ngroups = output_model.meta.exposure.ngroups + nframes = output_model.meta.exposure.nframes - # Create the output model as a copy of the input - output_model = input_model.copy() gdq = output_model.groupdq pdq = output_model.pixeldq + data = output_model.data - zframe = input_model.zeroframe if input_model.meta.exposure.zero_frame else None + zframe = output_model.zeroframe if output_model.meta.exposure.zero_frame else None # Extract subarray from saturation reference file, if necessary - if reffile_utils.ref_matches_sci(input_model, ref_model): + if reffile_utils.ref_matches_sci(output_model, ref_model): sat_thresh = ref_model.data sat_dq = ref_model.dq else: log.info('Extracting reference file subarray to match science data') - ref_sub_model = reffile_utils.get_subarray_model(input_model, ref_model) + ref_sub_model = reffile_utils.get_subarray_model(output_model, ref_model) sat_thresh = ref_sub_model.data.copy() sat_dq = ref_sub_model.dq.copy() ref_sub_model.close() @@ -96,7 +94,7 @@ def flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): return output_model -def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): +def irs2_flag_saturation(output_model, ref_model, n_pix_grow_sat, use_readpatt): """ Short Summary ------------- @@ -107,7 +105,7 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): Parameters ---------- - input_model : `~jwst.datamodels.RampModel` + output_model : `~jwst.datamodels.RampModel` The input science data to be corrected ref_model : `~jwst.datamodels.SaturationModel` @@ -128,11 +126,14 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): the GROUPDQ array """ - data = input_model.data + # Create the output model as a copy of the input + groupdq = output_model.groupdq + + data = output_model.data nints = data.shape[0] ngroups = data.shape[1] - detector = input_model.meta.instrument.detector - nframes = input_model.meta.exposure.nframes + detector = output_model.meta.instrument.detector + nframes = output_model.meta.exposure.nframes if use_readpatt: read_pattern = [[x + 1 + groupstart * nframes for x in range(nframes)] for groupstart in range(ngroups)] @@ -140,20 +141,17 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): else: read_pattern=None - # create a mask of the appropriate size - irs2_mask = x_irs2.make_mask(input_model) - # Create the output model as a copy of the input - output_model = input_model.copy() - groupdq = output_model.groupdq + # create a mask of the appropriate size + irs2_mask = x_irs2.make_mask(output_model) # Extract subarray from saturation reference file, if necessary - if reffile_utils.ref_matches_sci(input_model, ref_model): + if reffile_utils.ref_matches_sci(output_model, ref_model): sat_thresh = ref_model.data sat_dq = ref_model.dq else: log.info('Extracting reference file subarray to match science data') - ref_sub_model = reffile_utils.get_subarray_model(input_model, ref_model) + ref_sub_model = reffile_utils.get_subarray_model(output_model, ref_model) sat_thresh = ref_sub_model.data.copy() sat_dq = ref_sub_model.dq.copy() ref_sub_model.close() @@ -171,7 +169,7 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): flagarray = np.zeros(data.shape[-2:], dtype=groupdq.dtype) flaglowarray = np.zeros(data.shape[-2:], dtype=groupdq.dtype) - if input_model.meta.exposure.zero_frame: + if output_model.meta.exposure.zero_frame: zflagarray = np.zeros(data.shape[-2:], dtype=groupdq.dtype) zflaglowarray = np.zeros(data.shape[-2:], dtype=groupdq.dtype) @@ -229,8 +227,8 @@ def irs2_flag_saturation(input_model, ref_model, n_pix_grow_sat, use_readpatt): # Process ZEROFRAME. Instead of setting a ZEROFRAME DQ array, data # in the ZEROFRAME that is flagged will be set to 0. - if input_model.meta.exposure.zero_frame: - zplane = input_model.zeroframe[ints, :, :] + if output_model.meta.exposure.zero_frame: + zplane = output_model.zeroframe[ints, :, :] zdq = np.zeros(groupdq.shape[-2:], dtype=groupdq.dtype) ztemp = x_irs2.from_irs2(zplane, irs2_mask, detector) diff --git a/jwst/saturation/saturation_step.py b/jwst/saturation/saturation_step.py index 849abadb5e..d4bb08e70b 100755 --- a/jwst/saturation/saturation_step.py +++ b/jwst/saturation/saturation_step.py @@ -23,10 +23,10 @@ class SaturationStep(Step): reference_file_types = ['saturation'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.open(step_input) as input_model: # Get the name of the saturation reference file self.ref_name = self.get_reference_file(input_model, 'saturation') @@ -36,21 +36,23 @@ def process(self, input): if self.ref_name == 'N/A': self.log.warning('No SATURATION reference file found') self.log.warning('Saturation step will be skipped') - result = input_model.copy() - result.meta.cal_step.saturation = 'SKIPPED' - return result + input_model.meta.cal_step.saturation = 'SKIPPED' + return input_model # Open the reference file data model ref_model = datamodels.SaturationModel(self.ref_name) + # Work on a copy + result = input_model.copy() + # Do the saturation check - if pipe_utils.is_irs2(input_model): - sat = saturation.irs2_flag_saturation(input_model, ref_model, self.n_pix_grow_sat, self.use_readpatt) + if pipe_utils.is_irs2(result): + result = saturation.irs2_flag_saturation(result, ref_model, self.n_pix_grow_sat, self.use_readpatt) else: - sat = saturation.flag_saturation(input_model, ref_model, self.n_pix_grow_sat, self.use_readpatt) + result = saturation.flag_saturation(result, ref_model, self.n_pix_grow_sat, self.use_readpatt) + result.meta.cal_step.saturation = 'COMPLETE' - # Close the reference file and update the step status - ref_model.close() - sat.meta.cal_step.saturation = 'COMPLETE' + # Cleanup + del ref_model - return sat + return result diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 66103dfef3..02ae5eb92a 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -28,6 +28,7 @@ class JwstStep(Step): def _datamodels_open(cls, init, **kwargs): return datamodels.open(init, **kwargs) + def load_as_level2_asn(self, obj): """Load object as an association diff --git a/jwst/superbias/bias_sub.py b/jwst/superbias/bias_sub.py index fb26d61beb..4937d93e71 100644 --- a/jwst/superbias/bias_sub.py +++ b/jwst/superbias/bias_sub.py @@ -47,7 +47,7 @@ def do_correction(input_model, bias_model): return output_model -def subtract_bias(input, bias): +def subtract_bias(output, bias): """ Subtracts a superbias image from a science data set, subtracting the superbias from each group of each integration in the science data. @@ -56,7 +56,7 @@ def subtract_bias(input, bias): Parameters ---------- - input: data model object + output: data model object the input science data bias: superbias model object @@ -69,19 +69,18 @@ def subtract_bias(input, bias): """ - # Create output as a copy of the input science data model - output = input.copy() - # combine the science and superbias DQ arrays - output.pixeldq = np.bitwise_or(input.pixeldq, bias.dq) + output.pixeldq = np.bitwise_or(output.pixeldq, bias.dq) # Subtract the superbias image from all groups and integrations # of the science data + if not isinstance(type(output.data), float): + output.data = (output.data).astype(float) output.data -= bias.data # If ZEROFRAME is present, subtract the super bias. Zero values # indicate bad data, so should be kept zero. - if input.meta.exposure.zero_frame: + if output.meta.exposure.zero_frame: wh_zero = np.where(output.zeroframe == 0.) output.zeroframe -= bias.data output.zeroframe[wh_zero] = 0. # Zero values indicate unusable data diff --git a/jwst/superbias/superbias_step.py b/jwst/superbias/superbias_step.py index 26ddfc0758..8da93d5eed 100755 --- a/jwst/superbias/superbias_step.py +++ b/jwst/superbias/superbias_step.py @@ -15,15 +15,14 @@ class SuperBiasStep(Step): class_alias = "superbias" spec = """ - """ reference_file_types = ['superbias'] - def process(self, input): + def process(self, step_input): # Open the input data model - with datamodels.RampModel(input) as input_model: + with datamodels.open(step_input) as input_model: # Get the name of the superbias reference file to use self.bias_name = self.get_reference_file(input_model, 'superbias') @@ -33,19 +32,20 @@ def process(self, input): if self.bias_name == 'N/A': self.log.warning('No SUPERBIAS reference file found') self.log.warning('Superbias step will be skipped') - result = input_model.copy() - result.meta.cal_step.superbias = 'SKIPPED' - return result + input_model.meta.cal_step.superbias = 'SKIPPED' + return input_model # Open the superbias ref file data model bias_model = datamodels.SuperBiasModel(self.bias_name) - # Do the bias subtraction - result = bias_sub.do_correction(input_model, bias_model) + # Work on a copy + result = input_model.copy() - # Close the superbias reference file model and - # set the step status to complete - bias_model.close() + # Do the bias subtraction + result = bias_sub.do_correction(result, bias_model) result.meta.cal_step.superbias = 'COMPLETE' + # Cleanup + del bias_model + return result diff --git a/jwst/superbias/tests/test_bias_sub.py b/jwst/superbias/tests/test_bias_sub.py index b517b988df..266450df75 100755 --- a/jwst/superbias/tests/test_bias_sub.py +++ b/jwst/superbias/tests/test_bias_sub.py @@ -27,8 +27,8 @@ def test_basic_superbias_subtraction(setup_full_cube): data.data[:] = blevel bias.data[:] = blevel - # Run the pipeline - output = do_correction(data, bias) + # Run the pipeline on a copy as this is now being done in superbias_step + output = do_correction(data.copy(), bias) # Make sure that manual superbias subtraction matches pipeline output manual = data.data - bias.data @@ -54,8 +54,8 @@ def test_subarray_correction(setup_subarray_cube): data.data[:] = blevel manualbias[:] = blevel - # Run the pipeline - output = do_correction(data, bias) + # Run the pipeline on a copy as this is now being done in superbias_step + output = do_correction(data.copy(), bias) # Make sure the subarray was extracted correctly from superbias file # Make sure that manual superbias subtraction matches pipeline output @@ -106,8 +106,8 @@ def test_nans_in_superbias(setup_full_cube): # Set superbias value for pixel to NaN bias.data[500, 500] = np.nan - # Run the pipeline - output = do_correction(data, bias) + # Run the pipeline on a copy as this is now being done in superbias_step + output = do_correction(data.copy(), bias) # Check that subtraction was not done on pixel with NaN reference value # Check that subtraction was done on other pixels From 40a019e9193af0a48c5026bdacfcc1a6ae0aab11 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Fri, 20 Sep 2024 14:09:45 -0400 Subject: [PATCH 477/687] JP-3588: Use Pastasoss datamodel for NIRISS SOSS transform solution (#8763) --- CHANGES.rst | 6 + docs/jwst/extract_1d/arguments.rst | 5 - jwst/extract_1d/extract_1d_step.py | 10 +- jwst/extract_1d/soss_extract/atoca_utils.py | 74 --- jwst/extract_1d/soss_extract/pastasoss.py | 551 ++++++++++++++++++ .../soss_extract/soss_boxextract.py | 2 +- jwst/extract_1d/soss_extract/soss_extract.py | 376 +++++------- jwst/extract_1d/soss_extract/soss_solver.py | 508 ---------------- 8 files changed, 693 insertions(+), 839 deletions(-) create mode 100644 jwst/extract_1d/soss_extract/pastasoss.py delete mode 100644 jwst/extract_1d/soss_extract/soss_solver.py diff --git a/CHANGES.rst b/CHANGES.rst index 1a61f13086..3948e03994 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -122,6 +122,12 @@ emicorr - Removed unnecessary copies, and created a single copy at step.py level. [#8676] +extract_1d +---------- + +- Updated NIRISS SOSS extraction to utilize ``pastasoss`` + rotation solution. [#8763] + first_frame ----------- diff --git a/docs/jwst/extract_1d/arguments.rst b/docs/jwst/extract_1d/arguments.rst index dd049b4902..7b338a86f4 100644 --- a/docs/jwst/extract_1d/arguments.rst +++ b/docs/jwst/extract_1d/arguments.rst @@ -169,11 +169,6 @@ The ``extract_1d`` step has the following step-specific arguments. used when soss_wave_grid is not provided to make sure the computation time or the memory used stays reasonable. Default value is 20000. -``--soss_transform`` - This is a NIRISS-SOSS algorithm-specific parameter; this defines a rotation to - apply to the reference files to match the observation. It should be specified as - a list of three floats, with default values of None. - ``--soss_tikfac`` This is a NIRISS-SOSS algorithm-specific parameter; this is the regularization factor used in the SOSS extraction. If not specified, ATOCA will calculate a diff --git a/jwst/extract_1d/extract_1d_step.py b/jwst/extract_1d/extract_1d_step.py index 97399f2c72..f8b43b0178 100644 --- a/jwst/extract_1d/extract_1d_step.py +++ b/jwst/extract_1d/extract_1d_step.py @@ -174,14 +174,13 @@ class Extract1dStep(Step): soss_estimate = input_file(default = None) # Estimate used to generate the wavelength grid soss_rtol = float(default=1.0e-4) # Relative tolerance needed on a pixel model soss_max_grid_size = integer(default=20000) # Maximum grid size, if wave_grid not specified - soss_transform = list(default=None, min=3, max=3) # rotation applied to the ref files to match observation. soss_tikfac = float(default=None) # regularization factor for NIRISS SOSS extraction soss_width = float(default=40.) # aperture width used to extract the 1D spectrum from the de-contaminated trace. soss_bad_pix = option("model", "masking", default="masking") # method used to handle bad pixels soss_modelname = output_file(default = None) # Filename for optional model output of traces and pixel weights """ - reference_file_types = ['extract1d', 'apcorr', 'wavemap', 'spectrace', 'specprofile', 'speckernel'] + reference_file_types = ['extract1d', 'apcorr', 'pastasoss', 'specprofile', 'speckernel'] def process(self, input): """Execute the step. @@ -432,8 +431,7 @@ def process(self, input): return input_model # Load reference files. - spectrace_ref_name = self.get_reference_file(input_model, 'spectrace') - wavemap_ref_name = self.get_reference_file(input_model, 'wavemap') + pastasoss_ref_name = self.get_reference_file(input_model, 'pastasoss') specprofile_ref_name = self.get_reference_file(input_model, 'specprofile') speckernel_ref_name = self.get_reference_file(input_model, 'speckernel') @@ -444,7 +442,6 @@ def process(self, input): soss_kwargs['tikfac'] = self.soss_tikfac soss_kwargs['width'] = self.soss_width soss_kwargs['bad_pix'] = self.soss_bad_pix - soss_kwargs['transform'] = self.soss_transform soss_kwargs['subtract_background'] = self.subtract_background soss_kwargs['rtol'] = self.soss_rtol soss_kwargs['max_grid_size'] = self.soss_max_grid_size @@ -458,8 +455,7 @@ def process(self, input): # Run the extraction. result, ref_outputs, atoca_outputs = soss_extract.run_extract1d( input_model, - spectrace_ref_name, - wavemap_ref_name, + pastasoss_ref_name, specprofile_ref_name, speckernel_ref_name, subarray, diff --git a/jwst/extract_1d/soss_extract/atoca_utils.py b/jwst/extract_1d/soss_extract/atoca_utils.py index 60b26621d1..19a0144539 100644 --- a/jwst/extract_1d/soss_extract/atoca_utils.py +++ b/jwst/extract_1d/soss_extract/atoca_utils.py @@ -252,80 +252,6 @@ def get_wv_map_bounds(wave_map, dispersion_axis=1): return wave_top, wave_bottom -def check_dispersion_direction(wave_map, dispersion_axis=1, dwv_sign=-1): - """Check that the dispersion axis is increasing in the good direction - given by `dwv_sign`` - Parameters - ---------- - wave_map : array[float] - 2d-map of the pixel central wavelength - dispersion_axis : int, optional - Which axis is the dispersion axis (0 or 1) - dwv_sign : int, optional - Direction of increasing wavelengths (-1 or 1) - - Returns - ------- - bool_map : array[bool] - Boolean 2d map of the valid dispersion direction, same shape as `wave_map` - """ - - # Estimate the direction of increasing wavelength - wave_left, wave_right = get_wv_map_bounds(wave_map, dispersion_axis=dispersion_axis) - dwv = wave_right - wave_left - - # Return bool map of pixels following the good direction - bool_map = (dwv_sign * dwv >= 0) - # The bad value could be from left or right so mask both - bool_map &= np.roll(bool_map, 1, axis=dispersion_axis) - - return bool_map - - -def mask_bad_dispersion_direction(wave_map, n_max=10, fill_value=0, dispersion_axis=1, dwv_sign=-1): - """Change value of the pixels in `wave_map` that do not follow the - general dispersion direction. - - Parameters - ---------- - wave_map : array[float] - 2d-map of the pixel central wavelength - n_max : int - Maximum number of iterations - fill_value : float - Value use to replace pixels that do not follow the dispersion direction - dispersion_axis : int, optional - Which axis is the dispersion axis (0 or 1) - dwv_sign : int, optional - Direction of increasing wavelengths (-1 or 1) - - Returns - ------- - wave_map : array[float] - The corrected wave_map. - convergence flag : bool - Boolean set to True if all the pixels are now valid, False otherwise. - """ - # Do not modify the input - wave_map = wave_map.copy() - - # Make the correction iteratively - for i_try in range(n_max): - # Check which pixels are good - is_good_direction = check_dispersion_direction(wave_map, dispersion_axis, dwv_sign) - # Stop iteration if all good, or apply correction where needed. - if is_good_direction.all(): - convergence_flag = True - break - else: - wave_map[~is_good_direction] = fill_value - else: - # Did not succeed! :( - convergence_flag = False - - return wave_map, convergence_flag - - def oversample_grid(wave_grid, n_os=1): """Create an oversampled version of the input 1D wavelength grid. diff --git a/jwst/extract_1d/soss_extract/pastasoss.py b/jwst/extract_1d/soss_extract/pastasoss.py new file mode 100644 index 0000000000..4030df2224 --- /dev/null +++ b/jwst/extract_1d/soss_extract/pastasoss.py @@ -0,0 +1,551 @@ +from functools import partial +import logging +import numpy as np +from scipy.interpolate import interp1d + +log = logging.getLogger(__name__) + +SOSS_XDIM = 2048 +SOSS_YDIM = 300 +XTRACE_ORD1_LEN = SOSS_XDIM +XTRACE_ORD2_LEN = 1783 +WAVEMAP_WLMIN = 0.5 +WAVEMAP_WLMAX = 5.5 +WAVEMAP_NWL = 5001 +SUBARRAY_YMIN = 2048 - 256 + +def get_wavelengths(refmodel, x, pwcpos, order): + """Get the associated wavelength values for a given spectral order""" + if order == 1: + wavelengths = wavecal_model_order1_poly(refmodel, x, pwcpos) + elif order == 2: + wavelengths = wavecal_model_order2_poly(refmodel, x, pwcpos) + + return wavelengths + + +def min_max_scaler(x, x_min, x_max): + """ + Apply min-max scaling to input values. + + Parameters + ---------- + x : float or numpy.ndarray + The input value(s) to be scaled. + x_min : float + The minimum value in the range to which 'x' will be scaled. + x_max : float + The maximum value in the range to which 'x' will be scaled. + + Returns + ------- + float or numpy.ndarray + The scaled value(s) in the range [0, 1]. + + Notes + ----- + Min-max scaling is a data normalization technique that scales input values + 'x' to the range [0, 1] based on the provided minimum and maximum values, + 'x_min' and 'x_max'. This function is applicable to both individual values + and arrays of values. This function will use the min/max values from the + training data of the wavecal model. + """ + x_scaled = (x - x_min) / (x_max - x_min) + return x_scaled + + +def wavecal_model_order1_poly(refmodel, x, pwcpos): + """Compute order 1 wavelengths. + + Parameters + ---------- + refmodel : PastasossModel + The reference model holding the wavecal models + and scale extents + x : float or numpy.ndarray + The input pixel values for which the function + will estimate wavelengths + pwcpos : float + The position of the pupil wheel; used to determine + the difference between current and commanded position + to rotate the model + """ + x_scaler = partial( + min_max_scaler, + **{ + "x_min": refmodel.wavecal_models[0].scale_extents[0][0], + "x_max": refmodel.wavecal_models[0].scale_extents[1][0], + }, + ) + + pwcpos_offset_scaler = partial( + min_max_scaler, + **{ + "x_min": refmodel.wavecal_models[0].scale_extents[0][1], + "x_max": refmodel.wavecal_models[0].scale_extents[1][1], + }, + ) + + def get_poly_features(x, offset): + """Polynomial features for the order 1 wavecal model""" + poly_features = np.array( + [ + x, + offset, + x**2, + x * offset, + offset**2, + x**3, + x**2 * offset, + x * offset**2, + offset**3, + x**4, + x**3 * offset, + x**2 * offset**2, + x * offset**3, + offset**4, + x**5, + x**4 * offset, + x**3 * offset**2, + x**2 * offset**3, + x * offset**4, + offset**5, + ] + ) + return poly_features + + # extract model weights and intercept + coef = refmodel.wavecal_models[0].coefficients + + # get pixel columns and then scaled + x_scaled = x_scaler(x) + + # offset + offset = np.ones_like(x) * (pwcpos - refmodel.meta.pwcpos_cmd) + offset_scaled = pwcpos_offset_scaler(offset) + + # polynomial features + poly_features = get_poly_features(x_scaled, offset_scaled) + wavelengths = coef[0] + coef[1:] @ poly_features + + return wavelengths + + +def wavecal_model_order2_poly(refmodel, x, pwcpos): + """Compute order 2 wavelengths. + + Parameters + ---------- + refmodel : PastasossModel + The reference model holding the wavecal models + and scale extents + x : float or numpy.ndarray + The input pixel values for which the function + will estimate wavelengths + pwcpos : float + The position of the pupil wheel; used to determine + the difference between current and commanded position + to rotate the model + """ + x_scaler = partial( + min_max_scaler, + **{ + "x_min": refmodel.wavecal_models[1].scale_extents[0][0], + "x_max": refmodel.wavecal_models[1].scale_extents[1][0], + }, + ) + + pwcpos_offset_scaler = partial( + min_max_scaler, + **{ + "x_min": refmodel.wavecal_models[1].scale_extents[0][1], + "x_max": refmodel.wavecal_models[1].scale_extents[1][1], + }, + ) + + def get_poly_features(x, offset): + """Polynomial features for the order 2 wavecal model""" + poly_features = np.array( + [ + x, + offset, + x**2, + x * offset, + offset**2, + x**3, + x**2 * offset, + x * offset**2, + offset**3, + ] + ) + return poly_features + + # coef and intercept + coef = refmodel.wavecal_models[1].coefficients + + # get pixel columns and then scaled + x_scaled = x_scaler(x) + + offset = np.ones_like(x) * pwcpos + offset_scaled = pwcpos_offset_scaler(offset) + + # polynomial features + poly_features = get_poly_features(x_scaled, offset_scaled) + wavelengths = coef[0] + coef[1:] @ poly_features + + return wavelengths + + +def rotate(x, y, angle, origin=(0, 0), interp=True): + """ + Applies a rotation transformation to a set of 2D points. + + Parameters + ---------- + x : np.ndarray + The x-coordinates of the points to be transformed. + y : np.ndarray + The y-coordinates of the points to be transformed. + angle : float + The angle (in degrees) by which to rotate the points. + origin : Tuple[float, float], optional + The point about which to rotate the points. Default is (0, 0). + interp : bool, optional + Whether to interpolate the rotated positions onto the original x-pixel + column values. Default is True. + + Returns + ------- + Tuple[np.ndarray, np.ndarray] + The x and y coordinates of the rotated points. + + Examples + -------- + >>> x = np.array([0, 1, 2, 3]) + >>> y = np.array([0, 1, 2, 3]) + >>> x_rot, y_rot = rotate(x, y, 90) + """ + + # shift to rotate about center + xy_center = np.atleast_2d(origin).T + xy = np.vstack([x, y]) + + # Rotation transform matrix + radians = np.radians(angle) + c, s = np.cos(radians), np.sin(radians) + R = np.array([[c, -s], [s, c]]) + + # apply transformation + x_new, y_new = R @ (xy - xy_center) + xy_center + + # interpolate rotated positions onto x-pixel column values (default) + if interp: + # interpolate new coordinates onto original x values and mask values + # outside of the domain of the image 0<=x<=2047 and 0<=y<=255. + y_new = interp1d(x_new, y_new, fill_value="extrapolate")(x) + mask = np.where(y_new <= 255.0) + x = x[mask] + y_new = y_new[mask] + return x, y_new + + return x_new, y_new + + +def find_spectral_order_index(refmodel, order): + """Return index of trace and wavecal dict corresponding to order + + Parameters + ---------- + refmodel : datamodel + The reference file holding traces and wavelength calibration + models, under `refmodel.traces` and `refmodel.wavecal_models` + order: int + The spectral order to find trace and wavecal model indices for. + + Returns + ------- + int + The index to provide the reference file lists of traces and wavecal + models to retrieve the arrays for the desired spectral order + """ + + for i, entry in enumerate(refmodel.traces): + if entry.spectral_order == order: + return i + + log.warning("Order not found in reference file trace list.") + return -1 + + +def get_soss_traces(refmodel, pwcpos, order, subarray, interp=True): + """Generate the traces given a pupil wheel position. + + This is the primary method for generating the gr700xd trace position given a + pupil wheel position angle provided in the FITS header under keyword + PWCPOS. The traces for a given spectral order are found by performing a + rotation transformation using the refence trace positions at the commanded + PWCPOS=245.76 degrees. This method yields sub-pixel performance and will be + improved upon in later interations as more NIRISS/SOSS observations become + available. + + Parameters + ---------- + refmodel : PastasossModel + The reference file datamodel. + pwcpos : float + The pupil wheel positions angle provided in the FITS header under + keyword PWCPOS. + order : str + The spectral order for which a trace is computed. + Order 3 is currently unsupported. + subarray : str + Name of subarray in use, typically 'SUBSTRIP96' or 'SUBSTRIP256'. + interp : bool, optional + Whether to interpolate the rotated positions onto the original x-pixel + column values. Default is True. + + Returns + ------- + Tuple[np.ndarray, np.ndarray]] + If `order` is '1', a tuple of the x and y coordinates of the rotated + points for the first spectral order. + If `order` is '2', a tuple of the x and y coordinates of the rotated + points for the second spectral order. + If `order` is '3' or a combination of '1', '2', and '3', a list of + tuples of the x and y coordinates of the rotated points for each + spectral order. + + Raises + ------ + ValueError + If `order` is not '1', '2', '3', or a combination of '1', '2', and '3'. + """ + spectral_order_index = find_spectral_order_index(refmodel, int(order)) + + if spectral_order_index < 0: + error_message = f"Order {order} is not supported at this time." + log.error(error_message) + raise ValueError(error_message) + else: + # reference trace data + x, y = refmodel.traces[spectral_order_index].trace.T.copy() + origin = refmodel.traces[spectral_order_index].pivot_x, refmodel.traces[spectral_order_index].pivot_y + + # Offset for SUBSTRIP96 + if subarray == 'SUBSTRIP96': + y -= 10 + # rotated reference trace + x_new, y_new = rotate(x, y, pwcpos - refmodel.meta.pwcpos_cmd, origin, interp=interp) + + # wavelength associated to trace at given pwcpos value + wavelengths = get_wavelengths(refmodel, x_new, pwcpos, int(order)) + + return order, x_new, y_new, wavelengths + + +def extrapolate_to_wavegrid(w_grid, wavelength, quantity): + """ + Extrapolates quantities on the right and the left of a given array of quantity + + Parameters + ---------- + w_grid : sequence + The wavelength grid to interpolate onto + wavelength : sequence + The native wavelength values of the data + quantity : sequence + The data to interpolate + + Returns + ------- + Array + The interpolated quantities + """ + sorted = np.argsort(wavelength) + q = quantity[sorted] + w = wavelength[sorted] + + # Determine the slope on the right of the array + slope_right = (q[-1] - q[-2]) / (w[-1] - w[-2]) + # extrapolate at wavelengths larger than the max on the right + indright = np.where(w_grid > w[-1])[0] + q_right = q[-1] + (w_grid[indright] - w[-1]) * slope_right + # Determine the slope on the left of the array + slope_left = (q[1] - q[0]) / (w[1] - w[0]) + # extrapolate at wavelengths smaller than the min on the left + indleft = np.where(w_grid < w[0])[0] + q_left = q[0] + (w_grid[indleft] - w[0]) * slope_left + # Construct and extrapolated array of the quantity + w = np.concatenate((w_grid[indleft], w, w_grid[indright])) + q = np.concatenate((q_left, q, q_right)) + + # resample at the w_grid everywhere + q_grid = np.interp(w_grid, w, q) + + return q_grid + + +def calc_2d_wave_map(wave_grid, x_dms, y_dms, tilt, oversample=2, padding=0, maxiter=5, dtol=1e-2): + """Compute the 2D wavelength map on the detector. + + Parameters + ---------- + wave_grid : sequence + The wavelength corresponding to the x_dms, y_dms, and tilt values. + x_dms : sequence + The trace x position on the detector in DMS coordinates. + y_dms : sequence + The trace y position on the detector in DMS coordinates. + tilt : sequence + The trace tilt angle in degrees. + oversample : int + The oversampling factor of the input coordinates. + padding : int + The native pixel padding around the edge of the detector. + maxiter : int + The maximum number of iterations used when solving for the wavelength at each pixel. + dtol : float + The tolerance of the iterative solution in pixels. + + Returns + ------- + Array + An array containing the wavelength at each pixel on the detector. + """ + os = np.copy(oversample) + xpad = np.copy(padding) + ypad = np.copy(padding) + + # No need to compute wavelengths across the entire detector, slightly larger than SUBSTRIP256 will do. + dimx, dimy = SOSS_XDIM, SOSS_YDIM + y_dms = y_dms + (dimy - SOSS_XDIM) # Adjust y-coordinate to area of interest. + + # Generate the oversampled grid of pixel coordinates. + x_vec = np.arange((dimx + 2 * xpad) * os) / os - (os - 1) / (2 * os) - xpad + y_vec = np.arange((dimy + 2 * ypad) * os) / os - (os - 1) / (2 * os) - ypad + x_grid, y_grid = np.meshgrid(x_vec, y_vec) + + # Iteratively compute the wavelength at each pixel. + delta_x = 0.0 # A shift in x represents a shift in wavelength. + for niter in range(maxiter): + + # Assume all y have same wavelength. + wave_iterated = np.interp(x_grid - delta_x, x_dms[::-1], + wave_grid[::-1]) # Invert arrays to get increasing x. + + # Compute the tilt angle at the wavelengths. + tilt_tmp = np.interp(wave_iterated, wave_grid, tilt) + + # Compute the trace position at the wavelengths. + x_estimate = np.interp(wave_iterated, wave_grid, x_dms) + y_estimate = np.interp(wave_iterated, wave_grid, y_dms) + + # Project that back to pixel coordinates. + x_iterated = x_estimate + (y_grid - y_estimate) * np.tan(np.deg2rad(tilt_tmp)) + + # Measure error between requested and iterated position. + delta_x = delta_x + (x_iterated - x_grid) + + # If the desired precision has been reached end iterations. + if np.all(np.abs(x_iterated - x_grid) < dtol): + break + + # Evaluate the final wavelength map, this time setting out-of-bounds values to NaN. + wave_map_2d = np.interp(x_grid - delta_x, x_dms[::-1], wave_grid[::-1], left=np.nan, right=np.nan) + + # Extend to full detector size. + tmp = np.full((os * (dimx + 2 * xpad), os * (dimx + 2 * xpad)), fill_value=np.nan) + tmp[-os * (dimy + 2 * ypad):] = wave_map_2d + wave_map_2d = tmp + + return wave_map_2d + + +def get_soss_wavemaps(refmodel, pwcpos, subarray, padding=False, padsize=0, spectraces=False): + """ + Generate order 1 and 2 2D wavemaps from the rotated SOSS trace positions + + Parameters + ---------- + pwcpos : float + The pupil wheel position + subarray: str + The subarray name, ['FULL', 'SUBSTRIP256', 'SUBSTRIP96'] + padding : bool + Include padding on map edges (only needed for reference files) + padsize: int + The size of the padding to include on each side + spectraces : bool + Return the interpolated spectraces as well + + Returns + ------- + Array, Array + The 2D wavemaps and corresponding 1D spectraces + """ + _, order1_x, order1_y, order1_wl = get_soss_traces(refmodel, pwcpos, order='1', subarray=subarray, interp=True) + _, order2_x, order2_y, order2_wl = get_soss_traces(refmodel, pwcpos, order='2', subarray=subarray, interp=True) + + # Make wavemap from trace center wavelengths, padding to shape (296, 2088) + wavemin = WAVEMAP_WLMIN + wavemax = WAVEMAP_WLMAX + nwave = WAVEMAP_NWL + wave_grid = np.linspace(wavemin, wavemax, nwave) + + # Extrapolate wavelengths for order 1 trace + xtrace_order1 = extrapolate_to_wavegrid(wave_grid, order1_wl, order1_x) + ytrace_order1 = extrapolate_to_wavegrid(wave_grid, order1_wl, order1_y) + spectrace_1 = np.array([xtrace_order1, ytrace_order1, wave_grid]) + + # Set cutoff for order 2 where it runs off the detector + o2_cutoff = XTRACE_ORD2_LEN + w_o2_tmp = order2_wl[:o2_cutoff] + # Subtract 8 from FULL width to avoid reference pixels + w_o2 = np.zeros(SOSS_XDIM - 8) * np.nan + w_o2[:o2_cutoff] = w_o2_tmp + y_o2_tmp = order2_y[:o2_cutoff] + y_o2 = np.zeros(SOSS_XDIM - 8) * np.nan + y_o2[:o2_cutoff] = y_o2_tmp + x_o2 = np.copy(order1_x) + + # Fill for column > 1400 with linear extrapolation + m = w_o2[o2_cutoff - 1] - w_o2[o2_cutoff - 2] + dx = np.arange(SOSS_XDIM - 8 - o2_cutoff) + 1 + w_o2[o2_cutoff:] = w_o2[o2_cutoff - 1] + m * dx + m = y_o2[o2_cutoff - 1] - y_o2[o2_cutoff - 2] + dx = np.arange(SOSS_XDIM - 8 - o2_cutoff) + 1 + y_o2[o2_cutoff:] = y_o2[o2_cutoff - 1] + m * dx + + # Extrapolate wavelengths for order 2 trace + xtrace_order2 = extrapolate_to_wavegrid(wave_grid, w_o2, x_o2) + ytrace_order2 = extrapolate_to_wavegrid(wave_grid, w_o2, y_o2) + spectrace_2 = np.array([xtrace_order2, ytrace_order2, wave_grid]) + + # Make wavemap from wavelength solution for order 1 + wavemap_1 = calc_2d_wave_map(wave_grid, xtrace_order1, ytrace_order1, np.zeros_like(xtrace_order1), oversample=1, padding=padsize) + + # Make wavemap from wavelength solution for order 2 + wavemap_2 = calc_2d_wave_map(wave_grid, xtrace_order2, ytrace_order2, np.zeros_like(xtrace_order2), oversample=1, padding=padsize) + + # Extrapolate wavemap to FULL frame + wavemap_1[:SUBARRAY_YMIN - padsize, :] = wavemap_1[SUBARRAY_YMIN - padsize] + wavemap_2[:SUBARRAY_YMIN - padsize, :] = wavemap_2[SUBARRAY_YMIN - padsize] + + # Trim to subarray + if subarray == 'SUBSTRIP256': + wavemap_1 = wavemap_1[SUBARRAY_YMIN - padsize:SOSS_XDIM + padsize, :] + wavemap_2 = wavemap_2[SUBARRAY_YMIN - padsize:SOSS_XDIM + padsize, :] + if subarray == 'SUBSTRIP96': + wavemap_1 = wavemap_1[SUBARRAY_YMIN - padsize:SUBARRAY_YMIN + 96 + padsize, :] + wavemap_2 = wavemap_2[SUBARRAY_YMIN - padsize:SUBARRAY_YMIN + 96 + padsize, :] + + # Remove padding if necessary + if not padding and padsize != 0: + wavemap_1 = wavemap_1[padsize:-padsize, padsize:-padsize] + wavemap_2 = wavemap_2[padsize:-padsize, padsize:-padsize] + + if spectraces: + return np.array([wavemap_1, wavemap_2]), np.array([spectrace_1, spectrace_2]) + + else: + return np.array([wavemap_1, wavemap_2]) diff --git a/jwst/extract_1d/soss_extract/soss_boxextract.py b/jwst/extract_1d/soss_extract/soss_boxextract.py index fc560bbe2b..f499b21d7b 100644 --- a/jwst/extract_1d/soss_extract/soss_boxextract.py +++ b/jwst/extract_1d/soss_extract/soss_boxextract.py @@ -37,7 +37,7 @@ def get_box_weights(centroid, n_pix, shape, cols=None): # Row centers of all pixels. rows = np.indices((nrows, len(cols)))[0] - # Pixels that are entierly inside the box are set to one. + # Pixels that are entirely inside the box are set to one. cond = (rows <= (centroid - 0.5 + n_pix / 2)) cond &= ((centroid + 0.5 - n_pix / 2) <= rows) weights = cond.astype(float) diff --git a/jwst/extract_1d/soss_extract/soss_extract.py b/jwst/extract_1d/soss_extract/soss_extract.py index 1a23994197..77a0284463 100644 --- a/jwst/extract_1d/soss_extract/soss_extract.py +++ b/jwst/extract_1d/soss_extract/soss_extract.py @@ -1,7 +1,7 @@ import logging import numpy as np -from scipy.interpolate import UnivariateSpline +from scipy.interpolate import UnivariateSpline, CubicSpline from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import dqflags, SossWaveGridModel @@ -11,25 +11,24 @@ from astropy.nddata.bitmask import bitfield_to_boolean_mask from .soss_syscor import make_background_mask, soss_background -from .soss_solver import solve_transform, transform_wavemap, transform_profile, transform_coords from .atoca import ExtractionEngine, MaskOverlapError -from .atoca_utils import (ThroughputSOSS, WebbKernel, grid_from_map, mask_bad_dispersion_direction, +from .atoca_utils import (ThroughputSOSS, WebbKernel, grid_from_map, make_combined_adaptive_grid, get_wave_p_or_m, oversample_grid) from .soss_boxextract import get_box_weights, box_extract, estim_error_nearest_data +from .pastasoss import get_soss_wavemaps, XTRACE_ORD1_LEN, XTRACE_ORD2_LEN + log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -def get_ref_file_args(ref_files, transform): +def get_ref_file_args(ref_files): """Prepare the reference files for the extraction engine. Parameters ---------- ref_files : dict - A dictionary of the reference file DataModels. - transform : array or list - A 3-element array describing the rotation and translation to apply - to the reference files in order to match the observation. + A dictionary of the reference file DataModels, along with values + for the subarray and pwcpos, i.e. the pupil wheel position. Returns ------- @@ -38,39 +37,53 @@ def get_ref_file_args(ref_files, transform): (wavemaps, specprofiles, throughputs, kernels) """ - # The wavelength maps for order 1 and 2. - wavemap_ref = ref_files['wavemap'] - - ovs = wavemap_ref.map[0].oversampling - pad = wavemap_ref.map[0].padding - - wavemap_o1 = transform_wavemap(transform, wavemap_ref.map[0].data, ovs, pad) - wavemap_o2 = transform_wavemap(transform, wavemap_ref.map[1].data, ovs, pad) - - # Make sure all pixels follow the expected direction of the dispersion - wavemap_o1, flag_o1 = mask_bad_dispersion_direction(wavemap_o1) - wavemap_o2, flag_o2 = mask_bad_dispersion_direction(wavemap_o2) + pastasoss_ref = ref_files['pastasoss'] + pad = getattr(pastasoss_ref.traces[0], "padding", 0) + if pad > 0: + do_padding = True + else: + do_padding = False - # Warn if not all pixels were corrected - msg_warning = 'Some pixels in order {} do not follow the expected dispersion axis' - if not flag_o1: - log.warning(msg_warning.format(1)) - if not flag_o2: - log.warning(msg_warning.format(2)) + (wavemap_o1, wavemap_o2), (spectrace_o1, spectrace_o2) = \ + get_soss_wavemaps(pastasoss_ref, pwcpos=ref_files['pwcpos'], subarray=ref_files['subarray'], + padding=do_padding, padsize=pad, spectraces=True) # The spectral profiles for order 1 and 2. specprofile_ref = ref_files['specprofile'] - ovs = specprofile_ref.profile[0].oversampling - pad = specprofile_ref.profile[0].padding - specprofile_o1 = transform_profile(transform, specprofile_ref.profile[0].data, ovs, pad, norm=False) - specprofile_o2 = transform_profile(transform, specprofile_ref.profile[1].data, ovs, pad, norm=False) + specprofile_o1 = specprofile_ref.profile[0].data + specprofile_o2 = specprofile_ref.profile[1].data + + prof_shape0, prof_shape1 = specprofile_o1.shape + wavemap_shape0, wavemap_shape1 = wavemap_o1.shape + + if prof_shape0 != wavemap_shape0: + pad0 = (prof_shape0 - wavemap_shape0) // 2 + if pad0 > 0: + specprofile_o1 = specprofile_o1[pad0: -pad0, :] + specprofile_o2 = specprofile_o2[pad0: -pad0, :] + elif pad0 < 0: + wavemap_o1 = wavemap_o1[pad0: -pad0, :] + wavemap_o2 = wavemap_o2[pad0: -pad0, :] + if prof_shape1 != wavemap_shape1: + pad1 = (prof_shape1 - wavemap_shape1) // 2 + if pad1 > 0: + specprofile_o1 = specprofile_o1[:, pad1: -pad1] + specprofile_o2 = specprofile_o2[:, pad1: -pad1] + elif pad1 < 0: + wavemap_o1 = wavemap_o1[:, pad1: -pad1] + wavemap_o2 = wavemap_o2[:, pad1: -pad1] + # The throughput curves for order 1 and 2. - spectrace_ref = ref_files['spectrace'] + throughput_index_dict = dict() + for i, throughput in enumerate(pastasoss_ref.throughputs): + throughput_index_dict[throughput.spectral_order] = i - throughput_o1 = ThroughputSOSS(spectrace_ref.trace[0].data['WAVELENGTH'], spectrace_ref.trace[0].data['THROUGHPUT']) - throughput_o2 = ThroughputSOSS(spectrace_ref.trace[1].data['WAVELENGTH'], spectrace_ref.trace[1].data['THROUGHPUT']) + throughput_o1 = ThroughputSOSS(pastasoss_ref.throughputs[throughput_index_dict[1]].wavelength[:], + pastasoss_ref.throughputs[throughput_index_dict[1]].throughput[:]) + throughput_o2 = ThroughputSOSS(pastasoss_ref.throughputs[throughput_index_dict[2]].wavelength[:], + pastasoss_ref.throughputs[throughput_index_dict[2]].throughput[:]) # The spectral kernels. speckernel_ref = ref_files['speckernel'] @@ -85,13 +98,12 @@ def get_ref_file_args(ref_files, transform): # Needs the same number of columns as the detector. Put zeros where not define. wv_cent = np.zeros((1, wv_map.shape[1])) # Get central wavelength as a function of columns - col, _, wv = get_trace_1d(ref_files, transform, order) + col, _, wv = get_trace_1d(ref_files, order) wv_cent[:, col] = wv # Set invalid values to zero idx_invalid = ~np.isfinite(wv_cent) wv_cent[idx_invalid] = 0.0 centroid[order] = wv_cent - # Get kernels kernels_o1 = WebbKernel(speckernel_ref.wavelengths, speckernel_ref.kernels, centroid[1], ovs, n_pix) kernels_o2 = WebbKernel(speckernel_ref.wavelengths, speckernel_ref.kernels, centroid[2], ovs, n_pix) @@ -103,24 +115,19 @@ def get_ref_file_args(ref_files, transform): valid_wavemap = (speckernel_wv_range[0] <= wavemap_o2) & (wavemap_o2 <= speckernel_wv_range[1]) wavemap_o2 = np.where(valid_wavemap, wavemap_o2, 0.) - return [wavemap_o1, wavemap_o2], [specprofile_o1, specprofile_o2], [throughput_o1, throughput_o2], [kernels_o1, kernels_o2] + return [wavemap_o1, wavemap_o2], [specprofile_o1, specprofile_o2],\ + [throughput_o1, throughput_o2], [kernels_o1, kernels_o2] -def get_trace_1d(ref_files, transform, order, cols=None): +def get_trace_1d(ref_files, order): """Get the x, y, wavelength of the trace after applying the transform. Parameters ---------- ref_files : dict - A dictionary of the reference file DataModels. - transform : array or list - A 3-element list or array describing the rotation and translation - to apply to the reference files in order to match the - observation. + A dictionary of the reference file DataModels, along with values + for subarray and pwcpos, i.e. the pupil wheel position. order : int The spectral order for which to return the trace parameters. - cols : array[int], optional - The columns on the detector for which to compute the trace - parameters. If not given, all columns will be computed. Returns ------- @@ -128,27 +135,35 @@ def get_trace_1d(ref_files, transform, order, cols=None): The x, y and wavelength of the trace. """ - if cols is None: - xtrace = np.arange(2048) + pastasoss_ref = ref_files['pastasoss'] + pad = getattr(pastasoss_ref.traces[0], "padding", 0) + if pad > 0: + do_padding = True else: - xtrace = cols - - spectrace_ref = ref_files['spectrace'] - - # Read x, y, wavelength for the relevant order. - xref = spectrace_ref.trace[order - 1].data['X'] - yref = spectrace_ref.trace[order - 1].data['Y'] - waveref = spectrace_ref.trace[order - 1].data['WAVELENGTH'] - - # Rotate and shift the positions based on transform. - angle, xshift, yshift = transform - xrot, yrot = transform_coords(angle, xshift, yshift, xref, yref) - - # Interpolate y and wavelength to the requested columns. - sort = np.argsort(xrot) - ytrace = np.interp(xtrace, xrot[sort], yrot[sort]) - wavetrace = np.interp(xtrace, xrot[sort], waveref[sort]) - + do_padding = False + + (_, _), (spectrace_o1, spectrace_o2) = \ + get_soss_wavemaps(pastasoss_ref, pwcpos=ref_files['pwcpos'], subarray=ref_files['subarray'], + padding=do_padding, padsize=pad, spectraces=True) + if order == 1: + spectrace = spectrace_o1 + xtrace = np.arange(XTRACE_ORD1_LEN) + elif order == 2: + spectrace = spectrace_o2 + xtrace = np.arange(XTRACE_ORD2_LEN) + else: + errmsg = f"Order {order} is not covered by Pastasoss reference file!" + log.error(errmsg) + raise ValueError(errmsg) + + # CubicSpline requires monotonically increasing x arr + if spectrace[0][0] - spectrace[0][1] > 0: + spectrace = np.flip(spectrace, axis=1) + + trace_interp_y = CubicSpline(spectrace[0], spectrace[1]) + trace_interp_wave = CubicSpline(spectrace[0], spectrace[2]) + ytrace = trace_interp_y(xtrace) + wavetrace = trace_interp_wave(xtrace) return xtrace, ytrace, wavetrace @@ -204,43 +219,7 @@ def estim_flux_first_order(scidata_bkg, scierr, scimask, ref_file_args, mask_tra return estimate_spl -def _mask_wv_map_centroid_outside(wave_maps, ref_files, transform, y_max, orders=(1, 2)): - """Patch to mask wv_map when centroid outside - Parameters - ---------- - wave_maps : array or list - Wavelength maps - ref_files : dict - A dictionary of the reference file DataModels. - transform : array or list - A 3-element list or array describing the rotation and translation - to apply to the reference files in order to match the - observation. - y_max : int - Max value of column to check against centroid location - orders : tuple[int], optional - The spectral orders for each wave_map. If not specified, defaults to (1, 2). - - Returns - ------- - None - Modifies wave_maps in place to mask out values where centroid is off the detector. - """ - for wv_map, order in zip(wave_maps, orders): - # Get centroid wavelength and y position as a function of columns - _, y_pos, wv = get_trace_1d(ref_files, transform, order) - # Find min and max wavelengths with centroid inside of detector - wv = wv[np.isfinite(y_pos)] - y_pos = y_pos[np.isfinite(y_pos)] - idx_inside = (0 <= y_pos) & (y_pos <= y_max) - wv = wv[idx_inside] - # Set to zeros (mask) values outside - mask = np.isfinite(wv_map) - mask[mask] = (np.min(wv) > wv_map[mask]) | (wv_map[mask] > np.max(wv)) - wv_map[mask] = 0. - - -def get_native_grid_from_trace(ref_files, transform, spectral_order): +def get_native_grid_from_trace(ref_files, spectral_order): """ Make a 1d-grid of the pixels boundary and ready for ATOCA ExtractionEngine, based on the wavelength solution. @@ -248,22 +227,15 @@ def get_native_grid_from_trace(ref_files, transform, spectral_order): ---------- ref_files: dict A dictionary of the reference file DataModels. - transform: array_like - A 3-elemnt list or array describing the rotation and - translation to apply to the reference files in order to match the - observation. spectral_order: int The spectral order for which to return the trace parameters. - pix_center: bool - If True, use pixel center wavelength value to define the grid. - If False, use pixel boundaries. Default is False. Returns ------- Grid of the pixels boundaries at the native sampling (1d array) """ # From wavelenght solution - col, _, wave = get_trace_1d(ref_files, transform, spectral_order) + col, _, wave = get_trace_1d(ref_files, spectral_order) # Keep only valid solution ... idx_valid = np.isfinite(wave) @@ -284,7 +256,7 @@ def get_native_grid_from_trace(ref_files, transform, spectral_order): return wave, col -def get_grid_from_trace(ref_files, transform, spectral_order, n_os=1): +def get_grid_from_trace(ref_files, spectral_order, n_os=1): """ Make a 1d-grid of the pixels boundary and ready for ATOCA ExtractionEngine, based on the wavelength solution. @@ -292,10 +264,6 @@ def get_grid_from_trace(ref_files, transform, spectral_order, n_os=1): ---------- ref_files: dict A dictionary of the reference file DataModels. - transform: array_like - A 3-elemnt list or array describing the rotation and - translation to apply to the reference files in order to match the - observation. spectral_order: int The spectral order for which to return the trace parameters. Returns @@ -303,7 +271,7 @@ def get_grid_from_trace(ref_files, transform, spectral_order, n_os=1): Grid of the pixels boundaries at the native sampling (1d array) """ - wave, _ = get_native_grid_from_trace(ref_files, transform, spectral_order) + wave, _ = get_native_grid_from_trace(ref_files, spectral_order) # Use pixel boundaries instead of the center values wv_upper_bnd, wv_lower_bnd = get_wave_p_or_m(wave[None, :]) @@ -320,10 +288,10 @@ def get_grid_from_trace(ref_files, transform, spectral_order, n_os=1): return wave_grid -def make_decontamination_grid(ref_files, transform, rtol, max_grid_size, estimate, n_os, wv_range=None): +def make_decontamination_grid(ref_files, rtol, max_grid_size, estimate, n_os, wv_range=None): ''' Create the grid use for the simultaneous extraction of order 1 and 2. The grid is made by: - 1) requiring that it satifsfies the oversampling n_os + 1) requiring that it satisfies the oversampling n_os 2) trying to reach the specified tolerance for the spectral range shared between order 1 and 2 3) trying to reach the specified tolerance in the rest of spectral range The max_grid_size overrules steps 2) and 3), so the precision may not be reached if @@ -334,7 +302,7 @@ def make_decontamination_grid(ref_files, transform, rtol, max_grid_size, estimat spectral_orders = [2, 1] grids_ord = dict() for sp_ord in spectral_orders: - grids_ord[sp_ord] = get_grid_from_trace(ref_files, transform, sp_ord, n_os=n_os) + grids_ord[sp_ord] = get_grid_from_trace(ref_files, sp_ord, n_os=n_os) # Build the list of grids given to make_combined_grid. # It must be ordered in increasing priority. @@ -424,7 +392,7 @@ def f_to_spec(f_order, grid_order, ref_file_args, pixel_grid, mask, sp_ord=0): return spec -def _build_tracemodel_order(engine, ref_file_args, f_k, i_order, mask, ref_files, transform): +def _build_tracemodel_order(engine, ref_file_args, f_k, i_order, mask, ref_files): # Take only the order's specific ref_files ref_file_order = [[ref_f[i_order]] for ref_f in ref_file_args] @@ -456,7 +424,7 @@ def _build_tracemodel_order(engine, ref_file_args, f_k, i_order, mask, ref_files tracemodel_ord = model.rebuild(flux_order, fill_value=np.nan) # Build 1d spectrum integrated over pixels - pixel_wave_grid, valid_cols = get_native_grid_from_trace(ref_files, transform, sp_ord) + pixel_wave_grid, valid_cols = get_native_grid_from_trace(ref_files, sp_ord) spec_ord = f_to_spec(flux_order, grid_order, ref_file_order, pixel_wave_grid, np.all(mask, axis=0)[valid_cols], sp_ord=sp_ord) @@ -492,7 +460,7 @@ def _build_null_spec_table(wave_grid): return spec -def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, subarray, transform=None, +def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, tikfac=None, threshold=1e-4, n_os=2, wave_grid=None, estimate=None, rtol=1e-3, max_grid_size=1000000): """Perform the spectral extraction on a single image. @@ -508,27 +476,21 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s refmask : array[bool] Pixels that should never be reconstructed e.g. the reference pixels. ref_files : dict - A dictionary of the reference file DataModels. + A dictionary of the reference file DataModels, along with values for + subarray and pwcpos, i.e. the pupil wheel position. box_weights : dict A dictionary of the weights (for each order) used in the box extraction. The weights for each order are 2d arrays with the same size as the detector. - subarray : str - Subarray on which the data were recorded; one of 'SUBSTRIPT96', - 'SUBSTRIP256' or 'FULL'. - transform : array or list, optional - A 3-element list or array describing the rotation and translation to - apply to the reference files in order to match the observation. If not - specified, the transformation is computed. tikfac : float, optional The Tikhonov regularization factor used when solving for the uncontaminated flux. If not specified, the optimal Tikhonov factor is calculated. - n_os : int, optional - The oversampling factor of the wavelength grid used when solving for - the uncontaminated flux. If not specified, defaults to 5. threshold : float The threshold value for using pixels based on the spectral profile. Default value is 1e-4. + n_os : int, optional + The oversampling factor of the wavelength grid used when solving for + the uncontaminated flux. If not specified, defaults to 2. wave_grid : str or SossWaveGridModel or None Filename of reference file or SossWaveGridModel containing the wavelength grid used by ATOCA to model each pixel valid pixel of the detector. If not given, the grid is determined @@ -562,11 +524,13 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s # Init list of atoca 1d spectra spec_list = [] - # Orders to simulate - order_list = ['Order 1', 'Order 2'] + # Generate list of orders to simulate from pastasoss trace list + order_list = [] + for trace in ref_files['pastasoss'].traces: + order_list.append(f"Order {trace.spectral_order}") # Prepare the reference file arguments. - ref_file_args = get_ref_file_args(ref_files, transform) + ref_file_args = get_ref_file_args(ref_files) # Some error values are 0, we need to mask those pixels for the extraction engine. scimask = scimask | ~(scierr > 0) @@ -588,25 +552,11 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s # Generate grid based on estimate if not given if wave_grid is None: log.info(f'wave_grid not given: generating grid based on rtol={rtol}') - wave_grid = make_decontamination_grid(ref_files, transform, rtol, max_grid_size, estimate, n_os) + wave_grid = make_decontamination_grid(ref_files, rtol, max_grid_size, estimate, n_os) log.debug(f'wave_grid covering from {wave_grid.min()} to {wave_grid.max()}') else: log.info('Using previously computed or user specified wavelength grid.') -# # Use estimate to evaluate the contribution from each orders to pixels -# # (Used to determine which pixel to model later) -# ref_args_estimate = [ref_arg for ref_arg in ref_file_args] -# # No convolution needed (so give equivalent of identity) -# ref_args_estimate[3] = [np.array([1.]) for _ in order_list] -# engine_for_estimate = ExtractionEngine(*ref_args_estimate, wave_grid=wave_grid, mask_trace_profile=mask_trace_profile) -# models = {order: engine_for_estimate.rebuild(estimate, i_orders=[idx_ord], fill_value=np.nan) -# for idx_ord, order in enumerate(order_list)} -# total = np.nansum([models[order] for order in order_list], axis=0) -# total = np.where((total != 0), total, np.nan) -# contribution = {order: models[order] / total for order in order_list} - - log.debug('Extracting using transformation parameters {}'.format(transform)) - # Set the c_kwargs using the minimum value of the kernels c_kwargs = [{'thresh': webb_ker.min_value} for webb_ker in ref_file_args[3]] @@ -643,7 +593,7 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s for i_order, order in enumerate(order_list): for idx in range(len(all_tests['factors'])): f_k = all_tests['solution'][idx, :] - args = (engine, ref_file_args, f_k, i_order, global_mask, ref_files, transform) + args = (engine, ref_file_args, f_k, i_order, global_mask, ref_files) _, spec_ord = _build_tracemodel_order(*args) populate_tikho_attr(spec_ord, all_tests, idx, i_order + 1) spec_ord.meta.soss_extract1d.color_range = 'RED' @@ -672,7 +622,7 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s log.debug('Building the model image of {}.'.format(order)) - args = (engine, ref_file_args, f_k, i_order, global_mask, ref_files, transform) + args = (engine, ref_file_args, f_k, i_order, global_mask, ref_files) tracemodel_ord, spec_ord = _build_tracemodel_order(*args) spec_ord.meta.soss_extract1d.factor = tikfac spec_ord.meta.soss_extract1d.color_range = 'RED' @@ -687,7 +637,7 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s # ############################### # Model remaining part of order 2 # ############################### - if subarray != 'SUBSTRIP96': + if ref_files['subarray'] != 'SUBSTRIP96': idx_order2 = 1 order = idx_order2 + 1 order_str = 'Order 2' @@ -703,17 +653,13 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s # mask_fit |= already_modeled # Build 1d spectrum integrated over pixels - pixel_wave_grid, valid_cols = get_native_grid_from_trace(ref_files, transform, order) + pixel_wave_grid, valid_cols = get_native_grid_from_trace(ref_files, order) # Hardcode wavelength highest boundary as well. # Must overlap with lower limit in make_decontamination_grid is_in_wv_range = (pixel_wave_grid < 0.95) pixel_wave_grid, valid_cols = pixel_wave_grid[is_in_wv_range], valid_cols[is_in_wv_range] - # NOTE: This code is currently unused. - # Remove order 1 - # scidata_order2_decont = scidata_bkg - tracemodels['Order 1'] - # Range of initial tikhonov factors tikfac_log_range = np.log10(tikfac) + np.array([-2, 8]) @@ -725,7 +671,8 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s tikfac_log_range=tikfac_log_range) except MaskOverlapError: - log.error('Not enough unmasked pixels to model the remaining part of order 2. Model and spectrum will be NaN in that spectral region.') + log.error('Not enough unmasked pixels to model the remaining part of order 2.' + 'Model and spectrum will be NaN in that spectral region.') spec_ord = [_build_null_spec_table(pixel_wave_grid)] model = np.nan * np.ones_like(scidata_bkg) @@ -745,13 +692,12 @@ def model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, s return tracemodels, tikfac, logl, wave_grid, spec_list -def compute_box_weights(ref_files, transform, subarray, shape, width=40.): +def compute_box_weights(ref_files, shape, width=40.): - # Which orders to compute (for modeling, different than extraction). - if subarray == 'SUBSTRIP96': - order_list = [1, 2] - else: - order_list = [1, 2, 3] + # Generate list of orders from pastasoss trace list + order_list = [] + for trace in ref_files['pastasoss'].traces: + order_list.append(trace.spectral_order) # Extract each order from order list box_weights = dict() @@ -761,10 +707,10 @@ def compute_box_weights(ref_files, transform, subarray, shape, width=40.): # Order string-name is used more often than integer-name order = order_str[order_integer] - log.debug(f'Compute box weights for order {order}.') + log.debug(f'Compute box weights for {order}.') # Define the box aperture - xtrace, ytrace, wavelengths[order] = get_trace_1d(ref_files, transform, order_integer) + xtrace, ytrace, wavelengths[order] = get_trace_1d(ref_files, order_integer) box_weights[order] = get_box_weights(ytrace, width, shape, cols=xtrace) return box_weights, wavelengths @@ -772,7 +718,6 @@ def compute_box_weights(ref_files, transform, subarray, shape, width=40.): def decontaminate_image(scidata_bkg, tracemodels, subarray): """Perform decontamination of the image based on the trace models""" - # Which orders to extract. if subarray == 'SUBSTRIP96': order_list = [1, 2] @@ -809,7 +754,6 @@ def decontaminate_image(scidata_bkg, tracemodels, subarray): # TODO Add docstring -# TODO Add threshold like in model_image? TO use with the rough (but stable) estimate def model_single_order(data_order, err_order, ref_file_args, mask_fit, mask_rebuild, order, wave_grid, valid_cols, save_tiktests=False, tikfac_log_range=None): @@ -914,7 +858,6 @@ def throughput(wavelength): # Remove bad pixels that are not modeled for pixel number -# TODO Update docstring def extract_image(decontaminated_data, scierr, scimask, box_weights, bad_pix='model', tracemodels=None): """Perform the box-extraction on the image, while using the trace model to correct for contamination. @@ -926,16 +869,9 @@ def extract_image(decontaminated_data, scierr, scimask, box_weights, bad_pix='mo The uncertainties corresponding to the detector image. scimask : array[float] Pixel mask to apply to the detector image. - ref_files : dict - A dictionary of the reference file DataModels. - transform : array_like - A 3-element list or array describing the rotation and translation to - apply to the reference files in order to match the observation. - subarray : str - Subarray on which the data were recorded; one of 'SUBSTRIPT96', - 'SUBSTRIP256' or 'FULL'. - width : float - The width of the aperture used to extract the uncontaminated spectrum. + box_weights : dict + A dictionary of the weights (for each order) used in the box extraction. + The weights for each order are 2d arrays with the same size as the detector. bad_pix : str How to handle the bad pixels. Options are 'masking' and 'model'. 'masking' will simply mask the bad pixels, such that the number of pixels @@ -945,7 +881,7 @@ def extract_image(decontaminated_data, scierr, scimask, box_weights, bad_pix='mo Dictionary of the modeled detector images for each order. Returns ------- - wavelengths, fluxes, fluxerrs, npixels, box_weights : dict + fluxes, fluxerrs, npixels : dict Each output is a dictionary, with each extracted order as a key. """ # Init models with an empty dictionary if not given @@ -1015,7 +951,7 @@ def extract_image(decontaminated_data, scierr, scimask, box_weights, bad_pix='mo return fluxes, fluxerrs, npixels -def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, +def run_extract1d(input_model, pastasoss_ref_name, specprofile_ref_name, speckernel_ref_name, subarray, soss_filter, soss_kwargs): """Run the spectral extraction on NIRISS SOSS data. @@ -1023,10 +959,8 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, ---------- input_model : DataModel The input DataModel. - spectrace_ref_name : str - Name of the spectrace reference file. - wavemap_ref_name : str - Name of the wavemap reference file. + pastasoss_ref_name : str + Name of the pastasoss reference file. specprofile_ref_name : str Name of the specprofile reference file. speckernel_ref_name : str @@ -1052,25 +986,16 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, order_str_2_int = {f'Order {order}': order for order in [1, 2, 3]} # Read the reference files. - spectrace_ref = datamodels.SpecTraceModel(spectrace_ref_name) - wavemap_ref = datamodels.WaveMapModel(wavemap_ref_name) + pastasoss_ref = datamodels.PastasossModel(pastasoss_ref_name) specprofile_ref = datamodels.SpecProfileModel(specprofile_ref_name) speckernel_ref = datamodels.SpecKernelModel(speckernel_ref_name) ref_files = dict() - ref_files['spectrace'] = spectrace_ref - ref_files['wavemap'] = wavemap_ref + ref_files['pastasoss'] = pastasoss_ref ref_files['specprofile'] = specprofile_ref ref_files['speckernel'] = speckernel_ref - - # Initialize the theta, dx, dy transform parameters - transform = soss_kwargs.pop('transform') - if transform is None: - transform = [None, None, None] - else: - transform = [float(val) for val in transform] - # Save names for logging - param_name = np.array(['theta', 'x-offset', 'y-offset']) + ref_files['subarray'] = subarray + ref_files['pwcpos'] = input_model.meta.instrument.pupil_position # Unpack wave_grid if wave_grid_in was specified. wave_grid_in = soss_kwargs['wave_grid_in'] @@ -1167,40 +1092,8 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, scidata_bkg = scidata col_bkg = np.zeros(scidata.shape[1]) - # Determine the theta, dx, dy transform needed to match scidata trace position to ref file position. - if None in transform: - log.info('Solving for the transformation parameters.') - - # Unpack the expected order 1 & 2 positions. - spectrace_ref = ref_files['spectrace'] - xref_o1 = spectrace_ref.trace[0].data['X'] - yref_o1 = spectrace_ref.trace[0].data['Y'] - xref_o2 = spectrace_ref.trace[1].data['X'] - yref_o2 = spectrace_ref.trace[1].data['Y'] - - # Define which parameters to fit - is_fitted = np.array([value is None for value in transform]) - - # Show which parameters are fitted in log - log.info('Parameters used for fit: ' + ', '.join(param_name[is_fitted])) - log.info('Fixed parameters: ' + ', '.join(param_name[~is_fitted])) - - # Use the solver on the background subtracted image. - if subarray == 'SUBSTRIP96' or soss_filter == 'F277W': - # Use only order 1 to solve theta, dx, dy - transform = solve_transform(scidata_bkg, scimask, xref_o1, yref_o1, - soss_filter=soss_filter, is_fitted=is_fitted, - guess_transform=transform) - else: - transform = solve_transform(scidata_bkg, scimask, xref_o1, yref_o1, - xref_o2, yref_o2, is_fitted=is_fitted, - soss_filter=soss_filter, guess_transform=transform) - - string_list = [f'{name}={value}' for name, value in zip(param_name, transform)] - log.info('Measured to Reference trace position transform: ' + ', '.join(string_list)) - # Pre-compute the weights for box extraction (used in modeling and extraction) - args = (ref_files, transform, subarray, scidata_bkg.shape) + args = (ref_files, scidata_bkg.shape) box_weights, wavelengths = compute_box_weights(*args, width=soss_kwargs['width']) # Model the traces based on optics filter configuration (CLEAR or F277W) @@ -1208,7 +1101,6 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, # Model the image. kwargs = dict() - kwargs['transform'] = transform kwargs['estimate'] = estimate kwargs['tikfac'] = soss_kwargs['tikfac'] kwargs['max_grid_size'] = soss_kwargs['max_grid_size'] @@ -1217,7 +1109,7 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, kwargs['wave_grid'] = wave_grid kwargs['threshold'] = soss_kwargs['threshold'] - result = model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, subarray, **kwargs) + result = model_image(scidata_bkg, scierr, scimask, refmask, ref_files, box_weights, **kwargs) tracemodels, soss_kwargs['tikfac'], logl, wave_grid, spec_list = result # Add atoca spectra to multispec for output @@ -1272,19 +1164,18 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, if i == 0: all_box_weights[order] = [] all_box_weights[order].append(box_weights[order]) - # Copy spectral data for each order into the output model. for order in fluxes.keys(): table_size = len(wavelengths[order]) out_table = np.zeros(table_size, dtype=datamodels.SpecModel().spec_table.dtype) - out_table['WAVELENGTH'] = wavelengths[order] - out_table['FLUX'] = fluxes[order] - out_table['FLUX_ERROR'] = fluxerrs[order] + out_table['WAVELENGTH'] = wavelengths[order][:table_size] + out_table['FLUX'] = fluxes[order][:table_size] + out_table['FLUX_ERROR'] = fluxerrs[order][:table_size] out_table['DQ'] = np.zeros(table_size) - out_table['BACKGROUND'] = col_bkg - out_table['NPIXELS'] = npixels[order] + out_table['BACKGROUND'] = col_bkg[:table_size] + out_table['NPIXELS'] = npixels[order][:table_size] spec = datamodels.SpecModel(spec_table=out_table) @@ -1297,9 +1188,6 @@ def run_extract1d(input_model, spectrace_ref_name, wavemap_ref_name, output_model.meta.soss_extract1d.width = soss_kwargs['width'] output_model.meta.soss_extract1d.apply_decontamination = soss_kwargs['atoca'] output_model.meta.soss_extract1d.tikhonov_factor = soss_kwargs['tikfac'] - output_model.meta.soss_extract1d.delta_x = transform[1] - output_model.meta.soss_extract1d.delta_y = transform[2] - output_model.meta.soss_extract1d.theta = transform[0] output_model.meta.soss_extract1d.oversampling = soss_kwargs['n_os'] output_model.meta.soss_extract1d.threshold = soss_kwargs['threshold'] output_model.meta.soss_extract1d.bad_pix = soss_kwargs['bad_pix'] diff --git a/jwst/extract_1d/soss_extract/soss_solver.py b/jwst/extract_1d/soss_extract/soss_solver.py deleted file mode 100644 index 7f1cded6c7..0000000000 --- a/jwst/extract_1d/soss_extract/soss_solver.py +++ /dev/null @@ -1,508 +0,0 @@ -import logging - -import numpy as np -from scipy.ndimage import shift, rotate -from scipy.optimize import minimize -import warnings - -from .soss_syscor import aperture_mask -from .soss_centroids import get_centroids_com - -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -def transform_coords(angle, xshift, yshift, xpix, ypix, cenx=1024, ceny=50): - """Apply a rotation and shift to the trace centroids positions. This - assumes that the trace centroids are already in the CV3 coordinate system. - - Parameters - ---------- - angle : float - The angle by which to rotate the coordinates, in degrees. - xshift : float - The shift to apply to the x-coordinates after rotating. - yshift : float - The shift to apply to the y-coordinates after rotating. - xpix : array[float] - The x-coordinates to be transformed. - ypix : array[float] - The y-coordinates to be transformed. - cenx : float (optional) - The x-coordinate around which to rotate. - ceny : float (optional) - The y-coordinate around which to rotate. - - Returns - ------- - xrot : array[float] - The rotated and shifted x coordinate. - yrot : array[float] - The rotated and shifted y coordinate. - """ - - # Convert to numpy arrays. - xpix = np.atleast_1d(xpix) - ypix = np.atleast_1d(ypix) - - # Required rotation in the detector frame to match the data. - angle = np.deg2rad(angle) - rot_mat = np.array([[np.cos(angle), -np.sin(angle)], - [np.sin(angle), np.cos(angle)]]) - - # Rotation center set to o1 trace centroid halfway along spectral axis. - points = np.array([xpix - cenx, ypix - ceny]) - rot_points = rot_mat @ points - rot_points[0] += cenx - rot_points[1] += ceny - - # Apply the offsets. - xrot = rot_points[0] + xshift - yrot = rot_points[1] + yshift - - return xrot, yrot - - -def evaluate_model(xmod, transform, xref, yref): - """Evaluate the transformed reference coordinates at particular x-values. - - Parameters - ---------- - xmod : array[float] - The x-values at which to evaluate the transformed coordinates. - transform : array[float] - The transformation parameters. - xref : array[float] - The reference x-positions. - yref : array[float] - The reference y-positions. - - Returns - ------- - ymod : array[float] - The transformed y-coordinates corresponding to xmod. - """ - - angle, xshift, yshift = transform - - # Calculate rotated reference positions. - xrot, yrot = transform_coords(angle, xshift, yshift, xref, yref) - - # After rotation, need to re-sort the x-positions for interpolation. - sort = np.argsort(xrot) - xrot, yrot = xrot[sort], yrot[sort] - - # Interpolate rotated model onto same x scale as data. - ymod = np.interp(xmod, xrot, yrot) - - return ymod - - -def _chi_squared(transform, xref_o1, yref_o1, xref_o2, yref_o2, - xdat_o1, ydat_o1, xdat_o2, ydat_o2): - """Compute the chi-squared statistic for fitting the reference positions - to the true positions. - - Parameters - ---------- - transform : array[float] - The transformation parameters. - xref_o1 : array[float] - The order 1 reference x-positions. - yref_o1 : array[float] - The order 1 reference y-positions. - xref_o2 : array[float] - The order 2 reference x-positions. - yref_o2 : array[float] - The order 2 reference y-positions. - xdat_o1 : array[float] - The order 1 data x-positions. - ydat_o1 : array[float] - The order 1 data y-positions. - xdat_o2 : array[float] - The order 2 data x-positions. - ydat_o2 : array[float] - The order 2 data y-positions. - - Returns - ------- - chisq : float - The chi-squared value of the model fit. - """ - # Interpolate rotated model of first order onto same x scale as data. - ymod_o1 = evaluate_model(xdat_o1, transform, xref_o1, yref_o1) - - # Compute the chi-square. - chisq_o1 = np.nansum((ydat_o1 - ymod_o1)**2) - - # If second order centroids are provided, include them in the calculation. - if xdat_o2 is not None: - # Interpolate rotated model onto same x scale as data. - ymod_o2 = evaluate_model(xdat_o2, transform, xref_o2, yref_o2) - - # Compute the chi-square and add to the first order. - chisq_o2 = np.nansum((ydat_o2 - ymod_o2)**2) - chisq = chisq_o1 + chisq_o2 - # If not, use only the first order. - else: - chisq = chisq_o1 - - return chisq - - -def solve_transform(scidata_bkg, scimask, xref_o1, yref_o1, xref_o2=None, - yref_o2=None, halfwidth=30., is_fitted=(True, True, True), - soss_filter='CLEAR', guess_transform=(None, None, None), - bounds_theta=(-5., 5.), bounds_x=(-3, 3), bounds_y=(-3., 3.)): - """Given a science image, determine the centroids and find the simple - transformation (rotation + vertical & horizonal offset, or some combination - thereof) needed to match xref_o1 and yref_o1 to the image. - - Parameters - ---------- - scidata_bkg : array[float] - A background subtracted image of the SOSS trace. - scimask : array[float] - A boolean mask of pixels to be excluded. - xref_o1 : array[float] - A priori expectation of the order 1 trace x-positions. - yref_o1 : array[float] - A priori expectation of the order 1 trace y-positions. - xref_o2 : array[float] (optional) - A priori expectation of the order 2 trace x-positions. Providing these - will improve the accuracy of the solver. - yref_o2 : array[float] (optional) - A priori expectation of the order 2 trace y-positions. Providing these - will improve the accuracy of the solver. - halfwidth : float (optional) - Size of the aperture mask used when extracting the trace positions - from the data. - is_fitted: tuple, list or array [bool] - Tuple indicating which parameter is fitted in transform. - It is ordered as (rotation, x-shift, y-shift). - Default is (True, True, True), so all parameters are fitted. - When False, the value is fixed to the value in `guess_transform`. - guess_transform: tuple, list or array - Tuple of the initial guess value of each transformation parameters. - It is ordered as (rotation, x-shift, y-shift). If set to None, - the value 0. is given. Default is (None, None, None). - soss_filter : str (optional) - Designator for the SOSS filter used in the observation. Either CLEAR - or F277W. Setting F277W here will force shift to False. - bounds_theta : array[float] (optional) - Boundaries on the rotation angle to consider in the Chi-squared - minimization. - bounds_x : array[float] (optional) - Boundaries on the horizontal offset to consider in the Chi-squared - minimization. - bounds_y : array[float] (optional) - Boundaries on the vertical offset to consider in the Chi-squared - minimization. - - Returns - ------- - simple_transform : array[float] - Array containing the angle, x-shift and y-shift needed to match - xref_o1 and yref_o1 to the image. - """ - # Convert None to 0. in guess_transform and then convert to array - guess_transform = [val if val is not None else 0. for val in guess_transform] - guess_transform = np.array(guess_transform) - - # Convert is_fitted to array - is_fitted = np.array(is_fitted) - - # Start with order 1 centroids as they will be available for all subarrays. - # Remove any NaNs used to pad the xref, yref coordinates. - mask_o1 = np.isfinite(xref_o1) & np.isfinite(yref_o1) - xref_o1 = xref_o1[mask_o1] - yref_o1 = yref_o1[mask_o1] - - # Get centroids from data. - aper_mask_o1 = aperture_mask(xref_o1, yref_o1, halfwidth, scidata_bkg.shape) - mask = aper_mask_o1 | scimask - xdat_o1, ydat_o1, _ = get_centroids_com(scidata_bkg, mask=mask, - poly_order=None) - - # If order 2 centroids are provided, include them in the analysis. The - # inclusion of the order 2 centroids will allow for a more accurate - # determination of the rotation and offset, as the addition of the second - # order provides an anchor in the spatial direction. However, there are - # instances (a SUBSTRIP96, or F277W observation for example) where the - # second order is not available. In this case, work only with order 1. - if xref_o2 is not None and yref_o2 is not None and (soss_filter == 'CLEAR' or soss_filter == 'FULL'): - # Remove any NaNs used to pad the xref, yref coordinates. - log.info('Measuring trace position for orders 1 and 2.') - mask_o2 = np.isfinite(xref_o2) & np.isfinite(yref_o2) - xref_o2 = xref_o2[mask_o2] - yref_o2 = yref_o2[mask_o2] - - # Get centroids from data. - aper_mask_o2 = aperture_mask(xref_o2, yref_o2, halfwidth, scidata_bkg.shape) - mask = aper_mask_o2 | scimask - xdat_o2, ydat_o2, _ = get_centroids_com(scidata_bkg, mask=mask, - poly_order=None) - - # Use only the uncontaminated range between x=800 and x=1700. - mask = (xdat_o1 >= 800) & (xdat_o1 <= 1700) - xdat_o1 = xdat_o1[mask] - ydat_o1 = ydat_o1[mask] - - mask = (xdat_o2 >= 800) & (xdat_o2 <= 1700) - xdat_o2 = xdat_o2[mask] - ydat_o2 = ydat_o2[mask] - - elif soss_filter == 'F277W': - # If the exposure uses the F277W filter, there is no second order, and - # first order centroids are only useful at wavelengths greater than 2.5µm. - # Restrict centroids to lie within region lambda>~2.5µm, where the - # F277W filter response is strong. - log.info('Measuring trace position for order 1 spanning the F277W pixels.') - mask = (xdat_o1 >= 25) & (xdat_o1 <= 425) - xdat_o1 = xdat_o1[mask] - ydat_o1 = ydat_o1[mask] - # Force shift to False as there is not enough information to - # constrain dx, dy and dtheta simultaneously. - is_fitted[1:] = False - # Second order centroids are not available. - xdat_o2, ydat_o2 = None, None - - else: - # If the exposure is SUBSTRIP96 using the CLEAR filter, there is no - # order 2. Use the entire first order to enable the maximum possible - # positional constraint on the centroids. - log.info('Measuring trace position for order 1 only.') - xdat_o2, ydat_o2 = None, None - - # Find the simple transformation via a chi-squared minimization of the - # extracted and reference centroids. This transformation considers by - # default rotation as well as vertical and horizontal offsets. - - # Setup minimizing function depending on the parameters to fit - simple_transform = guess_transform.copy() - - def _chi2_to_fit(values, *args): - simple_transform[is_fitted] = values - return _chi_squared(simple_transform, *args) - - # Set up the optimization problem. - min_args = (xref_o1, yref_o1, xref_o2, yref_o2, - xdat_o1, ydat_o1, xdat_o2, ydat_o2) - - # Define the boundaries - bounds = [bounds_theta, bounds_x, bounds_y] - # Only keep the ones that are fitted - bounds = [bnds for idx, bnds in enumerate(bounds) if is_fitted[idx]] - - # Find the best-fit transformation. - result = minimize(_chi2_to_fit, guess_transform[is_fitted], bounds=bounds, - args=min_args) - simple_transform[is_fitted] = result.x - - return simple_transform - - -def rotate_image(image, angle, origin): - """Rotate an image around a specific pixel. - - Parameters - ---------- - image : array[float] - The image to rotate. - angle : float - The rotation angle in degrees. - origin : Tuple, List, Array - The x and y pixel position around which to rotate. - - Returns - ------- - image_rot : array[float] - The rotated image. - """ - - # Pad image so we can safely rotate around the origin. - padx = [image.shape[1] - origin[0], origin[0]] - pady = [image.shape[0] - origin[1], origin[1]] - image_pad = np.pad(image, [pady, padx], 'constant') - - # Rotate the image. - image_pad_rot = rotate(image_pad, angle, reshape=False) - - # Remove the padding. - image_rot = image_pad_rot[pady[0]:-pady[1], padx[0]:-padx[1]] - - return image_rot - - -def transform_image(angle, xshift, yshift, image, cenx=1024, ceny=50): - """Apply the transformation found by solve_transform() to a 2D reference - map. - - Parameters - ---------- - angle : float - The angle by which to rotate the file, in degrees. - xshift : float - The x-shift to apply in native pixels, will be rounded to the - nearest (oversampled) pixel. - yshift : float - The y-shift to apply in native pixels, will be rounded to the - nearest (oversampled) pixel. - image : array[float] - An image to transform. - cenx : float (optional) - The x-coordinate around which to rotate. - ceny : float (optional) - The y-coordinate around which to rotate. - - Returns - ------- - image_rot : array[float] - The image, after applying the shift and rotation. - """ - - # Rotate the image. - image_rot = rotate_image(image, angle, [cenx, ceny]) - - # Shift the image. - image_rot = shift(image_rot, [yshift, xshift]) - - return image_rot - - -def apply_transform(simple_transform, ref_map, oversample, pad, native=True): - """Apply the calculated rotation and offset to a 2D reference map, and bin - the map down the native size and resolution. - - Parameters - ---------- - simple_transform : Tuple, List, Array - The transformation parameters returned by solve_transform(). - ref_map : array[float] - A reference map: e.g., a 2D Wavelength map or Trace Profile map. - oversample : int - The oversampling factor the reference map. - pad : int - The padding (in native pixels) on the reference map. - native : bool (optional) - If True bin down to native pixel sizes and remove padding. - - Returns - ------- - trans_map : array[float] - The ref_map after having the transformation applied. - """ - - ovs = oversample - - # Unpack the transformation. - angle, xshift, yshift = simple_transform - - # Modify the transformation with the oversampling and padding. - xshift = ovs * xshift - yshift = ovs * yshift - cenx = ovs * (pad + 1024) - ceny = ovs * (pad + 50) - - # Apply the transformation to the reference map. - if (angle == 0 and xshift == 0 and yshift == 0): - trans_map = ref_map - else: - trans_map = transform_image(-angle, xshift, yshift, ref_map, cenx, ceny) - - if native: - if ovs > 1: - # Bin the transformed map down to native resolution. - nrows, ncols = trans_map.shape - trans_map = trans_map.reshape((nrows // ovs), ovs, (ncols // ovs), ovs) - trans_map = trans_map.mean(1).mean(-1) - - # Remove the padding. - if pad != 0: - trans_map = trans_map[pad:-pad, pad:-pad] - - return trans_map - - -def transform_wavemap(simple_transform, wavemap, oversample, pad, native=True): - """Apply the transformation found by solve_transform() to a 2D reference - wavelength map. - - Parameters - ---------- - simple_transform : Tuple, List, Array - The transformation parameters returned by solve_transform(). - wavemap : array[float] - A reference 2D wavelength map. - oversample : int - The oversampling factor the reference map. - pad : int - The padding (in native pixels) on the reference map. - native : bool (optional) - If True bin down to native pixel sizes and remove padding. - - Returns - ------- - trans_wavemap : array[float] - The ref_map after having the transformation applied. - """ - - # Find the minimum and maximum wavelength of the wavelength map. - minval = np.nanmin(wavemap) - maxval = np.nanmax(wavemap) - - # Set NaNs to zero to prevent errors when shifting/rotating. - mask = np.isnan(wavemap) - wavemap = np.where(mask, 0., wavemap) - - # Apply the transformation to the wavelength map. - trans_wavemap = apply_transform(simple_transform, wavemap, oversample, - pad, native=native) - - # Set pixels with interpolation artifacts to zero by enforcing the - # original min/max. - mask = (trans_wavemap < minval) | (trans_wavemap > maxval) - trans_wavemap[mask] = 0 - - return trans_wavemap - - -def transform_profile(simple_transform, profile, oversample, pad, native=True, - norm=True): - """Apply the transformation found by solve_transform() to a 2D reference - trace profile map. - - Parameters - ---------- - simple_transform : Tuple, List, Array - The transformation parameters returned by solve_transform(). - profile : array[float] - A reference 2D trace profile map. - oversample : int - The oversampling factor the reference map. - pad : int - The padding (in native pixels) on the reference map. - native : bool (optional) - If True bin down to native pixel sizes and remove padding. - norm : bool (optional) - If True, normalize each column of the trace profile to sum to one. - - Returns - ------- - trans_profile : array[float] - The ref_map after having the transformation applied. - """ - - # Apply the transformation to the 2D trace map. - trans_profile = apply_transform(simple_transform, profile, oversample, - pad, native=native) - - if norm: - # Normalize so that the columns sum to 1. - with warnings.catch_warnings(): - warnings.simplefilter(action="ignore", category=RuntimeWarning) - trans_profile = trans_profile / np.nansum(trans_profile, axis=0) - - trans_profile[~np.isfinite(trans_profile)] = 0. - - return trans_profile From 0f429162d67b4f1514db0bcdd034d0be37180f18 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 20 Sep 2024 14:12:28 -0400 Subject: [PATCH 478/687] Update coron tests for numpy 2 compatibility (#8812) Co-authored-by: Tyler Pauly --- jwst/coron/tests/test_coron.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index b113e9bb0d..cd90f80049 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -10,7 +10,7 @@ def test_fourier_imshift(): """ Test of fourier_imshift() in imageregistration.py """ - image = np.zeros((5, 5), dtype=np.float32) + image = np.zeros((5, 5), dtype=np.float64) image[1:4, 1:4] += 1.0 image[2, 2] += 2.0 shift = [1.2, 0.6] @@ -31,7 +31,7 @@ def test_fourier_imshift(): def test_shift_subtract(): """ Test of shift_subtract() in imageregistration.py """ - target = np.arange((15), dtype=np.float32).reshape((3, 5)) + target = np.arange((15), dtype=np.float64).reshape((3, 5)) reference = target + 0.1 reference[1, 0] -= 0.2 reference[2, 0] += 2.3 @@ -68,7 +68,7 @@ def test_shift_subtract(): def test_align_fourierLSQ(): """ Test of align_fourierLSQ() in imageregistration.py """ - target = np.arange((15), dtype=np.float32).reshape((3, 5)) + target = np.arange((15), dtype=np.float64).reshape((3, 5)) reference = target + 0.1 reference[1, 0] -= 0.2 reference[2, 0] += 2.3 @@ -85,8 +85,8 @@ def test_align_fourierLSQ(): def test_align_array(): """ Test of align_array() in imageregistration.py """ - temp = np.arange((15), dtype=np.float32).reshape((3, 5)) - targ = np.zeros((3, 3, 5)) + temp = np.arange((15), dtype=np.float64).reshape((3, 5)) + targ = np.zeros((3, 3, 5), dtype=np.float64) targ[:] = temp targ[0, 1, 1] += 0.3 targ[0, 2, 1] += 0.7 @@ -108,7 +108,7 @@ def test_align_array(): ref[0, 2] += 1.3 ref[1, 4] -= 0.6 - mask = temp * 0 + 1 + mask = np.full(temp.shape, 1) mask[1, 1] = 0 mask[1, 2] = 0 aligned, shifts = imageregistration.align_array(ref, targ, mask) @@ -149,7 +149,7 @@ def test_align_models(): """ Test of align_models() in imageregistration.py """ temp = np.arange((15), dtype=np.float32).reshape((3, 5)) - targ = np.zeros((3, 3, 5)) + targ = np.zeros((3, 3, 5), dtype=np.float32) targ[:] = temp targ[0, 1, 1] += 0.3 targ[0, 2, 1] += 0.7 @@ -168,8 +168,7 @@ def test_align_models(): ref[1, 2, 3] -= 5.0 ref[2, 0, 3] -= 1.6 - mask = ref[0, :, :] - mask = ref[0, :, :] * 0 + 1 + mask = np.full(ref[0].shape, 1) mask[1, 1] = 0 mask[1, 2] = 0 @@ -181,10 +180,10 @@ def test_align_models(): results_sub = am_results.data[:2, 2, :3] truth_results_sub = np.array( - [[10.0, 11.7, 12.0], [10.036278, 11.138131, 10.180669]], + [[10.0, 11.7, 12.0], [10.0, 11.2, 10.2]], ) - npt.assert_allclose(results_sub, truth_results_sub, atol=1e-6) + npt.assert_allclose(results_sub, truth_results_sub, rtol=1e-2) def test_KLT(): From 6ae8e137df69ae6352891e41ad31ed947ef4bf34 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Fri, 20 Sep 2024 14:14:20 -0400 Subject: [PATCH 479/687] update metadata for release `1.16.0` (#8810) Co-authored-by: Tyler Pauly --- CHANGES.rst | 2 +- README.md | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 3948e03994..63bb799b99 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,4 +1,4 @@ -1.15.2 (unreleased) +1.16.0 (2024-09-20) =================== align_refs diff --git a/README.md b/README.md index e830ffedfe..6d213ccd27 100644 --- a/README.md +++ b/README.md @@ -216,8 +216,9 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| -| 1.15.1 | B11.0rc2 | TBD | 1242 | 2024-07-08 | TBD | Second release candidate for B11.0 | -| 1.15.0 | B11.0rc1 | TBD | 1241 | 2024-06-26 | TBD | First release candidate for B11.0 | +| 1.16.0 | B11.1rc1 | TBD | 1281 | 2024-09-20 | TBD | First release candidate for B11.1 | +| 1.15.1 | B11.0 | 2024.2.2 | 1242 | 2024-07-08 | 2024-09-12 | Final release candidate for B11.0 | +| 1.15.0 | B11.0rc1 | | 1241 | 2024-06-26 | | First release candidate for B11.0 | | 1.14.1 | | | 1238 | 2024-06-27 | | PyPI-only release for external users | | 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | | 1.13.4 | | | 1185 | 2024-01-25 | | PyPI-only release for external users | From 217d6601083cb2e9de0440cc099afec77e405b9b Mon Sep 17 00:00:00 2001 From: zacharyburnett Date: Fri, 20 Sep 2024 14:19:51 -0400 Subject: [PATCH 480/687] freeze SDP environment --- requirements-sdp.txt | 77 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/requirements-sdp.txt b/requirements-sdp.txt index a110a4b011..794f475a21 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,3 +14,80 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt +asdf==3.4.0 +asdf-astropy==0.6.1 +asdf_coordinates_schemas==0.3.0 +asdf_standard==1.1.1 +asdf_transform_schemas==0.5.0 +asdf_wcs_schemas==0.4.0 +astropy==6.1.3 +astropy-iers-data==0.2024.9.16.0.32.21 +attrs==24.2.0 +BayesicFitting==3.2.1 +certifi==2024.8.30 +charset-normalizer==3.3.2 +ci_watson==0.7.0 +colorama==0.4.6 +contourpy==1.3.0 +coverage==7.6.1 +crds==11.18.4 +cycler==0.12.1 +drizzle==1.15.3 +et-xmlfile==1.1.0 +filelock==3.16.1 +fonttools==4.53.1 +future==1.0.0 +gwcs==0.21.0 +idna==3.10 +imageio==2.35.1 +importlib_metadata==8.5.0 +iniconfig==2.0.0 +jmespath==1.0.1 +jplephem==2.22 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +kiwisolver==1.4.7 +lazy_loader==0.4 +lxml==5.3.0 +matplotlib==3.9.2 +networkx==3.3 +numpy==1.26.4 +opencv-python-headless==4.10.0.84 +openpyxl==3.1.5 +packaging==24.1 +Parsley==1.3 +photutils==1.13.0 +pillow==10.4.0 +pluggy==1.5.0 +poppy==1.1.1 +psutil==6.0.0 +pyerfa==2.0.1.4 +pyparsing==3.1.4 +pysiaf==0.23.3 +pytest==8.3.3 +pytest-cov==5.0.0 +pytest-doctestplus==1.2.1 +python-dateutil==2.9.0.post0 +PyYAML==6.0.2 +readchar==4.2.0 +referencing==0.35.1 +requests==2.32.3 +requests-mock==1.12.1 +rpds-py==0.20.0 +ruff==0.6.6 +scikit-image==0.24.0 +scipy==1.14.1 +semantic-version==2.10.0 +six==1.16.0 +spherical_geometry==1.3.2 +stcal==1.9.0 +stdatamodels==2.1.0 +stpipe==0.7.0 +stsci.imagestats==1.8.3 +stsci.stimage==0.2.9 +synphot==1.4.0 +tifffile==2024.9.20 +tweakwcs==0.8.8 +urllib3==2.2.3 +wiimatch==0.3.2 +zipp==3.20.2 From 947d48ba596fdf09ec266286e5ccd520a34d70cf Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 23 Sep 2024 10:41:10 -0400 Subject: [PATCH 481/687] Make test for linalg error more robust across systems --- .../tests/test_clean_flicker_noise.py | 24 +++++++++++-------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py index 7bc237bc94..4b4fcf67bd 100644 --- a/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py +++ b/jwst/clean_flicker_noise/tests/test_clean_flicker_noise.py @@ -443,7 +443,6 @@ def test_fft_clean_error(monkeypatch, log_watcher): def raise_error(*args, **kwargs): raise np.linalg.LinAlgError('Linear algebra error') - clean_function = cfn.fft_clean_full_frame monkeypatch.setattr(cfn.NSClean, 'clean', raise_error) shape = (10, 10) @@ -451,7 +450,7 @@ def raise_error(*args, **kwargs): image = np.full(shape, 1.0) log_watcher.message = "Error cleaning image" - cleaned_image = clean_function(image.copy(), mask, 'NRS1') + cleaned_image = cfn.fft_clean_full_frame(image.copy(), mask, 'NRS1') assert cleaned_image is None log_watcher.assert_seen() @@ -467,14 +466,6 @@ def test_fft_subarray_clean_error(monkeypatch, log_watcher): assert cleaned_image is None log_watcher.assert_seen() - # Mask is nearly all bad except a pixel at the edge: - # triggers a linear algebra error - mask[0, 0] = True - log_watcher.message = "Error cleaning image" - cleaned_image = cfn.fft_clean_subarray(image.copy(), mask, 'NRS1') - assert cleaned_image is None - log_watcher.assert_seen() - # Mask is mostly bad: warns but continues mask[5, 5] = True log_watcher.message = "Insufficient reference pixels" @@ -482,6 +473,19 @@ def test_fft_subarray_clean_error(monkeypatch, log_watcher): assert np.allclose(cleaned_image, image) log_watcher.assert_seen() + # Trigger a linear algebra error + # This may occur when the mask is not completely bad, + # but there is insufficient data in some region. + def raise_error(*args, **kwargs): + raise np.linalg.LinAlgError('Linear algebra error') + + monkeypatch.setattr(cfn.NSCleanSubarray, 'clean', raise_error) + + log_watcher.message = "Error cleaning image" + cleaned_image = cfn.fft_clean_subarray(image.copy(), mask, 'NRS1') + assert cleaned_image is None + log_watcher.assert_seen() + def test_median_clean(): shape = (20, 20) From eb797c7a7f18b2db80e94c29197e104ae527f9df Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Mon, 23 Sep 2024 12:44:25 -0400 Subject: [PATCH 482/687] use `towncrier` to handle changelog entries (#8671) --- .github/pull_request_template.md | 82 ++++++- .github/workflows/changelog.yml | 29 ++- .readthedocs.yaml | 2 + CHANGES.rst | 1 - changes/.gitkeep | 0 changes/8671.docs.rst | 1 + docs/rtd_environment.yaml | 1 + pyproject.toml | 364 ++++++++++++++++++++++++++++++- 8 files changed, 469 insertions(+), 11 deletions(-) create mode 100644 changes/.gitkeep create mode 100644 changes/8671.docs.rst diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 9da3b0c69f..a7f4eabf61 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -12,11 +12,89 @@ This PR addresses ... ## Tasks - [ ] **request a review from someone specific**, to avoid making the maintainers review every PR - [ ] add a build milestone, i.e. `Build 11.3` (use the [latest build](https://github.com/spacetelescope/jwst/milestones) if not sure) -- [ ] Does this PR change user-facing code / API? - - [ ] add an entry to `CHANGES.rst` within the relevant release section (otherwise add the `no-changelog-entry-needed` label to this PR) +- [ ] Does this PR change user-facing code / API? (if not, label with `no-changelog-entry-needed`) + - [ ] write news fragment(s) in `changes/`: `echo "changed something" > changes/..rst` (see below for change types) - [ ] update or add relevant tests - [ ] update relevant docstrings and / or `docs/` page - [ ] [start a regression test](https://github.com/spacetelescope/RegressionTests/actions/workflows/jwst.yml) and include a link to the running job ([click here for instructions](https://github.com/spacetelescope/RegressionTests/blob/main/docs/running_regression_tests.md)) - [ ] Do truth files need to be updated ("okified")? - [ ] **after the reviewer has approved these changes**, run `okify_regtests` to update the truth files - [ ] if a JIRA ticket exists, [make sure it is resolved properly](https://github.com/spacetelescope/jwst/wiki/How-to-resolve-JIRA-issues) + +
news fragment change types... + + - ``changes/.general.rst``: infrastructure or miscellaneous change + - ``changes/.docs.rst`` + - ``changes/.stpipe.rst`` + - ``changes/.datamodels.rst`` + - ``changes/.scripts.rst`` + - ``changes/.fits_generator.rst`` + - ``changes/.set_telescope_pointing.rst`` + + ## stage 1 + - ``changes/.group_scale.rst`` + - ``changes/.dq_init.rst`` + - ``changes/.emicorr.rst`` + - ``changes/.saturation.rst`` + - ``changes/.ipc.rst`` + - ``changes/.firstframe.rst`` + - ``changes/.lastframe.rst`` + - ``changes/.reset.rst`` + - ``changes/.superbias.rst`` + - ``changes/.refpix.rst`` + - ``changes/.linearity.rst`` + - ``changes/.rscd.rst`` + - ``changes/.persistence.rst`` + - ``changes/.dark_current.rst`` + - ``changes/.charge_migration.rst`` + - ``changes/.jump.rst`` + - ``changes/.ramp_fitting.rst`` + - ``changes/.gain_scale.rst`` + + ## stage 2 + - ``changes/.assign_wcs.rst`` + - ``changes/.msaflagopen.rst`` + - ``changes/.nsclean.rst`` + - ``changes/.imprint.rst`` + - ``changes/.background.rst`` + - ``changes/.extract_2d.rst`` + - ``changes/.master_background.rst`` + - ``changes/.wavecorr.rst`` + - ``changes/.srctype.rst`` + - ``changes/.straylight.rst`` + - ``changes/.wfss_contam.rst`` + - ``changes/.flatfield.rst`` + - ``changes/.fringe.rst`` + - ``changes/.pathloss.rst`` + - ``changes/.barshadow.rst`` + - ``changes/.photom.rst`` + - ``changes/.pixel_replace.rst`` + - ``changes/.resample_spec.rst`` + - ``changes/.residual_fringe.rst`` + - ``changes/.cube_build.rst`` + - ``changes/.extract_1d.rst`` + - ``changes/.resample.rst`` + + ## stage 3 + - ``changes/.assign_mtwcs.rst`` + - ``changes/.mrs_imatch.rst`` + - ``changes/.tweakreg.rst`` + - ``changes/.skymatch.rst`` + - ``changes/.exp_to_source.rst`` + - ``changes/.outlier_detection.rst`` + - ``changes/.tso_photometry.rst`` + - ``changes/.stack_refs.rst`` + - ``changes/.align_refs.rst`` + - ``changes/.klip.rst`` + - ``changes/.spectral_leak.rst`` + - ``changes/.source_catalog.rst`` + - ``changes/.combine_1d.rst`` + - ``changes/.ami.rst`` + + ## other + - ``changes/.wfs_combine.rst`` + - ``changes/.white_light.rst`` + - ``changes/.cube_skymatch.rst`` + - ``changes/.engdb_tools.rst`` + - ``changes/.guider_cds.rst`` +
diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index 0ef4a043b0..a6889ab407 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -1,16 +1,31 @@ -name: Ensure changelog +name: changelog on: pull_request: - types: [labeled, unlabeled, opened, synchronize, reopened] + types: + - labeled + - unlabeled + - opened + - synchronize + - reopened + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true jobs: - ensure_changelog: - name: Verify that a changelog entry exists for this pull request + check: + if: ${{ !contains(github.event.pull_request.labels.*.name, 'no-changelog-entry-needed') }} runs-on: ubuntu-latest steps: + - uses: actions/setup-python@v5 + with: + python-version: 3 - uses: actions/checkout@v4 with: - submodules: true - - run: grep -P '\[[^\]]*#${{github.event.number}}[,\]]' CHANGES.rst - if: ${{ !contains(github.event.pull_request.labels.*.name, 'no-changelog-entry-needed') }} + fetch-depth: 0 + - run: pip install . + - run: pip install towncrier + - run: towncrier check + - run: towncrier build --draft | grep -P '#${{ github.event.number }}' + diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 6dff1248d4..107815b26e 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -27,6 +27,8 @@ build: - git fetch --all --tags || true pre_install: - git update-index --assume-unchanged docs/rtd_environment.yaml docs/conf.py + post_install: + - towncrier build --keep conda: environment: docs/rtd_environment.yaml diff --git a/CHANGES.rst b/CHANGES.rst index 63bb799b99..7500ad652f 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -416,7 +416,6 @@ tweakreg - Step now uses `ModelLibrary` to handle accessing models consistently whether they are in memory or on disk. [#8683] - 1.15.1 (2024-07-08) =================== diff --git a/changes/.gitkeep b/changes/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/changes/8671.docs.rst b/changes/8671.docs.rst new file mode 100644 index 0000000000..724817599d --- /dev/null +++ b/changes/8671.docs.rst @@ -0,0 +1 @@ +use ``towncrier`` to handle change log entries diff --git a/docs/rtd_environment.yaml b/docs/rtd_environment.yaml index aeebc4dff6..f6818e66e9 100644 --- a/docs/rtd_environment.yaml +++ b/docs/rtd_environment.yaml @@ -7,3 +7,4 @@ dependencies: - pip - graphviz - sphinx_rtd_theme>1.2.0 + - towncrier diff --git a/pyproject.toml b/pyproject.toml index dd65909821..def298ae11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -193,7 +193,6 @@ minversion = "6.0" norecursedirs = [ "docs/_build", "docs/exts", - "jwst/timeconversion", "jwst/associations/tests/data", "scripts", ".tox", @@ -286,3 +285,366 @@ lint.ignore = [ "jwst/ramp_fitting/tests/*" = [ "E402", # module-level import not at top of file ] + +[tool.towncrier] +filename = "CHANGES.rst" +directory = "changes" +package = "jwst" +title_format = "{version} ({project_date})" +ignore = [".gitkeep"] +wrap = true +issue_format = "`#{issue} `_" + +[[tool.towncrier.type]] +directory = "general" +name = "General" +showcontent = true + +[[tool.towncrier.type]] +directory = "docs" +name = "Documentation" +showcontent = true + +[[tool.towncrier.type]] +directory = "stpipe" +name = '``stpipe``' +showcontent = true + +[[tool.towncrier.type]] +directory = "datamodels" +name = "Data Models" +showcontent = true + +[[tool.towncrier.type]] +directory = "associations" +name = "Associations" +showcontent = true + +[[tool.towncrier.type]] +directory = "scripts" +name = "Scripts" +showcontent = true + +[[tool.towncrier.type]] +directory = "fits_generator" +name = "``fits_generator``" +showcontent = true + +[[tool.towncrier.type]] +directory = "set_telescope_pointing" +name = '``set_telescope_pointing``' +showcontent = true + +# pipeline + +[[tool.towncrier.type]] +directory = "pipeline" +name = 'Pipeline' +showcontent = true + +# steps + +[[tool.towncrier.type]] +directory = "align_refs" +name = 'align_refs (coron3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "ami" +name = 'ami_analyze / ami_normalize / ami_average (ami3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "assign_mtwcs" +name = 'assign_mtwcs (image3, spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "assign_wcs" +name = 'assign_wcs (image2, spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "background" +name = 'background (image2, spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "badpix_selfcal" +name = 'badpix_selfcal (spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "barshadow" +name = 'barshadow (spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "charge_migration" +name = "charge_migration (detector1 NIR)" +showcontent = true + +[[tool.towncrier.type]] +directory = "clean_flicker_noise" +name = "clean_flicker_noise (detector1)" +showcontent = true + +[[tool.towncrier.type]] +directory = "combine_1d" +name = 'combine_1d (spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "cube_build" +name = 'cube_build (spec2 IFU, spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "cube_skymatch" +name = 'cube_skymatch' +showcontent = true + +[[tool.towncrier.type]] +directory = "dark_current" +name = 'dark_current (detector1 NIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "dq_init" +name = 'dq_init (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "emicorr" +name = 'emicorr (detector1 MIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "engdb_tools" +name = 'engdb_tools' +showcontent = true + +[[tool.towncrier.type]] +directory = "exp_to_source" +name = 'exp_to_source (spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "extract_1d" +name = 'extract_1d (spec2, spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "extract_2d" +name = 'extract_2d (spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "firstframe" +name = 'firstframe (detector1 MIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "flatfield" +name = 'flatfield (image2, spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "fringe" +name = 'fringe (spec2 IFU)' +showcontent = true + +[[tool.towncrier.type]] +directory = "gain_scale" +name = 'gain_scale (detector1 NIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "group_scale" +name = 'group_scale (detector1 NIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "guider_cds" +name = 'guider_cds (FGS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "imprint" +name = 'imprint (spec2 IFU, spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "ipc" +name = 'ipc (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "jump" +name = 'jump (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "klip" +name = "klip (coron3)" +showcontent = true + +[[tool.towncrier.type]] +directory = "lastframe" +name = 'lastframe (detector1 MIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "linearity" +name = 'linearity (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "master_background" +name = 'master_background (spec2 MOS, spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "mrs_imatch" +name = 'mrs_imatch (spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "msaflagopen" +name = 'msaflagopen (spec2 IFU, spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "nsclean" +name = "nsclean (spec2 IFU, spec2 MOS)" +showcontent = true + +[[tool.towncrier.type]] +directory = "outlier_detection" +name = 'outlier_detection (image3, tso3, spec3, coron3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "pathloss" +name = 'pathloss (spec2 IFU, spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "persistence" +name = 'persistence (detector1 NIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "photom" +name = 'photom (image2, spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "pixel_replace" +name = 'pixel_replace (spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "ramp_fitting" +name = 'ramp_fitting (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "refpix" +name = 'refpix (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "resample" +name = 'resample (image2, image3, coron3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "resample_spec" +name = 'resample_spec (spec2 MOS, spec3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "reset" +name = 'reset (detector1 MIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "residual_fringe" +name = 'residual_fringe (spec2 IFU)' +showcontent = true + +[[tool.towncrier.type]] +directory = "rscd" +name = 'rscd (detector1 MIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "saturation" +name = 'saturation (detector1)' +showcontent = true + +[[tool.towncrier.type]] +directory = "skymatch" +name = 'skymatch (image3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "source_catalog" +name = 'source_catalog (image3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "spectral_leak" +name = "spectral_leak (spec3)" +showcontent = true + +[[tool.towncrier.type]] +directory = "srctype" +name = 'srctype (spec2)' +showcontent = true + +[[tool.towncrier.type]] +directory = "stack_refs" +name = "stack_refs (coron3)" +showcontent = true + +[[tool.towncrier.type]] +directory = "straylight" +name = 'straylight (spec2 IFU)' +showcontent = true + +[[tool.towncrier.type]] +directory = "superbias" +name = 'superbias (detector1 NIR)' +showcontent = true + +[[tool.towncrier.type]] +directory = "tso_photometry" +name = 'tso_photometry (tso3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "tweakreg" +name = 'tweakreg (image3)' +showcontent = true + +[[tool.towncrier.type]] +directory = "wavecorr" +name = 'wavecorr (spec2 MOS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "wfs_combine" +name = 'wfs_combine' +showcontent = true + +[[tool.towncrier.type]] +directory = "wfss_contam" +name = 'wfss_contam (spec2 WFSS)' +showcontent = true + +[[tool.towncrier.type]] +directory = "white_light" +name = 'white_light' +showcontent = true From b8cb30779586891a9709f727c6266831823c52ae Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Mon, 23 Sep 2024 14:02:59 -0400 Subject: [PATCH 483/687] switch default branch name to main in docs/ci (#8210) --- .github/dependabot.yml | 4 ++-- .github/workflows/ci.yml | 4 ++-- .github/workflows/tests_devdeps.yml | 4 ++-- CONTRIBUTING.md | 26 +++++++++++++------------- JenkinsfileRT | 2 +- JenkinsfileRT_dev | 2 +- README.md | 8 ++++---- changes/8210.general.rst | 1 + docs/getting_started/contributing.rst | 2 +- docs/getting_started/install.rst | 4 ++-- docs/jwst/stpipe/cfg_deprecation.rst | 2 +- 11 files changed, 30 insertions(+), 29 deletions(-) create mode 100644 changes/8210.general.rst diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 03b4fe324e..1b061aa0a4 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -4,7 +4,7 @@ updates: # Maintain dependencies for GitHub Actions - package-ecosystem: "github-actions" directory: "/" - target-branch: "master" + target-branch: "main" schedule: interval: "weekly" reviewers: @@ -13,7 +13,7 @@ updates: # Maintain dependencies for pip - package-ecosystem: "pip" directory: "/" - target-branch: "master" + target-branch: "main" schedule: interval: "weekly" reviewers: diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1b1f54cc37..45ab163eed 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -3,13 +3,13 @@ name: test on: push: branches: - - master + - main - '*x' tags: - '*' pull_request: branches: - - master + - main schedule: # Weekly Monday 9AM build - cron: "0 9 * * 1" diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index 6e0aef5081..2daf8941d5 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -3,13 +3,13 @@ name: test with development versions on: push: branches: - - master + - main - '*x' tags: - '*' pull_request: branches: - - master + - main schedule: # Weekly Monday 9AM build - cron: "0 9 * * 1" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 23fd25e57e..b085d74a1e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -15,7 +15,7 @@ opening a new [issue](https://github.com/spacetelescope/jwst/issues). ## Contributing code If you would like to contribute code, this is done by submitting a [pull request](https://github.com/spacetelescope/jwst/pulls) -to the "master" branch of `spacetelescope/jwst`. To do this, we recommend the +to the "main" branch of `spacetelescope/jwst`. To do this, we recommend the following workflow (which assumes you already have a Github account / command line tools). If you are also new to git, please refer to the [git reference manual](https://git-scm.com/docs) for an overview of git basics. @@ -33,7 +33,7 @@ First, to clarify some terms that will be commonly used here: The first step is to create your own 'remote' (online) and 'local' (on your machine) clones of the central `spacetelescope/jwst` repository. You will make code changes on your machine to your 'local' clone, push these to 'origin' (your online fork), -and finally, open a pull request to the ''master'' branch of `spacetelescope/jwst`. +and finally, open a pull request to the ''main'' branch of `spacetelescope/jwst`. 1. On the 'spacetelescope/jwst' Github repository page, 'fork' the JWST repository to your own account space by clicking the appropriate button on the upper right-hand @@ -77,16 +77,16 @@ If you ever want to reset these URLs, add references to other remote forks of ### Step 2: Creating a branch for your changes -It is a standard practice in git to create a new 'branch' (off `upstream/master`) +It is a standard practice in git to create a new 'branch' (off `upstream/main`) for each new feature or bug fix. You can call this branch whatever you like - in this example, we'll call it 'my_feature'. First, make sure you have all recent changes to upstream by 'fetching' them: >> git fetch upstream -The following will create a new branch off local/master called 'my_feature', and automatically switch you over to your new branch. +The following will create a new branch off local/main called 'my_feature', and automatically switch you over to your new branch. - >> git checkout -b my_feature upstream/master + >> git checkout -b my_feature upstream/main ### Step 3: Installing `jwst` for development @@ -149,14 +149,14 @@ you open a pull request, add the corresponding PR number. ### Step 4: Opening a pull request -Now, you can open a pull request on the master branch of the upstream `jwst` repository. +Now, you can open a pull request on the main branch of the upstream `jwst` repository. 1. On the `spacetelescope/jwst` web page, after you push your changes you should see a large green banner appear at the top prompting you to open a pull request with your recently pushed changes. You can also open a pull request from the [pull request tab](https://github.com/spacetelescope/jwst/pulls) on that page. Select your fork and your 'my_feature' branch, and open a pull request against -the 'master' branch. +the 'main' branch. 2. There is now a checklist of items that need to be done before your PR can be merged. * The continuous integration (CI) tests must complete and pass. The CI @@ -177,16 +177,16 @@ the 'master' branch. ## Keeping your development branch current - rebasing As `jwst` is constantly evolving, you will often encounter the situation where you've -made changes to your branch off 'master', but in the time its taken you to make those -changes, 'upstream/master' has evolved with new commits from other developers. In this +made changes to your branch off 'main', but in the time its taken you to make those +changes, 'upstream/main' has evolved with new commits from other developers. In this situation, you will want to make sure you incorporate these changes into your branch. Rebasing allows you to do two things - 1. apply others changes on top of yours, and 2. squash your commits, even if there aren't new changes to apply. Periodically, while writing code, to keep your branch up to date you will want to -do an interactive rebase against upstream/master to apply any new changes on top of yours: +do an interactive rebase against upstream/main to apply any new changes on top of yours: - >> git rebase -i upstream/master + >> git rebase -i upstream/main This will then prompt you to select commits and commit messages - if you select just the top commit, this will 'squash' the others and combine them into one. You @@ -204,7 +204,7 @@ commits into a single one, which is also done with `git rebase` `jwst` uses [sphinx](https://docs.readthedocs.io/en/stable/intro/getting-started-with-sphinx.html) to generate documentation, which is then hosted online on [readthedocs](https://readthedocs.org/). You can access two versions of the documentation on the [JWST readthedocs website](https://readthedocs.org/projects/jwst-pipeline/) -- the 'latest' version is whatever is currently on the master branch, and the 'stable' +- the 'latest' version is whatever is currently on the main branch, and the 'stable' version is the last released version. If you successfully merge a PR with documentation changes, they will only appear on 'latest' until the next JWST release. @@ -329,7 +329,7 @@ last released version of `stcal`, and stcal points to the last version of `jwst` issue becomes circular. What you will need to do is modify the `pyproject.toml` files in both packages to point to the other to demonstrate that CI tests pass (and make a comment noting this in your PR), and then change it back before the PR is merge so that changes -to `pyproject.toml` are not merged into master/main. In your `jwst` branch, to point to your +to `pyproject.toml` are not merged into main. In your `jwst` branch, to point to your branch in the dependent package (in this example `stcal`), change the required `stcal` version in `pyproject.toml` to: diff --git a/JenkinsfileRT b/JenkinsfileRT index e155c83ab1..4fcea13477 100644 --- a/JenkinsfileRT +++ b/JenkinsfileRT @@ -40,7 +40,7 @@ withCredentials([string( jobconfig = new JobConfig() jobconfig.enable_env_publication = true jobconfig.publish_env_on_success_only = true -jobconfig.publish_env_filter = "spacetelescope/master" +jobconfig.publish_env_filter = "spacetelescope/main" // Define python version for conda python_version = "3.11" diff --git a/JenkinsfileRT_dev b/JenkinsfileRT_dev index 2f88a1d81a..a3d7db5893 100644 --- a/JenkinsfileRT_dev +++ b/JenkinsfileRT_dev @@ -40,7 +40,7 @@ withCredentials([string( jobconfig = new JobConfig() jobconfig.enable_env_publication = false jobconfig.publish_env_on_success_only = true -jobconfig.publish_env_filter = "spacetelescope/master" +jobconfig.publish_env_filter = "spacetelescope/main" // Define python version for conda python_version = "3.11" diff --git a/README.md b/README.md index 6d213ccd27..b1bd23db8b 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # JWST Calibration Pipeline -[![Build Status](https://github.com/spacetelescope/jwst/workflows/CI/badge.svg?branch=master)](https://github.com/spacetelescope/jwst/actions) -[![codecov](https://codecov.io/gh/spacetelescope/jwst/branch/master/graph/badge.svg?token=Utf5Zs9g7z)](https://codecov.io/gh/spacetelescope/jwst) +[![Build Status](https://github.com/spacetelescope/jwst/workflows/CI/badge.svg)](https://github.com/spacetelescope/jwst/actions) +[![codecov](https://codecov.io/gh/spacetelescope/jwst/branch/main/graph/badge.svg?token=Utf5Zs9g7z)](https://codecov.io/gh/spacetelescope/jwst) [![Documentation Status](https://readthedocs.org/projects/jwst-pipeline/badge/?version=latest)](http://jwst-pipeline.readthedocs.io/en/latest/?badge=latest) [![Powered by STScI Badge](https://img.shields.io/badge/powered%20by-STScI-blue.svg?colorA=707170&colorB=3e8ddd&style=flat)](http://www.stsci.edu) [![Powered by Astropy Badge](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org/) @@ -64,7 +64,7 @@ You can also install a specific version: ### Installing the development version from Github You can install the latest development version (not as well tested) from the -Github master branch: +Github main branch: conda create -n python=3.11 conda activate @@ -178,7 +178,7 @@ environment variable: ## Documentation -Documentation (built daily from the Github `master` branch) is available at: +Documentation (built daily from the Github `main` branch) is available at: https://jwst-pipeline.readthedocs.io/en/latest/ diff --git a/changes/8210.general.rst b/changes/8210.general.rst new file mode 100644 index 0000000000..b923267b54 --- /dev/null +++ b/changes/8210.general.rst @@ -0,0 +1 @@ +Rename default branch to main. diff --git a/docs/getting_started/contributing.rst b/docs/getting_started/contributing.rst index 8d93493ac2..b1f7ae42e8 100644 --- a/docs/getting_started/contributing.rst +++ b/docs/getting_started/contributing.rst @@ -3,4 +3,4 @@ Contributing and Reporting Issues ================================= `jwst` is open source - if you would like to contribute code or file an issue, -please see the `the Github Contribution Guide `_. \ No newline at end of file +please see the `the Github Contribution Guide `_. diff --git a/docs/getting_started/install.rst b/docs/getting_started/install.rst index c978378641..410daa02ed 100644 --- a/docs/getting_started/install.rst +++ b/docs/getting_started/install.rst @@ -69,7 +69,7 @@ Installing the Development Version from Github ---------------------------------------------- You can install the latest development version (not as well tested) from the -Github master branch: +Github main branch: | >> conda create -n python=3.11 | >> conda activate @@ -93,7 +93,7 @@ environment of choice and then reinstall: This will ensure that all dependency packages are also upgraded. This also applies when using the development version of jwst - to upgrade and grab recent -changes, uninstall and re-install the master branch from Github: +changes, uninstall and re-install the main branch from Github: | >> pip uninstall jwst | >> pip install git+https://github.com/spacetelescope/jwst diff --git a/docs/jwst/stpipe/cfg_deprecation.rst b/docs/jwst/stpipe/cfg_deprecation.rst index 6b3fbe35b5..78bef68d93 100644 --- a/docs/jwst/stpipe/cfg_deprecation.rst +++ b/docs/jwst/stpipe/cfg_deprecation.rst @@ -4,7 +4,7 @@ CFG Usage Deprecation Notice ============================ As of March 18, 2021, a significant change to how JWST pipelines operate was -completed and pushed to the JWST master branch on github. Theoretically the +completed and pushed to the JWST main branch on github. Theoretically the change should be transparent. However, we are all familiar with the difference between theory and practice and hence we want to alert all users. From 6e28b95f17066e660b1a6fd99f620e3ba13c1003 Mon Sep 17 00:00:00 2001 From: zacharyburnett Date: Mon, 23 Sep 2024 14:10:19 -0400 Subject: [PATCH 484/687] clear SDP requirements --- requirements-sdp.txt | 77 -------------------------------------------- 1 file changed, 77 deletions(-) diff --git a/requirements-sdp.txt b/requirements-sdp.txt index 794f475a21..a110a4b011 100644 --- a/requirements-sdp.txt +++ b/requirements-sdp.txt @@ -14,80 +14,3 @@ # conda activate sdp # pip install -e .[test,sdp] # pip freeze | grep -v jwst >> requirements-sdp.txt -asdf==3.4.0 -asdf-astropy==0.6.1 -asdf_coordinates_schemas==0.3.0 -asdf_standard==1.1.1 -asdf_transform_schemas==0.5.0 -asdf_wcs_schemas==0.4.0 -astropy==6.1.3 -astropy-iers-data==0.2024.9.16.0.32.21 -attrs==24.2.0 -BayesicFitting==3.2.1 -certifi==2024.8.30 -charset-normalizer==3.3.2 -ci_watson==0.7.0 -colorama==0.4.6 -contourpy==1.3.0 -coverage==7.6.1 -crds==11.18.4 -cycler==0.12.1 -drizzle==1.15.3 -et-xmlfile==1.1.0 -filelock==3.16.1 -fonttools==4.53.1 -future==1.0.0 -gwcs==0.21.0 -idna==3.10 -imageio==2.35.1 -importlib_metadata==8.5.0 -iniconfig==2.0.0 -jmespath==1.0.1 -jplephem==2.22 -jsonschema==4.23.0 -jsonschema-specifications==2023.12.1 -kiwisolver==1.4.7 -lazy_loader==0.4 -lxml==5.3.0 -matplotlib==3.9.2 -networkx==3.3 -numpy==1.26.4 -opencv-python-headless==4.10.0.84 -openpyxl==3.1.5 -packaging==24.1 -Parsley==1.3 -photutils==1.13.0 -pillow==10.4.0 -pluggy==1.5.0 -poppy==1.1.1 -psutil==6.0.0 -pyerfa==2.0.1.4 -pyparsing==3.1.4 -pysiaf==0.23.3 -pytest==8.3.3 -pytest-cov==5.0.0 -pytest-doctestplus==1.2.1 -python-dateutil==2.9.0.post0 -PyYAML==6.0.2 -readchar==4.2.0 -referencing==0.35.1 -requests==2.32.3 -requests-mock==1.12.1 -rpds-py==0.20.0 -ruff==0.6.6 -scikit-image==0.24.0 -scipy==1.14.1 -semantic-version==2.10.0 -six==1.16.0 -spherical_geometry==1.3.2 -stcal==1.9.0 -stdatamodels==2.1.0 -stpipe==0.7.0 -stsci.imagestats==1.8.3 -stsci.stimage==0.2.9 -synphot==1.4.0 -tifffile==2024.9.20 -tweakwcs==0.8.8 -urllib3==2.2.3 -wiimatch==0.3.2 -zipp==3.20.2 From 56328cf07b82aece6d4ebb3eda62bb8870e7fb0c Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Tue, 24 Sep 2024 09:01:29 -0400 Subject: [PATCH 485/687] [docs] add new steps to pull request checklist as change fragment types (#8824) --- .github/pull_request_template.md | 143 ++++++++++++++++--------------- 1 file changed, 73 insertions(+), 70 deletions(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index a7f4eabf61..67aaee60b1 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -23,78 +23,81 @@ This PR addresses ...
news fragment change types... - - ``changes/.general.rst``: infrastructure or miscellaneous change - - ``changes/.docs.rst`` - - ``changes/.stpipe.rst`` - - ``changes/.datamodels.rst`` - - ``changes/.scripts.rst`` - - ``changes/.fits_generator.rst`` - - ``changes/.set_telescope_pointing.rst`` +- ``changes/.general.rst``: infrastructure or miscellaneous change +- ``changes/.docs.rst`` +- ``changes/.stpipe.rst`` +- ``changes/.datamodels.rst`` +- ``changes/.scripts.rst`` +- ``changes/.fits_generator.rst`` +- ``changes/.set_telescope_pointing.rst`` +- ``changes/.pipeline.rst`` - ## stage 1 - - ``changes/.group_scale.rst`` - - ``changes/.dq_init.rst`` - - ``changes/.emicorr.rst`` - - ``changes/.saturation.rst`` - - ``changes/.ipc.rst`` - - ``changes/.firstframe.rst`` - - ``changes/.lastframe.rst`` - - ``changes/.reset.rst`` - - ``changes/.superbias.rst`` - - ``changes/.refpix.rst`` - - ``changes/.linearity.rst`` - - ``changes/.rscd.rst`` - - ``changes/.persistence.rst`` - - ``changes/.dark_current.rst`` - - ``changes/.charge_migration.rst`` - - ``changes/.jump.rst`` - - ``changes/.ramp_fitting.rst`` - - ``changes/.gain_scale.rst`` +## stage 1 +- ``changes/.group_scale.rst`` +- ``changes/.dq_init.rst`` +- ``changes/.emicorr.rst`` +- ``changes/.saturation.rst`` +- ``changes/.ipc.rst`` +- ``changes/.firstframe.rst`` +- ``changes/.lastframe.rst`` +- ``changes/.reset.rst`` +- ``changes/.superbias.rst`` +- ``changes/.refpix.rst`` +- ``changes/.linearity.rst`` +- ``changes/.rscd.rst`` +- ``changes/.persistence.rst`` +- ``changes/.dark_current.rst`` +- ``changes/.charge_migration.rst`` +- ``changes/.jump.rst`` +- ``changes/.clean_flicker_noise.rst`` +- ``changes/.ramp_fitting.rst`` +- ``changes/.gain_scale.rst`` - ## stage 2 - - ``changes/.assign_wcs.rst`` - - ``changes/.msaflagopen.rst`` - - ``changes/.nsclean.rst`` - - ``changes/.imprint.rst`` - - ``changes/.background.rst`` - - ``changes/.extract_2d.rst`` - - ``changes/.master_background.rst`` - - ``changes/.wavecorr.rst`` - - ``changes/.srctype.rst`` - - ``changes/.straylight.rst`` - - ``changes/.wfss_contam.rst`` - - ``changes/.flatfield.rst`` - - ``changes/.fringe.rst`` - - ``changes/.pathloss.rst`` - - ``changes/.barshadow.rst`` - - ``changes/.photom.rst`` - - ``changes/.pixel_replace.rst`` - - ``changes/.resample_spec.rst`` - - ``changes/.residual_fringe.rst`` - - ``changes/.cube_build.rst`` - - ``changes/.extract_1d.rst`` - - ``changes/.resample.rst`` +## stage 2 +- ``changes/.assign_wcs.rst`` +- ``changes/.badpix_selfcal.rst`` +- ``changes/.msaflagopen.rst`` +- ``changes/.nsclean.rst`` +- ``changes/.imprint.rst`` +- ``changes/.background.rst`` +- ``changes/.extract_2d.rst`` +- ``changes/.master_background.rst`` +- ``changes/.wavecorr.rst`` +- ``changes/.srctype.rst`` +- ``changes/.straylight.rst`` +- ``changes/.wfss_contam.rst`` +- ``changes/.flatfield.rst`` +- ``changes/.fringe.rst`` +- ``changes/.pathloss.rst`` +- ``changes/.barshadow.rst`` +- ``changes/.photom.rst`` +- ``changes/.pixel_replace.rst`` +- ``changes/.resample_spec.rst`` +- ``changes/.residual_fringe.rst`` +- ``changes/.cube_build.rst`` +- ``changes/.extract_1d.rst`` +- ``changes/.resample.rst`` - ## stage 3 - - ``changes/.assign_mtwcs.rst`` - - ``changes/.mrs_imatch.rst`` - - ``changes/.tweakreg.rst`` - - ``changes/.skymatch.rst`` - - ``changes/.exp_to_source.rst`` - - ``changes/.outlier_detection.rst`` - - ``changes/.tso_photometry.rst`` - - ``changes/.stack_refs.rst`` - - ``changes/.align_refs.rst`` - - ``changes/.klip.rst`` - - ``changes/.spectral_leak.rst`` - - ``changes/.source_catalog.rst`` - - ``changes/.combine_1d.rst`` - - ``changes/.ami.rst`` +## stage 3 +- ``changes/.assign_mtwcs.rst`` +- ``changes/.mrs_imatch.rst`` +- ``changes/.tweakreg.rst`` +- ``changes/.skymatch.rst`` +- ``changes/.exp_to_source.rst`` +- ``changes/.outlier_detection.rst`` +- ``changes/.tso_photometry.rst`` +- ``changes/.stack_refs.rst`` +- ``changes/.align_refs.rst`` +- ``changes/.klip.rst`` +- ``changes/.spectral_leak.rst`` +- ``changes/.source_catalog.rst`` +- ``changes/.combine_1d.rst`` +- ``changes/.ami.rst`` - ## other - - ``changes/.wfs_combine.rst`` - - ``changes/.white_light.rst`` - - ``changes/.cube_skymatch.rst`` - - ``changes/.engdb_tools.rst`` - - ``changes/.guider_cds.rst`` +## other +- ``changes/.wfs_combine.rst`` +- ``changes/.white_light.rst`` +- ``changes/.cube_skymatch.rst`` +- ``changes/.engdb_tools.rst`` +- ``changes/.guider_cds.rst``
From 8f3eccf48c8a4b76533986a96a9dc429c78898f2 Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Tue, 24 Sep 2024 09:24:26 -0400 Subject: [PATCH 486/687] add CI check that `CHANGES.rst` is not modified (#8827) --- .github/workflows/changelog.yml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index a6889ab407..508e6e295a 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -28,4 +28,13 @@ jobs: - run: pip install towncrier - run: towncrier check - run: towncrier build --draft | grep -P '#${{ github.event.number }}' + prevent_manually_editing_changlog: + if: ${{ !contains(github.event.pull_request.labels.*.name, 'allow-manual-changelog-edit') }} + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: prevent direct changes to `CHANGES.rst` + run: git diff HEAD ${{ github.event.pull_request.base.sha }} --no-patch --exit-code CHANGES.rst From 54d84c6985006a7cb3c446e49c1a9e07bd7922a5 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 20 Sep 2024 09:12:11 -0400 Subject: [PATCH 487/687] split test_nirspec_ifu_spec3_regtest --- jwst/regtest/test_nirspec_ifu_spec3.py | 54 +------------------ .../test_nirspec_ifu_spec3_pixelreplace.py | 53 ++++++++++++++++++ 2 files changed, 54 insertions(+), 53 deletions(-) create mode 100644 jwst/regtest/test_nirspec_ifu_spec3_pixelreplace.py diff --git a/jwst/regtest/test_nirspec_ifu_spec3.py b/jwst/regtest/test_nirspec_ifu_spec3.py index f604b2ed9f..5efbcbd771 100644 --- a/jwst/regtest/test_nirspec_ifu_spec3.py +++ b/jwst/regtest/test_nirspec_ifu_spec3.py @@ -3,11 +3,6 @@ from jwst.regtest import regtestdata as rt -# Define artifactory source and truth -INPUT_PATH = 'nirspec/ifu' -TRUTH_PATH = 'truth/test_nirspec_ifu' -TRUTH_PATH_PIXEL_REPLACE = 'truth/test_nirspec_ifu_pixel_replace' - @pytest.fixture(scope='module') def run_spec3_multi(rtdata_module): @@ -31,27 +26,6 @@ def run_spec3_multi(rtdata_module): return rtdata -@pytest.fixture(scope='module') -def run_spec3_multi_pixel_replace(rtdata_module): - """Run Spec3Pipeline with pixel replacement""" - rtdata = rtdata_module - - step_params = { - 'input_path': 'nirspec/ifu/jw01249-o005_20230622t074431_rp_spec3_00001_asn.json', - 'step': 'calwebb_spec3', - 'args': { - '--steps.pixel_replace.skip=false', - '--steps.pixel_replace.save_results=true', - '--steps.pixel_replace.algorithm=mingrad', - '--steps.cube_build.save_results=true', - '--steps.extract_1d.save_results=true', - } - } - - rtdata = rt.run_step_from_dict(rtdata, **step_params) - return rtdata - - @pytest.mark.slow @pytest.mark.bigdata @pytest.mark.parametrize( @@ -73,32 +47,6 @@ def test_spec3_multi(run_spec3_multi, fitsdiff_default_kwargs, output): """Regression test matching output files""" rt.is_like_truth( run_spec3_multi, fitsdiff_default_kwargs, output, - truth_path=TRUTH_PATH, - is_suffix=False - ) - - -@pytest.mark.slow -@pytest.mark.bigdata -@pytest.mark.parametrize( - 'output', - [ - 'jw01249005001_03101_00001_nrs1_o005_pixel_replace.fits', - 'jw01249005001_03101_00001_nrs2_o005_pixel_replace.fits', - 'jw01249005001_03101_00002_nrs1_o005_pixel_replace.fits', - 'jw01249005001_03101_00002_nrs2_o005_pixel_replace.fits', - 'jw01249005001_03101_00003_nrs1_o005_pixel_replace.fits', - 'jw01249005001_03101_00003_nrs2_o005_pixel_replace.fits', - 'jw01249005001_03101_00004_nrs1_o005_pixel_replace.fits', - 'jw01249005001_03101_00004_nrs2_o005_pixel_replace.fits', - 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_s3d.fits', - 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_x1d.fits', - ] -) -def test_spec3_multi_pixel_replace(run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output): - """Regression test matching output files""" - rt.is_like_truth( - run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output, - truth_path=TRUTH_PATH_PIXEL_REPLACE, + truth_path='truth/test_nirspec_ifu', is_suffix=False ) diff --git a/jwst/regtest/test_nirspec_ifu_spec3_pixelreplace.py b/jwst/regtest/test_nirspec_ifu_spec3_pixelreplace.py new file mode 100644 index 0000000000..4b604ba5f0 --- /dev/null +++ b/jwst/regtest/test_nirspec_ifu_spec3_pixelreplace.py @@ -0,0 +1,53 @@ +"""Regression tests for NIRSpec IFU""" +import pytest + +from jwst.regtest import regtestdata as rt + +TRUTH_PATH_PIXEL_REPLACE = 'truth/test_nirspec_ifu_pixel_replace' + + +@pytest.fixture(scope='module') +def run_spec3_multi_pixel_replace(rtdata_module): + """Run Spec3Pipeline with pixel replacement""" + rtdata = rtdata_module + + step_params = { + 'input_path': 'nirspec/ifu/jw01249-o005_20230622t074431_rp_spec3_00001_asn.json', + 'step': 'calwebb_spec3', + 'args': { + '--steps.pixel_replace.skip=false', + '--steps.pixel_replace.save_results=true', + '--steps.pixel_replace.algorithm=mingrad', + '--steps.cube_build.save_results=true', + '--steps.extract_1d.save_results=true', + } + } + + rtdata = rt.run_step_from_dict(rtdata, **step_params) + return rtdata + + +@pytest.mark.slow +@pytest.mark.bigdata +@pytest.mark.parametrize( + 'output', + [ + 'jw01249005001_03101_00001_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00001_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00002_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00002_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00003_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00003_nrs2_o005_pixel_replace.fits', + 'jw01249005001_03101_00004_nrs1_o005_pixel_replace.fits', + 'jw01249005001_03101_00004_nrs2_o005_pixel_replace.fits', + 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_s3d.fits', + 'jw01249-o005_t001_nirspec_rp_test_g395h-f290lp_x1d.fits', + ] +) +def test_spec3_multi_pixel_replace(run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output): + """Regression test matching output files""" + rt.is_like_truth( + run_spec3_multi_pixel_replace, fitsdiff_default_kwargs, output, + truth_path='truth/test_nirspec_ifu_pixel_replace', + is_suffix=False + ) From 8091a35891e13feb7023e9317441632b3d3b5fb5 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Tue, 24 Sep 2024 14:04:34 -0400 Subject: [PATCH 488/687] Remove unnecessary test after pastasoss inclusion (#8825) --- jwst/regtest/test_niriss_soss.py | 33 -------------------------------- 1 file changed, 33 deletions(-) diff --git a/jwst/regtest/test_niriss_soss.py b/jwst/regtest/test_niriss_soss.py index ef9796d4fa..451b15adf7 100644 --- a/jwst/regtest/test_niriss_soss.py +++ b/jwst/regtest/test_niriss_soss.py @@ -126,24 +126,6 @@ def test_niriss_soss_extras(rtdata_module, run_atoca_extras, fitsdiff_default_kw assert diff.identical, diff.report() -@pytest.fixture(scope='module') -def run_extract1d_spsolve_failure(rtdata_module): - """ - Test coverage for fix to error thrown when spsolve fails to find - a good solution in ATOCA and needs to be replaced with a least- - squares solver. Note this failure is architecture-dependent - and also only trips for specific values of the transform parameters. - Pin tikfac for faster runtime. - """ - rtdata = rtdata_module - rtdata.get_data("niriss/soss/jw04098007001_04101_00001-seg003_nis_int01.fits") - args = ["extract_1d", rtdata.input, - "--soss_tikfac=3.1881637371089252e-15", - "--soss_transform=-0.00038201755227297866, -0.24237455427848956, 0.5404013401742825", - ] - Step.from_cmdline(args) - - @pytest.fixture(scope='module') def run_extract1d_null_order2(rtdata_module): """ @@ -156,25 +138,10 @@ def run_extract1d_null_order2(rtdata_module): rtdata.get_data("niriss/soss/jw01201008001_04101_00001-seg003_nis_int72.fits") args = ["extract_1d", rtdata.input, "--soss_tikfac=4.290665733550672e-17", - "--soss_transform=0.0794900761418923, -1.3197790951056494, -0.796875809148081", ] Step.from_cmdline(args) -@pytest.mark.bigdata -def test_extract1d_spsolve_failure(rtdata_module, run_extract1d_spsolve_failure, fitsdiff_default_kwargs): - - rtdata = rtdata_module - - output = "jw04098007001_04101_00001-seg003_nis_int01_extract1dstep.fits" - rtdata.output = output - - rtdata.get_truth(f"truth/test_niriss_soss_stages/{output}") - - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() - - @pytest.mark.bigdata def test_extract1d_null_order2(rtdata_module, run_extract1d_null_order2, fitsdiff_default_kwargs): From 163ad080ca62420e8fab0f5d321441dad7273245 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 26 Sep 2024 14:10:40 -0400 Subject: [PATCH 489/687] JP-3728: Deprecate `Step.__call__` (#8761) --- changes/8761.stpipe.rst | 1 + jwst/extract_2d/tests/test_grisms.py | 6 +++--- jwst/stpipe/core.py | 14 ++++++++++++++ jwst/stpipe/tests/test_step.py | 6 ++++++ jwst/tweakreg/tests/test_tweakreg.py | 8 ++++---- 5 files changed, 28 insertions(+), 7 deletions(-) create mode 100644 changes/8761.stpipe.rst diff --git a/changes/8761.stpipe.rst b/changes/8761.stpipe.rst new file mode 100644 index 0000000000..3d2985c04f --- /dev/null +++ b/changes/8761.stpipe.rst @@ -0,0 +1 @@ +Add warning that ``Step.__call__`` is deprecated. diff --git a/jwst/extract_2d/tests/test_grisms.py b/jwst/extract_2d/tests/test_grisms.py index 0da23a2ca1..1e50ace081 100644 --- a/jwst/extract_2d/tests/test_grisms.py +++ b/jwst/extract_2d/tests/test_grisms.py @@ -209,7 +209,7 @@ def test_create_box_fits(): # Add fake data to pass a shape to wfss_imaging_wcs im.data = np.zeros((512, 512)) aswcs = AssignWcsStep() - imwcs = aswcs(im) + imwcs = aswcs.run(im) imwcs.meta.source_catalog = source_catalog refs = get_reference_files(im) test_boxes = create_grism_bbox(imwcs, refs, @@ -242,7 +242,7 @@ def test_create_box_gwcs(): # The data array is not relevant im.data = np.zeros((512, 512)) aswcs = AssignWcsStep() - imwcs = aswcs(im) + imwcs = aswcs.run(im) imwcs.meta.source_catalog = source_catalog refs = get_reference_files(im) test_boxes = create_grism_bbox(imwcs, refs, @@ -268,7 +268,7 @@ def setup_image_cat(): im.data = np.zeros((512, 512)) im.meta.source_catalog = source_catalog aswcs = AssignWcsStep() - imwcs = aswcs(im) + imwcs = aswcs.run(im) refs = get_reference_files(im) return imwcs, refs diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 02ae5eb92a..21efa00ef0 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -3,6 +3,7 @@ """ from functools import wraps import logging +import warnings from stdatamodels.jwst.datamodels import JwstDataModel from stdatamodels.jwst import datamodels @@ -102,6 +103,19 @@ def run(self, *args, **kwargs): log.info(f"Results used jwst version: {__version__}") return result + @wraps(Step.__call__) + def __call__(self, *args, **kwargs): + if not self.parent: + warnings.warn( + "Step.__call__ is deprecated. It is equivalent to Step.run " + "and is not recommended. See " + "https://jwst-pipeline.readthedocs.io/en/latest/jwst/" + "user_documentation/running_pipeline_python.html" + "#advanced-use-pipeline-run-vs-pipeline-call for more details.", + UserWarning + ) + return super().__call__(*args, **kwargs) + # JwstPipeline needs to inherit from Pipeline, but also # be a subclass of JwstStep so that it will pass checks diff --git a/jwst/stpipe/tests/test_step.py b/jwst/stpipe/tests/test_step.py index 735b474a91..8ca804ff27 100644 --- a/jwst/stpipe/tests/test_step.py +++ b/jwst/stpipe/tests/test_step.py @@ -623,3 +623,9 @@ def test_finalize_logging(monkeypatch): monkeypatch.setattr(logging.getLogger("jwst.stpipe.core"), "info", watcher) pipeline.run(model) assert watcher.seen + + +def test_dunder_call_warning(): + pipeline = EmptyPipeline() + with pytest.warns(UserWarning, match="Step.__call__ is deprecated"): + pipeline(None) diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index a038410e69..9dc21267c9 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -193,7 +193,7 @@ def test_tweakreg_step(example_input, with_shift): step = tweakreg_step.TweakRegStep() # run the step on the example input modified above - result = step(example_input) + result = step.run(example_input) # check that step completed with result: @@ -227,7 +227,7 @@ def test_src_confusion_pars(example_input, alignment_type): "abs_refcat": REFCAT, } step = tweakreg_step.TweakRegStep(**pars) - result = step(example_input) + result = step.run(example_input) # check that step was skipped with result: @@ -356,7 +356,7 @@ def patched_construct_wcs_corrector(wcs, wcsinfo, catalog, group_id, _seen=[]): monkeypatch.setattr(twk, "construct_wcs_corrector", patched_construct_wcs_corrector) with pytest.raises(ValueError, match="done testing"): - step(str(asn_path)) + step.run(str(asn_path)) @pytest.mark.parametrize("with_shift", [True, False]) @@ -384,7 +384,7 @@ def test_sip_approx(example_input, with_shift): step.sip_npoints = 12 # run the step on the example input modified above - result = step(example_input) + result = step.run(example_input) with result: r0 = result.borrow(0) From 15fa0befecbce65cc526927f9b4076644c7ee975 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 27 Sep 2024 07:31:17 -0700 Subject: [PATCH 490/687] JP-3741: Faster temporary file I/O for outlier detection in on-disk mode (#8782) --- changes/8782.outlier_detection.0.rst | 1 + changes/8782.outlier_detection.1.rst | 1 + changes/8782.resample.rst | 1 + .../outlier_detection_imaging.rst | 12 +- jwst/outlier_detection/_fileio.py | 63 +- jwst/outlier_detection/coron.py | 4 +- jwst/outlier_detection/imaging.py | 71 +-- .../outlier_detection_step.py | 13 +- jwst/outlier_detection/spec.py | 77 +-- .../tests/test_algorithms.py | 12 + jwst/outlier_detection/tests/test_fileio.py | 40 ++ .../tests/test_outlier_detection.py | 76 ++- jwst/outlier_detection/tests/test_utils.py | 164 ++++++ jwst/outlier_detection/tso.py | 3 +- jwst/outlier_detection/utils.py | 547 +++++++++++++----- jwst/regtest/test_nirspec_fs_spec3.py | 12 +- jwst/resample/resample.py | 151 ++--- 17 files changed, 866 insertions(+), 382 deletions(-) create mode 100644 changes/8782.outlier_detection.0.rst create mode 100644 changes/8782.outlier_detection.1.rst create mode 100644 changes/8782.resample.rst create mode 100644 jwst/outlier_detection/tests/test_fileio.py create mode 100644 jwst/outlier_detection/tests/test_utils.py diff --git a/changes/8782.outlier_detection.0.rst b/changes/8782.outlier_detection.0.rst new file mode 100644 index 0000000000..469e927e44 --- /dev/null +++ b/changes/8782.outlier_detection.0.rst @@ -0,0 +1 @@ +Decrease the amount of file I/O required to compute the median when in_memory is set to False. \ No newline at end of file diff --git a/changes/8782.outlier_detection.1.rst b/changes/8782.outlier_detection.1.rst new file mode 100644 index 0000000000..a1f9bd8e55 --- /dev/null +++ b/changes/8782.outlier_detection.1.rst @@ -0,0 +1 @@ +Fix a bug that caused intermediate files to conflict for different slits when a MultiSlitModel was processed. \ No newline at end of file diff --git a/changes/8782.resample.rst b/changes/8782.resample.rst new file mode 100644 index 0000000000..05b3e87052 --- /dev/null +++ b/changes/8782.resample.rst @@ -0,0 +1 @@ +Permit creating drizzled models one at a time in many-to-many mode. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index c2cd6318b9..a6a6ec78e7 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -58,7 +58,8 @@ Specifically, this routine performs the following operations: should be used when resampling to create the output mosaic. Any pixel with a DQ value not included in this value (or list of values) will be ignored when resampling. - * Resampled images will be written out to disk with the suffix ``__outlier_i2d.fits`` + * When the ``save_intermediate_results`` parameter is set to True, + resampled images will be written out to disk with the suffix ``__outlier_i2d.fits`` if the input model container has an , otherwise the suffix will be ``_outlier_i2d.fits`` by default. * **If resampling is turned off** through the use of the ``resample_data`` parameter, @@ -162,9 +163,12 @@ during processing includes: :py:class:`~jwst.resample.ResampleStep` as well, to set whether or not to keep the resampled images in memory or not. -#. Computing the median image works section-by-section by only keeping 1Mb of each input - in memory at a time. As a result, only the final output product array for the final - median image along with a stack of 1Mb image sections are kept in memory. +#. Computing the median image works by writing the resampled data frames to appendable files + on disk that are split into sections spatially but contain the entire ngroups (i.e., time) + axis. The section size is set to use roughly the same amount of memory as a single resampled + model, and since the resampled models are discarded from memory after this write operation this + choice avoids increasing the memory usage beyond a single resampled model. + Those sections are then read in one at a time to compute the median image. These changes result in a minimum amount of memory usage during processing at the obvious expense of reading and writing the products from disk. diff --git a/jwst/outlier_detection/_fileio.py b/jwst/outlier_detection/_fileio.py index 6975ac1717..25dc3a26bd 100644 --- a/jwst/outlier_detection/_fileio.py +++ b/jwst/outlier_detection/_fileio.py @@ -1,17 +1,9 @@ -import os - import logging log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -def remove_file(fn): - if isinstance(fn, str) and os.path.isfile(fn): - os.remove(fn) - log.info(f"Removing file {fn}") - - -def save_median(median_model, make_output_path, asn_id=None): +def save_median(median_model, make_output_path): ''' Save median if requested by user @@ -20,13 +12,46 @@ def save_median(median_model, make_output_path, asn_id=None): median_model : ~jwst.datamodels.ImageModel The median ImageModel or CubeModel to save ''' - default_suffix = "_outlier_i2d.fits" - if asn_id is None: - suffix_to_remove = default_suffix - else: - suffix_to_remove = f"_{asn_id}{default_suffix}" - median_model_output_path = make_output_path( - basepath=median_model.meta.filename.replace(suffix_to_remove, '.fits'), - suffix='median') - median_model.save(median_model_output_path) - log.info(f"Saved model in {median_model_output_path}") + _save_intermediate_output(median_model, "median", make_output_path) + + +def save_drizzled(drizzled_model, make_output_path): + expected_tail = "outlier_?2d.fits" + suffix = drizzled_model.meta.filename[-len(expected_tail):-5] + _save_intermediate_output(drizzled_model, suffix, make_output_path) + + +def save_blot(input_model, blot, make_output_path): + blot_model = _make_blot_model(input_model, blot) + _save_intermediate_output(blot_model, "blot", make_output_path) + + +def _make_blot_model(input_model, blot): + blot_model = type(input_model)() + blot_model.data = blot + blot_model.update(input_model) + return blot_model + + +def _save_intermediate_output(model, suffix, make_output_path): + """ + Ensure all intermediate outputs from OutlierDetectionStep have consistent file naming conventions + + Notes + ----- + self.make_output_path() is updated globally for the step in the main pipeline + to include the asn_id in the output path, so no need to handle it here. + """ + + # outlier_?2d is not a known suffix, and make_output_path cannot handle an + # underscore in an unknown suffix, so do a manual string replacement + input_path = model.meta.filename.replace("_outlier_", "_") + + # Add a slit name to the output path for MultiSlitModel data if not present + if hasattr(model, "name") and model.name is not None: + if "_"+model.name.lower() not in input_path: + suffix = f"{model.name.lower()}_{suffix}" + + output_path = make_output_path(input_path, suffix=suffix) + model.save(output_path) + log.info(f"Saved {suffix} model in {output_path}") diff --git a/jwst/outlier_detection/coron.py b/jwst/outlier_detection/coron.py index 500ad49602..9a3bf9e984 100644 --- a/jwst/outlier_detection/coron.py +++ b/jwst/outlier_detection/coron.py @@ -2,7 +2,6 @@ Submodule for performing outlier detection on coronagraphy data. """ - import logging import numpy as np @@ -27,7 +26,6 @@ def detect_outliers( good_bits, maskpt, snr, - asn_id, make_output_path, ): """ @@ -56,7 +54,7 @@ def detect_outliers( median_model.update(input_model) median_model.meta.wcs = input_model.meta.wcs - save_median(median_model, make_output_path, asn_id) + save_median(median_model, make_output_path) del median_model # Perform outlier detection using statistical comparisons between diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 5678b45802..0dacd5ec43 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -2,19 +2,16 @@ Submodule for performing outlier detection on imaging data. """ -import copy import logging -import os - -from stdatamodels.jwst import datamodels from jwst.datamodels import ModelLibrary from jwst.resample import resample -from jwst.resample.resample_utils import build_driz_weight from jwst.stpipe.utilities import record_step_status -from .utils import create_median, flag_model_crs, flag_resampled_model_crs -from ._fileio import remove_file, save_median +from .utils import (flag_model_crs, + flag_resampled_model_crs, + median_without_resampling, + median_with_resampling) log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -40,7 +37,6 @@ def detect_outliers( fillval, allowed_memory, in_memory, - asn_id, make_output_path, ): """ @@ -58,20 +54,10 @@ def detect_outliers( log.warning("Outlier detection will be skipped") record_step_status(input_models, "outlier_detection", False) return input_models - + if resample_data: - # Start by creating resampled/mosaic images for - # each group of exposures - with input_models: - example_model = input_models.borrow(0) - output_path = make_output_path(basepath=example_model.meta.filename, - suffix='') - input_models.shelve(example_model, modify=False) - del example_model - output_path = os.path.dirname(output_path) resamp = resample.ResampleData( input_models, - output=output_path, single=True, blendheaders=False, wht_type=weight_type, @@ -80,46 +66,21 @@ def detect_outliers( fillval=fillval, good_bits=good_bits, in_memory=in_memory, - asn_id=asn_id, allowed_memory=allowed_memory, ) - median_wcs = resamp.output_wcs - drizzled_models = resamp.do_drizzle(input_models) + median_data, median_wcs = median_with_resampling(input_models, + resamp, + maskpt, + save_intermediate_results=save_intermediate_results, + make_output_path=make_output_path,) else: - # for non-dithered data, the resampled image is just the original image - drizzled_models = input_models - with input_models: - for i, model in enumerate(input_models): - model.wht = build_driz_weight( - model, - weight_type=weight_type, - good_bits=good_bits) - # copy for when saving median and input is a filename? - if i == 0: - median_wcs = copy.deepcopy(model.meta.wcs) - input_models.shelve(model, modify=True) - - # Perform median combination on set of drizzled mosaics - median_data = create_median(drizzled_models, maskpt) + median_data, median_wcs = median_without_resampling(input_models, + maskpt, + weight_type, + good_bits, + save_intermediate_results=save_intermediate_results, + make_output_path=make_output_path,) - if save_intermediate_results: - # make a median model - with drizzled_models: - example_model = drizzled_models.borrow(0) - drizzled_models.shelve(example_model, modify=False) - median_model = datamodels.ImageModel(median_data) - median_model.update(example_model) - median_model.meta.wcs = median_wcs - del example_model - - save_median(median_model, make_output_path, asn_id) - del median_model - else: - # since we're not saving intermediate results if the drizzled models - # were written to disk, remove them - if not in_memory: - for fn in drizzled_models.asn["products"][0]["members"]: - remove_file(fn["expname"]) # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index f18fb054e3..5ecc90a231 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -80,8 +80,7 @@ def process(self, input_data): self.log.info(f"Outlier Detection mode: {mode}") # determine the asn_id (if not set by the pipeline) - asn_id = self._get_asn_id(input_data) - self.log.info(f"Outlier Detection asn_id: {asn_id}") + self._get_asn_id(input_data) snr1, snr2 = [float(v) for v in self.snr.split()] scale1, scale2 = [float(v) for v in self.scale.split()] @@ -94,7 +93,6 @@ def process(self, input_data): self.maskpt, self.rolling_window_width, snr1, - asn_id, self.make_output_path, ) elif mode == 'coron': @@ -104,7 +102,6 @@ def process(self, input_data): self.good_bits, self.maskpt, snr1, - asn_id, self.make_output_path, ) elif mode == 'imaging': @@ -125,7 +122,6 @@ def process(self, input_data): self.fillval, self.allowed_memory, self.in_memory, - asn_id, self.make_output_path, ) elif mode == 'spec': @@ -145,7 +141,6 @@ def process(self, input_data): self.kernel, self.fillval, self.in_memory, - asn_id, self.make_output_path, ) elif mode == 'ifu': @@ -203,6 +198,9 @@ def _guess_mode(self, input_models): return None def _get_asn_id(self, input_models): + """Find association ID for any allowed input model type, + and update make_output_path such that the association ID + is included in intermediate and output file names.""" # handle if input_models isn't open if isinstance(input_models, (str, dict)): input_models = datamodels.open(input_models, asn_n_members=1) @@ -227,7 +225,8 @@ def _get_asn_id(self, input_models): _make_output_path, asn_id=asn_id ) - return asn_id + self.log.info(f"Outlier Detection asn_id: {asn_id}") + return def _set_status(self, input_models, status): # this might be called with the input which might be a filename or path diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index c482aad8b9..ad94551976 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -1,16 +1,14 @@ """ Submodule for performing outlier detection on spectra. """ -import copy -import os - -from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer, ModelLibrary from jwst.stpipe.utilities import record_step_status -from ..resample import resample_spec, resample_utils -from .utils import create_median, flag_crs_in_models, flag_crs_in_models_with_resampling -from ._fileio import remove_file +from ..resample import resample_spec +from .utils import (flag_crs_in_models, + flag_crs_in_models_with_resampling, + median_with_resampling, + median_without_resampling) import logging log = logging.getLogger(__name__) @@ -36,7 +34,6 @@ def detect_outliers( kernel, fillval, in_memory, - asn_id, make_output_path, ): """ @@ -53,15 +50,15 @@ def detect_outliers( record_step_status(input_models, "outlier_detection", False) return input_models + # convert to library for resample + # for compatibility with image3 pipeline + library = ModelLibrary(input_models, on_disk=False) + if resample_data is True: # Start by creating resampled/mosaic images for # each group of exposures - output_path = make_output_path( - basepath=input_models[0].meta.filename, suffix='') - output_path = os.path.dirname(output_path) resamp = resample_spec.ResampleSpecData( input_models, - output=output_path, single=True, blendheaders=False, wht_type=weight_type, @@ -70,54 +67,24 @@ def detect_outliers( fillval=fillval, good_bits=good_bits, in_memory=in_memory, - asn_id=asn_id, ) - median_wcs = resamp.output_wcs - - # convert to library for resample - # for compatibility with image3 pipeline - library = ModelLibrary(input_models, on_disk=False) - drizzled_models = resamp.do_drizzle(library) + median_data, median_wcs = median_with_resampling( + library, + resamp, + maskpt, + save_intermediate_results=save_intermediate_results, + make_output_path=make_output_path,) else: - drizzled_models = ModelLibrary(input_models) - with drizzled_models: - for i, model in enumerate(drizzled_models): - model.wht = resample_utils.build_driz_weight( - input_models[i], - weight_type=weight_type, - good_bits=good_bits) - drizzled_models.shelve(model) - # copy for when saving median and input is a filename? - median_wcs = copy.deepcopy(input_models[0].meta.wcs) - - # Perform median combination on set of drizzled mosaics - # create_median should be called as a method from parent class - median_data = create_median(drizzled_models, maskpt) - - if save_intermediate_results: - # Initialize intermediate products used in the outlier detection - median_model = datamodels.ImageModel(median_data) - with drizzled_models: - example_model = drizzled_models.borrow(0) - drizzled_models.shelve(example_model, 0, modify=False) - median_model.meta = example_model.meta - median_model.meta.filename = make_output_path( - basepath=input_models[0].meta.filename, - suffix='median' + median_data, median_wcs = median_without_resampling( + library, + maskpt, + weight_type, + good_bits, + save_intermediate_results=save_intermediate_results, + make_output_path=make_output_path, ) - log.info("Writing out MEDIAN image to: {}".format( - median_model.meta.filename)) - median_model.save(median_model.meta.filename) - del median_model - else: - # since we're not saving intermediate results if the drizzled models - # were written to disk, remove them - if not in_memory: - for fn in drizzled_models._members: - remove_file(fn["expname"]) - # Perform outlier detection using statistical comparisons between # each original input image and its blotted version of the median image if resample_data: diff --git a/jwst/outlier_detection/tests/test_algorithms.py b/jwst/outlier_detection/tests/test_algorithms.py index b3b9255bb4..d1c8ffd85d 100644 --- a/jwst/outlier_detection/tests/test_algorithms.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -1,6 +1,7 @@ import numpy as np from jwst.outlier_detection.tso import moving_median_over_zeroth_axis +from jwst.outlier_detection.utils import nanmedian3D def test_rolling_median(): @@ -14,3 +15,14 @@ def test_rolling_median(): result = moving_median_over_zeroth_axis(arr, w) expected = expected_time_axis[:, np.newaxis, np.newaxis] * spatial_axis[np.newaxis, :, :] assert np.allclose(result, expected) + + +def test_nanmedian3D(): + + shp = (11, 50, 60) + cube = np.random.normal(size=shp) + cube[5, 5:7, 5:8] = np.nan + med = nanmedian3D(cube) + + assert med.dtype == np.float32 + assert np.allclose(med, np.nanmedian(cube, axis=0), equal_nan=True) \ No newline at end of file diff --git a/jwst/outlier_detection/tests/test_fileio.py b/jwst/outlier_detection/tests/test_fileio.py new file mode 100644 index 0000000000..6e01557c8e --- /dev/null +++ b/jwst/outlier_detection/tests/test_fileio.py @@ -0,0 +1,40 @@ +import pytest +from jwst.outlier_detection._fileio import _save_intermediate_output +from jwst.datamodels import ImageModel +from jwst.step import OutlierDetectionStep +import os +import numpy as np +from functools import partial + + +SLIT_ID = "slit007" +ASN_ID = "a008" +INPUT_FILENAME = "foo_outlier_s2d.fits" +SUFFIX = "test" + +@pytest.fixture(scope="module") +def model(): + model = ImageModel(data=np.zeros((10,10))) + model.meta.filename = INPUT_FILENAME + return model + + +@pytest.fixture(scope="module") +def make_output_path(): + return OutlierDetectionStep().make_output_path + + +@pytest.mark.parametrize("asn_id", [None, ASN_ID]) +@pytest.mark.parametrize("slit_id", [None, SLIT_ID]) +def test_save(tmp_cwd, model, make_output_path, asn_id, slit_id): + + this_model = model.copy() + if slit_id is not None: + this_model.name = slit_id + make_output_path = partial(make_output_path, asn_id=asn_id) + _save_intermediate_output(this_model, SUFFIX, make_output_path) + + stem = model.meta.filename.split("_")[0] + inputs = [val for val in [stem, asn_id, slit_id, SUFFIX] if val is not None] + expected_filename = "_".join(inputs)+".fits" + assert os.path.isfile(expected_filename) \ No newline at end of file diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index ec469f4fb4..3155824fc8 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -4,13 +4,14 @@ from glob import glob import os +from gwcs.wcs import WCS from stdatamodels.jwst import datamodels from jwst.datamodels import ModelContainer, ModelLibrary from jwst.assign_wcs import AssignWcsStep from jwst.assign_wcs.pointing import create_fitswcs from jwst.outlier_detection import OutlierDetectionStep -from jwst.outlier_detection.utils import _flag_resampled_model_crs, create_median +from jwst.outlier_detection.utils import _flag_resampled_model_crs from jwst.outlier_detection.outlier_detection_step import ( IMAGE_MODES, TSO_SPEC_MODES, @@ -18,6 +19,8 @@ CORON_IMAGE_MODES, ) from jwst.resample.tests.test_resample_step import miri_rate_model +from jwst.outlier_detection.utils import median_with_resampling, median_without_resampling +from jwst.resample.resample import ResampleData OUTLIER_DO_NOT_USE = np.bitwise_or( datamodels.dqflags.pixel["DO_NOT_USE"], datamodels.dqflags.pixel["OUTLIER"] @@ -109,7 +112,7 @@ def we_many_sci( ): """Provide numsci science images with different noise but identical source and same background level""" - shape = (20, 20) + shape = (21, 20) sci1 = datamodels.ImageModel(shape) @@ -595,7 +598,7 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): assert result.dq[cr_timestep, 12, 12] == OUTLIER_DO_NOT_USE -def test_create_median(three_sci_as_asn, tmp_cwd): +def test_same_median_on_disk(three_sci_as_asn, tmp_cwd): """Test creation of median on disk vs in memory""" lib_on_disk = ModelLibrary(three_sci_as_asn, on_disk=True) lib_in_memory = ModelLibrary(three_sci_as_asn, on_disk=False) @@ -604,14 +607,71 @@ def test_create_median(three_sci_as_asn, tmp_cwd): with (lib_on_disk, lib_in_memory): for lib in [lib_on_disk, lib_in_memory]: for model in lib: - model.wht = np.ones_like(model.data) - model.wht[4,9] = 0.5 + model.var_rnoise = np.ones_like(model.data) + model.var_rnoise[4,9] = 2.0 lib.shelve(model, modify=True) - median_on_disk = create_median(lib_on_disk, 0.7) - median_in_memory = create_median(lib_in_memory, 0.7) + # 32-bit floats are 4 bytes each, min buffer size is one row of 20 pixels + # arbitrarily use 5 times that + buffer_size = 4 * 20 * 5 + median_on_disk, _ = median_without_resampling( + lib_on_disk, + 0.7, + "ivm", + "~DO_NOT_USE", + buffer_size=buffer_size,) + median_in_memory, _ = median_without_resampling( + lib_in_memory, + 0.7, + "ivm", + "~DO_NOT_USE", + buffer_size=buffer_size,) + + # Make sure the high-variance (low-weight) pixel is set to NaN assert np.isnan(median_in_memory[4,9]) # Make sure the median library is the same for on-disk and in-memory - assert np.allclose(median_on_disk, median_in_memory, equal_nan=True) \ No newline at end of file + assert np.allclose(median_on_disk, median_in_memory, equal_nan=True) + + +def test_drizzle_and_median_with_resample(three_sci_as_asn, tmp_cwd): + lib = ModelLibrary(three_sci_as_asn, on_disk=False) + + resamp = make_resamp(lib) + median, wcs = median_with_resampling( + lib, + resamp, + 0.7) + + assert isinstance(wcs, WCS) + assert median.shape == (21,20) + + resamp.single = False + with pytest.raises(ValueError): + # ensure failure if try to call when resamp.single is False + median_with_resampling( + lib, + resamp, + 0.7, + save_intermediate_results=True) + + +def make_resamp(input_models): + """All defaults are same as what is run by default by outlier detection""" + in_memory = not input_models._on_disk + resamp = ResampleData( + input_models, + output="", + single=True, + blendheaders=False, + wht_type="ivm", + pixfrac=1.0, + kernel="square", + fillval="INDEF", + good_bits="~DO_NOT_USE", + in_memory=in_memory, + asn_id="test", + allowed_memory=None, + ) + return resamp \ No newline at end of file diff --git a/jwst/outlier_detection/tests/test_utils.py b/jwst/outlier_detection/tests/test_utils.py new file mode 100644 index 0000000000..d1fc0bd576 --- /dev/null +++ b/jwst/outlier_detection/tests/test_utils.py @@ -0,0 +1,164 @@ +import pytest +from jwst.outlier_detection.utils import DiskAppendableArray, OnDiskMedian +from pathlib import Path +import numpy as np +import os + + +def test_disk_appendable_array(tmp_cwd): + + slice_shape = (8,7) + dtype = "float32" + tempdir = tmp_cwd / Path("tmptest") + os.mkdir(tempdir) + fname = tempdir / "test.bin" + + arr = DiskAppendableArray(slice_shape, dtype, fname) + + # check temporary file setup + assert str(arr._filename).split("/")[-1] in os.listdir(tempdir) + assert len(os.listdir(tempdir)) == 1 + assert arr.shape == (0,) + slice_shape + + # check cwd contains no files + assert all([not os.path.isfile(f) for f in os.listdir(tmp_cwd)]) + + # check expected append failures + with pytest.raises(ValueError): + candidate = np.zeros((7,7), dtype=dtype) + arr.append(candidate) + with pytest.raises(ValueError): + candidate = np.zeros((8,7), dtype='float64') + arr.append(candidate) + + # check append and read + candidate0 = np.zeros(slice_shape, dtype=dtype) + candidate1 = np.full(slice_shape, 2, dtype=dtype) + candidate2 = np.full(slice_shape, np.nan, dtype=dtype) + for candidate in [candidate0, candidate1, candidate2]: + arr.append(candidate) + + arr_in_memory = arr.read() + + assert arr_in_memory.shape == (3,) + slice_shape + assert np.all(arr_in_memory[0] == candidate0) + assert np.all(arr_in_memory[1] == candidate1) + assert np.allclose(arr_in_memory[2], candidate2, equal_nan=True) + + +def test_disk_appendable_array_bad_inputs(tmp_cwd): + + slice_shape = (8,7) + dtype = "float32" + tempdir = tmp_cwd / Path("tmptest") + fname = "test.bin" + + # test input directory does not exist + with pytest.raises(FileNotFoundError): + DiskAppendableArray(slice_shape, dtype, tempdir / fname) + + # make the input directory + os.mkdir(tempdir) + + # ensure failure if slice_shape is not 2-D + with pytest.raises(ValueError): + DiskAppendableArray((3,5,7), dtype, tempdir / fname) + + # ensure failure if dtype is not valid + with pytest.raises(TypeError): + DiskAppendableArray(slice_shape, "float3", tempdir / fname) + + # ensure failure if pass directory instead of filename + with pytest.raises(IsADirectoryError): + DiskAppendableArray(slice_shape, "float3", tempdir) + + +# filter warnings such that "ResourceWarning: Implictly cleaning up" is raised as error +# unfortunately need to do this indirectly by finding "finalize" string in PytestUnraisableException +@pytest.mark.filterwarnings("error:.*finalize.*:pytest.PytestUnraisableExceptionWarning") +def test_on_disk_median(tmp_cwd): + + library_length = 3 + frame_shape = (21,20) + dtype = 'float32' + tempdir = tmp_cwd / Path("tmptest") + os.mkdir(tempdir) + shape = (library_length,) + frame_shape + + median_computer = OnDiskMedian(shape, dtype=dtype, tempdir=tempdir) + + # test compute buffer indices + # buffer size equals size of single input model by default + # which means we expect same number of sections as library length + # in reality there is often one more section than that because + # of necessity of integer number of rows per section, but math is exact in this case + expected_buffer_size = frame_shape[0] * frame_shape[1] * np.dtype(dtype).itemsize + expected_section_nrows = frame_shape[0] // library_length + assert median_computer.nsections == library_length + assert median_computer.section_nrows == expected_section_nrows + assert median_computer.buffer_size == expected_buffer_size + + # test temp file setup + assert len(os.listdir(tempdir)) == 1 + assert str(median_computer._temp_path).startswith(str(tempdir)) + assert len(os.listdir(median_computer._temp_path)) == library_length + # check cwd and parent tempdir contain no files + assert all([not os.path.isfile(f) for f in os.listdir(tmp_cwd)]) + assert all([not os.path.isfile(f) for f in os.listdir(tempdir)]) + + # test validate data + with pytest.raises(ValueError): + candidate = np.zeros((20,20), dtype=dtype) + median_computer.add_image(candidate) + with pytest.raises(ValueError): + candidate = np.zeros((21,20), dtype='float64') + median_computer.add_image(candidate) + + # test add and compute + candidate0 = np.full(frame_shape, 3, dtype=dtype) + candidate1 = np.full(frame_shape, 2, dtype=dtype) + candidate2 = np.full(frame_shape, np.nan, dtype=dtype) + for candidate in [candidate0, candidate1, candidate2]: + median_computer.add_image(candidate) + median = median_computer.compute_median() + median_computer.cleanup() + assert median.shape == frame_shape + assert np.all(median == 2.5) + + # test expected error trying to add too many frames + # for loop to ensure always happens, not just the first time + candidate3 = np.zeros_like(candidate0) + for _ in range(2): + with pytest.raises(IndexError): + median_computer.add_image(candidate3) + + # test cleanup of tmpdir and everything else + assert not os.path.exists(median_computer._temp_path) + assert len(os.listdir(tempdir)) == 0 + assert len(os.listdir(os.getcwd())) == 1 # this is the "tmptest" directory + + +@pytest.mark.filterwarnings("error:.*finalize.*:pytest.PytestUnraisableExceptionWarning") +def test_on_disk_median_bad_inputs(tmp_cwd): + + library_length = 3 + frame_shape = (21,20) + dtype = 'float32' + tempdir = tmp_cwd / Path("tmptest") + os.mkdir(tempdir) + shape = (library_length,) + frame_shape + + with pytest.raises(ValueError): + OnDiskMedian(frame_shape, dtype=dtype, tempdir=tempdir) + + with pytest.raises(TypeError): + OnDiskMedian(shape, dtype="float3", tempdir=tempdir) + + with pytest.raises(FileNotFoundError): + OnDiskMedian(shape, dtype="float32", tempdir="dne") + + # ensure unreasonable buffer size will get set to minimum reasonable buffer + min_buffer = np.dtype(dtype).itemsize*frame_shape[1]*library_length + median_computer = OnDiskMedian(shape, dtype=dtype, tempdir=tempdir, buffer_size=-1) + assert median_computer.buffer_size == min_buffer + median_computer.cleanup() \ No newline at end of file diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 58aedd19b0..d0161dffe7 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -22,7 +22,6 @@ def detect_outliers( maskpt, rolling_window_width, snr, - asn_id, make_output_path, ): """ @@ -53,7 +52,7 @@ def detect_outliers( median_model = dm.CubeModel(data=medians) with dm.open(weighted_cube) as dm0: median_model.update(dm0) - save_median(median_model, make_output_path, asn_id) + save_median(median_model, make_output_path) del median_model # no need for blotting, resample is turned off for TSO diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 2bc6b2ff75..03446aac5e 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -1,14 +1,20 @@ """ The ever-present utils sub-module. A home for all... """ -import warnings +import copy +from functools import partial +import warnings import numpy as np +import tempfile +from pathlib import Path from jwst.lib.pipe_utils import match_nans_and_flags from jwst.resample.resample import compute_image_pixel_area +from jwst.resample.resample_utils import build_driz_weight from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_crs, flag_resampled_crs from stdatamodels.jwst import datamodels +from . import _fileio import logging log = logging.getLogger(__name__) @@ -20,177 +26,422 @@ _ONE_MB = 1 << 20 +def nanmedian3D(cube, overwrite_input=True): + """Compute the median of a cube ignoring warnings and with memory efficiency. + np.nanmedian always uses at least 64-bit precision internally, and this is too + memory-intensive. Instead, loop over the median calculation to avoid the + memory usage of the internal upcasting and temporary array allocation. + The additional runtime of this loop is indistinguishable from zero, + but this loop cuts overall step memory usage roughly in half for at least one + test association. + """ + with warnings.catch_warnings(): + warnings.filterwarnings(action="ignore", + message="All-NaN slice encountered", + category=RuntimeWarning) + output_arr = np.empty(cube.shape[1:], dtype=np.float32) + for i in range(output_arr.shape[0]): + # this for loop looks silly, but see docstring above + np.nanmedian(cube[:,i,:], axis=0, overwrite_input=overwrite_input, out=output_arr[i,:]) + return output_arr + + def create_cube_median(cube_model, maskpt): log.info("Computing median") weight_threshold = compute_weight_threshold(cube_model.wht, maskpt) - median = np.nanmedian( + # not safe to use overwrite_input=True here because we are operating on model.data directly + return nanmedian3D( np.ma.masked_array(cube_model.data, np.less(cube_model.wht, weight_threshold), fill_value=np.nan), - axis=0, - ) - return median + overwrite_input=False) -def create_median(resampled_models, maskpt, buffer_size=10.0): - """Create a median image from the singly resampled images. +def median_without_resampling(input_models, + maskpt, + weight_type, + good_bits, + save_intermediate_results=False, + make_output_path=None, + buffer_size=None): + """ + Shared code between imaging and spec modes for resampling and median computation Parameters ---------- - resampled_models : ModelLibrary - The singly resampled images. + input_models : ModelLibrary + The input datamodels. maskpt : float The weight threshold for masking out low weight pixels. - buffer_size : float - The size of chunk in MB, per input model, that will be read into memory. + weight_type : str + The type of weighting to use when combining images. Options are: + 'ivm' (inverse variance) or 'exptime' (exposure time). + + good_bits : int + The bit values that are considered good when determining the + data quality of the input. + + save_intermediate_results : bool + if True, save the drizzled models and median model to fits. + + make_output_path : function + The functools.partial instance to pass to save_median. Must be + specified if save_intermediate_results is True. Default None. + + buffer_size : int + The size of chunk in bytes that will be read into memory when computing the median. This parameter has no effect if the input library has its on_disk attribute set to False. + """ + in_memory = not input_models._on_disk + ngroups = len(input_models) + + with input_models: + for i in range(len(input_models)): + + drizzled_model = input_models.borrow(i) + drizzled_model.wht = build_driz_weight(drizzled_model, + weight_type=weight_type, + good_bits=good_bits) + median_wcs = copy.deepcopy(drizzled_model.meta.wcs) + input_models.shelve(drizzled_model, i, modify=True) + + if save_intermediate_results: + # write the drizzled model to file + _fileio.save_drizzled(drizzled_model, make_output_path) + + if i == 0: + input_shape = (ngroups,)+drizzled_model.data.shape + dtype = drizzled_model.data.dtype + median_computer = _make_median_computer(input_shape, in_memory, buffer_size, dtype) + + weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) + drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan + _append_to_median_computer(median_computer, i, drizzled_model.data, in_memory) + + # Perform median combination on set of drizzled mosaics + median_data = _evaluate_median_computer(median_computer, in_memory) + + if save_intermediate_results: + # Save median model to fits + median_model = datamodels.ImageModel(median_data) + median_model.update(drizzled_model) + median_model.meta.wcs = median_wcs + _fileio.save_median(median_model, make_output_path) + del drizzled_model + + return median_data, median_wcs + + +def median_with_resampling(input_models, + resamp, + maskpt, + save_intermediate_results=False, + make_output_path=None, + buffer_size=None): + """ + Shared code between imaging and spec modes for resampling and median computation - Returns - ------- - median_image : ndarray - The median image. + Parameters + ---------- + input_models : ModelLibrary + The input datamodels. + + resamp : resample.resample.ResampleData object + The controlling object for the resampling process. + + maskpt : float + The weight threshold for masking out low weight pixels. + + save_intermediate_results : bool + if True, save the drizzled models and median model to fits. + + make_output_path : function + The functools.partial instance to pass to save_median. Must be + specified if save_intermediate_results is True. Default None. + + buffer_size : int + The size of chunk in bytes that will be read into memory when computing the median. + This parameter has no effect if the input library has its on_disk attribute + set to False. """ - on_disk = resampled_models._on_disk - - # Compute the weight threshold for each input model - weight_thresholds = [] - model_list = [] - with resampled_models: - for resampled in resampled_models: - weight_threshold = compute_weight_threshold(resampled.wht, maskpt) - if not on_disk: - # handle weights right away for in-memory case - data = resampled.data.copy() - data[resampled.wht < weight_threshold] = np.nan - model_list.append(data) - del data - else: - weight_thresholds.append(weight_threshold) - resampled_models.shelve(resampled, modify=False) - del resampled + if not resamp.single: + raise ValueError("median_with_resampling should only be used for resample_many_to_many") - # easier case: all models in library can be loaded into memory at once - if not on_disk: - with warnings.catch_warnings(): - warnings.filterwarnings(action="ignore", - message="All-NaN slice encountered", - category=RuntimeWarning) - return np.nanmedian(np.array(model_list), axis=0) - else: - # set up buffered access to all input models - with resampled_models: - example_model = resampled_models.borrow(0) - shp = example_model.data.shape - dtype = example_model.data.dtype - nsections, section_nrows = _compute_buffer_indices(example_model, buffer_size) - resampled_models.shelve(example_model, modify=False) - del example_model + in_memory = not input_models._on_disk + indices_by_group = list(input_models.group_indices.values()) + ngroups = len(indices_by_group) - # get spatial sections of library and compute timewise median, one by one - resampled_sections = _get_sections(resampled_models, nsections, section_nrows, shp[0]) - median_image_empty = np.empty(shp, dtype) * np.nan - return _create_median(resampled_sections, resampled_models, weight_thresholds, median_image_empty) + with input_models: + for i, indices in enumerate(indices_by_group): + median_wcs = resamp.output_wcs + drizzled_model = resamp.resample_group(input_models, indices) -def _get_sections(library, nsections, section_nrows, imrows): - """Iterator to return sections from a ModelLibrary. + if save_intermediate_results: + # write the drizzled model to file + _fileio.save_drizzled(drizzled_model, make_output_path) + + if i == 0: + input_shape = (ngroups,)+drizzled_model.data.shape + dtype = drizzled_model.data.dtype + median_computer = _make_median_computer(input_shape, in_memory, buffer_size, dtype) + + weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) + drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan + _append_to_median_computer(median_computer, i, drizzled_model.data, in_memory) - Parameters - ---------- - library : ModelLibrary - The input data models. - nsections : int - The number of spatial sections in each model + # Perform median combination on set of drizzled mosaics + median_data = _evaluate_median_computer(median_computer, in_memory) - section_nrows : int - The number of rows in each section + if save_intermediate_results: + # Save median model to fits + median_model = datamodels.ImageModel(median_data) + median_model.update(drizzled_model) + median_model.meta.wcs = median_wcs + # drizzled model already contains asn_id + make_output_path = partial(make_output_path, asn_id=None) + _fileio.save_median(median_model, make_output_path) + del drizzled_model - imrows : int - The total number of rows in the image + return median_data, median_wcs - Yields - ------ - data_subset : ndarray - array of shape (len(library), section_nrows, ncols) representing a spatial - subset of all the data arrays in the library - weight_subset : ndarray - weights corresponding to data_list +def _make_median_computer(full_shape, in_memory, buffer_size, dtype): + + if in_memory: + # allocate memory for data arrays that go into median + median_computer = np.empty(full_shape, dtype=np.float32) + else: + # set up temporary storage for data arrays that go into median + median_computer = OnDiskMedian(full_shape, + dtype=dtype, + buffer_size=buffer_size) + return median_computer + + +def _append_to_median_computer(median_computer, idx, data, in_memory): + if in_memory: + # populate pre-allocated memory with the drizzled data + median_computer[idx] = data + else: + # distribute the drizzled data into the temporary storage + median_computer.add_image(data) + + +def _evaluate_median_computer(median_computer, in_memory): + if in_memory: + median_data = nanmedian3D(median_computer) + del median_computer + else: + median_data = median_computer.compute_median() + median_computer.cleanup() + return median_data + - row_range : tuple - The range of rows in the image covered by the data arrays +class DiskAppendableArray: """ - with library: - example_model = library.borrow(0) - dtype = example_model.data.dtype - dtype_wht = example_model.wht.dtype - shp = example_model.data.shape - library.shelve(example_model, 0, modify=False) - del example_model - for i in range(nsections): - row1 = i * section_nrows - row2 = min(row1 + section_nrows, imrows) + Creates a temporary file to which to append data, in order to perform + timewise operations on a stack of input images without holding all of them + in memory. + + This class is purpose-built for the median computation during outlier detection + and is not very flexible. It is assumed that each data array passed to `append` + represents the same spatial segment of the full dataset. It is also assumed that + each data array passed to `append` represents only a single instant in time; + the append operation will stack them along a new axis. + + The `read` operation is only capable of loading the full array back into memory. + When working with large datasets that do not fit in memory, the + required workflow is to create many DiskAppendableArray objects, each holding + a small spatial segment of the full dataset. + """ + + def __init__(self, slice_shape, dtype, filename): + """ + Parameters + ---------- + slice_shape : tuple + The shape of the spatial section of input data to be appended to the array. + + dtype : str + The data type of the array. Must be a valid numpy array datatype. + + filename : str + The full file path in which to store the array + """ + if len(slice_shape) != 2: + raise ValueError(f"Invalid slice_shape {slice_shape}. Only 2-D arrays are supported.") + self._filename = Path(filename) + with open(filename, "wb") as f: # noqa: F841 + pass + self._slice_shape = slice_shape + self._dtype = np.dtype(dtype) + self._append_count = 0 + + + @property + def shape(self): + return (self._append_count,) + self._slice_shape + + + def append(self, data): + """Add a new slice to the temporary file""" + if data.shape != self._slice_shape: + raise ValueError(f"Data shape {data.shape} does not match slice shape {self._slice_shape}") + if data.dtype != self._dtype: + raise ValueError(f"Data dtype {data.dtype} does not match array dtype {self._dtype}") + with open(self._filename, "ab") as f: + data.tofile(f, sep="") + self._append_count += 1 + + + def read(self): + """Read the 3-D array into memory""" + shp = (self._append_count,) + self._slice_shape + with open(self._filename, "rb") as f: + output = np.fromfile(f, dtype=self._dtype).reshape(shp) + return output + + +class OnDiskMedian: + + def __init__(self, shape, dtype='float32', tempdir="", buffer_size=None): + """ + Set up temporary files to perform operations on a stack of 2-D input arrays + along the stacking axis (e.g., a time axis) without + holding all of them in memory. Currently the only supported operation + is the median. + + Parameters + ---------- + shape: tuple + The shape of the entire input, (n_images, imrows, imcols). + + dtype : str + The data type of the input data. + + tempdir : str + The parent directory in which to create the temporary directory, + which itself holds all the DiskAppendableArray tempfiles. + Default is the current working directory. + + buffer_size : int, optional + The buffer size, units of bytes. + Default is the size of one input image. + """ + if len(shape) != 3: + raise ValueError(f"Invalid input shape {shape}; only three-dimensional data are supported.") + self._expected_nframes = shape[0] + self.frame_shape = shape[1:] + self.dtype = np.dtype(dtype) + self.itemsize = self.dtype.itemsize + self._temp_dir = tempfile.TemporaryDirectory(dir=tempdir) + self._temp_path = Path(self._temp_dir.name) + + # figure out number of sections and rows per section that are needed + self.nsections, self.section_nrows = self._get_buffer_indices(buffer_size=buffer_size) + self.slice_shape = (self.section_nrows, shape[2]) + self._n_adds = 0 + + # instantiate a temporary DiskAppendableArray for each section + self._temp_arrays = self._temparray_setup(dtype) + + + def _get_buffer_indices(self, buffer_size=None): + """ + Parameters + ---------- + buffer_size : int, optional + The buffer size for the median computation, units of bytes. + + Returns + ------- + nsections : int + The number of sections to divide the input data into. + + section_nrows : int + The number of rows in each section (except the last one). + """ + imrows, imcols = self.frame_shape + if buffer_size is None: + buffer_size = imrows * imcols * self.itemsize + per_model_buffer_size = buffer_size / self._expected_nframes + min_buffer_size = imcols * self.itemsize + section_nrows = min(imrows, int(per_model_buffer_size // min_buffer_size)) + + if section_nrows <= 0: + buffer_size = min_buffer_size * self._expected_nframes + log.warning("Buffer size is too small to hold a single row." + f"Increasing buffer size to {buffer_size / _ONE_MB}MB") + section_nrows = 1 + self.buffer_size = buffer_size + + nsections = int(np.ceil(imrows / section_nrows)) + log.info(f"Computing median over {self._expected_nframes} groups in {nsections} " + f"sections with total memory buffer {buffer_size / _ONE_MB} MB") + return nsections, section_nrows + + + def _temparray_setup(self, dtype): + """Set up temp file handlers for each spatial section""" + temp_arrays = [] + for i in range(0, self.nsections): + shp = self.slice_shape + if i == self.nsections - 1: + # last section has whatever shape is left over + shp = (self.frame_shape[0] - (self.nsections-1) * self.section_nrows, self.frame_shape[1]) + arr = DiskAppendableArray(shp, dtype, self._temp_path / f"{i}.bin") + temp_arrays.append(arr) + return temp_arrays + + + def add_image(self, data): + """Split resampled model data into spatial sections and write to disk.""" + if self._n_adds >= self.nsections: + raise IndexError(f"Too many calls to add_image. Expected at most {self.nsections} input models.") + self._validate_data(data) + self._n_adds += 1 + for i in range(self.nsections): + row1 = i * self.section_nrows + row2 = min(row1 + self.section_nrows, self.frame_shape[0]) + arr = self._temp_arrays[i] + arr.append(data[row1:row2]) + + + def _validate_data(self, data): + if data.shape != self.frame_shape: + raise ValueError(f"Data shape {data.shape} does not match expected shape {self.frame_shape}") + if data.dtype != self.dtype: + raise ValueError(f"Data dtype {data.dtype} does not match expected dtype {self.dtype}") - data_subset = np.empty((len(library), row2 - row1, shp[1]), dtype) - weight_subset = np.empty((len(library), row2 - row1, shp[1]), dtype_wht) - with library: - for j, model in enumerate(library): - data_subset[j] = model.data[row1:row2] - weight_subset[j] = model.wht[row1:row2] - library.shelve(model, j, modify=False) - del model - yield (data_subset, weight_subset, (row1, row2)) - - -def _compute_buffer_indices(model, buffer_size=None): - - imrows, imcols = model.data.shape - data_item_size = model.data.itemsize - min_buffer_size = imcols * data_item_size - buffer_size = min_buffer_size if buffer_size is None else (buffer_size * _ONE_MB) - section_nrows = min(imrows, int(buffer_size // min_buffer_size)) - if section_nrows == 0: - buffer_size = min_buffer_size - log.warning("WARNING: Buffer size is too small to hold a single row." - f"Increasing buffer size to {buffer_size / _ONE_MB}MB") - section_nrows = 1 - nsections = int(np.ceil(imrows / section_nrows)) - return nsections, section_nrows - - -def _create_median(resampled_sections, resampled_models, weight_thresholds, median_image_empty): - median_image = median_image_empty - for (resampled_sci, resampled_weight, (row1, row2)) in resampled_sections: - # Create a mask for each input image, masking out areas where there is - # no data or the data has very low weight - badmasks = [] - for weight, weight_threshold in zip(resampled_weight, weight_thresholds): - badmask = np.less(weight, weight_threshold) - log.debug("Percentage of pixels with low weight: {}".format( - np.sum(badmask) / len(weight.flat) * 100)) - badmasks.append(badmask) - - # Fill resampled_sci array with nan's where mask values are True - for f1, f2 in zip(resampled_sci, badmasks): - for elem1, elem2 in zip(f1, f2): - elem1[elem2] = np.nan - - del badmasks - - # For a stack of images with "bad" data replaced with Nan - # use np.nanmedian to compute the median. - with warnings.catch_warnings(): - warnings.filterwarnings(action="ignore", - message="All-NaN slice encountered", - category=RuntimeWarning) - median_image[row1:row2] = np.nanmedian(resampled_sci, axis=0) - del resampled_sci, resampled_weight - - return median_image + + def cleanup(self): + """Remove the temporary files and directory when finished""" + self._temp_dir.cleanup() + return + + + def compute_median(self): + """Read spatial sections from disk and compute the median across groups + (median over number of exposures on a per-pixel basis)""" + row_indices = [(i * self.section_nrows, min((i+1) * self.section_nrows, self.frame_shape[0])) + for i in range(self.nsections)] + + output_rows = row_indices[-1][1] + output_cols = self._temp_arrays[0].shape[2] + median_image = np.full((output_rows, output_cols), np.nan, dtype=self.dtype) + + for i, disk_arr in enumerate(self._temp_arrays): + row1, row2 = row_indices[i] + arr = disk_arr.read() + median_image[row1:row2] = nanmedian3D(arr) + del arr, disk_arr + + return median_image def flag_crs_in_models( @@ -226,14 +477,7 @@ def flag_resampled_model_crs( blot = gwcs_blot(median_data, median_wcs, input_model.data.shape, input_model.meta.wcs, pix_ratio) if save_blot: - if make_output_path is None: - raise ValueError("make_output_path must be provided if save_blot is True") - model_path = make_output_path(input_model.meta.filename, suffix='blot') - blot_model = _make_blot_model(input_model, blot) - blot_model.meta.filename = model_path - blot_model.save(model_path) - log.info(f"Saved model in {model_path}") - del blot_model + _fileio.save_blot(input_model, blot, make_output_path) # dq flags will be updated in-place _flag_resampled_model_crs(input_model, blot, snr1, snr2, scale1, scale2, backg) @@ -304,10 +548,3 @@ def flag_model_crs(image, blot, snr): match_nans_and_flags(image) log.info(f"{np.count_nonzero(cr_mask)} pixels marked as outliers") - - -def _make_blot_model(input_model, blot): - blot_model = type(input_model)() - blot_model.data = blot - blot_model.update(input_model) - return blot_model \ No newline at end of file diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index 4f3bca7edd..4fd2d62cf5 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -1,6 +1,7 @@ from astropy.io.fits.diff import FITSDiff import pytest import numpy as np +import os from gwcs import wcstools from jwst.stpipe import Step @@ -20,20 +21,27 @@ def run_pipeline(rtdata_module): # Run the calwebb_spec3 pipeline; save results from intermediate steps args = ["calwebb_spec3", rtdata.input, "--steps.outlier_detection.save_results=true", + "--steps.outlier_detection.save_intermediate_results=true", "--steps.resample_spec.save_results=true", "--steps.extract_1d.save_results=true"] Step.from_cmdline(args) @pytest.mark.bigdata -@pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) +@pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d", "median"]) @pytest.mark.parametrize("source_id,slit_name", [("s000000001","s200a2"), ("s000000021","s200a1"), ("s000000023","s400a1"), ("s000000024","s1600a1"), ("s000000025","s200b1")]) def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, suffix, source_id, slit_name): """Test spec3 pipeline on a set of NIRSpec FS exposures.""" rtdata = rtdata_module - output = f"jw01309-o022_{source_id}_nirspec_f290lp-g395h-{slit_name}-allslits_{suffix}.fits" + if suffix == "median": + output = f"jw01309022001_04102_00002_nrs2_{slit_name}_{suffix}.fits" + # also ensure drizzled and blot models were created with the correct names + assert os.path.isfile(output.replace("median", "outlier_s2d")) + assert os.path.isfile(output.replace("median", "blot")) + else: + output = f"jw01309-o022_{source_id}_nirspec_f290lp-g395h-{slit_name}-allslits_{suffix}.fits" rtdata.output = output rtdata.get_truth(f"truth/test_nirspec_fs_spec3/{output}") diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 6350c3496e..7d70ae840d 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -251,6 +251,82 @@ def _get_intensity_scale(self, img): else: iscale = 1.0 return iscale + + def resample_group(self, input_models, indices): + """Apply resample_many_to_many for one group + + Parameters + ---------- + input_models : ModelLibrary + + indices : list + """ + output_model = self.blank_output.copy() + + copy_asn_info_from_library(input_models, output_model) + + with input_models: + example_image = input_models.borrow(indices[0]) + + # Determine output file type from input exposure filenames + # Use this for defining the output filename + indx = example_image.meta.filename.rfind('.') + output_type = example_image.meta.filename[indx:] + output_root = '_'.join(example_image.meta.filename.replace( + output_type, '').split('_')[:-1]) + output_model.meta.filename = ( + f'{output_root}_' + f'{self.intermediate_suffix}{output_type}') + input_models.shelve(example_image, indices[0], modify=False) + del example_image + + # Initialize the output with the wcs + driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, + kernel=self.kernel, fillval=self.fillval) + + log.info(f"{len(indices)} exposures to drizzle together") + for index in indices: + img = input_models.borrow(index) + if isinstance(img, datamodels.SlitModel): + # must call this explicitly to populate area extension + # although the existence of this extension may not be necessary + img.area = img.area + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') + + inwht = resample_utils.build_driz_weight( + img, + weight_type=self.weight_type, + good_bits=self.good_bits + ) + + # apply sky subtraction + blevel = img.meta.background.level + if not img.meta.background.subtracted and blevel is not None: + data = img.data - blevel + else: + data = img.data + + xmin, xmax, ymin, ymax = resample_utils._resample_range( + data.shape, + img.meta.wcs.bounding_box + ) + + driz.add_image( + data, + img.meta.wcs, + iscale=iscale, + inwht=inwht, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax + ) + del data + input_models.shelve(img, index, modify=False) + del img + + return output_model def resample_many_to_many(self, input_models): """Resample many inputs to many outputs where outputs have a common frame. @@ -263,75 +339,8 @@ def resample_many_to_many(self, input_models): """ output_models = [] for group_id, indices in input_models.group_indices.items(): - output_model = self.blank_output - - copy_asn_info_from_library(input_models, output_model) - - with input_models: - example_image = input_models.borrow(indices[0]) - - # Determine output file type from input exposure filenames - # Use this for defining the output filename - indx = example_image.meta.filename.rfind('.') - output_type = example_image.meta.filename[indx:] - output_root = '_'.join(example_image.meta.filename.replace( - output_type, '').split('_')[:-1]) - if self.asn_id is not None: - output_model.meta.filename = ( - f'{output_root}_{self.asn_id}_' - f'{self.intermediate_suffix}{output_type}') - else: - output_model.meta.filename = ( - f'{output_root}_' - f'{self.intermediate_suffix}{output_type}') - input_models.shelve(example_image, indices[0], modify=False) - del example_image - - # Initialize the output with the wcs - driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, - kernel=self.kernel, fillval=self.fillval) - - log.info(f"{len(indices)} exposures to drizzle together") - for index in indices: - img = input_models.borrow(index) - if isinstance(img, datamodels.SlitModel): - # must call this explicitly to populate area extension - # although the existence of this extension may not be necessary - img.area = img.area - iscale = self._get_intensity_scale(img) - log.debug(f'Using intensity scale iscale={iscale}') - - inwht = resample_utils.build_driz_weight( - img, - weight_type=self.weight_type, - good_bits=self.good_bits - ) - - # apply sky subtraction - blevel = img.meta.background.level - if not img.meta.background.subtracted and blevel is not None: - data = img.data - blevel - else: - data = img.data - - xmin, xmax, ymin, ymax = resample_utils._resample_range( - data.shape, - img.meta.wcs.bounding_box - ) - - driz.add_image( - data, - img.meta.wcs, - iscale=iscale, - inwht=inwht, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax - ) - del data - input_models.shelve(img, index, modify=False) - del img + + output_model = self.resample_group(input_models, indices) if not self.in_memory: # Write out model to disk, then return filename @@ -342,9 +351,7 @@ def resample_many_to_many(self, input_models): log.info(f"Saved model in {output_name}") output_models.append(output_name) else: - output_models.append(output_model.copy()) - output_model.data *= 0. - output_model.wht *= 0. + output_models.append(output_model) if not self.in_memory: # build ModelLibrary as an association from the output files From 3232016ef46eab48f67081ae9619b72f9f6521a4 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Thu, 26 Sep 2024 13:33:10 -0400 Subject: [PATCH 491/687] refactor accumulate sums and add option for sigma clipping --- jwst/combine_1d/combine1d.py | 150 ++++++++++++++++++++++------- jwst/combine_1d/combine_1d_step.py | 1 + 2 files changed, 118 insertions(+), 33 deletions(-) diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index c4a6021e9d..9f7f9de4ef 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -3,6 +3,7 @@ # import logging +import warnings import numpy as np @@ -10,7 +11,7 @@ from jwst.datamodels import ModelContainer -from ..extract_1d.spec_wcs import create_spectral_wcs +from jwst.extract_1d.spec_wcs import create_spectral_wcs log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -157,7 +158,7 @@ def assign_wavelengths(self, input_spectra): input_spectra[0].declination[0], self.wavelength) - def accumulate_sums(self, input_spectra): + def accumulate_sums(self, input_spectra, sigma_clip=None): """Compute a weighted sum of all the input spectra. Each pixel of each input spectrum will be added to one pixel of @@ -180,6 +181,8 @@ def accumulate_sums(self, input_spectra): ---------- input_spectra : list of InputSpectrumModel objects List of input spectra. + sigma_clip : float, optional + Factor for clipping outliers in spectral combination. """ # This is the data type for the output spectrum. We'll use double @@ -189,21 +192,23 @@ def accumulate_sums(self, input_spectra): dq_dtype = cmb_dtype.fields["DQ"][0] nelem = self.wavelength.shape[0] + nspec = len(input_spectra) - self.flux = np.zeros(nelem, dtype=np.float64) - self.flux_error = np.zeros(nelem, dtype=np.float64) - self.surf_bright = np.zeros(nelem, dtype=np.float64) - self.sb_error = np.zeros(nelem, dtype=np.float64) - self.dq = np.zeros(nelem, dtype=dq_dtype) - self.weight = np.zeros(nelem, dtype=np.float64) - self.count = np.zeros(nelem, dtype=np.float64) + dq = np.zeros(nelem, dtype=dq_dtype) + + flux = np.zeros((nspec, nelem), dtype=np.float64) + flux_error = np.zeros((nspec, nelem), dtype=np.float64) + surf_bright = np.zeros((nspec, nelem), dtype=np.float64) + sb_error = np.zeros((nspec, nelem), dtype=np.float64) + weight = np.zeros((nspec, nelem), dtype=np.float64) + count = np.zeros((nspec, nelem), dtype=np.float64) self.flux_unit = input_spectra[0].flux_unit self.sb_unit = input_spectra[0].sb_unit n_nan = 0 # initial value ninputs = 0 - for in_spec in input_spectra: + for s, in_spec in enumerate(input_spectra): ninputs += 1 if in_spec.name is not None: slit_name = f'{ninputs}, slit {in_spec.name}' @@ -220,7 +225,8 @@ def accumulate_sums(self, input_spectra): nan_flag = np.isnan(out_pixel) n_nan += nan_flag.sum() for i in range(len(out_pixel)): - if in_spec.dq[i] & datamodels.dqflags.pixel['DO_NOT_USE'] > 0: + # Need to check on dq and nan flux because dq is not set for some x1d + if (in_spec.dq[i] & datamodels.dqflags.pixel['DO_NOT_USE'] > 0) | np.isnan(in_spec.flux[i]): continue # Round to the nearest pixel. if nan_flag[i]: # skip if the pixel number is NaN @@ -228,17 +234,30 @@ def accumulate_sums(self, input_spectra): k = round(float(out_pixel[i])) if k < 0 or k >= nelem: continue - weight = in_spec.weight[i] - self.dq[k] |= in_spec.dq[i] - self.flux[k] += in_spec.flux[i] * weight - self.flux_error[k] += (in_spec.flux_error[i] * weight)**2 - self.surf_bright[k] += (in_spec.surf_bright[i] * weight) - self.sb_error[k] += (in_spec.sb_error[i] * weight)**2 - self.weight[k] += weight - self.count[k] += 1. + dq[k] |= in_spec.dq[i] + flux[s, k] = in_spec.flux[i] + flux_error[s, k] = in_spec.flux_error[i] + surf_bright[s, k] = in_spec.surf_bright[i] + sb_error[s, k] = in_spec.sb_error[i] + weight[s, k] = in_spec.weight[i] + count[s, k] = 1. + + (flux, flux_error, surf_bright, sb_error, + weight, count) = self.combine_spectra(flux, flux_error, + surf_bright, sb_error, + weight, count, sigma_clip=sigma_clip) + if n_nan > 0: log.warning("%d output pixel numbers were NaN", n_nan) + self.flux = flux + self.flux_error = flux_error + self.surf_bright = surf_bright + self.sb_error = sb_error + self.dq = dq + self.weight = weight + self.count = count + # Since the output wavelengths will not usually be exactly the same # as the input wavelengths, it's possible that there will be output # pixels for which there is no corresponding pixel in any of the @@ -259,18 +278,84 @@ def accumulate_sums(self, input_spectra): self.count = self.count[index] del index - self.normalized = False + def combine_spectra(self, flux, flux_error, surf_bright, sb_error, + weight, count, sigma_clip=None): + """Combine accumulated spectra. - def compute_combination(self): - """Compute the combined values.""" + Parameters + ---------- + flux : ndarray, 2-D + Tabulated fluxes for the input spectra in the format + [N spectra, M wavelengths]. + flux_error : ndarray, 2-D + Flux errors of input spectra. + surf_bright : ndarray, 2-D + Surface brightnesses of input spectra. + sb_error : ndarray, 2-D + Surface brightness errors for input spectra. + weight : ndarray, 2-D + Pixel weights for input spectra + count : ndarray, 2-D + Count of how many values at each index in the input arrays. + sigma_clip : float, optional + Factor for clipping outliers. Compares input spectra to the + median and medaian absolute devaition, by default None. - if not self.normalized: - sum_weight = np.where(self.weight > 0., self.weight, 1.) - self.surf_bright /= sum_weight - self.flux /= sum_weight - self.flux_error = np.sqrt(self.flux_error) / sum_weight - self.sb_error = np.sqrt(self.sb_error) / sum_weight - self.normalized = True + Returns + ------- + flux : ndarray, 1-D + Combined 1-D fluxes. + flux_error : ndarray, 1-D + Combined 1-D flux errors. + surf_bright : ndarray, 1-D + Combined 1-D surface brightnesses. + sb_error : ndarray, 1-D + Combined 1-D surface brightness errors. + weight : ndarray, 1-D + Total, per wavelength weights. + count : ndarray, 1-D + Total count of spectra contributing to each wavelength. + + """ + + # Catch warnings for all NaN slices in an array. + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + + if sigma_clip is not None: + # Copy the fluxes for modifying + flux_2d = np.array(flux * weight) + + # Mask any missing pixels in the input spectra + missing = (count < 1) | (flux_2d == 0.0) + flux_2d[missing] = np.nan + # Calculate median and median absolute deviation + med_flux = np.nanmedian(flux_2d, axis=0) + mad = np.nanmedian(np.abs(flux_2d - med_flux)) + + # Clip any outlier pixels in the input spectra + clipped = np.abs(flux * weight - med_flux) > sigma_clip * mad + flux[clipped] = np.nan + flux_error[clipped] = np.nan + surf_bright[clipped] = np.nan + sb_error[clipped] = np.nan + count[clipped] = 0 + weight[clipped] = 0 + + # Perform a weighted sum of the input spectra + sum_weight = np.nansum(weight, axis=0) + sum_weight_nonzero = np.where(sum_weight > 0., sum_weight, 1.) + + flux= np.nansum(flux * weight, axis=0) / sum_weight_nonzero + flux_error = np.sqrt(np.nansum((flux_error * weight)**2, axis=0)) / sum_weight_nonzero + surf_bright = np.nansum(surf_bright * weight, axis=0) / sum_weight_nonzero + sb_error = np.sqrt(np.nansum((sb_error * weight)**2, axis=0)) / sum_weight_nonzero + count = np.nansum(count, axis=0) + + self.normalized = True + + return flux, flux_error, surf_bright, sb_error, sum_weight, count + def create_output_data(self): """Create the output data. @@ -397,7 +482,7 @@ def count_input(input_spectra): return wl, n_input_spectra -def compute_output_wl(wl, n_input_spectra): +def compute_output_wl(wl, n_input_spectra, ): """Compute output wavelengths. In summary, the output wavelengths are computed by binning the @@ -557,7 +642,7 @@ def check_exptime(exptime_key): return exptime_key -def combine_1d_spectra(input_model, exptime_key): +def combine_1d_spectra(input_model, exptime_key, sigma_clip=None): """Combine the input spectra. Parameters @@ -602,8 +687,7 @@ def combine_1d_spectra(input_model, exptime_key): for order in input_spectra: output_spectra[order] = OutputSpectrumModel() output_spectra[order].assign_wavelengths(input_spectra[order]) - output_spectra[order].accumulate_sums(input_spectra[order]) - output_spectra[order].compute_combination() + output_spectra[order].accumulate_sums(input_spectra[order], sigma_clip=sigma_clip) output_model = datamodels.MultiCombinedSpecModel() diff --git a/jwst/combine_1d/combine_1d_step.py b/jwst/combine_1d/combine_1d_step.py index 76b5172a83..a5d23d6389 100644 --- a/jwst/combine_1d/combine_1d_step.py +++ b/jwst/combine_1d/combine_1d_step.py @@ -20,6 +20,7 @@ class Combine1dStep(Step): spec = """ exptime_key = string(default="exposure_time") # use for weight + sigma_clip = float(default=None) # Factor for clipping outliers """ def process(self, input_file): From 9490e300452231664c3687dad670c4ecaa1b7c9a Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Thu, 26 Sep 2024 13:33:51 -0400 Subject: [PATCH 492/687] set sigma_clip default and option to save bkg x1d spectra --- .../master_background_mos_step.py | 21 +++++++++++++------ jwst/master_background/nirspec_utils.py | 12 +++++++---- 2 files changed, 23 insertions(+), 10 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 45c641ff5c..a9460d288d 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -47,6 +47,7 @@ class MasterBackgroundMosStep(Pipeline): class_alias = "master_background_mos" spec = """ + sigma_clip = float(default=3) # Factor for clipping outliers when combining background spectra force_subtract = boolean(default=False) # Force subtracting master background save_background = boolean(default=False) # Save computed master background user_background = string(default=None) # Path to user-supplied master background @@ -132,7 +133,8 @@ def process(self, data): return data self.log.info('Calculating master background') - master_background, mb_multislit = self._calc_master_background(data_model) + master_background, mb_multislit, bkg_x1d_spectra = self._calc_master_background( + data_model, sigma_clip=self.sigma_clip) # Check that a master background was actually determined. if master_background is None: @@ -152,6 +154,7 @@ def process(self, data): if self.save_background: self.save_model(master_background, suffix='masterbg1d', force=True) self.save_model(mb_multislit, suffix='masterbg2d', force=True) + self.save_model(bkg_x1d_spectra, suffix='bkgx1d', force=True) return result @@ -199,23 +202,27 @@ def _classify_slits(self, data): return num_bkg, num_src - def _calc_master_background(self, data, user_background=None): + def _calc_master_background(self, data, user_background=None, sigma_clip=3): """Calculate master background from background slits Parameters ---------- data : `~jwst.datamodels.MultiSlitModel` The data to operate on. - user_background : None, string, or `~jwst.datamodels.CombinedSpecModel` Optional user-supplied master background 1D spectrum, path to file or opened datamodel + sigma_clip : None or float + Optional factor for sigma clipping outliers when combining background spectra. Returns ------- masterbkg_1d, masterbkg_2d : `~jwst.datamodels.CombinedSpecModel`, `~jwst.datamodels.MultiSlitModel` The master background in 1d and 2d, multislit formats. None is returned when a master background could not be determined. + bkg_x1d_spectra : `~jwst.datamodels.MultiSlitModel` + The 1D extracted background spectra used to determine the master background. + Returns None when a user_background is provided. """ # Since the parameters for the substeps are modified during processing, @@ -244,12 +251,14 @@ def _calc_master_background(self, data, user_background=None): if user_background: self.log.debug(f'User background provided {user_background}') master_background = user_background + bkg_x1d_spectra = None else: self.log.debug('Calculating 1D master background') - master_background = nirspec_utils.create_background_from_multislit(pre_calibrated) + master_background, bkg_x1d_spectra = nirspec_utils.create_background_from_multislit( + pre_calibrated, sigma_clip=sigma_clip) if master_background is None: self.log.debug('No master background could be calculated. Returning None') - return None, None + return None, None, None # Now decalibrate the master background for each individual science slit. # First step is to map the master background into a MultiSlitModel @@ -274,4 +283,4 @@ def _calc_master_background(self, data, user_background=None): mb_multislit = self.pathloss(mb_multislit) mb_multislit = self.flat_field(mb_multislit) - return master_background, mb_multislit + return master_background, mb_multislit, bkg_x1d_spectra diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 153bf9f504..9bff793a99 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -76,7 +76,7 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(input_model): +def create_background_from_multislit(input_model, sigma_clip=3): """Create a 1D master background spectrum from a set of calibrated background MOS slitlets in the input MultiSlitModel. @@ -85,11 +85,15 @@ def create_background_from_multislit(input_model): ---------- input_model : `~jwst.datamodels.MultiSlitModel` The input data model containing all slit instances. + sigma_clip : None or float + Optional factor for sigma clipping outliers when combining background spectra. Returns ------- master_bkg: `~jwst.datamodels.CombinedSpecModel` The 1D master background spectrum created from the inputs. + x1d: `jwst.datamodels.MultiSpecModel` + The 1D extracted background spectra of the inputs. """ from ..resample import resample_spec_step from ..extract_1d import extract_1d_step @@ -119,13 +123,13 @@ def create_background_from_multislit(input_model): # Call combine_1d to combine the 1D background spectra log.info('Combining 1D background spectra into master background') - master_bkg = combine_1d_spectra(x1d, exptime_key='exposure_time') + master_bkg = combine_1d_spectra( + x1d, exptime_key='exposure_time', sigma_clip=sigma_clip) del bkg_model del resamp - del x1d - return master_bkg + return master_bkg, x1d def correct_nrs_ifu_bkg(input_model): From 473c58610eac4c0f2dc5097285c227fb6283394f Mon Sep 17 00:00:00 2001 From: Thomas WIlliams Date: Tue, 24 Sep 2024 09:14:48 +0100 Subject: [PATCH 493/687] Fix crash combining full and subarray for background --- changes/8787.background.rst | 1 + jwst/background/background_sub.py | 14 +++++++++++--- 2 files changed, 12 insertions(+), 3 deletions(-) create mode 100644 changes/8787.background.rst diff --git a/changes/8787.background.rst b/changes/8787.background.rst new file mode 100644 index 0000000000..9f04f7e0bd --- /dev/null +++ b/changes/8787.background.rst @@ -0,0 +1 @@ +Fixed crash when combining full and subarray observations for background subtraction. diff --git a/jwst/background/background_sub.py b/jwst/background/background_sub.py index 27463c32a9..5f2aa0e0e7 100755 --- a/jwst/background/background_sub.py +++ b/jwst/background/background_sub.py @@ -91,10 +91,18 @@ def get_subset_array(self, other): if jmax < jmin: jmax = jmin + # To ensure that we mix and match subarray obs, take + # the x/y shape from self.data. To ensure we can work + # with mismatched NINTS, if we have that third dimension + # use the value from other + data_shape = copy.deepcopy(self.data.shape) + if self.im_dim == 3: + data_shape[0] = other.data.shape[0] + # Set up arrays, NaN out data/err for sigma clipping, keep DQ as 0 for bitwise_or - data_overlap = np.ones_like(other.data) * np.nan - err_overlap = np.ones_like(other.data) * np.nan - dq_overlap = np.zeros_like(other.data, dtype=np.uint32) + data_overlap = np.ones(data_shape) * np.nan + err_overlap = np.ones(data_shape) * np.nan + dq_overlap = np.zeros(data_shape, dtype=np.uint32) if self.im_dim == 2: idx = (slice(jmin - other.jmin, jmax - other.jmin), From 586843578c3ad27142dfde8b8f15b6277fd8069e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 25 Sep 2024 13:30:26 -0400 Subject: [PATCH 494/687] Fix indexing errors, add tests --- jwst/background/background_sub.py | 16 ++--- jwst/background/tests/test_background.py | 92 +++++++++++++++++++++++- 2 files changed, 99 insertions(+), 9 deletions(-) diff --git a/jwst/background/background_sub.py b/jwst/background/background_sub.py index 5f2aa0e0e7..b5fa267bfb 100755 --- a/jwst/background/background_sub.py +++ b/jwst/background/background_sub.py @@ -91,24 +91,24 @@ def get_subset_array(self, other): if jmax < jmin: jmax = jmin - # To ensure that we mix and match subarray obs, take + # To ensure that we can mix and match subarray obs, take # the x/y shape from self.data. To ensure we can work # with mismatched NINTS, if we have that third dimension # use the value from other - data_shape = copy.deepcopy(self.data.shape) + data_shape = list(self.data.shape) if self.im_dim == 3: data_shape[0] = other.data.shape[0] # Set up arrays, NaN out data/err for sigma clipping, keep DQ as 0 for bitwise_or - data_overlap = np.ones(data_shape) * np.nan - err_overlap = np.ones(data_shape) * np.nan + data_overlap = np.full(data_shape, np.nan, dtype=other.data.dtype) + err_overlap = np.full(data_shape, np.nan, dtype=other.data.dtype) dq_overlap = np.zeros(data_shape, dtype=np.uint32) if self.im_dim == 2: idx = (slice(jmin - other.jmin, jmax - other.jmin), slice(imin - other.imin, imax - other.imin), ) - if self.im_dim == 3: + else: idx = (slice(None, None), slice(jmin - other.jmin, jmax - other.jmin), slice(imin - other.imin, imax - other.imin), @@ -120,9 +120,9 @@ def get_subset_array(self, other): dq_cutout = other.dq[idx] # Put them into the right place in the original image array shape - data_overlap[:data_cutout.shape[0], :data_cutout.shape[1]] = copy.deepcopy(data_cutout) - err_overlap[:data_cutout.shape[0], :data_cutout.shape[1]] = copy.deepcopy(err_cutout) - dq_overlap[:data_cutout.shape[0], :data_cutout.shape[1]] = copy.deepcopy(dq_cutout) + data_overlap[..., :data_cutout.shape[-2], :data_cutout.shape[-1]] = copy.deepcopy(data_cutout) + err_overlap[..., :data_cutout.shape[-2], :data_cutout.shape[-1]] = copy.deepcopy(err_cutout) + dq_overlap[..., :data_cutout.shape[-2], :data_cutout.shape[-1]] = copy.deepcopy(dq_cutout) return data_overlap, err_overlap, dq_overlap diff --git a/jwst/background/tests/test_background.py b/jwst/background/tests/test_background.py index 04eeba15b5..7e895b7ddd 100644 --- a/jwst/background/tests/test_background.py +++ b/jwst/background/tests/test_background.py @@ -80,6 +80,47 @@ def science_image(): return image +def miri_rate_model(data_shape, value=1.0): + """ + Generate a MIRI image subarray rate or rateints image. + + Parameters + ---------- + data_shape : tuple of int + Shape of the rate data. 2 values for rate, 3 for rateints. + value : float, optional + Value to set in the data array. + + Returns + ------- + image : DataModel + An open datamodel containing MIRI subarray rate or rateints + data. + """ + + if len(data_shape) == 2: + image = datamodels.ImageModel(data_shape) + else: + image = datamodels.CubeModel(data_shape) + + image.data[:, :] = value + image.meta.instrument.name = 'MIRI' + image.meta.instrument.detector = 'MIRIMAGE' + image.meta.instrument.filter = 'F2100W' + image.meta.exposure.type = 'MIR_IMAGE' + image.meta.observation.date = '2019-02-27' + image.meta.observation.time = '13:37:18.548' + image.meta.date = '2019-02-27T13:37:18.548' + + image.meta.subarray.xstart = 1 + image.meta.subarray.ystart = 1 + + image.meta.subarray.xsize = image.data.shape[-1] + image.meta.subarray.ysize = image.data.shape[-2] + + return image + + def test_nirspec_gwa(tmp_cwd, background, science_image): """Verify NIRSPEC GWA logic for in the science and background""" @@ -281,6 +322,55 @@ def test_nis_wfss_background(filters, pupils, make_wfss_datamodel): assert np.isclose([pipeline_reference_mean], [test_reference_mean], rtol=1e-1) +@pytest.mark.parametrize('data_shape,background_shape', + [((10, 10), (10, 10)), + ((10, 10), (20, 20)), + ((2, 10, 10), (2, 10, 10)), + ((2, 10, 10), (2, 20, 20)), + ((2, 10, 10), (3, 10, 10)), + ((2, 10, 10), (3, 20, 20)), + ((3, 10, 10), (2, 10, 10)), + ((3, 10, 10), (2, 20, 20))]) +def test_miri_subarray_full_overlap(data_shape, background_shape): + image_value = 10.0 + background_value = 1.0 + image = miri_rate_model(data_shape, value=image_value) + background = miri_rate_model(background_shape, value=background_value) + + result = BackgroundStep.call(image, [background]) + + assert_allclose(result.data, image_value - background_value) + assert type(result) is type(image) + assert result.meta.cal_step.back_sub == 'COMPLETE' + + image.close() + background.close() + + +@pytest.mark.parametrize('data_shape,background_shape', + [((20, 20), (10, 10)), + ((2, 20, 20), (2, 10, 10),), + ((3, 20, 20), (2, 10, 10),), + ((2, 20, 20), (3, 10, 10),)]) +def test_miri_subarray_partial_overlap(data_shape, background_shape): + image_value = 10.0 + background_value = 1.0 + image = miri_rate_model(data_shape, value=image_value) + background = miri_rate_model(background_shape, value=background_value) + + result = BackgroundStep.call(image, [background]) + + assert_allclose(result.data[..., :background_shape[-2], :background_shape[-1]], + image_value - background_value) + assert_allclose(result.data[..., background_shape[-2]:, :], image_value) + assert_allclose(result.data[..., :, background_shape[-1]:], image_value) + assert type(result) is type(image) + assert result.meta.cal_step.back_sub == 'COMPLETE' + + image.close() + background.close() + + def test_robust_mean(): """Test robust mean calculation""" data = np.random.rand(2048, 2048) @@ -290,7 +380,7 @@ def test_robust_mean(): assert np.isclose([test], [result], rtol=1e-3) -def test_no_Nan(): +def test_no_nan(): """Make sure that nan values are filled with fill value""" # Make data model model = datamodels.ImageModel() From 78b6adb2e6b718a7997dffe54664a127bf3407c7 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 30 Sep 2024 16:26:26 -0400 Subject: [PATCH 495/687] Fix tolerance for coron test on Linux --- jwst/coron/tests/test_coron.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/coron/tests/test_coron.py b/jwst/coron/tests/test_coron.py index cd90f80049..4c3d3dbebe 100644 --- a/jwst/coron/tests/test_coron.py +++ b/jwst/coron/tests/test_coron.py @@ -141,8 +141,8 @@ def test_align_array(): ] ) - npt.assert_allclose(aligned, truth_aligned, atol=1e-5) - npt.assert_allclose(shifts, truth_shifts, atol=1e-5) + npt.assert_allclose(aligned, truth_aligned, atol=1e-4, rtol=1e-5) + npt.assert_allclose(shifts, truth_shifts, atol=1e-4, rtol=1e-5) def test_align_models(): From 829cb54ac0f293de6fc3cc7a7fa0ee99ff441bb7 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 1 Oct 2024 11:52:27 -0400 Subject: [PATCH 496/687] changes to include resample and extract_1d into mos bg pipe --- jwst/master_background/master_background_mos_step.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 45c641ff5c..73af6bee9b 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -9,6 +9,8 @@ from ..flatfield import flat_field_step from ..pathloss import pathloss_step from ..photom import photom_step +from ..resample import resample_spec_step +from ..extract_1d import extract_1d_step from ..stpipe import Pipeline __all__ = ['MasterBackgroundMosStep'] @@ -60,6 +62,8 @@ class MasterBackgroundMosStep(Pipeline): 'pathloss': pathloss_step.PathLossStep, 'barshadow': barshadow_step.BarShadowStep, 'photom': photom_step.PhotomStep, + 'resample_spec': resample_spec_step.ResampleSpecStep, + 'extract_1d': extract_1d_step.Extract1dStep, } # No need to prefetch. This will have been done by the parent step. From 8a68010913aa1a478e2d277dd0c8505cc048a7df Mon Sep 17 00:00:00 2001 From: "P. L. Lim" <2090236+pllim@users.noreply.github.com> Date: Tue, 1 Oct 2024 14:41:53 -0400 Subject: [PATCH 497/687] MNT: Use hash for Action workflow versions and update if needed (#8839) --- .github/workflows/build.yml | 4 ++-- .github/workflows/changelog.yml | 6 +++--- .github/workflows/ci.yml | 12 ++++++------ .github/workflows/ci_cron.yml | 4 ++-- .github/workflows/label_pull_request.yml | 2 +- .github/workflows/tests_devdeps.yml | 4 ++-- 6 files changed, 16 insertions(+), 16 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index cc6c3dfd50..61fb8cf6ef 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -16,12 +16,12 @@ jobs: name: check that `requirements-sdp.txt` is populated runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 - run: grep -v '^ *#' requirements-sdp.txt build: needs: [ check ] if: always() && (needs.check.result == 'success' || needs.check.result == 'skipped') - uses: OpenAstronomy/github-actions-workflows/.github/workflows/publish.yml@v1 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/publish.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 with: upload_to_pypi: ${{ (github.event_name == 'release') && (github.event.action == 'released') }} targets: | diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index 508e6e295a..1b933ff5ad 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -18,10 +18,10 @@ jobs: if: ${{ !contains(github.event.pull_request.labels.*.name, 'no-changelog-entry-needed') }} runs-on: ubuntu-latest steps: - - uses: actions/setup-python@v5 + - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 with: python-version: 3 - - uses: actions/checkout@v4 + - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 with: fetch-depth: 0 - run: pip install . @@ -32,7 +32,7 @@ jobs: if: ${{ !contains(github.event.pull_request.labels.*.name, 'allow-manual-changelog-edit') }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 with: fetch-depth: 0 - name: prevent direct changes to `CHANGES.rst` diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 45ab163eed..5df1c376c8 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,17 +34,17 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - - uses: pre-commit/action@v3.0.1 + - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 + - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 + - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 check: - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 with: default_python: "3.12" envs: | - linux: check-dependencies latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@master + uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest @@ -54,7 +54,7 @@ jobs: outputs: context: ${{ steps.context.outputs.context }} test: - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 needs: [ crds_context ] with: setenv: | diff --git a/.github/workflows/ci_cron.yml b/.github/workflows/ci_cron.yml index 33addc0358..be1d323b31 100644 --- a/.github/workflows/ci_cron.yml +++ b/.github/workflows/ci_cron.yml @@ -23,7 +23,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@master + uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest @@ -34,7 +34,7 @@ jobs: context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run scheduled tests'))) - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 needs: [ crds_context ] with: setenv: | diff --git a/.github/workflows/label_pull_request.yml b/.github/workflows/label_pull_request.yml index b0ff2f0ec0..2c20b43584 100644 --- a/.github/workflows/label_pull_request.yml +++ b/.github/workflows/label_pull_request.yml @@ -10,7 +10,7 @@ jobs: triage: runs-on: ubuntu-latest steps: - - uses: actions/labeler@v5 + - uses: actions/labeler@8558fd74291d67161a8a78ce36a881fa63b766a9 # v5.0.0 if: github.event_name == 'pull_request_target' || github.event_name == 'pull_request' with: repo-token: "${{ secrets.GITHUB_TOKEN }}" \ No newline at end of file diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index 2daf8941d5..72b7ad9fee 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -32,7 +32,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@master + uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest @@ -43,7 +43,7 @@ jobs: context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run devdeps tests'))) - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@v1 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 needs: [ crds_context ] with: setenv: | From e5c73fa23d157f4aa65416b4070c2b838e978494 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 1 Oct 2024 13:52:01 -0700 Subject: [PATCH 498/687] JP-3768: Move outlier detection median computers to stcal (#8840) --- changes/8840.outlier_detection.rst | 1 + .../tests/test_algorithms.py | 12 - jwst/outlier_detection/tests/test_utils.py | 164 ----------- jwst/outlier_detection/utils.py | 272 +----------------- pyproject.toml | 2 +- 5 files changed, 9 insertions(+), 442 deletions(-) create mode 100644 changes/8840.outlier_detection.rst delete mode 100644 jwst/outlier_detection/tests/test_utils.py diff --git a/changes/8840.outlier_detection.rst b/changes/8840.outlier_detection.rst new file mode 100644 index 0000000000..0d1fe91bb7 --- /dev/null +++ b/changes/8840.outlier_detection.rst @@ -0,0 +1 @@ +Moved median computers out of the jwst repository and into stcal. \ No newline at end of file diff --git a/jwst/outlier_detection/tests/test_algorithms.py b/jwst/outlier_detection/tests/test_algorithms.py index d1c8ffd85d..b3b9255bb4 100644 --- a/jwst/outlier_detection/tests/test_algorithms.py +++ b/jwst/outlier_detection/tests/test_algorithms.py @@ -1,7 +1,6 @@ import numpy as np from jwst.outlier_detection.tso import moving_median_over_zeroth_axis -from jwst.outlier_detection.utils import nanmedian3D def test_rolling_median(): @@ -15,14 +14,3 @@ def test_rolling_median(): result = moving_median_over_zeroth_axis(arr, w) expected = expected_time_axis[:, np.newaxis, np.newaxis] * spatial_axis[np.newaxis, :, :] assert np.allclose(result, expected) - - -def test_nanmedian3D(): - - shp = (11, 50, 60) - cube = np.random.normal(size=shp) - cube[5, 5:7, 5:8] = np.nan - med = nanmedian3D(cube) - - assert med.dtype == np.float32 - assert np.allclose(med, np.nanmedian(cube, axis=0), equal_nan=True) \ No newline at end of file diff --git a/jwst/outlier_detection/tests/test_utils.py b/jwst/outlier_detection/tests/test_utils.py deleted file mode 100644 index d1fc0bd576..0000000000 --- a/jwst/outlier_detection/tests/test_utils.py +++ /dev/null @@ -1,164 +0,0 @@ -import pytest -from jwst.outlier_detection.utils import DiskAppendableArray, OnDiskMedian -from pathlib import Path -import numpy as np -import os - - -def test_disk_appendable_array(tmp_cwd): - - slice_shape = (8,7) - dtype = "float32" - tempdir = tmp_cwd / Path("tmptest") - os.mkdir(tempdir) - fname = tempdir / "test.bin" - - arr = DiskAppendableArray(slice_shape, dtype, fname) - - # check temporary file setup - assert str(arr._filename).split("/")[-1] in os.listdir(tempdir) - assert len(os.listdir(tempdir)) == 1 - assert arr.shape == (0,) + slice_shape - - # check cwd contains no files - assert all([not os.path.isfile(f) for f in os.listdir(tmp_cwd)]) - - # check expected append failures - with pytest.raises(ValueError): - candidate = np.zeros((7,7), dtype=dtype) - arr.append(candidate) - with pytest.raises(ValueError): - candidate = np.zeros((8,7), dtype='float64') - arr.append(candidate) - - # check append and read - candidate0 = np.zeros(slice_shape, dtype=dtype) - candidate1 = np.full(slice_shape, 2, dtype=dtype) - candidate2 = np.full(slice_shape, np.nan, dtype=dtype) - for candidate in [candidate0, candidate1, candidate2]: - arr.append(candidate) - - arr_in_memory = arr.read() - - assert arr_in_memory.shape == (3,) + slice_shape - assert np.all(arr_in_memory[0] == candidate0) - assert np.all(arr_in_memory[1] == candidate1) - assert np.allclose(arr_in_memory[2], candidate2, equal_nan=True) - - -def test_disk_appendable_array_bad_inputs(tmp_cwd): - - slice_shape = (8,7) - dtype = "float32" - tempdir = tmp_cwd / Path("tmptest") - fname = "test.bin" - - # test input directory does not exist - with pytest.raises(FileNotFoundError): - DiskAppendableArray(slice_shape, dtype, tempdir / fname) - - # make the input directory - os.mkdir(tempdir) - - # ensure failure if slice_shape is not 2-D - with pytest.raises(ValueError): - DiskAppendableArray((3,5,7), dtype, tempdir / fname) - - # ensure failure if dtype is not valid - with pytest.raises(TypeError): - DiskAppendableArray(slice_shape, "float3", tempdir / fname) - - # ensure failure if pass directory instead of filename - with pytest.raises(IsADirectoryError): - DiskAppendableArray(slice_shape, "float3", tempdir) - - -# filter warnings such that "ResourceWarning: Implictly cleaning up" is raised as error -# unfortunately need to do this indirectly by finding "finalize" string in PytestUnraisableException -@pytest.mark.filterwarnings("error:.*finalize.*:pytest.PytestUnraisableExceptionWarning") -def test_on_disk_median(tmp_cwd): - - library_length = 3 - frame_shape = (21,20) - dtype = 'float32' - tempdir = tmp_cwd / Path("tmptest") - os.mkdir(tempdir) - shape = (library_length,) + frame_shape - - median_computer = OnDiskMedian(shape, dtype=dtype, tempdir=tempdir) - - # test compute buffer indices - # buffer size equals size of single input model by default - # which means we expect same number of sections as library length - # in reality there is often one more section than that because - # of necessity of integer number of rows per section, but math is exact in this case - expected_buffer_size = frame_shape[0] * frame_shape[1] * np.dtype(dtype).itemsize - expected_section_nrows = frame_shape[0] // library_length - assert median_computer.nsections == library_length - assert median_computer.section_nrows == expected_section_nrows - assert median_computer.buffer_size == expected_buffer_size - - # test temp file setup - assert len(os.listdir(tempdir)) == 1 - assert str(median_computer._temp_path).startswith(str(tempdir)) - assert len(os.listdir(median_computer._temp_path)) == library_length - # check cwd and parent tempdir contain no files - assert all([not os.path.isfile(f) for f in os.listdir(tmp_cwd)]) - assert all([not os.path.isfile(f) for f in os.listdir(tempdir)]) - - # test validate data - with pytest.raises(ValueError): - candidate = np.zeros((20,20), dtype=dtype) - median_computer.add_image(candidate) - with pytest.raises(ValueError): - candidate = np.zeros((21,20), dtype='float64') - median_computer.add_image(candidate) - - # test add and compute - candidate0 = np.full(frame_shape, 3, dtype=dtype) - candidate1 = np.full(frame_shape, 2, dtype=dtype) - candidate2 = np.full(frame_shape, np.nan, dtype=dtype) - for candidate in [candidate0, candidate1, candidate2]: - median_computer.add_image(candidate) - median = median_computer.compute_median() - median_computer.cleanup() - assert median.shape == frame_shape - assert np.all(median == 2.5) - - # test expected error trying to add too many frames - # for loop to ensure always happens, not just the first time - candidate3 = np.zeros_like(candidate0) - for _ in range(2): - with pytest.raises(IndexError): - median_computer.add_image(candidate3) - - # test cleanup of tmpdir and everything else - assert not os.path.exists(median_computer._temp_path) - assert len(os.listdir(tempdir)) == 0 - assert len(os.listdir(os.getcwd())) == 1 # this is the "tmptest" directory - - -@pytest.mark.filterwarnings("error:.*finalize.*:pytest.PytestUnraisableExceptionWarning") -def test_on_disk_median_bad_inputs(tmp_cwd): - - library_length = 3 - frame_shape = (21,20) - dtype = 'float32' - tempdir = tmp_cwd / Path("tmptest") - os.mkdir(tempdir) - shape = (library_length,) + frame_shape - - with pytest.raises(ValueError): - OnDiskMedian(frame_shape, dtype=dtype, tempdir=tempdir) - - with pytest.raises(TypeError): - OnDiskMedian(shape, dtype="float3", tempdir=tempdir) - - with pytest.raises(FileNotFoundError): - OnDiskMedian(shape, dtype="float32", tempdir="dne") - - # ensure unreasonable buffer size will get set to minimum reasonable buffer - min_buffer = np.dtype(dtype).itemsize*frame_shape[1]*library_length - median_computer = OnDiskMedian(shape, dtype=dtype, tempdir=tempdir, buffer_size=-1) - assert median_computer.buffer_size == min_buffer - median_computer.cleanup() \ No newline at end of file diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 03446aac5e..1563c2565d 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -4,15 +4,13 @@ import copy from functools import partial -import warnings import numpy as np -import tempfile -from pathlib import Path from jwst.lib.pipe_utils import match_nans_and_flags from jwst.resample.resample import compute_image_pixel_area from jwst.resample.resample_utils import build_driz_weight from stcal.outlier_detection.utils import compute_weight_threshold, gwcs_blot, flag_crs, flag_resampled_crs +from stcal.outlier_detection.median import MedianComputer, nanmedian3D from stdatamodels.jwst import datamodels from . import _fileio @@ -23,27 +21,6 @@ DO_NOT_USE = datamodels.dqflags.pixel['DO_NOT_USE'] OUTLIER = datamodels.dqflags.pixel['OUTLIER'] -_ONE_MB = 1 << 20 - - -def nanmedian3D(cube, overwrite_input=True): - """Compute the median of a cube ignoring warnings and with memory efficiency. - np.nanmedian always uses at least 64-bit precision internally, and this is too - memory-intensive. Instead, loop over the median calculation to avoid the - memory usage of the internal upcasting and temporary array allocation. - The additional runtime of this loop is indistinguishable from zero, - but this loop cuts overall step memory usage roughly in half for at least one - test association. - """ - with warnings.catch_warnings(): - warnings.filterwarnings(action="ignore", - message="All-NaN slice encountered", - category=RuntimeWarning) - output_arr = np.empty(cube.shape[1:], dtype=np.float32) - for i in range(output_arr.shape[0]): - # this for loop looks silly, but see docstring above - np.nanmedian(cube[:,i,:], axis=0, overwrite_input=overwrite_input, out=output_arr[i,:]) - return output_arr def create_cube_median(cube_model, maskpt): @@ -115,14 +92,14 @@ def median_without_resampling(input_models, if i == 0: input_shape = (ngroups,)+drizzled_model.data.shape dtype = drizzled_model.data.dtype - median_computer = _make_median_computer(input_shape, in_memory, buffer_size, dtype) + computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan - _append_to_median_computer(median_computer, i, drizzled_model.data, in_memory) + computer.append(drizzled_model.data, i) # Perform median combination on set of drizzled mosaics - median_data = _evaluate_median_computer(median_computer, in_memory) + median_data = computer.evaluate() if save_intermediate_results: # Save median model to fits @@ -187,15 +164,14 @@ def median_with_resampling(input_models, if i == 0: input_shape = (ngroups,)+drizzled_model.data.shape dtype = drizzled_model.data.dtype - median_computer = _make_median_computer(input_shape, in_memory, buffer_size, dtype) + computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan - _append_to_median_computer(median_computer, i, drizzled_model.data, in_memory) - + computer.append(drizzled_model.data, i) # Perform median combination on set of drizzled mosaics - median_data = _evaluate_median_computer(median_computer, in_memory) + median_data = computer.evaluate() if save_intermediate_results: # Save median model to fits @@ -210,240 +186,6 @@ def median_with_resampling(input_models, return median_data, median_wcs -def _make_median_computer(full_shape, in_memory, buffer_size, dtype): - - if in_memory: - # allocate memory for data arrays that go into median - median_computer = np.empty(full_shape, dtype=np.float32) - else: - # set up temporary storage for data arrays that go into median - median_computer = OnDiskMedian(full_shape, - dtype=dtype, - buffer_size=buffer_size) - return median_computer - - -def _append_to_median_computer(median_computer, idx, data, in_memory): - if in_memory: - # populate pre-allocated memory with the drizzled data - median_computer[idx] = data - else: - # distribute the drizzled data into the temporary storage - median_computer.add_image(data) - - -def _evaluate_median_computer(median_computer, in_memory): - if in_memory: - median_data = nanmedian3D(median_computer) - del median_computer - else: - median_data = median_computer.compute_median() - median_computer.cleanup() - return median_data - - -class DiskAppendableArray: - """ - Creates a temporary file to which to append data, in order to perform - timewise operations on a stack of input images without holding all of them - in memory. - - This class is purpose-built for the median computation during outlier detection - and is not very flexible. It is assumed that each data array passed to `append` - represents the same spatial segment of the full dataset. It is also assumed that - each data array passed to `append` represents only a single instant in time; - the append operation will stack them along a new axis. - - The `read` operation is only capable of loading the full array back into memory. - When working with large datasets that do not fit in memory, the - required workflow is to create many DiskAppendableArray objects, each holding - a small spatial segment of the full dataset. - """ - - def __init__(self, slice_shape, dtype, filename): - """ - Parameters - ---------- - slice_shape : tuple - The shape of the spatial section of input data to be appended to the array. - - dtype : str - The data type of the array. Must be a valid numpy array datatype. - - filename : str - The full file path in which to store the array - """ - if len(slice_shape) != 2: - raise ValueError(f"Invalid slice_shape {slice_shape}. Only 2-D arrays are supported.") - self._filename = Path(filename) - with open(filename, "wb") as f: # noqa: F841 - pass - self._slice_shape = slice_shape - self._dtype = np.dtype(dtype) - self._append_count = 0 - - - @property - def shape(self): - return (self._append_count,) + self._slice_shape - - - def append(self, data): - """Add a new slice to the temporary file""" - if data.shape != self._slice_shape: - raise ValueError(f"Data shape {data.shape} does not match slice shape {self._slice_shape}") - if data.dtype != self._dtype: - raise ValueError(f"Data dtype {data.dtype} does not match array dtype {self._dtype}") - with open(self._filename, "ab") as f: - data.tofile(f, sep="") - self._append_count += 1 - - - def read(self): - """Read the 3-D array into memory""" - shp = (self._append_count,) + self._slice_shape - with open(self._filename, "rb") as f: - output = np.fromfile(f, dtype=self._dtype).reshape(shp) - return output - - -class OnDiskMedian: - - def __init__(self, shape, dtype='float32', tempdir="", buffer_size=None): - """ - Set up temporary files to perform operations on a stack of 2-D input arrays - along the stacking axis (e.g., a time axis) without - holding all of them in memory. Currently the only supported operation - is the median. - - Parameters - ---------- - shape: tuple - The shape of the entire input, (n_images, imrows, imcols). - - dtype : str - The data type of the input data. - - tempdir : str - The parent directory in which to create the temporary directory, - which itself holds all the DiskAppendableArray tempfiles. - Default is the current working directory. - - buffer_size : int, optional - The buffer size, units of bytes. - Default is the size of one input image. - """ - if len(shape) != 3: - raise ValueError(f"Invalid input shape {shape}; only three-dimensional data are supported.") - self._expected_nframes = shape[0] - self.frame_shape = shape[1:] - self.dtype = np.dtype(dtype) - self.itemsize = self.dtype.itemsize - self._temp_dir = tempfile.TemporaryDirectory(dir=tempdir) - self._temp_path = Path(self._temp_dir.name) - - # figure out number of sections and rows per section that are needed - self.nsections, self.section_nrows = self._get_buffer_indices(buffer_size=buffer_size) - self.slice_shape = (self.section_nrows, shape[2]) - self._n_adds = 0 - - # instantiate a temporary DiskAppendableArray for each section - self._temp_arrays = self._temparray_setup(dtype) - - - def _get_buffer_indices(self, buffer_size=None): - """ - Parameters - ---------- - buffer_size : int, optional - The buffer size for the median computation, units of bytes. - - Returns - ------- - nsections : int - The number of sections to divide the input data into. - - section_nrows : int - The number of rows in each section (except the last one). - """ - imrows, imcols = self.frame_shape - if buffer_size is None: - buffer_size = imrows * imcols * self.itemsize - per_model_buffer_size = buffer_size / self._expected_nframes - min_buffer_size = imcols * self.itemsize - section_nrows = min(imrows, int(per_model_buffer_size // min_buffer_size)) - - if section_nrows <= 0: - buffer_size = min_buffer_size * self._expected_nframes - log.warning("Buffer size is too small to hold a single row." - f"Increasing buffer size to {buffer_size / _ONE_MB}MB") - section_nrows = 1 - self.buffer_size = buffer_size - - nsections = int(np.ceil(imrows / section_nrows)) - log.info(f"Computing median over {self._expected_nframes} groups in {nsections} " - f"sections with total memory buffer {buffer_size / _ONE_MB} MB") - return nsections, section_nrows - - - def _temparray_setup(self, dtype): - """Set up temp file handlers for each spatial section""" - temp_arrays = [] - for i in range(0, self.nsections): - shp = self.slice_shape - if i == self.nsections - 1: - # last section has whatever shape is left over - shp = (self.frame_shape[0] - (self.nsections-1) * self.section_nrows, self.frame_shape[1]) - arr = DiskAppendableArray(shp, dtype, self._temp_path / f"{i}.bin") - temp_arrays.append(arr) - return temp_arrays - - - def add_image(self, data): - """Split resampled model data into spatial sections and write to disk.""" - if self._n_adds >= self.nsections: - raise IndexError(f"Too many calls to add_image. Expected at most {self.nsections} input models.") - self._validate_data(data) - self._n_adds += 1 - for i in range(self.nsections): - row1 = i * self.section_nrows - row2 = min(row1 + self.section_nrows, self.frame_shape[0]) - arr = self._temp_arrays[i] - arr.append(data[row1:row2]) - - - def _validate_data(self, data): - if data.shape != self.frame_shape: - raise ValueError(f"Data shape {data.shape} does not match expected shape {self.frame_shape}") - if data.dtype != self.dtype: - raise ValueError(f"Data dtype {data.dtype} does not match expected dtype {self.dtype}") - - - def cleanup(self): - """Remove the temporary files and directory when finished""" - self._temp_dir.cleanup() - return - - - def compute_median(self): - """Read spatial sections from disk and compute the median across groups - (median over number of exposures on a per-pixel basis)""" - row_indices = [(i * self.section_nrows, min((i+1) * self.section_nrows, self.frame_shape[0])) - for i in range(self.nsections)] - - output_rows = row_indices[-1][1] - output_cols = self._temp_arrays[0].shape[2] - median_image = np.full((output_rows, output_cols), np.nan, dtype=self.dtype) - - for i, disk_arr in enumerate(self._temp_arrays): - row1, row2 = row_indices[i] - arr = disk_arr.read() - median_image[row1:row2] = nanmedian3D(arr) - del arr, disk_arr - - return median_image - - def flag_crs_in_models( input_models, median_data, diff --git a/pyproject.toml b/pyproject.toml index def298ae11..c73a69fef9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ dependencies = [ "scikit-image>=0.19", "scipy>=1.9.3", "spherical-geometry>=1.2.22", - "stcal>=1.9.0,<1.10.0", + "stcal @ git+https://github.com/spacetelescope/stcal.git@main", "stdatamodels>=2.1.0,<2.2.0", "stpipe>=0.7.0,<0.8.0", "stsci.imagestats>=1.6.3", From a5d996babc27b90cb5b8d2106c01daa33606c8d6 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 1 Oct 2024 18:49:34 -0400 Subject: [PATCH 499/687] changes entry --- changes/8847 | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8847 diff --git a/changes/8847 b/changes/8847 new file mode 100644 index 0000000000..05eee27ee2 --- /dev/null +++ b/changes/8847 @@ -0,0 +1 @@ +include resample and extract_1d into MOS background pipeline From 4f09856d60a846b17874d3af496c9ae67fd7c1b0 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Wed, 2 Oct 2024 09:32:42 -0400 Subject: [PATCH 500/687] JP-3305: Clean up bad mosaic associations (#8843) --- changes/8843.associations.rst | 1 + jwst/associations/lib/rules_level3_base.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 changes/8843.associations.rst diff --git a/changes/8843.associations.rst b/changes/8843.associations.rst new file mode 100644 index 0000000000..e6d2334960 --- /dev/null +++ b/changes/8843.associations.rst @@ -0,0 +1 @@ +Add mosaic association candidates to list of level three candidate types requiring members from more than one observation \ No newline at end of file diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index b072f6cbea..a1da4f135b 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -83,7 +83,7 @@ LEVEL2B_EXPTYPES.extend(SPEC2_SCIENCE_EXP_TYPES) # Association Candidates that should have more than one observations -MULTI_OBS_AC_TYPES = ['group', 'background'] +MULTI_OBS_AC_TYPES = ['group', 'background', 'mosaic'] class DMS_Level3_Base(DMSBaseMixin, Association): From c21b9c2dfb922563b0e90a44f9b5042229743c81 Mon Sep 17 00:00:00 2001 From: Maria Date: Wed, 2 Oct 2024 10:17:46 -0400 Subject: [PATCH 501/687] Delete changes/8847 mistake --- changes/8847 | 1 - 1 file changed, 1 deletion(-) delete mode 100644 changes/8847 diff --git a/changes/8847 b/changes/8847 deleted file mode 100644 index 05eee27ee2..0000000000 --- a/changes/8847 +++ /dev/null @@ -1 +0,0 @@ -include resample and extract_1d into MOS background pipeline From a4cbdb39ccededd9cf59ad355bb865a7b2e6e76c Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Wed, 2 Oct 2024 10:20:03 -0400 Subject: [PATCH 502/687] adding changes --- changes/8847.refpix.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8847.refpix.rst diff --git a/changes/8847.refpix.rst b/changes/8847.refpix.rst new file mode 100644 index 0000000000..44b6fe0d39 --- /dev/null +++ b/changes/8847.refpix.rst @@ -0,0 +1 @@ +Include resample and extract_1d into MOS background pipeline From 4a33784119f32522ffe4432f4b1c390acd2f2c9e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 2 Oct 2024 14:13:31 -0400 Subject: [PATCH 503/687] Fix intermediate issues with non-resampled outlier methods --- .../tests/test_outlier_detection.py | 43 +++++++++++++------ jwst/outlier_detection/utils.py | 14 +++--- 2 files changed, 35 insertions(+), 22 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 3155824fc8..de42b4fd2a 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -203,6 +203,7 @@ def we_three_sci(): def test_outlier_step_no_outliers(we_three_sci, do_resample, tmp_cwd): """Test whole step, no outliers""" container = ModelContainer(list(we_three_sci)) + container[0].var_rnoise[10, 10] = 1E9 pristine = ModelContainer([m.copy() for m in container]) OutlierDetectionStep.call(container, in_memory=True, resample_data=do_resample) @@ -261,7 +262,8 @@ def test_outlier_step_base(we_three_sci, tmp_cwd): assert len(median_files) != 0 -def test_outlier_step_spec(tmp_cwd, tmp_path): +@pytest.mark.parametrize('resample', [True, False]) +def test_outlier_step_spec(tmp_cwd, tmp_path, resample): """Test outlier step for spec data including saving intermediate results.""" output_dir = tmp_path / 'output' output_dir.mkdir(exist_ok=True) @@ -274,18 +276,25 @@ def test_outlier_step_spec(tmp_cwd, tmp_path): # Make it an exposure type outlier detection expects miri_cal.meta.exposure.type = "MIR_LRS-FIXEDSLIT" - # Make a couple copies, give them unique exposure numbers and filename - container = ModelContainer([miri_cal, miri_cal.copy(), miri_cal.copy()]) - for i, model in enumerate(container): - model.meta.filename = f'test_{i}_cal.fits' + def make_input(): + # Make a couple copies, give them unique exposure numbers and filename + container = ModelContainer([miri_cal.copy(), miri_cal.copy(), miri_cal.copy()]) + for i, model in enumerate(container): + model.meta.filename = f'test_{i}_cal.fits' - # Drop a CR on the science array in the first image - container[0].data[209, 37] += 1 + # Drop a CR on the science array in the first image + container[0].data[209, 37] += 1 + + return container # Verify that intermediate files are removed when not saved - # (s2d files are expected, i2d files are not, but we'll check - # for them to make sure the imaging extension didn't creep back in) - OutlierDetectionStep.call(container, output_dir=output_dir, save_results=True) + # If resampling, s2d files are expected, i2d files are not, + # but we'll check for them to make sure the imaging extension + # didn't creep back in. + container = make_input() + OutlierDetectionStep.call(container, + output_dir=output_dir, save_results=True, + resample_data=resample) for dirname in [output_dir, tmp_cwd]: result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) @@ -306,9 +315,10 @@ def test_outlier_step_spec(tmp_cwd, tmp_path): assert len(result_files) == 0 # Call again, but save intermediate to the output path + container = make_input() result = OutlierDetectionStep.call( container, save_results=True, save_intermediate_results=True, - output_dir=output_dir + output_dir=output_dir, resample_data=resample ) # Make sure nothing changed in SCI array @@ -331,8 +341,13 @@ def test_outlier_step_spec(tmp_cwd, tmp_path): assert len(result_files) == len(container) # s2d, median, and blot files are written to the output directory - assert len(s2d_files) == len(container) - assert len(blot_files) == len(container) + if resample: + assert len(s2d_files) == len(container) + assert len(blot_files) == len(container) + else: + assert len(s2d_files) == 0 + assert len(blot_files) == 0 + assert len(median_files) == 1 # i2d files not written @@ -674,4 +689,4 @@ def make_resamp(input_models): asn_id="test", allowed_memory=None, ) - return resamp \ No newline at end of file + return resamp diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index 1563c2565d..a552087687 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -78,16 +78,14 @@ def median_without_resampling(input_models, with input_models: for i in range(len(input_models)): - drizzled_model = input_models.borrow(i) + input_model = input_models.borrow(i) + drizzled_model = copy.deepcopy(input_model) + input_models.shelve(input_model, i, modify=False) + drizzled_model.wht = build_driz_weight(drizzled_model, - weight_type=weight_type, - good_bits=good_bits) + weight_type=weight_type, + good_bits=good_bits) median_wcs = copy.deepcopy(drizzled_model.meta.wcs) - input_models.shelve(drizzled_model, i, modify=True) - - if save_intermediate_results: - # write the drizzled model to file - _fileio.save_drizzled(drizzled_model, make_output_path) if i == 0: input_shape = (ngroups,)+drizzled_model.data.shape From 72613a3b0a1d7b1e339c83683744e6c3a4b3b0ba Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 2 Oct 2024 14:24:42 -0400 Subject: [PATCH 504/687] Add change note --- changes/8853.outlier_detection.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8853.outlier_detection.rst diff --git a/changes/8853.outlier_detection.rst b/changes/8853.outlier_detection.rst new file mode 100644 index 0000000000..abb775f6c8 --- /dev/null +++ b/changes/8853.outlier_detection.rst @@ -0,0 +1 @@ +Avoid modifying input and saving duplicate files when resample_data=False. From 0a1cd93dc1c9af9c800a16be412cbd58fde3acd6 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 2 Oct 2024 14:31:25 -0400 Subject: [PATCH 505/687] Copy only the needed data --- jwst/outlier_detection/utils.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index a552087687..f8cea4995b 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -78,23 +78,22 @@ def median_without_resampling(input_models, with input_models: for i in range(len(input_models)): - input_model = input_models.borrow(i) - drizzled_model = copy.deepcopy(input_model) - input_models.shelve(input_model, i, modify=False) - - drizzled_model.wht = build_driz_weight(drizzled_model, - weight_type=weight_type, - good_bits=good_bits) + drizzled_model = input_models.borrow(i) + drizzled_data = drizzled_model.data.copy() + weight = build_driz_weight(drizzled_model, + weight_type=weight_type, + good_bits=good_bits) median_wcs = copy.deepcopy(drizzled_model.meta.wcs) + input_models.shelve(drizzled_model, i, modify=False) if i == 0: - input_shape = (ngroups,)+drizzled_model.data.shape - dtype = drizzled_model.data.dtype + input_shape = (ngroups,) + drizzled_data.shape + dtype = drizzled_data.dtype computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) - weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) - drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan - computer.append(drizzled_model.data, i) + weight_threshold = compute_weight_threshold(weight, maskpt) + drizzled_data[weight < weight_threshold] = np.nan + computer.append(drizzled_data, i) # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() From 290b2263dbfb8b02b0f8b9d69c598849b80d80bc Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 2 Oct 2024 14:35:03 -0400 Subject: [PATCH 506/687] Only copy the wcs for the first model --- jwst/outlier_detection/utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index f8cea4995b..ecebe8fd2b 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -83,10 +83,8 @@ def median_without_resampling(input_models, weight = build_driz_weight(drizzled_model, weight_type=weight_type, good_bits=good_bits) - median_wcs = copy.deepcopy(drizzled_model.meta.wcs) - input_models.shelve(drizzled_model, i, modify=False) - if i == 0: + median_wcs = copy.deepcopy(drizzled_model.meta.wcs) input_shape = (ngroups,) + drizzled_data.shape dtype = drizzled_data.dtype computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) @@ -95,6 +93,8 @@ def median_without_resampling(input_models, drizzled_data[weight < weight_threshold] = np.nan computer.append(drizzled_data, i) + input_models.shelve(drizzled_model, i, modify=False) + # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() @@ -151,7 +151,6 @@ def median_with_resampling(input_models, with input_models: for i, indices in enumerate(indices_by_group): - median_wcs = resamp.output_wcs drizzled_model = resamp.resample_group(input_models, indices) if save_intermediate_results: @@ -159,6 +158,7 @@ def median_with_resampling(input_models, _fileio.save_drizzled(drizzled_model, make_output_path) if i == 0: + median_wcs = resamp.output_wcs input_shape = (ngroups,)+drizzled_model.data.shape dtype = drizzled_model.data.dtype computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) From fb3c2cd092e676609da4ee7a14b45137ed898048 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 2 Oct 2024 15:03:05 -0400 Subject: [PATCH 507/687] Refactor test to avoid repeated step call --- .../tests/test_outlier_detection.py | 94 ++++++++----------- 1 file changed, 38 insertions(+), 56 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index de42b4fd2a..4c4d3cdfb3 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -263,7 +263,8 @@ def test_outlier_step_base(we_three_sci, tmp_cwd): @pytest.mark.parametrize('resample', [True, False]) -def test_outlier_step_spec(tmp_cwd, tmp_path, resample): +@pytest.mark.parametrize('save_intermediate', [True, False]) +def test_outlier_step_spec(tmp_cwd, tmp_path, resample, save_intermediate): """Test outlier step for spec data including saving intermediate results.""" output_dir = tmp_path / 'output' output_dir.mkdir(exist_ok=True) @@ -276,59 +277,34 @@ def test_outlier_step_spec(tmp_cwd, tmp_path, resample): # Make it an exposure type outlier detection expects miri_cal.meta.exposure.type = "MIR_LRS-FIXEDSLIT" - def make_input(): - # Make a couple copies, give them unique exposure numbers and filename - container = ModelContainer([miri_cal.copy(), miri_cal.copy(), miri_cal.copy()]) - for i, model in enumerate(container): - model.meta.filename = f'test_{i}_cal.fits' - - # Drop a CR on the science array in the first image - container[0].data[209, 37] += 1 - - return container - - # Verify that intermediate files are removed when not saved - # If resampling, s2d files are expected, i2d files are not, - # but we'll check for them to make sure the imaging extension - # didn't creep back in. - container = make_input() - OutlierDetectionStep.call(container, - output_dir=output_dir, save_results=True, - resample_data=resample) - for dirname in [output_dir, tmp_cwd]: - result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) - i2d_files = glob(os.path.join(dirname, '*i2d*.fits')) - s2d_files = glob(os.path.join(dirname, '*outlier_s2d.fits')) - median_files = glob(os.path.join(dirname, '*median.fits')) - blot_files = glob(os.path.join(dirname, '*blot.fits')) + # Make a couple copies, give them unique exposure numbers and filename + container = ModelContainer([miri_cal.copy(), miri_cal.copy(), miri_cal.copy()]) + for i, model in enumerate(container): + model.meta.filename = f'test_{i}_cal.fits' - # intermediate files are removed - assert len(i2d_files) == 0 - assert len(s2d_files) == 0 - assert len(median_files) == 0 - assert len(blot_files) == 0 + # Drop a CR on the science array in the first image + container[0].data[209, 37] += 1 - # result files are written to the output directory - if dirname == output_dir: - assert len(result_files) == len(container) - else: - assert len(result_files) == 0 - - # Call again, but save intermediate to the output path - container = make_input() + # Call outlier detection result = OutlierDetectionStep.call( - container, save_results=True, save_intermediate_results=True, - output_dir=output_dir, resample_data=resample - ) + container, resample_data=resample, + output_dir=output_dir, save_results=True, + save_intermediate_results=save_intermediate) # Make sure nothing changed in SCI array - for image, corrected in zip(container, result): - np.testing.assert_allclose(image.data, corrected.data) + for image in result: + nn = ~np.isnan(image.data) + np.testing.assert_allclose(image.data[nn], miri_cal.data[nn]) # Verify CR is flagged + assert np.isnan(result[0].data[209, 37]) assert result[0].dq[209, 37] == OUTLIER_DO_NOT_USE # Verify that intermediate files are saved at the specified location + if save_intermediate: + expected_intermediate = len(container) + else: + expected_intermediate = 0 for dirname in [output_dir, tmp_cwd]: all_files = glob(os.path.join(dirname, '*.fits')) result_files = glob(os.path.join(dirname, '*outlierdetectionstep.fits')) @@ -337,29 +313,35 @@ def make_input(): median_files = glob(os.path.join(dirname, '*median.fits')) blot_files = glob(os.path.join(dirname, '*blot.fits')) if dirname == output_dir: - # result files are written to the output directory + # Result files are always written to the output directory assert len(result_files) == len(container) - # s2d, median, and blot files are written to the output directory + # s2d and blot files are written to the output directory + # if save_intermediate is True and resampling is set if resample: - assert len(s2d_files) == len(container) - assert len(blot_files) == len(container) + assert len(s2d_files) == expected_intermediate + assert len(blot_files) == expected_intermediate else: assert len(s2d_files) == 0 assert len(blot_files) == 0 - assert len(median_files) == 1 + # Only one median file is saved if save_intermediate is True, + # no matter how many input files there are + if save_intermediate: + assert len(median_files) == 1 + else: + assert len(median_files) == 0 - # i2d files not written + # i2d files are never written assert len(i2d_files) == 0 - # nothing else was written - assert len(all_files) == len(s2d_files) + \ - len(median_files) + \ - len(result_files) + \ - len(blot_files) + # Nothing else was written + assert len(all_files) == (len(s2d_files) + + len(median_files) + + len(result_files) + + len(blot_files)) else: - # nothing should be written to the current directory + # Nothing should be written to the current directory assert len(result_files) == 0 assert len(s2d_files) == 0 assert len(median_files) == 0 From b47cac816d7a7c4608238d93bda029d3654cb681 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 2 Oct 2024 14:05:39 -0700 Subject: [PATCH 508/687] Documentation and code updates clarifying the behavior of the ``in_memory`` flag (#8851) --- changes/8851.outlier_detection.rst | 1 + docs/jwst/outlier_detection/arguments.rst | 2 ++ docs/jwst/outlier_detection/outlier_detection_imaging.rst | 6 +++++- jwst/outlier_detection/imaging.py | 1 - jwst/outlier_detection/outlier_detection_step.py | 2 +- jwst/outlier_detection/spec.py | 1 - 6 files changed, 9 insertions(+), 4 deletions(-) create mode 100644 changes/8851.outlier_detection.rst diff --git a/changes/8851.outlier_detection.rst b/changes/8851.outlier_detection.rst new file mode 100644 index 0000000000..da2e49986d --- /dev/null +++ b/changes/8851.outlier_detection.rst @@ -0,0 +1 @@ +Update documentation to clarify the interaction between pipeline-level and step-level `--in_memory` flags. \ No newline at end of file diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index b2acf485d4..08ce00219a 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -89,6 +89,8 @@ that control the behavior of the processing: Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when needed and all intermediate files are stored on disk, rather than in memory. + This flag is superseded by the pipeline-level ``--in-memory`` flag, and thus + has no effect when running the full level 3 pipeline. Step Arguments for IFU data =========================== diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index a6a6ec78e7..5bee8c30ef 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -171,6 +171,10 @@ during processing includes: Those sections are then read in one at a time to compute the median image. These changes result in a minimum amount of memory usage during processing at the obvious -expense of reading and writing the products from disk. +expense of reading and writing the products from disk. Note that if a ModelLibrary object +is input to the step, the memory behavior of the step is read from the ``on_disk`` status +of the ModelLibrary object, and the ``in_memory`` parameter of the step is ignored. +When running ``calwebb_image3``, the ``in_memory`` flag should therefore be set at the pipeline level, +e.g., ``strun calwebb_image3 asn.json --in-memory=True``; the step-specific flag will be ignored. .. automodapi:: jwst.outlier_detection.imaging diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index 0dacd5ec43..c964f3e9ad 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -65,7 +65,6 @@ def detect_outliers( kernel=kernel, fillval=fillval, good_bits=good_bits, - in_memory=in_memory, allowed_memory=allowed_memory, ) median_data, median_wcs = median_with_resampling(input_models, diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 5ecc90a231..828bf47323 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -67,7 +67,7 @@ class OutlierDetectionStep(Step): good_bits = string(default="~DO_NOT_USE") # DQ flags to allow search_output_file = boolean(default=False) allowed_memory = float(default=None) # Fraction of memory to use for the combined image - in_memory = boolean(default=False) + in_memory = boolean(default=False) # ignored if run within the pipeline; set at pipeline level instead """ def process(self, input_data): diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index ad94551976..a0d6f56d2c 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -66,7 +66,6 @@ def detect_outliers( kernel=kernel, fillval=fillval, good_bits=good_bits, - in_memory=in_memory, ) median_data, median_wcs = median_with_resampling( From f548375d205f5a770daccb486c50dfd77a7d0c45 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 4 Oct 2024 19:29:02 -0400 Subject: [PATCH 509/687] adding changes for JP-3748 --- changes/8847.refpix.rst | 2 +- jwst/master_background/master_background_mos_step.py | 2 ++ jwst/master_background/nirspec_utils.py | 6 ++++-- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/changes/8847.refpix.rst b/changes/8847.refpix.rst index 44b6fe0d39..f394cd8e5d 100644 --- a/changes/8847.refpix.rst +++ b/changes/8847.refpix.rst @@ -1 +1 @@ -Include resample and extract_1d into MOS background pipeline +Include resample, pixel_replace, and extract_1d into MOS background pipeline diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 73af6bee9b..7514532a4e 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -9,6 +9,7 @@ from ..flatfield import flat_field_step from ..pathloss import pathloss_step from ..photom import photom_step +from ..pixel_replace import pixel_replace_step from ..resample import resample_spec_step from ..extract_1d import extract_1d_step from ..stpipe import Pipeline @@ -62,6 +63,7 @@ class MasterBackgroundMosStep(Pipeline): 'pathloss': pathloss_step.PathLossStep, 'barshadow': barshadow_step.BarShadowStep, 'photom': photom_step.PhotomStep, + 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'resample_spec': resample_spec_step.ResampleSpecStep, 'extract_1d': extract_1d_step.Extract1dStep, } diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 153bf9f504..1c9cd33fa4 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -91,6 +91,7 @@ def create_background_from_multislit(input_model): master_bkg: `~jwst.datamodels.CombinedSpecModel` The 1D master background spectrum created from the inputs. """ + from ..pixel_replace import pixel_replace_step from ..resample import resample_spec_step from ..extract_1d import extract_1d_step from ..combine_1d.combine1d import combine_1d_spectra @@ -113,8 +114,9 @@ def create_background_from_multislit(input_model): bkg_model.slits.extend(slits) # Apply resample_spec and extract_1d to all background slitlets - log.info('Applying resampling and 1D extraction to background slits') - resamp = resample_spec_step.ResampleSpecStep.call(bkg_model) + log.info('Applying pixel_replace, resampling and 1D extraction to background slits') + pixrepl = pixel_replace_step.PixelReplaceStep.call(bkg_model) + resamp = resample_spec_step.ResampleSpecStep.call(pixrepl) x1d = extract_1d_step.Extract1dStep.call(resamp) # Call combine_1d to combine the 1D background spectra From f78d05ab0d88b15fbe77cab7dfaaa4a3277a412b Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 4 Oct 2024 19:34:06 -0400 Subject: [PATCH 510/687] fixing comment and adding delete array statement --- jwst/master_background/nirspec_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 1c9cd33fa4..af48c81603 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -113,7 +113,7 @@ def create_background_from_multislit(input_model): bkg_model.slits.extend(slits) - # Apply resample_spec and extract_1d to all background slitlets + # Apply pixel_replace, resample_spec and extract_1d to all background slitlets log.info('Applying pixel_replace, resampling and 1D extraction to background slits') pixrepl = pixel_replace_step.PixelReplaceStep.call(bkg_model) resamp = resample_spec_step.ResampleSpecStep.call(pixrepl) @@ -124,6 +124,7 @@ def create_background_from_multislit(input_model): master_bkg = combine_1d_spectra(x1d, exptime_key='exposure_time') del bkg_model + del pixrepl del resamp del x1d From 496dd4f570538158a8f27ea36ca6abcb53b8a63c Mon Sep 17 00:00:00 2001 From: David Law Date: Sun, 6 Oct 2024 14:48:46 -0400 Subject: [PATCH 511/687] Add median filtering to background spectrum --- jwst/master_background/master_background_step.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 1ea83855ee..f88b76d2e8 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -2,6 +2,7 @@ import numpy as np from stdatamodels.properties import merge_tree from stdatamodels.jwst import datamodels +from scipy.signal import medfilt from jwst.datamodels import ModelContainer from jwst.stpipe import record_step_status @@ -25,6 +26,7 @@ class MasterBackgroundStep(Step): save_background = boolean(default=False) # Save computed master background force_subtract = boolean(default=False) # Force subtracting master background output_use_model = boolean(default=True) + median_kernel = integer(default=1) """ def process(self, input): @@ -51,6 +53,10 @@ def process(self, input): has already been applied and, if so, the master background step is skipped. If set to True, the step logic is bypassed and the master background is subtracted. + median_kernel : integer, optional + Optional user-supplied kernel with which to moving-median boxcar filter the master background + spectrum. Must be an odd integer. + Returns ------- result : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`, `~jwst.datamodels.ModelContainer` @@ -140,6 +146,12 @@ def process(self, input): exptime_key='exposure_time', ) + # If requested, apply a moving-median boxcar filter to the master background spectrum + if (self.median_kernel > 1): + self.log.info('Applying moving-median boxcar of width %i',self.median_kernel) + master_background.spec[0].spec_table['surf_bright'] = medfilt( + master_background.spec[0].spec_table['surf_bright'], kernel_size=[self.median_kernel]) + background_data.close() result = ModelContainer() From c2720b8890885f86857114df90837b9fb4f9c690 Mon Sep 17 00:00:00 2001 From: Maria Date: Mon, 7 Oct 2024 12:21:38 -0400 Subject: [PATCH 512/687] JP-3770: emicorr memory and run time improvement (#8849) --- changes/8849.emicorr.rst | 1 + jwst/emicorr/emicorr.py | 16 +++++++--------- jwst/emicorr/tests/test_emicorr.py | 4 ++-- 3 files changed, 10 insertions(+), 11 deletions(-) create mode 100644 changes/8849.emicorr.rst diff --git a/changes/8849.emicorr.rst b/changes/8849.emicorr.rst new file mode 100644 index 0000000000..aee1210d87 --- /dev/null +++ b/changes/8849.emicorr.rst @@ -0,0 +1 @@ +Removed the interleaved noise array and do interleaving of noise and subtraction in-place to avoid creating 2 arrays of equal dimensions to data. diff --git a/jwst/emicorr/emicorr.py b/jwst/emicorr/emicorr.py index 3c714a1e7d..6386b8e76c 100644 --- a/jwst/emicorr/emicorr.py +++ b/jwst/emicorr/emicorr.py @@ -438,25 +438,23 @@ def apply_emicorr(output_model, emicorr_model, # This is the phase matched noise model to subtract from each pixel of the input image dd_noise = lut[(phaseall * period_in_pixels).astype(int)] - # Interleave (straight copy) into 4 amps - noise = np.zeros((nints, ngroups, ny, nx)) # same size as input data - noise_x = np.arange(nx4) * 4 - for k in range(4): - noise[:, :, :, noise_x + k] = dd_noise - # Safety catch; anywhere the noise value is not finite, set it to zero - noise[~np.isfinite(noise)] = 0.0 + dd_noise[~np.isfinite(dd_noise)] = 0.0 # Subtract EMI noise from the input data log.info('Subtracting EMI noise from data') - output_model.data = output_model.data - noise + + # Interleave (straight copy) into 4 amps + noise_x = np.arange(nx4) * 4 + for k in range(4): + output_model.data[..., noise_x + k] -= dd_noise # clean up del data del dd_all del times_this_int del phaseall - del noise + del dd_noise if save_intermediate_results and save_onthefly_reffile is not None: if 'FAST' in readpatt: diff --git a/jwst/emicorr/tests/test_emicorr.py b/jwst/emicorr/tests/test_emicorr.py index 412e6033e6..1dcfb19cd3 100644 --- a/jwst/emicorr/tests/test_emicorr.py +++ b/jwst/emicorr/tests/test_emicorr.py @@ -7,7 +7,7 @@ import numpy as np from jwst.emicorr import emicorr, emicorr_step -from stdatamodels.jwst.datamodels import Level1bModel, EmiModel +from stdatamodels.jwst.datamodels import RampModel, EmiModel subarray_example_case = { @@ -33,7 +33,7 @@ def mk_data_mdl(data, subarray, readpatt, detector): # create input_model - input_model = Level1bModel(data=data) + input_model = RampModel(data=data) input_model.meta.instrument.name = 'MIRI' input_model.meta.instrument.detector = detector input_model.meta.exposure.type = 'MIR_4QPM' From 303c0411b03cbfeed095029a66e69673f7d11764 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 7 Oct 2024 22:40:17 +0000 Subject: [PATCH 513/687] Bump spacetelescope/crds from 12.0.0 to 12.0.2 Bumps [spacetelescope/crds](https://github.com/spacetelescope/crds) from 12.0.0 to 12.0.2. - [Release notes](https://github.com/spacetelescope/crds/releases) - [Changelog](https://github.com/spacetelescope/crds/blob/master/CHANGES.rst) - [Commits](https://github.com/spacetelescope/crds/compare/d96060f99a7ca75969f6652b050243592f4ebaeb...94138b4501c9487535fd6b977492fc1a2c319708) --- updated-dependencies: - dependency-name: spacetelescope/crds dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/ci.yml | 2 +- .github/workflows/ci_cron.yml | 2 +- .github/workflows/tests_devdeps.yml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5df1c376c8..8d058499cb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,7 +44,7 @@ jobs: envs: | - linux: check-dependencies latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 + uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest diff --git a/.github/workflows/ci_cron.yml b/.github/workflows/ci_cron.yml index be1d323b31..d116e6e938 100644 --- a/.github/workflows/ci_cron.yml +++ b/.github/workflows/ci_cron.yml @@ -23,7 +23,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 + uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index 72b7ad9fee..6beefbdaa6 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -32,7 +32,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@d96060f99a7ca75969f6652b050243592f4ebaeb # 12.0.0 + uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest From 3acd82a266dcca74d085884e2721811ca82a31a6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 7 Oct 2024 22:40:21 +0000 Subject: [PATCH 514/687] Bump actions/checkout from 4.2.0 to 4.2.1 Bumps [actions/checkout](https://github.com/actions/checkout) from 4.2.0 to 4.2.1. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/d632683dd7b4114ad314bca15554477dd762a938...eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/build.yml | 2 +- .github/workflows/changelog.yml | 4 ++-- .github/workflows/ci.yml | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 61fb8cf6ef..e563348d7b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -16,7 +16,7 @@ jobs: name: check that `requirements-sdp.txt` is populated runs-on: ubuntu-latest steps: - - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 + - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 - run: grep -v '^ *#' requirements-sdp.txt build: needs: [ check ] diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index 1b933ff5ad..dd09228b0d 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -21,7 +21,7 @@ jobs: - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 with: python-version: 3 - - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 + - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 with: fetch-depth: 0 - run: pip install . @@ -32,7 +32,7 @@ jobs: if: ${{ !contains(github.event.pull_request.labels.*.name, 'allow-manual-changelog-edit') }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 + - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 with: fetch-depth: 0 - name: prevent direct changes to `CHANGES.rst` diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5df1c376c8..a9e318b415 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,7 +34,7 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@d632683dd7b4114ad314bca15554477dd762a938 # v4.2.0 + - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 check: From bcdc0a7dec493448b530a5cf703ff49223c36cbd Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 8 Oct 2024 10:51:20 -0400 Subject: [PATCH 515/687] Update NIRCam image moving target test to in-flight data --- jwst/regtest/test_nircam_mtimage.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/jwst/regtest/test_nircam_mtimage.py b/jwst/regtest/test_nircam_mtimage.py index 21b5013042..919d1620b1 100644 --- a/jwst/regtest/test_nircam_mtimage.py +++ b/jwst/regtest/test_nircam_mtimage.py @@ -11,11 +11,11 @@ @pytest.mark.parametrize("in_memory", [True, False]) def test_nircam_image_moving_target_i2d(rtdata, fitsdiff_default_kwargs, in_memory): """Test resampled i2d of moving target exposures for NIRCam imaging""" - rtdata.get_asn("nircam/image/mt_asn.json") - rtdata.output = "mt_assoc_i2d.fits" + rtdata.get_asn("nircam/image/jw01252-o005_20240905t222322_image3_00001_asn.json") + rtdata.output = "jw01252-o005_t003_nircam_clear-f277w_i2d.fits" args = ["calwebb_image3", rtdata.input, "--in_memory=" + str(in_memory)] Step.from_cmdline(args) - rtdata.get_truth("truth/test_nircam_mtimage/mt_assoc_i2d.fits") + rtdata.get_truth("truth/test_nircam_mtimage/jw01252-o005_t003_nircam_clear-f277w_i2d.fits") fitsdiff_default_kwargs["atol"] = 1e-5 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From e18462d8b26ee27d355275458123cf083428c57e Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 8 Oct 2024 13:13:49 -0400 Subject: [PATCH 516/687] Expand tolerance for i2d product --- jwst/regtest/test_nircam_mtimage.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/regtest/test_nircam_mtimage.py b/jwst/regtest/test_nircam_mtimage.py index 919d1620b1..472dd37f1e 100644 --- a/jwst/regtest/test_nircam_mtimage.py +++ b/jwst/regtest/test_nircam_mtimage.py @@ -17,7 +17,8 @@ def test_nircam_image_moving_target_i2d(rtdata, fitsdiff_default_kwargs, in_memo Step.from_cmdline(args) rtdata.get_truth("truth/test_nircam_mtimage/jw01252-o005_t003_nircam_clear-f277w_i2d.fits") - fitsdiff_default_kwargs["atol"] = 1e-5 + fitsdiff_default_kwargs["rtol"] = 1e-4 + fitsdiff_default_kwargs["atol"] = 2e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From bf57c2d8a3cfa3e176a9d2923518b0b3a64a41a2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 8 Oct 2024 14:20:37 -0400 Subject: [PATCH 517/687] Update NIRCam WFS regression test data --- jwst/regtest/test_nircam_wfs.py | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/jwst/regtest/test_nircam_wfs.py b/jwst/regtest/test_nircam_wfs.py index 3b4a0a6262..e0e708878a 100644 --- a/jwst/regtest/test_nircam_wfs.py +++ b/jwst/regtest/test_nircam_wfs.py @@ -15,16 +15,19 @@ def run_pipelines(rtdata_module): rtdata = rtdata_module - # Get the image2 ASN and members - rtdata.get_asn("nircam/wavefront/jw00632-o003_20191210t194815_wfs-image2_001_asn.json") - - # Run the calwebb_wfs-image2 pipeline - args = ["jwst.pipeline.Image2Pipeline", rtdata.input] - Step.from_cmdline(args) + # Get the rate files and run the image2 pipeline on each + rate_files = [ + "nircam/wavefront/jw02586173001_03104_00001_nrcalong_rate.fits", + "nircam/wavefront/jw02586173001_03104_00002_nrcalong_rate.fits" + ] + for rate_file in rate_files: + rtdata.get_data(rate_file) + args = ["calwebb_image2", rtdata.input] + Step.from_cmdline(args) # Get the image3 ASN file only, not the members. The members were just - # produced by the calwebb_wfs-image2 pipeline run. - rtdata.get_data("nircam/wavefront/jw00632-o003_20191210t194815_wfs-image3_001_asn.json") + # produced by the calwebb_image2 pipeline run. + rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00006_asn.json") # Run the calwebb_wfs-image3 pipeline with no refinement (default) args = ["calwebb_wfs-image3", rtdata.input] @@ -32,7 +35,7 @@ def run_pipelines(rtdata_module): # Get the second version of the image3 ASN, which has a unique output # product name. Only get the ASN file, not the members. - rtdata.get_data("nircam/wavefront/jw00632-o003_20191210t194815_wfs-image3_002_asn.json") + rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00006_asn_v2.json") # Run the calwebb_wfs-image3 pipeline with refinement args = ["calwebb_wfs-image3", rtdata.input, "--do_refine=True"] @@ -51,10 +54,10 @@ def test_nicam_wfsimage_noextras(run_pipelines): @pytest.mark.bigdata @pytest.mark.parametrize("output",[ - "jw00632003002_03105_00001_nrca4_cal.fits", - "jw00632003002_03105_00002_nrca4_cal.fits", - "jw00632-o003_t001_nircam_f212n-wlp8-nrca4_wfscmb-05.fits", - "jw00632-o003_t001_nircam_f212n-wlp8-nrca4_wfscmb-05_refine.fits"], + "jw02586173001_03104_00001_nrcalong_cal.fits", + "jw02586173001_03104_00002_nrcalong_cal.fits", + "jw02586-o173_t035_nircam_clear-f356w-nrcalong_wfscmb-04.fits", + "jw02586-o173_t035_nircam_clear-f356w-nrcalong_wfscmb-04_refine.fits"], ids=["cal1", "cal2", "wfscmb1", "wfscmb2"]) def test_nircam_wfsimage(run_pipelines, fitsdiff_default_kwargs, output): """Regression test of the calwebb_wfs-image2 and calwebb_wfs-image3 @@ -65,7 +68,7 @@ def test_nircam_wfsimage(run_pipelines, fitsdiff_default_kwargs, output): rtdata.output = output # Get the truth files - rtdata.get_truth(os.path.join("truth/nircam/test_wfs-image2and3", output)) + rtdata.get_truth(os.path.join("truth/test_nircam_wfs", output)) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From a84347b64cacb71044485c9e6f30c2216c3c1328 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 9 Oct 2024 10:58:41 -0400 Subject: [PATCH 518/687] Use nrca3 data --- jwst/regtest/test_nircam_wfs.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/jwst/regtest/test_nircam_wfs.py b/jwst/regtest/test_nircam_wfs.py index e0e708878a..2ebaadd7b7 100644 --- a/jwst/regtest/test_nircam_wfs.py +++ b/jwst/regtest/test_nircam_wfs.py @@ -17,8 +17,8 @@ def run_pipelines(rtdata_module): # Get the rate files and run the image2 pipeline on each rate_files = [ - "nircam/wavefront/jw02586173001_03104_00001_nrcalong_rate.fits", - "nircam/wavefront/jw02586173001_03104_00002_nrcalong_rate.fits" + "nircam/wavefront/jw02586173001_03104_00001_nrca3_rate.fits", + "nircam/wavefront/jw02586173001_03104_00002_nrca3_rate.fits" ] for rate_file in rate_files: rtdata.get_data(rate_file) @@ -27,7 +27,7 @@ def run_pipelines(rtdata_module): # Get the image3 ASN file only, not the members. The members were just # produced by the calwebb_image2 pipeline run. - rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00006_asn.json") + rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00008_asn.json") # Run the calwebb_wfs-image3 pipeline with no refinement (default) args = ["calwebb_wfs-image3", rtdata.input] @@ -35,7 +35,7 @@ def run_pipelines(rtdata_module): # Get the second version of the image3 ASN, which has a unique output # product name. Only get the ASN file, not the members. - rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00006_asn_v2.json") + rtdata.get_data("nircam/wavefront/jw02586-o173_20240923t165435_wfs-image3_00008_asn_v2.json") # Run the calwebb_wfs-image3 pipeline with refinement args = ["calwebb_wfs-image3", rtdata.input, "--do_refine=True"] @@ -54,10 +54,10 @@ def test_nicam_wfsimage_noextras(run_pipelines): @pytest.mark.bigdata @pytest.mark.parametrize("output",[ - "jw02586173001_03104_00001_nrcalong_cal.fits", - "jw02586173001_03104_00002_nrcalong_cal.fits", - "jw02586-o173_t035_nircam_clear-f356w-nrcalong_wfscmb-04.fits", - "jw02586-o173_t035_nircam_clear-f356w-nrcalong_wfscmb-04_refine.fits"], + "jw02586173001_03104_00001_nrca3_cal.fits", + "jw02586173001_03104_00002_nrca3_cal.fits", + "jw02586-o173_t035_nircam_f212n-wlm8-nrca3_wfscmb-04.fits", + "jw02586-o173_t035_nircam_f212n-wlm8-nrca3_wfscmb-04_refine.fits"], ids=["cal1", "cal2", "wfscmb1", "wfscmb2"]) def test_nircam_wfsimage(run_pipelines, fitsdiff_default_kwargs, output): """Regression test of the calwebb_wfs-image2 and calwebb_wfs-image3 From d6818d56dc6e9017e0551924ee7faaa505bc9f21 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 9 Oct 2024 19:55:05 -0400 Subject: [PATCH 519/687] vendorize and fix crds workflow (#8869) --- .github/workflows/ci.yml | 2 +- .github/workflows/ci_cron.yml | 2 +- .github/workflows/contexts.yml | 26 ++++++++++++++++++++++++++ .github/workflows/tests_devdeps.yml | 2 +- 4 files changed, 29 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/contexts.yml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 932ae63efe..1e6e0a2ff9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -44,7 +44,7 @@ jobs: envs: | - linux: check-dependencies latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 + uses: ./.github/workflows/contexts.yml crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest diff --git a/.github/workflows/ci_cron.yml b/.github/workflows/ci_cron.yml index d116e6e938..05146f9fb2 100644 --- a/.github/workflows/ci_cron.yml +++ b/.github/workflows/ci_cron.yml @@ -23,7 +23,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 + uses: ./.github/workflows/contexts.yml crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest diff --git a/.github/workflows/contexts.yml b/.github/workflows/contexts.yml new file mode 100644 index 0000000000..b3047bdb63 --- /dev/null +++ b/.github/workflows/contexts.yml @@ -0,0 +1,26 @@ +name: contexts + +on: + workflow_call: + outputs: + jwst: + value: ${{ jobs.contexts.outputs.jwst }} + workflow_dispatch: + +jobs: + contexts: + name: retrieve latest CRDS contexts + runs-on: ubuntu-latest + outputs: + jwst: ${{ steps.jwst_crds_context.outputs.pmap }} + steps: + - id: jwst_crds_context + env: + OBSERVATORY: jwst + CRDS_SERVER_URL: https://jwst-crds.stsci.edu + run: > + echo "pmap=$( + curl -s -X POST -d '{"jsonrpc": "1.0", "method": "get_default_context", "params": ["${{ env.OBSERVATORY }}", null], "id": 1}' ${{ env.CRDS_SERVER_URL }}/json/ --retry 8 --connect-timeout 10 | + python -c "import sys, json; print(json.load(sys.stdin)['result'])" + )" >> $GITHUB_OUTPUT + - run: if [[ ! -z "${{ steps.jwst_crds_context.outputs.pmap }}" ]]; then echo ${{ steps.jwst_crds_context.outputs.pmap }}; else exit 1; fi diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index 6beefbdaa6..e5ad86b187 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -32,7 +32,7 @@ concurrency: jobs: latest_crds_contexts: - uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708 # 12.0.2 + uses: ./.github/workflows/contexts.yml crds_context: needs: [ latest_crds_contexts ] runs-on: ubuntu-latest From eb3afde0c5736f218b4126e0e0a3cc1f5f756b4a Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 10 Oct 2024 11:35:35 -0700 Subject: [PATCH 520/687] JP-3705: Remove deprecated nlow and nhigh parameters from outlier detection step (#8870) Co-authored-by: Melanie Clarke --- changes/8870.outlier_detection.rst | 1 + docs/jwst/outlier_detection/arguments.rst | 8 -------- jwst/outlier_detection/outlier_detection_step.py | 10 ---------- 3 files changed, 1 insertion(+), 18 deletions(-) create mode 100644 changes/8870.outlier_detection.rst diff --git a/changes/8870.outlier_detection.rst b/changes/8870.outlier_detection.rst new file mode 100644 index 0000000000..53ce01221d --- /dev/null +++ b/changes/8870.outlier_detection.rst @@ -0,0 +1 @@ +Remove deprecated nlow and nhigh parameters from outlier detection step. diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index 08ce00219a..8d551122c0 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -25,14 +25,6 @@ that control the behavior of the processing: Any floating-point value, given as a string, is valid. A value of 'INDEF' will use the last zero weight flux. -``--nlow`` - Deprecated and has no effect. This parameter will be removed - in a future version. - -``--nhigh`` - Deprecated and has no effect. This parameter will be removed - in a future version. - ``--maskpt`` The percent of maximum weight to use as lower-limit for valid data; valid values go from 0.0 to 1.0. diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 828bf47323..e44afee505 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -20,14 +20,6 @@ TSO_IMAGE_MODES = ['NRC_TSIMAGE'] # missing MIR_IMAGE with TSOVIST=True, not really addable CORON_IMAGE_MODES = ['NRC_CORON', 'MIR_LYOT', 'MIR_4QPM'] -_STEP_MODES = { - 'coron': coron, - 'ifu': ifu, - 'imaging': imaging, - 'tso': tso, - 'spec': spec, -} - __all__ = ["OutlierDetectionStep"] @@ -52,8 +44,6 @@ class OutlierDetectionStep(Step): pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel fillval = string(default='INDEF') - nlow = integer(default=0) # DEPRECATED this setting has no effect and will be removed - nhigh = integer(default=0) # DEPRECATED this setting has no effect and will be removed maskpt = float(default=0.7) snr = string(default='5.0 4.0') scale = string(default='1.2 0.7') From 7669260fd093142467ad518377faefac581e29f5 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 11 Oct 2024 07:11:33 -0700 Subject: [PATCH 521/687] JP-3749: Add mypy type checking (#8852) --- .github/workflows/ci.yml | 1 + changes/8852.general.rst | 1 + jwst/__init__.py | 2 +- jwst/ami/ami_analyze.py | 2 +- jwst/ami/leastsqnrm.py | 2 +- jwst/ami/matrix_dft.py | 2 +- jwst/assign_wcs/util.py | 9 +- jwst/associations/__init__.py | 2 +- jwst/associations/association.py | 4 +- jwst/associations/association_io.py | 2 +- jwst/associations/lib/constraint.py | 2 +- jwst/associations/lib/rules_level3_base.py | 2 +- jwst/associations/tests/helpers.py | 4 +- jwst/badpix_selfcal/badpix_selfcal.py | 11 +- jwst/cube_skymatch/cube_skymatch_step.py | 2 +- jwst/datamodels/__init__.py | 2 +- jwst/dq_init/dq_initialization.py | 2 +- jwst/dq_init/tests/test_dq_init.py | 4 +- jwst/extract_1d/apply_apcorr.py | 37 ++- jwst/extract_1d/extract.py | 297 ++++++++------------ jwst/fits_generator/generators.py | 8 +- jwst/fits_generator/input_file_types.py | 6 +- jwst/lib/engdb_direct.py | 2 +- jwst/lib/progress.py | 2 +- jwst/lib/set_telescope_pointing.py | 69 +++-- jwst/lib/set_velocity_aberration.py | 2 +- jwst/lib/siafdb.py | 8 +- jwst/lib/tests/test_siafdb.py | 2 +- jwst/mrs_imatch/mrs_imatch_step.py | 2 +- jwst/outlier_detection/tests/test_fileio.py | 2 +- jwst/outlier_detection/tso.py | 10 +- jwst/pipeline/tests/test_calwebb_image2.py | 2 +- jwst/pipeline/tests/test_calwebb_image3.py | 2 +- jwst/pipeline/tests/test_calwebb_spec2.py | 2 +- jwst/pixel_replace/pixel_replace_step.py | 2 +- jwst/regtest/test_associations_sdp_pools.py | 4 +- jwst/regtest/test_associations_standards.py | 2 +- jwst/regtest/test_fgs_pointing.py | 2 +- jwst/resample/resample_step.py | 4 +- jwst/skymatch/skymatch_step.py | 2 +- jwst/stpipe/core.py | 2 +- jwst/tests/test_import.py | 2 +- jwst/tweakreg/tweakreg_step.py | 2 +- jwst/wfss_contam/tests/test_observations.py | 2 +- pyproject.toml | 19 ++ tox.ini | 8 + 46 files changed, 258 insertions(+), 302 deletions(-) create mode 100644 changes/8852.general.rst diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1e6e0a2ff9..73df104f48 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -43,6 +43,7 @@ jobs: default_python: "3.12" envs: | - linux: check-dependencies + - linux: check-types latest_crds_contexts: uses: ./.github/workflows/contexts.yml crds_context: diff --git a/changes/8852.general.rst b/changes/8852.general.rst new file mode 100644 index 0000000000..0a584e8d63 --- /dev/null +++ b/changes/8852.general.rst @@ -0,0 +1 @@ +Added mypy type checking to CI checks diff --git a/jwst/__init__.py b/jwst/__init__.py index 8ec268c7e6..6fdd1e4afa 100644 --- a/jwst/__init__.py +++ b/jwst/__init__.py @@ -7,6 +7,6 @@ _regex_git_hash = re.compile(r".*\+g(\w+)") __version_commit__ = "" if "+" in __version__: - commit = _regex_git_hash.match(__version__).groups() + commit = _regex_git_hash.match(__version__).groups() # type: ignore if commit: __version_commit__ = commit[0] diff --git a/jwst/ami/ami_analyze.py b/jwst/ami/ami_analyze.py index dd7e24132c..631c780004 100644 --- a/jwst/ami/ami_analyze.py +++ b/jwst/ami/ami_analyze.py @@ -3,7 +3,7 @@ import numpy as np import copy -from jwst.datamodels import CubeModel, ImageModel +from jwst.datamodels import CubeModel, ImageModel # type: ignore[attr-defined] from .find_affine2d_parameters import find_rotation from . import instrument_data diff --git a/jwst/ami/leastsqnrm.py b/jwst/ami/leastsqnrm.py index fbbcd98b5f..d1c1a89524 100644 --- a/jwst/ami/leastsqnrm.py +++ b/jwst/ami/leastsqnrm.py @@ -613,7 +613,7 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None): if linfit: try: - from linearfit import linearfit + from linearfit import linearfit # type: ignore[import-not-found] # dependent variables M = np.asmatrix(flatimg) diff --git a/jwst/ami/matrix_dft.py b/jwst/ami/matrix_dft.py index 63ab8074b3..3f47a230c3 100644 --- a/jwst/ami/matrix_dft.py +++ b/jwst/ami/matrix_dft.py @@ -198,7 +198,7 @@ def matrix_idft(*args, **kwargs): return matrix_dft(*args, **kwargs) -matrix_idft.__doc__ = matrix_dft.__doc__.replace( +matrix_idft.__doc__ = matrix_dft.__doc__.replace( # type: ignore 'Perform a matrix discrete Fourier transform', 'Perform an inverse matrix discrete Fourier transform' ) diff --git a/jwst/assign_wcs/util.py b/jwst/assign_wcs/util.py index 1b30ce2320..b33620ed8b 100644 --- a/jwst/assign_wcs/util.py +++ b/jwst/assign_wcs/util.py @@ -99,7 +99,7 @@ def _reproject(x, y): def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray], - disp_axis: int = None, pscale_ratio: float = None) -> float: + disp_axis: int | None = None, pscale_ratio: float | None = None) -> float: """Compute scaling transform. Parameters @@ -137,8 +137,8 @@ def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray], crval_with_offsets = wcs(*crpix_with_offsets, with_bounding_box=False) coords = SkyCoord(ra=crval_with_offsets[spatial_idx[0]], dec=crval_with_offsets[spatial_idx[1]], unit="deg") - xscale = np.abs(coords[0].separation(coords[1]).value) - yscale = np.abs(coords[0].separation(coords[2]).value) + xscale: float = np.abs(coords[0].separation(coords[1]).value) + yscale: float = np.abs(coords[0].separation(coords[2]).value) if pscale_ratio is not None: xscale *= pscale_ratio @@ -149,7 +149,8 @@ def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray], # Assuming disp_axis is consistent with DataModel.meta.wcsinfo.dispersion.direction return yscale if disp_axis == 1 else xscale - return np.sqrt(xscale * yscale) + scale: float = np.sqrt(xscale * yscale) + return scale def calc_rotation_matrix(roll_ref: float, v3i_yang: float, vparity: int = 1) -> List[float]: diff --git a/jwst/associations/__init__.py b/jwst/associations/__init__.py index 0697dca651..da7b016279 100644 --- a/jwst/associations/__init__.py +++ b/jwst/associations/__init__.py @@ -9,7 +9,7 @@ """ # Take version from the upstream package -from .. import __version__ +from jwst import __version__ # Utility diff --git a/jwst/associations/association.py b/jwst/associations/association.py index e6edce4b75..6acfec5c17 100644 --- a/jwst/associations/association.py +++ b/jwst/associations/association.py @@ -79,12 +79,12 @@ class Association(MutableMapping): GLOBAL_CONSTRAINT = None """Global constraints""" - INVALID_VALUES = None + INVALID_VALUES: tuple | None = None """Attribute values that indicate the attribute is not specified. """ - ioregistry = IORegistry() + ioregistry: IORegistry = IORegistry() """The association IO registry""" def __init__( diff --git a/jwst/associations/association_io.py b/jwst/associations/association_io.py index 5ce8c451a1..d768a20118 100644 --- a/jwst/associations/association_io.py +++ b/jwst/associations/association_io.py @@ -14,7 +14,7 @@ logger = logging.getLogger(__name__) logger.addHandler(logging.NullHandler()) -__all__ = [] +__all__: list = [] # Define JSON encoder to convert `Member` to `dict` diff --git a/jwst/associations/lib/constraint.py b/jwst/associations/lib/constraint.py index ed3c3d1c35..a3c270426f 100644 --- a/jwst/associations/lib/constraint.py +++ b/jwst/associations/lib/constraint.py @@ -56,7 +56,7 @@ class SimpleConstraintABC(abc.ABC): """ # Attributes to show in the string representation. - _str_attrs = ('name', 'value') + _str_attrs: tuple = ('name', 'value') def __new__(cls, *args, **kwargs): """Force creation of the constraint attribute dict before anything else.""" diff --git a/jwst/associations/lib/rules_level3_base.py b/jwst/associations/lib/rules_level3_base.py index a1da4f135b..944bc2b63b 100644 --- a/jwst/associations/lib/rules_level3_base.py +++ b/jwst/associations/lib/rules_level3_base.py @@ -97,7 +97,7 @@ class DMS_Level3_Base(DMSBaseMixin, Association): INVALID_VALUES = _EMPTY # Make sequences type-dependent - _sequences = defaultdict(Counter) + _sequences: defaultdict = defaultdict(Counter) def __init__(self, *args, **kwargs): diff --git a/jwst/associations/tests/helpers.py b/jwst/associations/tests/helpers.py index 11878d4dbe..82c77cc41a 100644 --- a/jwst/associations/tests/helpers.py +++ b/jwst/associations/tests/helpers.py @@ -56,11 +56,11 @@ class BasePoolRule(): # Define the pools and testing parameters related to them. # Each entry is a tuple starting with the path of the pool. - pools = [] + pools: list = [] # Define the rules that SHOULD be present. # Each entry is the class name of the rule. - valid_rules = [] + valid_rules: list = [] def test_rules_exist(self): rules = registry_level3_only() diff --git a/jwst/badpix_selfcal/badpix_selfcal.py b/jwst/badpix_selfcal/badpix_selfcal.py index 0691926552..c2fd84bbfd 100644 --- a/jwst/badpix_selfcal/badpix_selfcal.py +++ b/jwst/badpix_selfcal/badpix_selfcal.py @@ -3,7 +3,7 @@ import numpy as np -import jwst.datamodels as dm +from jwst.datamodels import IFUImageModel # type: ignore[attr-defined] from stcal.outlier_detection.utils import medfilt from stdatamodels.jwst.datamodels.dqflags import pixel @@ -12,7 +12,7 @@ def badpix_selfcal(minimg: np.ndarray, flagfrac_lower: float = 0.001, flagfrac_upper: float = 0.001, kernel_size: int = 15, - dispaxis=None) -> np.ndarray: + dispaxis=None) -> tuple: """ Flag residual artifacts as bad pixels in the DQ array of a JWST exposure @@ -59,18 +59,17 @@ def badpix_selfcal(minimg: np.ndarray, flag_low, flag_high = np.nanpercentile(minimg_hpf, [flagfrac_lower * 100, (1 - flagfrac_upper) * 100]) bad = (minimg_hpf > flag_high) | (minimg_hpf < flag_low) flagged_indices = np.where(bad) - return flagged_indices -def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> dm.IFUImageModel: +def apply_flags(input_model: IFUImageModel, flagged_indices: np.ndarray) -> IFUImageModel: """ Apply the flagged indices to the input model. Sets the flagged pixels to NaN and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL Parameters ---------- - input_model : dm.IFUImageModel + input_model : IFUImageModel Input science data to be corrected flagged_indices : np.ndarray Indices of the flagged pixels, @@ -78,7 +77,7 @@ def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> d Returns ------- - output_model : dm.IFUImageModel + output_model : IFUImageModel Flagged data model """ diff --git a/jwst/cube_skymatch/cube_skymatch_step.py b/jwst/cube_skymatch/cube_skymatch_step.py index db61c79bb2..d8f6edb685 100644 --- a/jwst/cube_skymatch/cube_skymatch_step.py +++ b/jwst/cube_skymatch/cube_skymatch_step.py @@ -52,7 +52,7 @@ class CubeSkyMatchStep(Step): binwidth = float(min=0.0, default=0.1) # Bin width for 'mode' and 'midpt' `skystat`, in sigma """ - reference_file_types = [] + reference_file_types: list = [] def process(self, input1, input2): cube_models = ModelContainer(input1) diff --git a/jwst/datamodels/__init__.py b/jwst/datamodels/__init__.py index 1d3e0b23d6..a3d64a70e3 100644 --- a/jwst/datamodels/__init__.py +++ b/jwst/datamodels/__init__.py @@ -34,7 +34,7 @@ _deprecated_modules = ['schema'] # Deprecated models in stdatamodels -_deprecated_models = [] +_deprecated_models: list[str] = [] # Import all submodules from stdatamodels.jwst.datamodels for attr in dir(stdatamodels.jwst.datamodels): diff --git a/jwst/dq_init/dq_initialization.py b/jwst/dq_init/dq_initialization.py index 9c67d16a35..424ef2dbe8 100644 --- a/jwst/dq_init/dq_initialization.py +++ b/jwst/dq_init/dq_initialization.py @@ -5,7 +5,7 @@ from stdatamodels.jwst import datamodels from ..lib import reffile_utils -from jwst.datamodels import dqflags +from jwst.datamodels import dqflags # type: ignore[attr-defined] log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) diff --git a/jwst/dq_init/tests/test_dq_init.py b/jwst/dq_init/tests/test_dq_init.py index f4c5ac73f9..e18fae409c 100644 --- a/jwst/dq_init/tests/test_dq_init.py +++ b/jwst/dq_init/tests/test_dq_init.py @@ -259,12 +259,12 @@ def test_dq_add1_groupdq(): # Set parameters for multiple runs of guider data args = "xstart, ystart, xsize, ysize, nints, ngroups, instrument, exp_type, detector" -test_data = [(1, 1, 2048, 2048, 2, 2, 'FGS', 'FGS_ID-IMAGE', 'GUIDER1'), +test_data_multiple = [(1, 1, 2048, 2048, 2, 2, 'FGS', 'FGS_ID-IMAGE', 'GUIDER1'), (1, 1, 1032, 1024, 1, 5, 'MIRI', 'MIR_IMAGE', 'MIRIMAGE')] ids = ["GuiderRawModel-Image", "RampModel"] -@pytest.mark.parametrize(args, test_data, ids=ids) +@pytest.mark.parametrize(args, test_data_multiple, ids=ids) def test_fullstep(xstart, ystart, xsize, ysize, nints, ngroups, instrument, exp_type, detector): """Test that the full step runs""" diff --git a/jwst/extract_1d/apply_apcorr.py b/jwst/extract_1d/apply_apcorr.py index daf9f67588..3618719f89 100644 --- a/jwst/extract_1d/apply_apcorr.py +++ b/jwst/extract_1d/apply_apcorr.py @@ -1,9 +1,6 @@ import abc -from typing import Tuple, Union, Type from scipy.interpolate import RectBivariateSpline, interp1d -from astropy.io import fits -from stdatamodels import DataModel from stdatamodels.jwst.datamodels import MultiSlitModel from ..assign_wcs.util import compute_scale @@ -55,10 +52,8 @@ class ApCorrBase(abc.ABC): } } - size_key = None - - def __init__(self, input_model: DataModel, apcorr_table: fits.FITS_rec, sizeunit: str, - location: Tuple[float, float, float] = None, slit_name: str = None, **match_kwargs): + def __init__(self, input_model, apcorr_table, sizeunit, + location = None, slit_name = None, **match_kwargs): self.correction = None self.model = input_model @@ -75,6 +70,10 @@ def __init__(self, input_model: DataModel, apcorr_table: fits.FITS_rec, sizeunit self.apcorr_func = self.approximate() self.tabulated_correction = None + @property + def size_key(self): + return None + def _convert_size_units(self): """If the SIZE or Radius column is in units of arcseconds, convert to pixels.""" if self.apcorr_sizeunits.startswith('arcsec'): @@ -102,7 +101,7 @@ def _convert_size_units(self): 'pixels.' ) - def _get_match_keys(self) -> dict: + def _get_match_keys(self): """Get column keys needed for reducing the reference table based on input.""" instrument = self.model.meta.instrument.name.upper() exptype = self.model.meta.exposure.type.upper() @@ -113,7 +112,7 @@ def _get_match_keys(self) -> dict: if key in exptype: return relevant_pars[key] - def _get_match_pars(self) -> dict: + def _get_match_pars(self): """Get meta parameters required for reference table row-selection.""" match_pars = {} @@ -125,7 +124,7 @@ def _get_match_pars(self) -> dict: return match_pars - def _reduce_reftable(self) -> fits.FITS_record: + def _reduce_reftable(self): """Reduce full reference table to a single matched row.""" table = self._reference_table.copy() @@ -145,7 +144,7 @@ def approximate(self): """Generate an approximate aperture correction function based on input data.""" pass - def apply(self, spec_table: fits.FITS_rec): + def apply(self, spec_table): """Apply interpolated aperture correction value to source-related extraction results in-place. Parameters @@ -181,14 +180,14 @@ class ApCorrPhase(ApCorrBase): """ size_key = 'size' - def __init__(self, *args, pixphase: float = 0.5, **kwargs): + def __init__(self, *args, pixphase = 0.5, **kwargs): self.phase = pixphase # In the future we'll attempt to measure the pixel phase from inputs. super().__init__(*args, **kwargs) def approximate(self): """Generate an approximate function for interpolating apcorr values to input wavelength and size.""" - def _approx_func(wavelength: float, size: float, pixel_phase: float) -> RectBivariateSpline: + def _approx_func(wavelength, size, pixel_phase): """Create a 'custom' approximation function that approximates the aperture correction in two stages based on input data. @@ -228,7 +227,7 @@ def _approx_func(wavelength: float, size: float, pixel_phase: float) -> RectBiva def measure_phase(self): # Future method in determining pixel phase pass - def tabulate_correction(self, spec_table: fits.FITS_rec): + def tabulate_correction(self, spec_table): """Tabulate the interpolated aperture correction value. This will save time when applying it later, especially if it is to be applied to multiple integrations. @@ -255,7 +254,7 @@ def tabulate_correction(self, spec_table: fits.FITS_rec): self.tabulated_correction = np.asarray(coefs) - def apply(self, spec_table: fits.FITS_rec, use_tabulated=False): + def apply(self, spec_table, use_tabulated=False): """Apply interpolated aperture correction value to source-related extraction results in-place. Parameters @@ -297,8 +296,8 @@ def apply(self, spec_table: fits.FITS_rec, use_tabulated=False): class ApCorrRadial(ApCorrBase): """Aperture correction class used with spectral data produced from an extraction aperture radius.""" - def __init__(self, input_model: DataModel, apcorr_table, - location: Tuple[float, float, float] = None): + def __init__(self, input_model, apcorr_table, + location = None): self.correction = None self.model = input_model @@ -329,7 +328,7 @@ def _convert_size_units(self): 'pixels.' ) - def apply(self, spec_table: fits.FITS_rec): + def apply(self, spec_table): """Apply interpolated aperture correction value to source-related extraction results in-place. Parameters @@ -410,7 +409,7 @@ def approximate(self): return RectBivariateSpline(size, wavelength, apcorr.T, ky=1, kx=1) -def select_apcorr(input_model: DataModel) -> Union[Type[ApCorr], Type[ApCorrPhase], Type[ApCorrRadial]]: +def select_apcorr(input_model): """Select appropriate Aperture correction class based on input DataModel. Parameters diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index 7c0198bad7..bd7cda6d8c 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -5,14 +5,11 @@ import math import numpy as np -from typing import Union, Tuple, NamedTuple, List +from dataclasses import dataclass from astropy.modeling import polynomial from astropy.io import fits -from gwcs import WCS -from stdatamodels import DataModel -from stdatamodels.properties import ObjectNode from stdatamodels.jwst import datamodels -from stdatamodels.jwst.datamodels import dqflags, SlitModel, SpecModel +from stdatamodels.jwst.datamodels import dqflags from stdatamodels.jwst.datamodels.apcorr import ( MirLrsApcorrModel, MirMrsApcorrModel, NrcWfssApcorrModel, NrsFsApcorrModel, NrsMosApcorrModel, NrsIfuApcorrModel, NisWfssApcorrModel @@ -88,11 +85,12 @@ EXACT = "exact match" -class Aperture(NamedTuple): # When python 3.6 is no longer supported, consider converting to DataClass - xstart: Union[int, float] - xstop: Union[int, float] - ystart: Union[int, float] - ystop: Union[int, float] +@dataclass +class Aperture: + xstart: float + xstop: float + ystart: float + ystop: float class Extract1dError(Exception): @@ -109,7 +107,7 @@ class ContinueError(Exception): pass -def open_extract1d_ref(refname: str, exptype: str) -> dict: +def open_extract1d_ref(refname, exptype): """Open the extract1d reference file. Parameters @@ -174,7 +172,7 @@ def open_extract1d_ref(refname: str, exptype: str) -> dict: return ref_dict -def open_apcorr_ref(refname: str, exptype: str) -> DataModel: +def open_apcorr_ref(refname, exptype): """Determine the appropriate DataModel class to use when opening the input APCORR reference file. Parameters @@ -212,16 +210,16 @@ def open_apcorr_ref(refname: str, exptype: str) -> DataModel: def get_extract_parameters( - ref_dict: Union[dict, None], - input_model: DataModel, - slitname: str, - sp_order: int, - meta: ObjectNode, - smoothing_length: Union[int, None], - bkg_fit: str, - bkg_order: Union[int, None], - use_source_posn: Union[bool, None] -) -> dict: + ref_dict, + input_model, + slitname, + sp_order, + meta, + smoothing_length, + bkg_fit, + bkg_order, + use_source_posn +): """Get extract1d reference file values. Parameters @@ -449,7 +447,7 @@ def get_extract_parameters( return extract_params -def log_initial_parameters(extract_params: dict): +def log_initial_parameters(extract_params): """Log some of the initial extraction parameters. Parameters @@ -477,9 +475,7 @@ def log_initial_parameters(extract_params: dict): log.debug(f"use_source_posn = {extract_params['use_source_posn']}") -def get_aperture( - im_shape: tuple, wcs: WCS, extract_params: dict -) -> Union[Aperture, dict]: +def get_aperture(im_shape, wcs, extract_params): """Get the extraction limits xstart, xstop, ystart, ystop. Parameters @@ -521,7 +517,7 @@ def get_aperture( return ap_ref -def aperture_from_ref(extract_params: dict, im_shape: Tuple[int]) -> Aperture: +def aperture_from_ref(extract_params, im_shape): """Get extraction region from reference file or image shape. Parameters @@ -560,9 +556,7 @@ def aperture_from_ref(extract_params: dict, im_shape: Tuple[int]) -> Aperture: return ap_ref -def update_from_width( - ap_ref: Aperture, extract_width: Union[int, None], direction: int -) -> Aperture: +def update_from_width(ap_ref, extract_width, direction): """Update XD extraction limits based on extract_width. If extract_width was specified, that value should override @@ -623,9 +617,7 @@ def update_from_width( return ap_width -def update_from_shape( - ap: Aperture, im_shape: Tuple[int] -) -> Tuple[Aperture, bool]: +def update_from_shape(ap, im_shape): """Truncate extraction region based on input image shape. Parameters @@ -675,7 +667,7 @@ def update_from_shape( return ap_shape, truncated -def aperture_from_wcs(wcs: WCS) -> Union[NamedTuple, None]: +def aperture_from_wcs(wcs): """Get the limits over which the WCS is defined. Parameters @@ -685,7 +677,7 @@ def aperture_from_wcs(wcs: WCS) -> Union[NamedTuple, None]: Returns ------- - ap_wcs : namedtuple or None + ap_wcs : Aperture or None Keys are 'xstart', 'xstop', 'ystart', and 'ystop'. These are the limits copied directly from wcs.bounding_box. """ @@ -722,12 +714,7 @@ def aperture_from_wcs(wcs: WCS) -> Union[NamedTuple, None]: return ap_wcs -def update_from_wcs( - ap_ref: Aperture, - ap_wcs: Union[Aperture, None], - extract_width: int, - direction: int, -) -> Aperture: +def update_from_wcs(ap_ref, ap_wcs, extract_width, direction,): """Limit the extraction region to the WCS bounding box. Parameters @@ -783,9 +770,7 @@ def update_from_wcs( return ap -def sanity_check_limits( - ap_ref: Aperture, ap_wcs: Aperture, -) -> bool: +def sanity_check_limits(ap_ref, ap_wcs,): """Sanity check. Parameters @@ -818,9 +803,7 @@ def sanity_check_limits( return flag -def compare_start( - aperture_start: Union[int, float], wcs_bb_lower_lim: Union[int, float] -) -> Union[int, float]: +def compare_start(aperture_start, wcs_bb_lower_lim): """Compare the start limit from the aperture with the WCS lower limit. Extended summary @@ -855,9 +838,7 @@ def compare_start( return math.ceil(wcs_bb_lower_lim) -def compare_stop( - aperture_stop: Union[int, float], wcs_bb_upper_lim: Union[int, float] -) -> Union[int, float]: +def compare_stop(aperture_stop, wcs_bb_upper_lim): """Compare the stop limit from the aperture with the WCS upper limit. The more restrictive (i.e. smaller) limit is the one upon which the @@ -890,7 +871,7 @@ def compare_stop( return math.floor(wcs_bb_upper_lim) -def create_poly(coeff: List[float]) -> Union[polynomial.Polynomial1D, None]: +def create_poly(coeff): """Create a polynomial model from coefficients. Parameters @@ -1035,27 +1016,27 @@ class ExtractBase(abc.ABC): def __init__( self, - input_model: DataModel, - slit: Union[DataModel, None] = None, - ref_image: Union[DataModel, None] = None, - dispaxis: int = HORIZONTAL, - spectral_order: int = 1, - xstart: int = None, - xstop: int = None, - ystart: int = None, - ystop: int = None, - extract_width: int = None, - src_coeff: Union[List[List[float]], None] = None, - bkg_coeff: Union[List[List[float]], None] = None, - independent_var: str = "pixel", - smoothing_length: int = 0, - bkg_fit: str = "poly", - bkg_order: int = 0, - position_correction: float = 0., - subtract_background: Union[bool, None] = None, - use_source_posn: Union[bool, None] = None, - match: Union[str, None] = None, - ref_file_type: Union[str, None] = None + input_model, + slit = None, + ref_image = None, + dispaxis = HORIZONTAL, + spectral_order = 1, + xstart = None, + xstop = None, + ystart = None, + ystop = None, + extract_width = None, + src_coeff = None, + bkg_coeff = None, + independent_var = "pixel", + smoothing_length = 0, + bkg_fit = "poly", + bkg_order = 0, + position_correction = 0., + subtract_background = None, + use_source_posn = None, + match = None, + ref_file_type = None ): """ Parameters @@ -1222,9 +1203,7 @@ def assign_polynomial_limits(self): pass @staticmethod - def get_target_coordinates( - input_model: DataModel, slit: Union[DataModel, None] - ) -> Tuple[Union[float, None], Union[float, None]]: + def get_target_coordinates(input_model, slit): """Get the right ascension and declination of the target. For MultiSlitModel (or similar) data, each slit has the source @@ -1277,9 +1256,7 @@ def get_target_coordinates( return targ_ra, targ_dec - def offset_from_offset( - self, input_model: DataModel, slit: DataModel, - ) -> Tuple[float, Union[float, None]]: + def offset_from_offset(self, input_model, slit): """Get position offset from the target coordinates. Parameters @@ -1347,13 +1324,7 @@ def offset_from_offset( return offset, locn - def locn_from_wcs( - self, - input_model: DataModel, - slit: Union[DataModel, None], - targ_ra: Union[float, None], - targ_dec: Union[float, None], - ) -> Union[Tuple[int, float, float], None]: + def locn_from_wcs(self, input_model, slit, targ_ra, targ_dec): """Get the location of the spectrum, based on the WCS. Parameters @@ -1544,7 +1515,7 @@ def __init__(self, *base_args, **base_kwargs): log.debug("Background subtraction was specified in the reference file,") log.debug("but has been overridden by the step parameter.") - def nominal_locn(self, middle: int, middle_wl: float) -> Union[float, None]: + def nominal_locn(self, middle, middle_wl): """Find the nominal cross-dispersion location of the target spectrum. This version is for the case that the reference file is a JSON file, @@ -1604,7 +1575,7 @@ def nominal_locn(self, middle: int, middle_wl: float) -> Union[float, None]: return location - def add_position_correction(self, shape: tuple): + def add_position_correction(self, shape): """Add the position offset to the extraction location (in-place). Extended summary @@ -1666,7 +1637,7 @@ def _apply_position_corr(self, coeffs): coeff_list[0] += self.position_correction coeffs[i] = copy.copy(coeff_list) - def update_extraction_limits(self, ap: Aperture): + def update_extraction_limits(self, ap): """Update start and stop limits. Extended summary @@ -1796,19 +1767,7 @@ def _poly_per_region(self, coeffs): return result - def extract( - self, - data: np.ndarray, - var_poisson: np.ndarray, - var_rnoise: np.ndarray, - var_flat: np.ndarray, - wl_array: Union[np.ndarray, None], - ) -> Tuple[ - float, float, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray - ]: + def extract(self, data, var_poisson, var_rnoise, var_flat, wl_array): """Do the extraction. Extended summary @@ -2048,9 +2007,7 @@ def __init__(self, *base_args, **base_kwargs): """ super().__init__(*base_args, **base_kwargs) - def nominal_locn( - self, middle: float, middle_wl: float - ) -> Union[float, None]: + def nominal_locn(self, middle, middle_wl): """Find the nominal cross-dispersion location of the target spectrum. This version is for the case that the reference file is an image. @@ -2106,7 +2063,7 @@ def nominal_locn( return location - def add_position_correction(self, shape: tuple): + def add_position_correction(self, shape): """Shift the reference image (in-place). Parameters @@ -2163,18 +2120,7 @@ def log_extraction_parameters(self): log.debug(f"smoothing_length = {self.smoothing_length}") log.debug(f"position_correction = {self.position_correction}") - def extract(self, - data: np.ndarray, - var_poisson: np.ndarray, - var_rnoise: np.ndarray, - var_flat: np.ndarray, - wl_array: np.ndarray, - ) -> \ - Tuple[ - float, float, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray]: + def extract(self, data, var_poisson, var_rnoise, var_flat, wl_array): """ Do the actual extraction, for the case that the extract1d reference file is an image. @@ -2455,7 +2401,7 @@ def extract(self, background, b_var_poisson, b_var_rnoise, b_var_flat, npixels, dq) - def match_shape(self, shape: tuple) -> np.ndarray: + def match_shape(self, shape): """Truncate or expand reference image to match the science data. Extended summary @@ -2495,9 +2441,7 @@ def match_shape(self, shape: tuple) -> np.ndarray: return buf @staticmethod - def separate_target_and_background( - ref - ) -> Tuple[np.ndarray, Union[np.ndarray, None]]: + def separate_target_and_background(ref): """Create masks for target and background. Parameters @@ -2533,24 +2477,24 @@ def separate_target_and_background( def run_extract1d( - input_model: DataModel, - extract_ref_name: str, - apcorr_ref_name: Union[str, None], - smoothing_length: Union[int, None], - bkg_fit: str, - bkg_order: Union[int, None], - bkg_sigma_clip: float, - log_increment: int, - subtract_background: Union[bool, None], - use_source_posn: Union[bool, None], - center_xy: Union[float, None], - ifu_autocen: Union[bool, None], - ifu_rfcorr: Union[bool, None], - ifu_set_srctype: str, - ifu_rscale: float, - ifu_covar_scale: float, - was_source_model: bool = False, -) -> DataModel: + input_model, + extract_ref_name, + apcorr_ref_name, + smoothing_length, + bkg_fit, + bkg_order, + bkg_sigma_clip, + log_increment, + subtract_background, + use_source_posn, + center_xy, + ifu_autocen, + ifu_rfcorr, + ifu_set_srctype, + ifu_rscale, + ifu_covar_scale, + was_source_model, +): """Extract 1-D spectra. This just reads the reference files (if any) and calls do_extract1d. @@ -2688,7 +2632,7 @@ def run_extract1d( return output_model -def ref_dict_sanity_check(ref_dict: Union[dict, None]) -> Union[dict, None]: +def ref_dict_sanity_check(ref_dict): """Check for required entries. Parameters @@ -2723,24 +2667,24 @@ def ref_dict_sanity_check(ref_dict: Union[dict, None]) -> Union[dict, None]: def do_extract1d( - input_model: DataModel, - extract_ref_dict: Union[dict, None], - apcorr_ref_model=None, - smoothing_length: Union[int, None] = None, - bkg_fit: str = "poly", - bkg_order: Union[int, None] = None, - bkg_sigma_clip: float = 0, - log_increment: int = 50, - subtract_background: Union[int, None] = None, - use_source_posn: Union[bool, None] = None, - center_xy: Union[int, None] = None, - ifu_autocen: Union[bool, None] = None, - ifu_rfcorr: Union[bool, None] = None, - ifu_set_srctype: str = None, - ifu_rscale: float = None, - ifu_covar_scale: float = 1.0, - was_source_model: bool = False -) -> DataModel: + input_model, + extract_ref_dict, + apcorr_ref_model = None, + smoothing_length = None, + bkg_fit = "poly", + bkg_order = None, + bkg_sigma_clip = 0, + log_increment = 50, + subtract_background = None, + use_source_posn = None, + center_xy = None, + ifu_autocen = None, + ifu_rfcorr = None, + ifu_set_srctype = None, + ifu_rscale = None, + ifu_covar_scale = 1.0, + was_source_model = False +): """Extract 1-D spectra. In the pipeline, this function would be called by run_extract1d. @@ -3067,9 +3011,7 @@ def do_extract1d( return output_model -def populate_time_keywords( - input_model: DataModel, output_model: DataModel -) -> Union[DataModel, None]: +def populate_time_keywords(input_model, output_model): """Copy the integration times keywords to header keywords. Parameters @@ -3212,7 +3154,7 @@ def populate_time_keywords( n += 1 -def get_spectral_order(slit: DataModel) -> int: +def get_spectral_order(slit): """Get the spectral order number. Parameters @@ -3241,7 +3183,7 @@ def get_spectral_order(slit: DataModel) -> int: return sp_order -def is_prism(input_model: DataModel) -> bool: +def is_prism(input_model): """Determine whether the current observing mode used a prism. Extended summary @@ -3289,9 +3231,7 @@ def is_prism(input_model: DataModel) -> bool: return prism_mode -def copy_keyword_info( - slit: SlitModel, slitname: Union[str, None], spec: SpecModel -): +def copy_keyword_info(slit, slitname, spec): """Copy metadata from the input to the output spectrum. Parameters @@ -3343,16 +3283,7 @@ def copy_keyword_info( spec.shutter_state = slit.shutter_state -def extract_one_slit( - input_model: DataModel, - slit: SlitModel, - integ: int, - prev_offset: Union[float, str], - extract_params: dict -) -> Tuple[float, float, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray, np.ndarray, np.ndarray, - np.ndarray, np.ndarray, float]: +def extract_one_slit(input_model, slit, integ, prev_offset, extract_params): """Extract data for one slit, or spectral order, or plane. Parameters @@ -3567,11 +3498,7 @@ def extract_one_slit( extraction_values) -def replace_bad_values( - data: np.ndarray, - input_dq: Union[np.ndarray, None], - wl_array: np.ndarray -) -> np.ndarray: +def replace_bad_values(data, input_dq, wl_array): """Replace values flagged with DO_NOT_USE or that have NaN wavelengths. Parameters @@ -3611,10 +3538,7 @@ def replace_bad_values( return data -def nans_at_endpoints( - wavelength: np.ndarray, - dq: np.ndarray, -) -> Tuple[np.ndarray, np.ndarray, slice]: +def nans_at_endpoints(wavelength, dq): """Flag NaNs in the wavelength array. Extended summary @@ -3684,7 +3608,8 @@ def create_extraction(extract_ref_dict, output_model, apcorr_ref_model, log_increment, - is_multiple_slits): + is_multiple_slits +): if slit is None: meta_source = input_model else: diff --git a/jwst/fits_generator/generators.py b/jwst/fits_generator/generators.py index f7f8499a60..8e217a79f5 100644 --- a/jwst/fits_generator/generators.py +++ b/jwst/fits_generator/generators.py @@ -41,10 +41,10 @@ from . import util from jwst import __version__ -bool = __builtins__['bool'] -str = __builtins__['str'] -int = __builtins__['int'] -float = __builtins__['float'] +bool = __builtins__['bool'] # type: ignore[index] +str = __builtins__['str'] # type: ignore[index] +int = __builtins__['int'] # type: ignore[index] +float = __builtins__['float'] # type: ignore[index] def date_and_time_to_cds(date_str, time_str): """ diff --git a/jwst/fits_generator/input_file_types.py b/jwst/fits_generator/input_file_types.py index 163ea9dd28..c0333a572c 100644 --- a/jwst/fits_generator/input_file_types.py +++ b/jwst/fits_generator/input_file_types.py @@ -453,8 +453,8 @@ def is_niriss_spec_horiz(hdulist): InputAPTFile ] -file_type_names = [ +type_names = [ x.type_name for x in input_file_types] -file_type_names[-1] = 'or ' + file_type_names[-1] -file_type_names = ', '.join(file_type_names) +type_names[-1] = 'or ' + type_names[-1] +file_type_names = ', '.join(type_names) diff --git a/jwst/lib/engdb_direct.py b/jwst/lib/engdb_direct.py index 39ddf977ce..906e9b1200 100644 --- a/jwst/lib/engdb_direct.py +++ b/jwst/lib/engdb_direct.py @@ -80,7 +80,7 @@ def __init__(self, base_url=None, default_format='dict', **service_kwargs): ]), timeout=self.timeout) response.raise_for_status() - @property + @property # type: ignore[no-redef] def default_format(self): # noqa: F811 return self._default_format diff --git a/jwst/lib/progress.py b/jwst/lib/progress.py index 4091b62569..c0c9067fce 100644 --- a/jwst/lib/progress.py +++ b/jwst/lib/progress.py @@ -32,7 +32,7 @@ def next(self): # Stub the Bar functionality if the actual module does not exist. try: - from progress.bar import Bar as _Bar + from progress.bar import Bar as _Bar # type: ignore[import-not-found] except ModuleNotFoundError: _Bar = _BarStub diff --git a/jwst/lib/set_telescope_pointing.py b/jwst/lib/set_telescope_pointing.py index 4f46a443ab..6ea54cc81b 100644 --- a/jwst/lib/set_telescope_pointing.py +++ b/jwst/lib/set_telescope_pointing.py @@ -63,7 +63,7 @@ from enum import Enum import logging from math import (cos, sin, sqrt) -import typing +from typing import Any, Callable from astropy import units as U from astropy.table import Table @@ -205,7 +205,7 @@ class Methods(Enum): #: Default algorithm under PCS_MODE TRACK/FINEGUIDE/MOVING. TRACK = TRACK_TR_202111 - def __new__(cls: object, value: str, func_name: str, calc_func: str, mnemonics: dict): + def __new__(cls, value, func_name, calc_func, mnemonics): obj = object.__new__(cls) obj._value_ = value obj._func_name = func_name @@ -312,33 +312,33 @@ class Transforms: """The matrices used in calculation of the M_eci2siaf transformation """ #: ECI to FGS1 - m_eci2fgs1: np.array = None + m_eci2fgs1: np.ndarray | None = None #: ECI to Guide Star - m_eci2gs: np.array = None + m_eci2gs: np.ndarray | None = None #: ECI to J-Frame - m_eci2j: np.array = None + m_eci2j: np.ndarray | Any = None #: ECI to SIAF - m_eci2siaf: np.array = None + m_eci2siaf: np.ndarray | None = None #: ECI to SIFOV - m_eci2sifov: np.array = None + m_eci2sifov: np.ndarray | None = None #: ECI to V - m_eci2v: np.array = None + m_eci2v: np.ndarray | Any = None #: FGSX to Guide Stars transformation - m_fgsx2gs: np.array = None + m_fgsx2gs: np.ndarray | Any = None #: FGS1 to SIFOV - m_fgs12sifov: np.array = None + m_fgs12sifov: np.ndarray | None = None #: Velocity aberration - m_gs2gsapp: np.array = None + m_gs2gsapp: np.ndarray | Any = None #: J-Frame to FGS1 - m_j2fgs1: np.array = None + m_j2fgs1: np.ndarray | Any = None #: FSM correction - m_sifov_fsm_delta: np.array = None + m_sifov_fsm_delta: np.ndarray | None = None #: SIFOV to V1 - m_sifov2v: np.array = None + m_sifov2v: np.ndarray | None = None #: V to SIAF - m_v2siaf: np.array = None + m_v2siaf: np.ndarray | Any = None #: Override values. Either another Transforms or dict-like object - override: object = None + override: object | None = None @classmethod def from_asdf(cls, asdf_file): @@ -426,50 +426,50 @@ class TransformParameters: #: The V3 position angle to use if the pointing information is not found. default_pa_v3: float = 0. #: Detector in use. - detector: str = None + detector: str = "" #: Do not write out the modified file. dry_run: bool = False #: URL of the engineering telemetry database REST interface. - engdb_url: str = None + engdb_url: str | None = None #: Exposure type - exp_type: str = None + exp_type: str | None = None #: FGS to use as the guiding FGS. If None, will be set to what telemetry provides. - fgsid: int = None + fgsid: int | None = None #: The version of the FSM correction calculation to use. See `calc_sifov_fsm_delta_matrix` fsmcorr_version: str = 'latest' #: Units of the FSM correction values. Default is 'arcsec'. See `calc_sifov_fsm_delta_matrix` fsmcorr_units: str = 'arcsec' #: Guide star WCS info, typically from the input model. - guide_star_wcs: WCSRef = WCSRef() + guide_star_wcs: WCSRef = WCSRef(None, None, None) #: Transpose the `j2fgs1` matrix. j2fgs_transpose: bool = True #: The [DX, DY, DZ] barycentri velocity vector - jwst_velocity: np.array = None + jwst_velocity: np.ndarray | None = None #: The method, or algorithm, to use in calculating the transform. If not specified, the default method is used. method: Methods = Methods.default #: Observation end time - obsend: float = None + obsend: float | None = None #: Observation start time - obsstart: float = None + obsstart: float | None = None #: If set, matrices that should be used instead of the calculated one. - override_transforms: Transforms = None + override_transforms: Transforms | None = None #: The tracking mode in use. - pcs_mode: str = None + pcs_mode: str | None = None #: The observatory orientation, represented by the ECI quaternion, and other engineering mnemonics - pointing: Pointing = None + pointing: Pointing | Any = None #: Reduction function to use on values. - reduce_func: typing.Callable = None + reduce_func: Callable | None = None #: The SIAF information for the input model - siaf: SIAF = None + siaf: SIAF | Any = None #: The SIAF database - siaf_db: SiafDb = None + siaf_db: SiafDb | Any = None #: If no telemetry can be found during the observation, #: the time, in seconds, beyond the observation time to search for telemetry. tolerance: float = 60. #: The date of observation (`jwst.datamodels.JwstDataModel.meta.date`) - useafter: str = None + useafter: str | None = None #: V3 position angle at Guide Star (`jwst.datamodels.JwstDataModel.meta.guide_star.gs_v3_pa_science`) - v3pa_at_gs: float = None + v3pa_at_gs: float | None = None def as_reprdict(self): """Return a dict where all values are REPR of their values""" @@ -878,7 +878,6 @@ def update_wcs_from_telem(model, t_pars: TransformParameters): logger.warning('Exception is %s', exception) logger.info("Setting ENGQLPTG keyword to PLANNED") model.meta.visit.engdb_pointing_quality = "PLANNED" - t_pars.pointing = None else: logger.info('Successful read of engineering quaternions:') logger.info('\tPointing: %s', t_pars.pointing) @@ -2987,7 +2986,7 @@ def gs_ideal_to_subarray(gs_position, aperture, flip=False): return x, y -def calc_wcs_guiding(model, t_pars, default_roll_ref=0.0, default_vparity=1, default_v3yangle=0.0): +def calc_wcs_guiding(model, t_pars: TransformParameters, default_roll_ref=0.0, default_vparity=1, default_v3yangle=0.0): """Calculate WCS info for FGS guiding For Fine Guidance guiding observations, nearly everything @@ -3028,7 +3027,7 @@ def calc_wcs_guiding(model, t_pars, default_roll_ref=0.0, default_vparity=1, def # Retrieve the appropriate mnemonics that represent the X/Y position of guide star # in the image. if t_pars.exp_type in ['fgs_acq1', 'fgs_acq2']: - mnemonics_to_read = FGS_ACQ_MNEMONICS + mnemonics_to_read = set(FGS_ACQ_MNEMONICS.keys()) elif t_pars.exp_type in ['fgs_fineguide', 'fgs_track']: mnemonics_to_read = FGS_GUIDED_MNEMONICS elif t_pars.exp_type in FGS_ID_EXP_TYPES: diff --git a/jwst/lib/set_velocity_aberration.py b/jwst/lib/set_velocity_aberration.py index 4e05f42ed9..7fcff79ffa 100644 --- a/jwst/lib/set_velocity_aberration.py +++ b/jwst/lib/set_velocity_aberration.py @@ -23,7 +23,7 @@ from gwcs.geometry import SphericalToCartesian, CartesianToSpherical from scipy.constants import speed_of_light import jwst.datamodels as dm -from jwst.datamodels import Level1bModel +from jwst.datamodels import Level1bModel # type: ignore[attr-defined] # Configure logging logger = logging.getLogger(__name__) diff --git a/jwst/lib/siafdb.py b/jwst/lib/siafdb.py index a90b74d205..939159a3e2 100644 --- a/jwst/lib/siafdb.py +++ b/jwst/lib/siafdb.py @@ -33,14 +33,14 @@ # in Level1bModel data models. SIAF = namedtuple("SIAF", ["v2_ref", "v3_ref", "v3yangle", "vparity", "crpix1", "crpix2", "cdelt1", "cdelt2", - "vertices_idl"]) + "vertices_idl"], + defaults = (None, None, None, None, 0, 0, 3600, 3600, + (0, 1, 1, 0, 0, 0, 1, 1))) # Set default values for the SIAF. # Values which are needed by the pipeline are set to None which # triggers a ValueError if missing in the SIAF database. # Quantities not used by the pipeline get a default value - # FITS keywords and aperture vertices. -SIAF.__new__.__defaults__ = (None, None, None, None, 0, 0, 3600, 3600, - (0, 1, 1, 0, 0, 0, 1, 1)) SIAF_REQUIRED = ['V2Ref', 'V3Ref', 'V3IdlYAngle', 'VIdlParity'] SIAF_OPTIONAL = ['XSciRef', 'YSciRef', 'XSciScale', 'YSciScale'] @@ -76,7 +76,7 @@ def __init__(self, source=None, prd=None): log_level = logging.ERROR try: with LoggingContext(logger_pysiaf, level=log_level): - import pysiaf + import pysiaf # type: ignore[import-not-found] except ImportError: raise ValueError('Package "pysiaf" is not installed. Cannot use the pysiaf api') self.pysiaf = pysiaf diff --git a/jwst/lib/tests/test_siafdb.py b/jwst/lib/tests/test_siafdb.py index 2cf0357850..f070140105 100644 --- a/jwst/lib/tests/test_siafdb.py +++ b/jwst/lib/tests/test_siafdb.py @@ -7,7 +7,7 @@ from jwst.lib import siafdb pytest.importorskip('pysiaf') -import pysiaf # noqa: E402 +import pysiaf # type: ignore[import-not-found] # noqa: E402 # Database paths DATA_PATH = Path(__file__).parent / 'data' diff --git a/jwst/mrs_imatch/mrs_imatch_step.py b/jwst/mrs_imatch/mrs_imatch_step.py index 543da7b208..3abf11aa12 100644 --- a/jwst/mrs_imatch/mrs_imatch_step.py +++ b/jwst/mrs_imatch/mrs_imatch_step.py @@ -31,7 +31,7 @@ class MRSIMatchStep(Step): skip = boolean(default=True) # Step must be turned on by parameter reference or user """ - reference_file_types = [] + reference_file_types: list = [] def process(self, images): diff --git a/jwst/outlier_detection/tests/test_fileio.py b/jwst/outlier_detection/tests/test_fileio.py index 6e01557c8e..41ef2c1b2f 100644 --- a/jwst/outlier_detection/tests/test_fileio.py +++ b/jwst/outlier_detection/tests/test_fileio.py @@ -1,6 +1,6 @@ import pytest from jwst.outlier_detection._fileio import _save_intermediate_output -from jwst.datamodels import ImageModel +from jwst.datamodels import ImageModel # type: ignore[attr-defined] from jwst.step import OutlierDetectionStep import os import numpy as np diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index d0161dffe7..2c0020986f 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -49,7 +49,7 @@ def detect_outliers( # Save median model if pars['save_intermediate_results'] is True # this will be a CubeModel with rolling median values. if save_intermediate_results: - median_model = dm.CubeModel(data=medians) + median_model = dm.CubeModel(data=medians) # type: ignore[name-defined] with dm.open(weighted_cube) as dm0: median_model.update(dm0) save_median(median_model, make_output_path) @@ -89,7 +89,11 @@ def weight_no_resample(input_model, good_bits): return weighted_cube -def compute_rolling_median(model: dm.CubeModel, weight_threshold: np.ndarray, w: int=25) -> np.ndarray: +def compute_rolling_median( + model: dm.CubeModel, # type: ignore[name-defined] + weight_threshold: np.ndarray, + w: int=25 +) -> np.ndarray: ''' Set bad and low-weight data to NaN, then compute the rolling median over the time axis. @@ -159,7 +163,7 @@ def moving_median_over_zeroth_axis(x: np.ndarray, w: int) -> np.ndarray: for idx in range(w - 1): shifted[idx:-w + idx + 1, idx] = x shifted[idx + 1:, idx + 1] = x - medians = np.median(shifted, axis=1) + medians: np.ndarray = np.median(shifted, axis=1) for idx in range(w - 1): medians[idx] = np.median(shifted[idx, :idx + 1]) medians[-idx - 1] = np.median(shifted[-idx - 1, -idx - 1:]) diff --git a/jwst/pipeline/tests/test_calwebb_image2.py b/jwst/pipeline/tests/test_calwebb_image2.py index e0389f42e3..0e24cc6768 100644 --- a/jwst/pipeline/tests/test_calwebb_image2.py +++ b/jwst/pipeline/tests/test_calwebb_image2.py @@ -2,7 +2,7 @@ import os import shutil from jwst.stpipe import Step -from jwst.datamodels import ImageModel +from jwst.datamodels import ImageModel # type: ignore[attr-defined] INPUT_FILE = "dummy_rate.fits" diff --git a/jwst/pipeline/tests/test_calwebb_image3.py b/jwst/pipeline/tests/test_calwebb_image3.py index 8531674664..3c7f813ac6 100644 --- a/jwst/pipeline/tests/test_calwebb_image3.py +++ b/jwst/pipeline/tests/test_calwebb_image3.py @@ -3,7 +3,7 @@ import shutil from jwst.stpipe import Step from jwst.assign_wcs import AssignWcsStep -from jwst.datamodels import ImageModel +from jwst.datamodels import ImageModel # type: ignore[attr-defined] INPUT_FILE = "dummy_cal.fits" diff --git a/jwst/pipeline/tests/test_calwebb_spec2.py b/jwst/pipeline/tests/test_calwebb_spec2.py index 71a39232f3..1baf57ac9d 100644 --- a/jwst/pipeline/tests/test_calwebb_spec2.py +++ b/jwst/pipeline/tests/test_calwebb_spec2.py @@ -3,7 +3,7 @@ import shutil from jwst.pipeline.calwebb_spec2 import Spec2Pipeline from jwst.stpipe import Step -from jwst.datamodels import IFUImageModel +from jwst.datamodels import IFUImageModel # type: ignore[attr-defined] INPUT_FILE = "dummy_rate.fits" diff --git a/jwst/pixel_replace/pixel_replace_step.py b/jwst/pixel_replace/pixel_replace_step.py index 7aa07b3dfd..b76260d0a0 100644 --- a/jwst/pixel_replace/pixel_replace_step.py +++ b/jwst/pixel_replace/pixel_replace_step.py @@ -3,7 +3,7 @@ from ..stpipe import Step from jwst.stpipe import record_step_status -from .. import datamodels +from jwst import datamodels from .pixel_replace import PixelReplacement __all__ = ["PixelReplaceStep"] diff --git a/jwst/regtest/test_associations_sdp_pools.py b/jwst/regtest/test_associations_sdp_pools.py index 3cbe4afe08..bda356691d 100644 --- a/jwst/regtest/test_associations_sdp_pools.py +++ b/jwst/regtest/test_associations_sdp_pools.py @@ -22,11 +22,11 @@ # Mark expected failures. Key is the pool name # and value is the reason message. -EXPECTED_FAILS = { +EXPECTED_FAILS: dict = { } # Pools that require special handling -SPECIAL_DEFAULT = { +SPECIAL_DEFAULT: dict = { 'args': [], 'xfail': None, 'slow': False, diff --git a/jwst/regtest/test_associations_standards.py b/jwst/regtest/test_associations_standards.py index b8ba75c0b6..6f103385ed 100644 --- a/jwst/regtest/test_associations_standards.py +++ b/jwst/regtest/test_associations_standards.py @@ -34,7 +34,7 @@ ] # Produce general associations -DEF_ARGS = [] +DEF_ARGS: list = [] # Define the standards diff --git a/jwst/regtest/test_fgs_pointing.py b/jwst/regtest/test_fgs_pointing.py index f905da5f6b..ba90dce043 100644 --- a/jwst/regtest/test_fgs_pointing.py +++ b/jwst/regtest/test_fgs_pointing.py @@ -9,7 +9,7 @@ from numpy import array # noqa: E402 from numpy import allclose, isclose # noqa: E402 -from jwst.datamodels import Level1bModel # noqa: E402 +from jwst.datamodels import Level1bModel # type: ignore[attr-defined] # noqa: E402 from jwst.lib import engdb_mast # noqa: E402 from jwst.lib import engdb_tools # noqa: E402 from jwst.lib import set_telescope_pointing as stp # noqa: E402 diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 5790fb4f58..4132850918 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -4,7 +4,7 @@ import asdf -from jwst.datamodels import ModelLibrary, ImageModel +from jwst.datamodels import ModelLibrary, ImageModel # type: ignore[attr-defined] from jwst.lib.pipe_utils import match_nans_and_flags from . import resample @@ -56,7 +56,7 @@ class ResampleStep(Step): in_memory = boolean(default=True) # Keep images in memory """ - reference_file_types = [] + reference_file_types: list = [] def process(self, input): diff --git a/jwst/skymatch/skymatch_step.py b/jwst/skymatch/skymatch_step.py index c64b63c00d..9762a72048 100644 --- a/jwst/skymatch/skymatch_step.py +++ b/jwst/skymatch/skymatch_step.py @@ -63,7 +63,7 @@ class SkyMatchStep(Step): in_memory = boolean(default=True) # If False, preserve memory using temporary files """ # noqa: E501 - reference_file_types = [] + reference_file_types: list = [] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) diff --git a/jwst/stpipe/core.py b/jwst/stpipe/core.py index 21efa00ef0..0c3df08d71 100644 --- a/jwst/stpipe/core.py +++ b/jwst/stpipe/core.py @@ -11,7 +11,7 @@ from stpipe import Step from stpipe import Pipeline -from .. import __version_commit__, __version__ +from jwst import __version_commit__, __version__ from ..lib.suffix import remove_suffix diff --git a/jwst/tests/test_import.py b/jwst/tests/test_import.py index 5df92128d5..63cd440361 100644 --- a/jwst/tests/test_import.py +++ b/jwst/tests/test_import.py @@ -8,7 +8,7 @@ import jwst -def dependencies(package, exclude: [str]): +def dependencies(package, exclude: list[str]): return [ module[1] for module in pkgutil.walk_packages( diff --git a/jwst/tweakreg/tweakreg_step.py b/jwst/tweakreg/tweakreg_step.py index 0d8363771b..1638189daf 100644 --- a/jwst/tweakreg/tweakreg_step.py +++ b/jwst/tweakreg/tweakreg_step.py @@ -126,7 +126,7 @@ class TweakRegStep(Step): in_memory = boolean(default=True) # If False, preserve memory using temporary files at expense of runtime """ - reference_file_types = [] + reference_file_types: list = [] def process(self, input): if isinstance(input, ModelLibrary): diff --git a/jwst/wfss_contam/tests/test_observations.py b/jwst/wfss_contam/tests/test_observations.py index 71228f6479..422b2900ec 100644 --- a/jwst/wfss_contam/tests/test_observations.py +++ b/jwst/wfss_contam/tests/test_observations.py @@ -12,7 +12,7 @@ from jwst.wfss_contam.observations import background_subtract from jwst.wfss_contam.disperse import dispersed_pixel from jwst.wfss_contam.tests import data -from jwst.datamodels import SegmentationMapModel, ImageModel +from jwst.datamodels import SegmentationMapModel, ImageModel # type: ignore[attr-defined] data_path = os.path.split(os.path.abspath(data.__file__))[0] DIR_IMAGE = "direct_image.fits" diff --git a/pyproject.toml b/pyproject.toml index c73a69fef9..0ef1fc7253 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -264,6 +264,8 @@ line-length = 130 lint.ignore = [ "E741", # ambiguous variable name (O/0, l/I, etc.) "E722", # Do not use bare `except` + "ANN101", # Missing type annotation for self in method + "ANN102", # Missing type annotation for cls in classmethod ] [tool.ruff.lint.per-file-ignores] @@ -286,6 +288,23 @@ lint.ignore = [ "E402", # module-level import not at top of file ] +[tool.mypy] +warn_return_any = true +warn_unused_configs = true +disable_error_code = "import-untyped" # do not check imports +exclude = ["build"] + +[[tool.mypy.overrides]] +# don't complain about the installed c parts of this library +module = [ + "jwst.cube_build.cube_match_internal", + "jwst.cube_build.cube_match_sky_driz", + "jwst.cube_build.cube_match_sky_pointcloud", + "jwst.lib.winclip", + "jwst.straylight.calc_xart", +] +ignore_missing_imports = true + [tool.towncrier] filename = "CHANGES.rst" directory = "changes" diff --git a/tox.ini b/tox.ini index 8813c9998a..77df7bbe27 100644 --- a/tox.ini +++ b/tox.ini @@ -13,6 +13,14 @@ envlist = # tox -l -v # +[testenv:check-types] +description = check type hints, e.g. with mypy +deps = + mypy + types-requests +commands = + mypy jwst --config-file pyproject.toml + [testenv:check-dependencies] description = verify that install_requires in setup.cfg has correct dependencies # `extras` needs to be empty to check modules without additional dependencies From e56eea6a4ac755182cba2d5261fe4e0d236b781a Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 10 Oct 2024 15:47:52 -0400 Subject: [PATCH 522/687] Move moving target updates to unit test --- jwst/lib/tests/test_set_telescope_pointing.py | 81 +++++++++++++++++++ jwst/regtest/test_nircam_mtimage.py | 33 -------- 2 files changed, 81 insertions(+), 33 deletions(-) diff --git a/jwst/lib/tests/test_set_telescope_pointing.py b/jwst/lib/tests/test_set_telescope_pointing.py index 1455b7127d..cdc8228b58 100644 --- a/jwst/lib/tests/test_set_telescope_pointing.py +++ b/jwst/lib/tests/test_set_telescope_pointing.py @@ -12,6 +12,7 @@ pytest.importorskip('pysiaf') from astropy.io import fits # noqa: E402 +from astropy.table import Table # noqa: E402 from astropy.time import Time # noqa: E402 from stdatamodels.jwst import datamodels # noqa: E402 @@ -586,6 +587,46 @@ def test_mirim_tamrs_siaf_values(eng_db_ngas, data_file_nosiaf): assert model.meta.wcsinfo.crpix2 == 993.5 +def test_moving_target_update(caplog, eng_db_ngas, data_file_moving_target): + """Test moving target table updates.""" + with datamodels.open(data_file_moving_target) as model: + stp.update_mt_kwds(model) + + expected_ra = 6.200036603575057e-05 + expected_dec = 1.7711407285091175e-10 + assert np.isclose(model.meta.wcsinfo.mt_ra, expected_ra) + assert np.isclose(model.meta.wcsinfo.mt_dec, expected_dec) + assert np.isclose(model.meta.target.ra, expected_ra) + assert np.isclose(model.meta.target.dec, expected_dec) + + assert "Moving target RA and Dec updated" in caplog.text + + +def test_moving_target_no_mtt(caplog, eng_db_ngas, data_file): + """Test moving target table updates, no table present.""" + with datamodels.open(data_file) as model: + stp.update_mt_kwds(model) + + # No update without table + assert model.meta.wcsinfo.mt_ra is None + assert model.meta.wcsinfo.mt_dec is None + + assert "Moving target position table not found" in caplog.text + + +def test_moving_target_tnotinrange(caplog, eng_db_ngas, data_file_moving_target): + """Test moving target table updates, time not in range.""" + with datamodels.open(data_file_moving_target) as model: + model.meta.exposure.mid_time -= 0.2 + stp.update_mt_kwds(model) + + # No update without times in range + assert model.meta.wcsinfo.mt_ra is None + assert model.meta.wcsinfo.mt_dec is None + + assert "is not in the moving_target table range" in caplog.text + + # ###################### # Utilities and fixtures # ###################### @@ -789,6 +830,46 @@ def data_file_acq1(tmp_path): yield file_path +@pytest.fixture +def data_file_moving_target(tmp_path): + """Example data from simulation.""" + # Values are from simulated data file jw00634_nrcblong_mttest_uncal.fits + model = datamodels.Level1bModel() + model.meta.exposure.start_time = 58738.82598848102 + model.meta.exposure.end_time = 58738.82747969907 + model.meta.exposure.mid_time = 58738.82673409005 + model.meta.target.ra = 0.0 + model.meta.target.dec = 0.0 + model.meta.guidestar.gs_ra = 0.0001 + model.meta.guidestar.gs_dec = 0.0001 + model.meta.aperture.name = "MIRIM_FULL" + model.meta.observation.date = '2019-09-12' + model.meta.exposure.type = "MIR_IMAGE" + model.meta.ephemeris.velocity_x = 0.00651191175424979 + model.meta.ephemeris.velocity_y = 0.160769793796114 + model.meta.ephemeris.velocity_z = 0.147663026601154 + + model.meta.target.type = 'MOVING' + model.meta.moving_target = None + + times = ['2019-09-12T19:49:25.405', '2019-09-12T19:50:29.825', '2019-09-12T19:51:34.246'] + apparent_ra = [0.0, 6.2e-5, 1.24e-4] + apparent_dec = [-6.2e-5, 0.0, 3.0e-5] + default = [0.0, 0.0, 0.0] + col_names = [item['name'] for item in model.schema['properties']['moving_target']['datatype']] + mt_table = Table([times, apparent_ra, apparent_dec], + names=('time', 'mt_apparent_RA', 'mt_apparent_Dec')) + for column in col_names: + if column not in {'time', 'mt_apparent_RA', 'mt_apparent_Dec'}: + mt_table.add_column(default, name=column) + model.moving_target = mt_table.as_array() + + file_path = tmp_path / 'file.fits' + model.save(file_path) + model.close() + yield file_path + + @pytest.fixture() def eng_db_jw703(): """Setup the test engineering database""" diff --git a/jwst/regtest/test_nircam_mtimage.py b/jwst/regtest/test_nircam_mtimage.py index 472dd37f1e..c0e28b1bfb 100644 --- a/jwst/regtest/test_nircam_mtimage.py +++ b/jwst/regtest/test_nircam_mtimage.py @@ -1,9 +1,6 @@ import pytest from astropy.io.fits.diff import FITSDiff -from stdatamodels.jwst import datamodels - -from jwst.lib.set_telescope_pointing import update_mt_kwds from jwst.stpipe import Step @@ -21,33 +18,3 @@ def test_nircam_image_moving_target_i2d(rtdata, fitsdiff_default_kwargs, in_memo fitsdiff_default_kwargs["atol"] = 2e-4 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - - -@pytest.mark.parametrize("input_file", - [ - 'jw00634_nrcblong_mttest_tnotinrange_uncal.fits', - 'jw00634_nrcblong_no_mtt_uncal.fits', - 'jw00634_nrcblong_mttest_uncal.fits', - ], - ids=["midpt_not_in_mt_table_range", "no_mt_table", "with_mt_table"] - ) -@pytest.mark.bigdata -def test_nircam_image_moving_target_kwds(input_file, rtdata, fitsdiff_default_kwargs): - """Tests for moving target table nkeyword additions""" - - # Get the input file - rtdata.get_data(f"nircam/image/{input_file}") - - # The add_mt_kwds function overwrites its input, so output = input - rtdata.output = rtdata.input - - with datamodels.open(rtdata.output) as model: - update_mt_kwds(model) - # since the model is updated in place we need to write out the update - model.write(rtdata.input) - - rtdata.get_truth(f"truth/test_nircam_mtimage/{input_file}") - - # Compare the results and the truth - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From dcf3b92d666d663ee678f5565e2c9ae2d3b27857 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 8 Oct 2024 15:28:31 -0400 Subject: [PATCH 523/687] Remove old test, no longer needed --- jwst/regtest/test_nircam_image.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/jwst/regtest/test_nircam_image.py b/jwst/regtest/test_nircam_image.py index 398c3c720c..66c94f094b 100644 --- a/jwst/regtest/test_nircam_image.py +++ b/jwst/regtest/test_nircam_image.py @@ -188,25 +188,6 @@ def test_nircam_image_stage3_segm(run_image3pipeline, rtdata_module, fitsdiff_de assert diff.identical, diff.report() -@pytest.fixture() -def run_image3_closedfile(rtdata): - """Run calwebb_image3 on NIRCam imaging with data that had a closed file issue.""" - rtdata.get_asn("nircam/image/fail_short_image3_asn.json") - - args = ["calwebb_image3", rtdata.input] - Step.from_cmdline(args) - - -@pytest.mark.bigdata -def test_image3_closedfile(run_image3_closedfile, rtdata, fitsdiff_default_kwargs): - """Ensure production of Image3Pipeline output with data having closed file issues""" - rtdata.output = 'jw00617-o082_t001_nircam_clear-f090w-sub320_i2d.fits' - rtdata.get_truth('truth/test_nircam_image/jw00617-o082_t001_nircam_clear-f090w-sub320_i2d.fits') - - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() - - @pytest.mark.bigdata def test_nircam_frame_averaged_darks(rtdata, fitsdiff_default_kwargs): """Test optional frame-averaged darks output from DarkCurrentStep""" From 8cf4c4bb98c93c4ad485a3f338a97e1317bcae02 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 10 Oct 2024 10:17:17 -0400 Subject: [PATCH 524/687] Update data for frame averaged darks test --- jwst/regtest/test_nircam_image.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_nircam_image.py b/jwst/regtest/test_nircam_image.py index 66c94f094b..89b08f7de8 100644 --- a/jwst/regtest/test_nircam_image.py +++ b/jwst/regtest/test_nircam_image.py @@ -191,15 +191,16 @@ def test_nircam_image_stage3_segm(run_image3pipeline, rtdata_module, fitsdiff_de @pytest.mark.bigdata def test_nircam_frame_averaged_darks(rtdata, fitsdiff_default_kwargs): """Test optional frame-averaged darks output from DarkCurrentStep""" - rtdata.get_data("nircam/image/jw00312007001_02102_00001_nrcblong_ramp.fits") + rtdata.get_data("nircam/image/jw01205015001_03101_00001_nrcb1_ramp.fits") + dark_file = 'jw01205015001_03101_00001_nrcb1_frame_averaged_dark.fits' args = ["jwst.dark_current.DarkCurrentStep", rtdata.input, - "--dark_output='frame_averaged_darks.fits'", + f"--dark_output={dark_file}", ] Step.from_cmdline(args) - rtdata.output = "frame_averaged_darks.fits" + rtdata.output = dark_file - rtdata.get_truth("truth/test_nircam_image/frame_averaged_darks.fits") + rtdata.get_truth(f"truth/test_nircam_image/{dark_file}") diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From 4553284d7950c07439659afa26469b8eaa8921ab Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 7 Oct 2024 15:14:18 -0400 Subject: [PATCH 525/687] Update NIRCam tsgrism regression test data --- jwst/regtest/test_nircam_tsgrism.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/jwst/regtest/test_nircam_tsgrism.py b/jwst/regtest/test_nircam_tsgrism.py index de92f1015b..b6a1da891f 100644 --- a/jwst/regtest/test_nircam_tsgrism.py +++ b/jwst/regtest/test_nircam_tsgrism.py @@ -12,7 +12,7 @@ def run_pipelines(rtdata_module): rtdata = rtdata_module # Run tso-spec2 pipeline on the _rateints file, saving intermediate products - rtdata.get_data("nircam/tsgrism/jw00721012001_03103_00001-seg001_nrcalong_rateints.fits") + rtdata.get_data("nircam/tsgrism/jw01366002001_04103_00001-seg001_nrcalong_rateints.fits") args = ["calwebb_spec2", rtdata.input, "--steps.flat_field.save_results=True", "--steps.extract_2d.save_results=True", @@ -22,7 +22,7 @@ def run_pipelines(rtdata_module): # Get the level3 association json file (though not its members) and run # the tso3 pipeline on all _calints files listed in association - rtdata.get_data("nircam/tsgrism/jw00721-o012_20191119t043909_tso3_001_asn.json") + rtdata.get_data("nircam/tsgrism/jw01366-o002_20230107t004627_tso3_00001_asn.json") args = ["calwebb_tso3", rtdata.input] Step.from_cmdline(args) @@ -60,12 +60,12 @@ def test_nircam_tsgrism_stage2_offsetSR(run_pipeline_offsetSR, fitsdiff_default_ @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["calints", "extract_2d", "flat_field", - "o012_crfints", "srctype", "x1dints"]) + "o002_crfints", "srctype", "x1dints"]) def test_nircam_tsgrism_stage2(run_pipelines, fitsdiff_default_kwargs, suffix): """Regression test of tso-spec2 pipeline performed on NIRCam TSO grism data.""" rtdata = run_pipelines - rtdata.input = "jw00721012001_03103_00001-seg001_nrcalong_rateints.fits" - output = "jw00721012001_03103_00001-seg001_nrcalong_" + suffix + ".fits" + rtdata.input = "jw01366002001_04103_00001-seg001_nrcalong_rateints.fits" + output = "jw01366002001_04103_00001-seg001_nrcalong_" + suffix + ".fits" rtdata.output = output rtdata.get_truth("truth/test_nircam_tsgrism_stages/" + output) @@ -77,9 +77,9 @@ def test_nircam_tsgrism_stage2(run_pipelines, fitsdiff_default_kwargs, suffix): @pytest.mark.bigdata def test_nircam_tsgrism_stage3_x1dints(run_pipelines, fitsdiff_default_kwargs): rtdata = run_pipelines - rtdata.input = "jw00721-o012_20191119t043909_tso3_001_asn.json" - rtdata.output = "jw00721-o012_t004_nircam_f444w-grismr-subgrism256_x1dints.fits" - rtdata.get_truth("truth/test_nircam_tsgrism_stages/jw00721-o012_t004_nircam_f444w-grismr-subgrism256_x1dints.fits") + rtdata.input = "jw01366-o002_20230107t004627_tso3_00001_asn.json" + rtdata.output = "jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_x1dints.fits" + rtdata.get_truth("truth/test_nircam_tsgrism_stages/jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_x1dints.fits") diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -88,9 +88,10 @@ def test_nircam_tsgrism_stage3_x1dints(run_pipelines, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_nircam_tsgrism_stage3_whtlt(run_pipelines): rtdata = run_pipelines - rtdata.input = "jw00721-o012_20191119t043909_tso3_001_asn.json" - rtdata.output = "jw00721-o012_t004_nircam_f444w-grismr-subgrism256_whtlt.ecsv" - rtdata.get_truth("truth/test_nircam_tsgrism_stages/jw00721-o012_t004_nircam_f444w-grismr-subgrism256_whtlt.ecsv") + rtdata.input = "jw01366-o002_20230107t004627_tso3_00001_asn.json" + rtdata.output = "jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_whtlt.ecsv" + rtdata.get_truth("truth/test_nircam_tsgrism_stages/jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_whtlt.ecsv") + table = Table.read(rtdata.output) table_truth = Table.read(rtdata.truth) From d2dfb6a1325d71174f893e3ee83434b27fa0d1e8 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 7 Oct 2024 17:33:37 -0400 Subject: [PATCH 526/687] Remove non-functional setpointing test --- jwst/regtest/test_nircam_tsgrism.py | 32 +++-------------------------- 1 file changed, 3 insertions(+), 29 deletions(-) diff --git a/jwst/regtest/test_nircam_tsgrism.py b/jwst/regtest/test_nircam_tsgrism.py index b6a1da891f..05f88773ff 100644 --- a/jwst/regtest/test_nircam_tsgrism.py +++ b/jwst/regtest/test_nircam_tsgrism.py @@ -2,7 +2,6 @@ from astropy.io.fits.diff import FITSDiff from astropy.table import Table, setdiff -from jwst.lib.set_telescope_pointing import add_wcs from jwst.stpipe import Step @@ -33,7 +32,6 @@ def run_pipelines(rtdata_module): params=["jw01185015001_03104_00001-seg004_nrcalong_rate.fits", "jw01185013001_04103_00001-seg003_nrcalong_rate.fits"]) def run_pipeline_offsetSR(request, rtdata_module): - '''''' rtdata = rtdata_module rtdata.get_data("nircam/tsgrism/" + request.param) args = ["calwebb_spec2", rtdata.input, @@ -44,12 +42,13 @@ def run_pipeline_offsetSR(request, rtdata_module): @pytest.mark.bigdata def test_nircam_tsgrism_stage2_offsetSR(run_pipeline_offsetSR, fitsdiff_default_kwargs): - ''' + """ Test coverage for offset special requirement specifying nonzero offset in X. + Test data are two observations of Gliese 436, one with offset specified and one without. Quantitatively we just ensure that the outputs are identical to the inputs, but qualitatively can check that the spectral lines fall at the same wavelengths in both cases, - which is why the zero-offset case is also included here.''' + which is why the zero-offset case is also included here.""" rtdata = run_pipeline_offsetSR rtdata.output = rtdata.input.replace("rate", "x1d") rtdata.get_truth("truth/test_nircam_tsgrism_stages/" + rtdata.output.split('/')[-1]) @@ -92,33 +91,8 @@ def test_nircam_tsgrism_stage3_whtlt(run_pipelines): rtdata.output = "jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_whtlt.ecsv" rtdata.get_truth("truth/test_nircam_tsgrism_stages/jw01366-o002_t001_nircam_f322w2-grismr-subgrism256_whtlt.ecsv") - table = Table.read(rtdata.output) table_truth = Table.read(rtdata.truth) # setdiff returns a table of length zero if there is no difference assert len(setdiff(table, table_truth)) == 0 - - -@pytest.mark.bigdata -def test_nircam_setpointing_tsgrism(rtdata, fitsdiff_default_kwargs): - """ - Regression test of the set_telescope_pointing script on a level-1b NIRCam file. - """ - # Get SIAF PRD database file - siaf_path = rtdata.get_data("common/prd.db") - rtdata.get_data("nircam/tsgrism/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") - # The add_wcs function overwrites its input - rtdata.output = rtdata.input - - # Call the WCS routine, using the ENGDB_Service - try: - add_wcs(rtdata.input, siaf_path=siaf_path) - except ValueError: - pytest.skip('Engineering Database not available.') - - rtdata.get_truth("truth/test_nircam_setpointing/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") - - fitsdiff_default_kwargs['rtol'] = 1e-6 - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From 47a0005f8071af4d304d19d64e30514eef553e6f Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 7 Oct 2024 17:36:01 -0400 Subject: [PATCH 527/687] Update tsimg data, remove old setpointing test --- jwst/regtest/test_nircam_tsimg.py | 57 ++++--------------------------- 1 file changed, 7 insertions(+), 50 deletions(-) diff --git a/jwst/regtest/test_nircam_tsimg.py b/jwst/regtest/test_nircam_tsimg.py index 05684588c4..508a2a2bc4 100644 --- a/jwst/regtest/test_nircam_tsimg.py +++ b/jwst/regtest/test_nircam_tsimg.py @@ -1,12 +1,7 @@ -from pathlib import Path import pytest - from astropy.io.fits.diff import FITSDiff from astropy.table import Table, setdiff -from jwst.lib import engdb_tools -from jwst.lib.set_telescope_pointing import add_wcs -from jwst.lib.tests.engdb_mock import EngDB_Mocker from jwst.stpipe import Step @@ -18,8 +13,7 @@ def run_pipelines(rtdata_module): # Run the calwebb_tso-image2 pipeline on each of the 2 inputs rate_files = [ - "nircam/tsimg/jw00312006001_02102_00001-seg001_nrcb1_rateints.fits", - "nircam/tsimg/jw00312006001_02102_00001-seg002_nrcb1_rateints.fits" + "nircam/tsimg/jw01068006001_03103_00001-seg001_nrcb1_rateints.fits", ] for rate_file in rate_files: rtdata.get_data(rate_file) @@ -28,7 +22,7 @@ def run_pipelines(rtdata_module): # Get the level3 association json file (though not its members) and run # the tso3 pipeline on all _calints files listed in association - rtdata.get_data("nircam/tsimg/jw00312-o006_20191225t115310_tso3_001_asn.json") + rtdata.get_data("nircam/tsimg/jw01068-o006_20240401t151322_tso3_00002_asn.json") args = ["calwebb_tso3", rtdata.input] Step.from_cmdline(args) @@ -40,8 +34,8 @@ def run_pipelines(rtdata_module): def test_nircam_tsimg_stage2(run_pipelines, fitsdiff_default_kwargs, suffix): """Regression test of tso-image2 pipeline performed on NIRCam TSIMG data.""" rtdata = run_pipelines - rtdata.input = "jw00312006001_02102_00001-seg001_nrcb1_rateints.fits" - output = "jw00312006001_02102_00001-seg001_nrcb1_" + suffix + ".fits" + rtdata.input = "jw01068006001_03103_00001-seg001_nrcb1_rateints.fits" + output = "jw01068006001_03103_00001-seg001_nrcb1_" + suffix + ".fits" rtdata.output = output rtdata.get_truth("truth/test_nircam_tsimg_stage23/" + output) @@ -53,49 +47,12 @@ def test_nircam_tsimg_stage2(run_pipelines, fitsdiff_default_kwargs, suffix): @pytest.mark.bigdata def test_nircam_tsimage_stage3_phot(run_pipelines): rtdata = run_pipelines - rtdata.input = "jw00312-o006_20191225t115310_tso3_001_asn.json" - rtdata.output = "jw00312-o006_t001_nircam_f210m-clear-sub64p_phot.ecsv" - rtdata.get_truth("truth/test_nircam_tsimg_stage23/jw00312-o006_t001_nircam_f210m-clear-sub64p_phot.ecsv") + rtdata.input = "jw01068-o006_20240401t151322_tso3_00002_asn.json" + rtdata.output = "jw01068-o006_t004_nircam_f150w2-f164n-sub64p_phot.ecsv" + rtdata.get_truth("truth/test_nircam_tsimg_stage23/jw01068-o006_t004_nircam_f150w2-f164n-sub64p_phot.ecsv") table = Table.read(rtdata.output) table_truth = Table.read(rtdata.truth) # setdiff returns a table of length zero if there is no difference assert len(setdiff(table, table_truth)) == 0 - - -@pytest.mark.bigdata -def test_nircam_setpointing_tsimg(rtdata, engdb, fitsdiff_default_kwargs): - """ - Regression test of the set_telescope_pointing script on a level-1b - NIRCam TSO imaging file. - """ - # Get SIAF PRD database file - siaf_path = rtdata.get_data("common/prd.db") - rtdata.get_data("nircam/tsimg/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") - # The add_wcs function overwrites its input, so output = input - rtdata.output = rtdata.input - - # Call the WCS routine, using the ENGDB_Service - try: - add_wcs(rtdata.input, engdb_url='http://localhost', siaf_path=siaf_path) - except ValueError: - pytest.skip('Engineering Database not available.') - - rtdata.get_truth("truth/test_nircam_setpointing/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") - - fitsdiff_default_kwargs['rtol'] = 1e-6 - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() - - -# ######## -# Fixtures -# ######## -@pytest.fixture -def engdb(): - """Setup the mock engineering database""" - db_path = Path(__file__).parents[1] / 'lib' / 'tests' / 'data' / 'engdb' - with EngDB_Mocker(db_path=db_path): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb From dcd6bb2dcded2fe366e2c37b27f209cbdef01233 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 8 Oct 2024 09:37:20 -0400 Subject: [PATCH 528/687] Diff tables with tolerances --- jwst/regtest/test_nircam_tsimg.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/jwst/regtest/test_nircam_tsimg.py b/jwst/regtest/test_nircam_tsimg.py index 508a2a2bc4..5795ac23ef 100644 --- a/jwst/regtest/test_nircam_tsimg.py +++ b/jwst/regtest/test_nircam_tsimg.py @@ -1,6 +1,5 @@ import pytest from astropy.io.fits.diff import FITSDiff -from astropy.table import Table, setdiff from jwst.stpipe import Step @@ -45,14 +44,10 @@ def test_nircam_tsimg_stage2(run_pipelines, fitsdiff_default_kwargs, suffix): @pytest.mark.bigdata -def test_nircam_tsimage_stage3_phot(run_pipelines): +def test_nircam_tsimage_stage3_phot(run_pipelines, diff_astropy_tables): rtdata = run_pipelines rtdata.input = "jw01068-o006_20240401t151322_tso3_00002_asn.json" rtdata.output = "jw01068-o006_t004_nircam_f150w2-f164n-sub64p_phot.ecsv" rtdata.get_truth("truth/test_nircam_tsimg_stage23/jw01068-o006_t004_nircam_f150w2-f164n-sub64p_phot.ecsv") - table = Table.read(rtdata.output) - table_truth = Table.read(rtdata.truth) - - # setdiff returns a table of length zero if there is no difference - assert len(setdiff(table, table_truth)) == 0 + assert diff_astropy_tables(rtdata.output, rtdata.truth) From ed61ed757259bb99c0d578fa71d9b179a64431a7 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 9 Oct 2024 09:58:28 -0400 Subject: [PATCH 529/687] Restore setpointing tests --- jwst/regtest/test_nircam_tsgrism.py | 25 ++++++++++++++++++ jwst/regtest/test_nircam_tsimg.py | 41 +++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+) diff --git a/jwst/regtest/test_nircam_tsgrism.py b/jwst/regtest/test_nircam_tsgrism.py index 05f88773ff..4af5c81748 100644 --- a/jwst/regtest/test_nircam_tsgrism.py +++ b/jwst/regtest/test_nircam_tsgrism.py @@ -2,6 +2,7 @@ from astropy.io.fits.diff import FITSDiff from astropy.table import Table, setdiff +from jwst.lib.set_telescope_pointing import add_wcs from jwst.stpipe import Step @@ -96,3 +97,27 @@ def test_nircam_tsgrism_stage3_whtlt(run_pipelines): # setdiff returns a table of length zero if there is no difference assert len(setdiff(table, table_truth)) == 0 + + +@pytest.mark.bigdata +def test_nircam_setpointing_tsgrism(rtdata, fitsdiff_default_kwargs): + """ + Regression test of the set_telescope_pointing script on a level-1b NIRCam file. + """ + # Get SIAF PRD database file + siaf_path = rtdata.get_data("common/prd.db") + rtdata.get_data("nircam/tsgrism/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") + # The add_wcs function overwrites its input + rtdata.output = rtdata.input + + # Call the WCS routine, using the ENGDB_Service + try: + add_wcs(rtdata.input, siaf_path=siaf_path) + except ValueError: + pytest.skip('Engineering Database not available.') + + rtdata.get_truth("truth/test_nircam_setpointing/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") + + fitsdiff_default_kwargs['rtol'] = 1e-6 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() diff --git a/jwst/regtest/test_nircam_tsimg.py b/jwst/regtest/test_nircam_tsimg.py index 5795ac23ef..cf9f38ce23 100644 --- a/jwst/regtest/test_nircam_tsimg.py +++ b/jwst/regtest/test_nircam_tsimg.py @@ -1,6 +1,10 @@ +from pathlib import Path import pytest from astropy.io.fits.diff import FITSDiff +from jwst.lib import engdb_tools +from jwst.lib.set_telescope_pointing import add_wcs +from jwst.lib.tests.engdb_mock import EngDB_Mocker from jwst.stpipe import Step @@ -51,3 +55,40 @@ def test_nircam_tsimage_stage3_phot(run_pipelines, diff_astropy_tables): rtdata.get_truth("truth/test_nircam_tsimg_stage23/jw01068-o006_t004_nircam_f150w2-f164n-sub64p_phot.ecsv") assert diff_astropy_tables(rtdata.output, rtdata.truth) + + +@pytest.mark.bigdata +def test_nircam_setpointing_tsimg(rtdata, engdb, fitsdiff_default_kwargs): + """ + Regression test of the set_telescope_pointing script on a level-1b + NIRCam TSO imaging file. + """ + # Get SIAF PRD database file + siaf_path = rtdata.get_data("common/prd.db") + rtdata.get_data("nircam/tsimg/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") + # The add_wcs function overwrites its input, so output = input + rtdata.output = rtdata.input + + # Call the WCS routine, using the ENGDB_Service + try: + add_wcs(rtdata.input, engdb_url='http://localhost', siaf_path=siaf_path) + except ValueError: + pytest.skip('Engineering Database not available.') + + rtdata.get_truth("truth/test_nircam_setpointing/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") + + fitsdiff_default_kwargs['rtol'] = 1e-6 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + +# ######## +# Fixtures +# ######## +@pytest.fixture +def engdb(): + """Setup the mock engineering database""" + db_path = Path(__file__).parents[1] / 'lib' / 'tests' / 'data' / 'engdb' + with EngDB_Mocker(db_path=db_path): + engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') + yield engdb From c3541d76a535affe649a19a49235262582a29dc1 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 10 Oct 2024 17:32:39 -0400 Subject: [PATCH 530/687] Update regression test data; speed up ifu calculations --- jwst/regtest/test_nirspec_wcs.py | 51 +++++++++++++++++++------------- 1 file changed, 31 insertions(+), 20 deletions(-) diff --git a/jwst/regtest/test_nirspec_wcs.py b/jwst/regtest/test_nirspec_wcs.py index f49c0cbbda..c29f06af68 100644 --- a/jwst/regtest/test_nirspec_wcs.py +++ b/jwst/regtest/test_nirspec_wcs.py @@ -1,3 +1,4 @@ +import numpy as np import pytest from numpy.testing import assert_allclose from gwcs.wcstools import grid_from_bounding_box @@ -8,9 +9,12 @@ @pytest.mark.bigdata -def test_nirspec_fixedslit_wcs(rtdata): +@pytest.mark.parametrize('input_file', + ['jw02072002001_05101_00001_nrs1_rate.fits', + 'jw01309022001_04102_00001_nrs2_rate.fits'], + ids=['full', 'allslits']) +def test_nirspec_fixedslit_wcs(rtdata, input_file): """Test NIRSpec fixed slit wcs""" - input_file = 'jw00023001001_01101_00001_nrs1_rate.fits' rtdata.get_data(f"nirspec/fs/{input_file}") AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs') @@ -29,21 +33,28 @@ def test_nirspec_fixedslit_wcs(rtdata): @pytest.mark.bigdata -def test_nirspec_mos_wcs(rtdata): +@pytest.mark.parametrize('input_file,msa_file', + [('jw01345066001_05101_00001_nrs1_rate.fits', 'jw01345066001_01_msa.fits'), + ('jw02674004001_03101_00001_nrs1_rate.fits', 'jw02674004001_01_msa.fits')], + ids=['mos', 'mos_fs']) +def test_nirspec_mos_wcs(rtdata, input_file, msa_file): """Test NIRSpec MOS wcs""" - input_file = 'msa_patt_num.fits' # Get MSA meta file - rtdata.get_data('nirspec/mos/V9621500100101_short_msa.fits') + rtdata.get_data(f'nirspec/mos/{msa_file}') rtdata.get_data(f"nirspec/mos/{input_file}") AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs') - output = input_file.replace('.fits', '_assign_wcs.fits') + output = input_file.replace('rate', 'assign_wcs') rtdata.output = output rtdata.get_truth(f"truth/test_nirspec_wcs/{output}") with datamodels.open(rtdata.output) as im, datamodels.open(rtdata.truth) as im_truth: - names = [slit.name for slit in nirspec.get_open_slits(im)] + # Get validated open slits from WCS transform + slit2msa = im_truth.meta.wcs.get_transform('slit_frame', 'msa_frame') + open_slits = slit2msa.slits[:] + names = [slit.name for slit in open_slits] + for name in names: wcs = nirspec.nrs_wcs_set_input(im, name) wcs_truth = nirspec.nrs_wcs_set_input(im_truth, name) @@ -51,30 +62,30 @@ def test_nirspec_mos_wcs(rtdata): assert_wcs_grid_allclose(wcs, wcs_truth) -@pytest.mark.parametrize("input_file", - [ - 'jw00011001001_01120_00001_nrs1_rate.fits', - 'jw00011001001_01120_00002_nrs1_rate.fits', - 'jw00011001001_01120_00003_nrs2_rate.fits', - ], - ids=['nrs1_f170lp', 'nrs1_opaque', 'nrs2_f170lp']) @pytest.mark.bigdata -def test_nirspec_ifu_wcs(input_file, rtdata): +@pytest.mark.parametrize('input_file', + ['jw01251004001_03107_00001_nrs1_rate.fits', + 'jw01251004001_03107_00001_nrs2_rate.fits'], + ids=['nrs1', 'nrs2']) +def test_nirspec_ifu_wcs(rtdata, input_file): """Test NIRSpec IFU wcs""" rtdata.get_data(f"nirspec/ifu/{input_file}") AssignWcsStep.call(input_file, save_results=True, suffix='assign_wcs') - output = input_file.replace('rate.fits', 'assign_wcs.fits') + output = input_file.replace('rate', 'assign_wcs') rtdata.output = output rtdata.get_truth(f"truth/test_nirspec_wcs/{output}") with datamodels.open(rtdata.output) as im, datamodels.open(rtdata.truth) as im_truth: - # Test several slices in the IFU, range(30) - for slice_ in [0, 9, 16, 23, 29]: - wcs = nirspec.nrs_wcs_set_input(im, slice_) - wcs_truth = nirspec.nrs_wcs_set_input(im_truth, slice_) + # Test all the IFU slices + wcsobj, tr1, tr2, tr3 = nirspec._get_transforms(im, np.arange(30)) + t_wcsobj, t_tr1, t_tr2, t_tr3 = nirspec._get_transforms(im_truth, np.arange(30)) + for k in range(len(tr2)): + wcs = nirspec._nrs_wcs_set_input_lite(im, wcsobj, k, [tr1, tr2[k], tr3[k]]) + wcs_truth = nirspec._nrs_wcs_set_input_lite( + im_truth, t_wcsobj, k, [t_tr1, t_tr2[k], t_tr3[k]]) assert_wcs_grid_allclose(wcs, wcs_truth) From 32f744d7107f42a949e2b036d29f9e79425db181 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 14 Oct 2024 13:47:18 -0400 Subject: [PATCH 531/687] fix pre-commit action --- .github/workflows/ci.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 73df104f48..161b70a1db 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -36,6 +36,8 @@ jobs: steps: - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 + with: + python-version: '3.12' - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 check: uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 From f13c2de31601e181fafa1e18a7a27cebe44ad0f0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 11 Oct 2024 14:58:52 -0400 Subject: [PATCH 532/687] Update test data for NIRSpec IFU steps test --- jwst/regtest/test_nirspec_steps_spec2.py | 33 +++++++++++++++--------- 1 file changed, 21 insertions(+), 12 deletions(-) diff --git a/jwst/regtest/test_nirspec_steps_spec2.py b/jwst/regtest/test_nirspec_steps_spec2.py index 0314d77a1e..854e1b1286 100644 --- a/jwst/regtest/test_nirspec_steps_spec2.py +++ b/jwst/regtest/test_nirspec_steps_spec2.py @@ -18,13 +18,18 @@ @pytest.mark.bigdata def test_nirspec_ifu_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test using predefined interpolated flat""" - with dm.open(rtdata.get_data('nirspec/ifu/nrs_ifu_nrs1_assign_wcs.fits')) as data: - with dm.open(rtdata.get_data('nirspec/ifu/nrs_ifu_nrs1_interpolated_flat.fits')) as user_supplied_flat: + output_file = 'jw01251004001_03107_00001_nrs1_flat_from_user_model.fits' + with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_assign_wcs.fits')) as data: + with dm.open(rtdata.get_data( + 'nirspec/ifu/jw01251004001_03107_00001_nrs1_interpolatedflat.fits')) as user_supplied_flat: + + # Call the flat field function directly with a user flat nirspec_ifu(data, None, None, None, None, user_supplied_flat=user_supplied_flat) - rtdata.output = 'ff_using_interpolated.fits' + + rtdata.output = output_file data.write(rtdata.output) - rtdata.get_truth(TRUTH_PATH + '/' + 'ff_using_interpolated.fits') + rtdata.get_truth(TRUTH_PATH + '/' + output_file) diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -32,15 +37,19 @@ def test_nirspec_ifu_user_supplied_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test providing a user-supplied flat field to the FlatFieldStep""" - data = rtdata.get_data('nirspec/ifu/nrs_ifu_nrs1_assign_wcs.fits') - user_supplied_flat = rtdata.get_data('nirspec/ifu/nrs_ifu_nrs1_interpolated_flat.fits') + output_file = 'jw01251004001_03107_00001_nrs1_flat_from_user_file.fits' + + data = rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_assign_wcs.fits') + user_supplied_flat = rtdata.get_data( + 'nirspec/ifu/jw01251004001_03107_00001_nrs1_interpolatedflat.fits') + # Call the step with a user flat data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat) - rtdata.output = 'flat_fielded_step_user_supplied.fits' + rtdata.output = output_file data_flat_fielded.write(rtdata.output) del data_flat_fielded - rtdata.get_truth(TRUTH_PATH + '/' + 'flat_fielded_step_user_supplied.fits') + rtdata.get_truth(TRUTH_PATH + '/' + output_file) diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -49,7 +58,7 @@ def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_ff_inv(rtdata, fitsdiff_default_kwargs): """Test flat field inversion""" - with dm.open(rtdata.get_data('nirspec/ifu/nrs_ifu_nrs1_assign_wcs.fits')) as data: + with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_assign_wcs.fits')) as data: flatted = FlatFieldStep.call(data) unflatted = FlatFieldStep.call(flatted, inverse=True) @@ -71,7 +80,7 @@ def test_ff_inv(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_pathloss_corrpars(rtdata): """Test PathLossStep using correction_pars""" - with dm.open(rtdata.get_data('nirspec/ifu/nrs1_flat_field.fits')) as data: + with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_flat_field.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -85,7 +94,7 @@ def test_pathloss_corrpars(rtdata): @pytest.mark.bigdata def test_pathloss_inverse(rtdata): """Test PathLossStep using correction_pars""" - with dm.open(rtdata.get_data('nirspec/ifu/nrs1_flat_field.fits')) as data: + with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_flat_field.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -100,7 +109,7 @@ def test_pathloss_inverse(rtdata): @pytest.mark.bigdata def test_pathloss_source_type(rtdata): """Test PathLossStep forcing source type""" - with dm.open(rtdata.get_data('nirspec/ifu/nrs1_flat_field.fits')) as data: + with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_flat_field.fits')) as data: pls = PathLossStep() pls.source_type = 'extended' pls.run(data) From 157c0831f5b1371134b9813fe7c475fdaecd860f Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 14 Oct 2024 12:38:07 -0400 Subject: [PATCH 533/687] Update MOS input for spec2 steps tests --- jwst/regtest/test_nirspec_mos_spec2_steps.py | 32 +++++++++++--------- jwst/regtest/test_nirspec_steps_spec2.py | 18 ++++++----- 2 files changed, 28 insertions(+), 22 deletions(-) diff --git a/jwst/regtest/test_nirspec_mos_spec2_steps.py b/jwst/regtest/test_nirspec_mos_spec2_steps.py index b26a38bfa5..853cea7962 100644 --- a/jwst/regtest/test_nirspec_mos_spec2_steps.py +++ b/jwst/regtest/test_nirspec_mos_spec2_steps.py @@ -13,14 +13,18 @@ @pytest.mark.bigdata def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test providing a user-supplied flat field to the FlatFieldStep""" - data = rtdata.get_data('nirspec/mos/usf_wavecorr.fits') - user_supplied_flat = rtdata.get_data('nirspec/mos/usf_flat.fits') + basename = 'jw01345066001_05101_00001_nrs1' + output_file = f'{basename}_flat_from_user_file.fits' - data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat) - rtdata.output = 'flat_fielded_step_user_supplied.fits' + data = rtdata.get_data(f'nirspec/mos/{basename}_wavecorr.fits') + user_supplied_flat = rtdata.get_data(f'nirspec/mos/{basename}_interpolatedflat.fits') + + data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat, + save_results=False) + rtdata.output = output_file data_flat_fielded.write(rtdata.output) - rtdata.get_truth('truth/test_nirspec_mos_spec2/flat_fielded_step_user_supplied.fits') + rtdata.get_truth(f'truth/test_nirspec_mos_spec2/{output_file}') diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -28,7 +32,7 @@ def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_ff_inv(rtdata, fitsdiff_default_kwargs): """Test flat field inversion""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: flatted = FlatFieldStep.call(data) unflatted = FlatFieldStep.call(flatted, inverse=True) @@ -52,7 +56,7 @@ def test_ff_inv(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_pathloss_corrpars(rtdata): """Test PathLossStep using correction_pars""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -70,7 +74,7 @@ def test_pathloss_corrpars(rtdata): @pytest.mark.bigdata def test_pathloss_inverse(rtdata): """Test PathLossStep using inversion""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -90,7 +94,7 @@ def test_pathloss_inverse(rtdata): @pytest.mark.bigdata def test_pathloss_source_type(rtdata): """Test PathLossStep forcing source type""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = PathLossStep() pls.source_type = 'extended' pls.run(data) @@ -106,7 +110,7 @@ def test_pathloss_source_type(rtdata): @pytest.mark.bigdata def test_barshadow_corrpars(rtdata): """BarShadowStep using correction_pars""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = BarShadowStep() corrected = pls.run(data) @@ -124,7 +128,7 @@ def test_barshadow_corrpars(rtdata): @pytest.mark.bigdata def test_barshadow_inverse(rtdata): """BarShadowStep using inversion""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = BarShadowStep() corrected = pls.run(data) @@ -144,7 +148,7 @@ def test_barshadow_inverse(rtdata): @pytest.mark.bigdata def test_barshadow_source_type(rtdata): """Test BarShadowStep forcing source type""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = BarShadowStep() pls.source_type = 'extended' corrected = pls.run(data) @@ -159,7 +163,7 @@ def test_barshadow_source_type(rtdata): @pytest.mark.bigdata def test_photom_corrpars(rtdata): """Test for photom correction parameters""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = PhotomStep() corrected = pls.run(data) @@ -177,7 +181,7 @@ def test_photom_corrpars(rtdata): @pytest.mark.bigdata def test_photom_inverse(rtdata): """PhotomStep using inversion""" - with dm.open(rtdata.get_data('nirspec/mos/usf_wavecorr.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01345066001_05101_00001_nrs1_wavecorr.fits')) as data: pls = PhotomStep() corrected = pls.run(data) diff --git a/jwst/regtest/test_nirspec_steps_spec2.py b/jwst/regtest/test_nirspec_steps_spec2.py index 854e1b1286..3fd5c2340c 100644 --- a/jwst/regtest/test_nirspec_steps_spec2.py +++ b/jwst/regtest/test_nirspec_steps_spec2.py @@ -18,10 +18,11 @@ @pytest.mark.bigdata def test_nirspec_ifu_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test using predefined interpolated flat""" - output_file = 'jw01251004001_03107_00001_nrs1_flat_from_user_model.fits' - with dm.open(rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_assign_wcs.fits')) as data: + basename = 'jw01251004001_03107_00001_nrs1' + output_file = f'{basename}_flat_from_user_model.fits' + with dm.open(rtdata.get_data(f'nirspec/ifu/{basename}_assign_wcs.fits')) as data: with dm.open(rtdata.get_data( - 'nirspec/ifu/jw01251004001_03107_00001_nrs1_interpolatedflat.fits')) as user_supplied_flat: + f'nirspec/ifu/{basename}_interpolatedflat.fits')) as user_supplied_flat: # Call the flat field function directly with a user flat nirspec_ifu(data, None, None, None, None, user_supplied_flat=user_supplied_flat) @@ -37,14 +38,15 @@ def test_nirspec_ifu_user_supplied_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test providing a user-supplied flat field to the FlatFieldStep""" - output_file = 'jw01251004001_03107_00001_nrs1_flat_from_user_file.fits' + basename = 'jw01251004001_03107_00001_nrs1' + output_file = f'{basename}_flat_from_user_file.fits' - data = rtdata.get_data('nirspec/ifu/jw01251004001_03107_00001_nrs1_assign_wcs.fits') - user_supplied_flat = rtdata.get_data( - 'nirspec/ifu/jw01251004001_03107_00001_nrs1_interpolatedflat.fits') + data = rtdata.get_data(f'nirspec/ifu/{basename}_assign_wcs.fits') + user_supplied_flat = rtdata.get_data(f'nirspec/ifu/{basename}_interpolatedflat.fits') # Call the step with a user flat - data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat) + data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat, + save_results=False) rtdata.output = output_file data_flat_fielded.write(rtdata.output) del data_flat_fielded From 3c86d10fc22adc48eed03cd992587d373d661e7c Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 14 Oct 2024 13:39:37 -0400 Subject: [PATCH 534/687] put rtdata okify results in unique directories --- jwst/regtest/conftest.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/jwst/regtest/conftest.py b/jwst/regtest/conftest.py index 856309bed1..5eb902d6c3 100644 --- a/jwst/regtest/conftest.py +++ b/jwst/regtest/conftest.py @@ -74,6 +74,16 @@ def postmortem(request, fixturename): return None +def pytest_collection_modifyitems(config, items): + # add the index of each item in the list of items + # this is used below for artifactory_result_path + # to produce a unique result subdirectory for + # each test (even if that test shares a name with + # another test which is the case for parametrized tests). + for i, item in enumerate(items): + item.index = i + + @pytest.fixture(scope='function', autouse=True) def generate_artifactory_json(request, artifactory_repos): """Pytest fixture that leaves behind JSON upload and okify specfiles @@ -91,8 +101,9 @@ def artifactory_result_path(): build_matrix_suffix = os.environ.get('BUILD_MATRIX_SUFFIX', '0') subdir = '{}_{}_{}'.format(TODAYS_DATE, build_tag, build_matrix_suffix) testname = request.node.originalname or request.node.name + basename = f"{request.node.index}_{testname}" - return os.path.join(results_root, subdir, testname) + os.sep + return os.path.join(results_root, subdir, basename) + os.sep yield # Execute the following at test teardown From 898e028f89ff235b8ef1bdef62a9e164abd4b416 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 14 Oct 2024 13:40:19 -0400 Subject: [PATCH 535/687] add changelog fragment --- changes/8885.general.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8885.general.rst diff --git a/changes/8885.general.rst b/changes/8885.general.rst new file mode 100644 index 0000000000..435de02426 --- /dev/null +++ b/changes/8885.general.rst @@ -0,0 +1 @@ +Give regtest okify results unique subdirectories. From 414fef060097fd6a592779c249f947141c398d02 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 16 Oct 2024 07:10:28 -0700 Subject: [PATCH 536/687] Use nanmedian3D for TSO data in outlier detection (#8859) --- .../tests/test_outlier_detection.py | 15 ++++++++++----- jwst/outlier_detection/tso.py | 4 ++-- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 4c4d3cdfb3..bca8e6361f 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -553,13 +553,13 @@ def test_outlier_step_image_weak_cr_coron(exptype, tsovisit, tmp_cwd): @pytest.mark.parametrize("exptype, tsovisit", exptypes_tso) -def test_outlier_step_weak_cr_tso(exptype, tsovisit): +@pytest.mark.parametrize("rolling_window_width", [7, 0]) +def test_outlier_step_weak_cr_tso(exptype, tsovisit, rolling_window_width): '''Test outlier detection with rolling median on time-varying source - This test fails if rolling_window_width is set to 100, i.e., take simple median + This test fails if rolling_window_width is set to 0, i.e., take simple median ''' bkg = 1.5 sig = 0.02 - rolling_window_width = 7 numsci = 50 signal = 7.0 im = we_many_sci( @@ -588,8 +588,13 @@ def test_outlier_step_weak_cr_tso(exptype, tsovisit): assert np.all(np.isnan(result.data[i][dnu])) assert np.allclose(model.data[~dnu], result.data[i][~dnu]) - # Verify source is not flagged - assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) + # Verify source is not flagged for rolling median + if rolling_window_width == 7: + assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) + # But this fails for simple median + elif rolling_window_width == 0: + with pytest.raises(AssertionError): + assert np.all(result.dq[:, 7, 7] == datamodels.dqflags.pixel["GOOD"]) # Verify CR is flagged assert result.dq[cr_timestep, 12, 12] == OUTLIER_DO_NOT_USE diff --git a/jwst/outlier_detection/tso.py b/jwst/outlier_detection/tso.py index 2c0020986f..e5524bc920 100644 --- a/jwst/outlier_detection/tso.py +++ b/jwst/outlier_detection/tso.py @@ -4,7 +4,7 @@ from jwst import datamodels as dm from stcal.outlier_detection.utils import compute_weight_threshold -from .utils import flag_model_crs +from .utils import flag_model_crs, nanmedian3D from ._fileio import save_median import logging @@ -41,7 +41,7 @@ def detect_outliers( medians = compute_rolling_median(weighted_cube, weight_threshold, w=rolling_window_width) else: - medians = np.nanmedian(weighted_cube.data, axis=0) + medians = nanmedian3D(weighted_cube.data, overwrite_input=False) # this is a 2-D array, need to repeat it into the time axis # for consistent shape with rolling median case medians = np.broadcast_to(medians, weighted_cube.shape) From ba343e2e99ff9cacd4e88829a1829d033fb9ac79 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 15 Oct 2024 15:52:10 -0400 Subject: [PATCH 537/687] Move group scale test to irs2 detector1 run --- jwst/regtest/test_nirspec_irs2_detector1.py | 3 +- jwst/regtest/test_nirspec_subarray.py | 42 --------------------- 2 files changed, 2 insertions(+), 43 deletions(-) delete mode 100644 jwst/regtest/test_nirspec_subarray.py diff --git a/jwst/regtest/test_nirspec_irs2_detector1.py b/jwst/regtest/test_nirspec_irs2_detector1.py index 1cb6756241..6203f8b9bf 100644 --- a/jwst/regtest/test_nirspec_irs2_detector1.py +++ b/jwst/regtest/test_nirspec_irs2_detector1.py @@ -17,6 +17,7 @@ def run_detector1pipeline(rtdata_module): Step.from_cmdline([ "calwebb_detector1", rtdata.input, + "--steps.group_scale.save_results=True", "--steps.dq_init.save_results=True", "--steps.saturation.save_results=True", "--steps.superbias.save_results=True", @@ -52,7 +53,7 @@ def run_detector1_with_clean_flicker_noise(rtdata_module): @pytest.mark.bigdata -@pytest.mark.parametrize("suffix", ['dq_init', 'saturation', 'superbias', +@pytest.mark.parametrize("suffix", ['group_scale', 'dq_init', 'saturation', 'superbias', 'refpix', 'linearity', 'dark_current', 'jump', '0_ramp_fit', 'gain_scale', 'rate']) diff --git a/jwst/regtest/test_nirspec_subarray.py b/jwst/regtest/test_nirspec_subarray.py deleted file mode 100644 index 1114086d05..0000000000 --- a/jwst/regtest/test_nirspec_subarray.py +++ /dev/null @@ -1,42 +0,0 @@ -import os - -import pytest -from astropy.io.fits.diff import FITSDiff - -from jwst.stpipe import Step - -""" -nrs1_group_subarray.fits the input (uncal) file -nrs1_group_subarray_group_scale.fits output from group_scale -nrs1_group_subarray_rate.fits output -""" - - -@pytest.fixture(scope="module") -def run_pipeline(rtdata_module): - """Run calwebb_detector1 pipeline on NIRSpec subarray data.""" - rtdata = rtdata_module - rtdata.get_data("nirspec/fs/nrs1_group_subarray.fits") - - args = ["calwebb_detector1", rtdata.input, - "--steps.group_scale.save_results=True"] - Step.from_cmdline(args) - - return rtdata - - -@pytest.mark.bigdata -@pytest.mark.parametrize("output", [ - 'nrs1_group_subarray_group_scale.fits', - 'nrs1_group_subarray_rate.fits', ], - ids=['group_scale', 'rate']) -def test_nirspec_detector1(run_pipeline, fitsdiff_default_kwargs, output): - """ - Regression test of calwebb_detector1 pipeline performed on NIRSpec data. - """ - rtdata = run_pipeline - rtdata.output = output - rtdata.get_truth(os.path.join("truth/test_nirspec_subarray", output)) - - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From 99575cc06b03118fccffc3c3dc7f80f885c38824 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 15 Oct 2024 16:27:38 -0400 Subject: [PATCH 538/687] Update data for exceptions tests --- jwst/regtest/test_nirspec_exceptions.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/jwst/regtest/test_nirspec_exceptions.py b/jwst/regtest/test_nirspec_exceptions.py index f28bc36761..4c17bf8fd0 100644 --- a/jwst/regtest/test_nirspec_exceptions.py +++ b/jwst/regtest/test_nirspec_exceptions.py @@ -15,8 +15,8 @@ def test_nirspec_missing_msa_fail(rtdata, fitsdiff_default_kwargs, monkeypatch): that's missing an MSAMETFL. Exception should be raised. """ - # Get the input file - rtdata.get_data('nirspec/mos/f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod.fits') + # Get the input file, don't get the MSA file + rtdata.get_data('nirspec/mos/jw01180025001_05101_00001_nrs2_rate.fits') # Run the calwebb_spec2 pipeline args = ["calwebb_spec2", rtdata.input] @@ -41,8 +41,8 @@ def test_nirspec_missing_msa_nofail(rtdata, fitsdiff_default_kwargs, monkeypatch that's missing an MSAMETFL. Exception should NOT be raised. """ - # Get the input file - rtdata.get_data('nirspec/mos/f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod.fits') + # Get the input file, don't get the MSA file + rtdata.get_data('nirspec/mos/jw01180025001_05101_00001_nrs2_rate.fits') # Run the calwebb_spec2 pipeline args = ["calwebb_spec2", @@ -69,7 +69,7 @@ def test_nirspec_assignwcs_skip(rtdata, fitsdiff_default_kwargs, caplog): """ # Get the input file - rtdata.get_data('nirspec/mos/f170lp-g235m_mos_observation-6-c0e0_001_dn_nrs1_mod.fits') + rtdata.get_data('nirspec/mos/jw01180025001_05101_00001_nrs2_rate.fits') # Run the calwebb_spec2 pipeline args = ["calwebb_spec2", @@ -90,7 +90,7 @@ def test_nirspec_nrs2_nodata_api(rtdata, fitsdiff_default_kwargs): """ # Get the input file - rtdata.get_data('nirspec/ifu/jw84700006001_02101_00001_nrs2_rate.fits') + rtdata.get_data('nirspec/ifu/jw01128009001_0310c_00004_nrs2_rate.fits') # Call the Spec2Pipeline step = Spec2Pipeline() @@ -109,7 +109,7 @@ def test_nirspec_nrs2_nodata_strun(rtdata, fitsdiff_default_kwargs, caplog): """ # Get the input file - rtdata.get_data('nirspec/ifu/jw84700006001_02101_00001_nrs2_rate.fits') + rtdata.get_data('nirspec/ifu/jw01128009001_0310c_00004_nrs2_rate.fits') # Call the Spec2Pipeline cmd = [ From 266fa8a55a9cc9c7e99a4b4671ea62c666ae03f0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 15 Oct 2024 17:07:29 -0400 Subject: [PATCH 539/687] Update NIRSpec imaging regression test data --- jwst/regtest/test_nirspec_image2.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/jwst/regtest/test_nirspec_image2.py b/jwst/regtest/test_nirspec_image2.py index fba5f82eac..b8dbe252c4 100644 --- a/jwst/regtest/test_nirspec_image2.py +++ b/jwst/regtest/test_nirspec_image2.py @@ -11,13 +11,14 @@ @pytest.mark.bigdata def test_nirspec_image2(rtdata, fitsdiff_default_kwargs): - rtdata.get_data("nirspec/imaging/jw84600010001_02102_00001_nrs2_rate.fits") + basename = 'jw03290001001_03101_00001_nrs2' + rtdata.get_data(f"nirspec/imaging/{basename}_rate.fits") args = ["calwebb_image2", rtdata.input] Step.from_cmdline(args) - rtdata.output = "jw84600010001_02102_00001_nrs2_cal.fits" + rtdata.output = f"{basename}_cal.fits" - rtdata.get_truth("truth/test_nirspec_image2/jw84600010001_02102_00001_nrs2_cal.fits") + rtdata.get_truth(f"truth/test_nirspec_image2/{basename}_cal.fits") diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -26,14 +27,16 @@ def test_nirspec_image2(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test providing a user-supplied flat field to the FlatFieldStep""" - data = rtdata.get_data('nirspec/imaging/usf_assign_wcs.fits') - user_supplied_flat = rtdata.get_data('nirspec/imaging/usf_flat.fits') + basename = 'jw03290001001_03101_00001_nrs2' + output_file = f'{basename}_flat_from_user_file.fits' + data = rtdata.get_data(f'nirspec/imaging/{basename}_assign_wcs.fits') + user_supplied_flat = rtdata.get_data(f'nirspec/imaging/{basename}_interpolatedflat.fits') data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat) - rtdata.output = 'flat_fielded_step_user_supplied.fits' + rtdata.output = output_file data_flat_fielded.write(rtdata.output) - rtdata.get_truth('truth/test_nirspec_image2/flat_fielded_step_user_supplied.fits') + rtdata.get_truth(f'truth/test_nirspec_image2/{output_file}') diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -41,7 +44,8 @@ def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_ff_inv(rtdata, fitsdiff_default_kwargs): """Test flat field inversion""" - with dm.open(rtdata.get_data('nirspec/imaging/usf_assign_wcs.fits')) as data: + input_file = 'nirspec/imaging/jw03290001001_03101_00001_nrs2_assign_wcs.fits' + with dm.open(rtdata.get_data(input_file)) as data: flatted = FlatFieldStep.call(data) unflatted = FlatFieldStep.call(flatted, inverse=True) @@ -56,7 +60,8 @@ def test_ff_inv(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_correction_pars(rtdata, fitsdiff_default_kwargs): """Test use of correction parameters""" - with dm.open(rtdata.get_data('nirspec/imaging/usf_assign_wcs.fits')) as data: + input_file = 'nirspec/imaging/jw03290001001_03101_00001_nrs2_assign_wcs.fits' + with dm.open(rtdata.get_data(input_file)) as data: # First use of FlatFieldStep will store the correction. # The next use will use that correction From 677252406274979198ec25c217cca2cf16a2bc18 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 16 Oct 2024 14:02:02 -0400 Subject: [PATCH 540/687] Update nirspec verify test to check a NRS_IMAGE file --- jwst/regtest/test_nirspec_verify.py | 29 ++++++++++------------------- 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/jwst/regtest/test_nirspec_verify.py b/jwst/regtest/test_nirspec_verify.py index 112217aafa..822abb4b6a 100644 --- a/jwst/regtest/test_nirspec_verify.py +++ b/jwst/regtest/test_nirspec_verify.py @@ -4,12 +4,16 @@ from jwst.stpipe import Step -ROOT = 'nrs_verify_nrs1' +# Input is jw01970001001_02102_00001_nrs1_uncal.fits, a verify-only image +# preceding an IFU observation, with EXP_TYPE=NRS_IMAGE. +# There is currently no data with EXP_TYPE=NRS_VERIFY available in +# the archive. +ROOT = 'jw01970002001_03101_00001_nrs1' @pytest.fixture(scope='module') def run_detector1(rtdata_module): - """Run NRS_VERIFY through detector1""" + """Run a verify-only NIRSpec image through detector1.""" rtdata = rtdata_module rtdata.get_data(f'nirspec/imaging/{ROOT}_uncal.fits') @@ -28,28 +32,15 @@ def run_detector1(rtdata_module): @pytest.fixture(scope='module') def run_image2(run_detector1, rtdata_module): - """Run NRS_VERIFY through image2""" + """Run a verify-only NIRSpec image through image2.""" rtdata = rtdata_module - - # Get some predefined references due to insufficient coverage - # from CRDS. - refs = ['jwst_nirspec_fpa_0005.asdf', 'jwst_nirspec_flat_0061.fits', 'jwst_nirspec_area_0001.fits'] - for ref in refs: - rtdata.get_data(f'nirspec/imaging/{ref}') - rtdata.input = f'{ROOT}_rate.fits' args = [ 'calwebb_image2', rtdata.input, '--steps.assign_wcs.save_results=true', - '--steps.assign_wcs.override_fpa=jwst_nirspec_fpa_0005.asdf', '--steps.flat_field.save_results=true', - '--steps.flat_field.override_flat=jwst_nirspec_flat_0061.fits', - '--steps.flat_field.override_sflat=N/A', - '--steps.flat_field.override_fflat=N/A', - '--steps.flat_field.override_dflat=N/A', - '--steps.photom.save_results=true', - '--steps.photom.override_area=jwst_nirspec_area_0001.fits', + '--steps.photom.save_results=true' ] Step.from_cmdline(args) @@ -61,7 +52,7 @@ def run_image2(run_detector1, rtdata_module): 'dark_current', 'jump', 'rate', ]) def test_verify_detector1(run_detector1, rtdata_module, fitsdiff_default_kwargs, suffix): - """Test results of the detector1 and image2 processing""" + """Test results of the detector1 processing.""" rtdata = rtdata_module output = f'{ROOT}_{suffix}.fits' rtdata.output = output @@ -80,7 +71,7 @@ def test_verify_detector1(run_detector1, rtdata_module, fitsdiff_default_kwargs, 'assign_wcs', 'flat_field', 'cal', ]) def test_verify_image2(run_image2, rtdata_module, fitsdiff_default_kwargs, suffix): - """Test results of the detector1 and image2 processing""" + """Test results of the image2 processing.""" rtdata = rtdata_module output = f'{ROOT}_{suffix}.fits' rtdata.output = output From ae78da248903ff32cbdbe2b90b2e71e62008c233 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 17 Oct 2024 10:55:08 -0400 Subject: [PATCH 541/687] Switch okify to GitHub Actions (#8762) Co-authored-by: Tyler Pauly --- jwst/scripts/okify_regtests.py | 27 +++++++++------------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/jwst/scripts/okify_regtests.py b/jwst/scripts/okify_regtests.py index 9c0b78ae94..4a2494bb1d 100755 --- a/jwst/scripts/okify_regtests.py +++ b/jwst/scripts/okify_regtests.py @@ -30,15 +30,9 @@ def parse_args(): parser = ArgumentParser(description='Okify regression test results') parser.add_argument( 'build_number', - help='Jenkins build number for JWST builds', + help='GitHub Actions build number for JWST builds', metavar='build-number', ) - parser.add_argument( - '--job-name', - help='Jenkins job name under [RT] (default: JWST)', - default='JWST', - metavar='job-name', - ) parser.add_argument( '--dry-run', action='store_true', help='pass the --dry-run flag to JFrog CLI' ) @@ -103,21 +97,19 @@ def artifactory_dispatch(okify_op, specfile, dry_run): raise RuntimeError(f'Unknown artifactory operation: {okify_op}') -def artifactory_get_breadcrumbs(build_number, job_name, suffix): +def artifactory_get_breadcrumbs(build_number, suffix): """Download specfiles or other breadcrumb from Artifactory associated with a build number and return a list of their locations on the local file system - An example search would be: + An example search for build 586 would be: - jfrog rt search jwst-pipeline-results/*/*_okify.json --props='build.number=540;build.name=RT :: JWST' + jfrog rt search jwst-pipeline-results/*_GITHUB_CI_*-586/*_okify.json """ - build_name = f'RT :: {job_name}' # Retrieve all the okify specfiles for failed tests. args = list( ['jfrog', 'rt', 'dl'] - + [f'{ARTIFACTORY_REPO}/*/*{suffix}'] - + [f'--props=build.number={build_number};build.name={build_name}'] + + [f"{ARTIFACTORY_REPO}/*_GITHUB_CI_*-{build_number}/*{suffix}"] + ['--flat'] ) subprocess.run(args, check=True, capture_output=True) @@ -125,9 +117,9 @@ def artifactory_get_breadcrumbs(build_number, job_name, suffix): return sorted(glob(f'*{suffix}')) -def artifactory_get_build_artifacts(build_number, job_name): - specfiles = artifactory_get_breadcrumbs(build_number, job_name, SPECFILE_SUFFIX) - asdffiles = artifactory_get_breadcrumbs(build_number, job_name, RTDATA_SUFFIX) +def artifactory_get_build_artifacts(build_number): + specfiles = artifactory_get_breadcrumbs(build_number, SPECFILE_SUFFIX) + asdffiles = artifactory_get_breadcrumbs(build_number, RTDATA_SUFFIX) if len(specfiles) != len(asdffiles): raise RuntimeError('Different number of _okify.json and _rtdata.asdf files') @@ -153,14 +145,13 @@ def main(): args = parse_args() build = args.build_number - name = args.job_name # Create and chdir to a temporary directory to store specfiles with tempfile.TemporaryDirectory() as tmp_path: print(f'Downloading test logs to {tmp_path}') with pushd(tmp_path): # Retrieve all the okify specfiles for failed tests. - specfiles, asdffiles = artifactory_get_build_artifacts(build, name) + specfiles, asdffiles = artifactory_get_build_artifacts(build) number_failed_tests = len(specfiles) From bc3d035fde8235cb31f713ccadc9900e15ebf5b0 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 16 Oct 2024 17:32:56 -0400 Subject: [PATCH 542/687] Update regression test data for NIRSpec lamps --- jwst/regtest/test_nirspec_lamp_fs_spec2.py | 10 ++++++---- jwst/regtest/test_nirspec_lamp_ifu_spec2.py | 10 ++++++---- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/jwst/regtest/test_nirspec_lamp_fs_spec2.py b/jwst/regtest/test_nirspec_lamp_fs_spec2.py index 735a3b6024..c8961e782f 100644 --- a/jwst/regtest/test_nirspec_lamp_fs_spec2.py +++ b/jwst/regtest/test_nirspec_lamp_fs_spec2.py @@ -10,11 +10,14 @@ @pytest.fixture(scope="module") def run_pipeline(rtdata_module): - """Run the calwebb_spec2 pipeline on a NIRSpec lamp exposure.""" + """Run the calwebb_spec2 pipeline on a NIRSpec lamp exposure. + + Input data has EXP_TYPE = NRS_AUTOWAVE, OPMODE = FIXEDSLIT + """ rtdata = rtdata_module # Get the input exposure - rtdata.get_data('nirspec/lamp/jw84600010001_02101_00004_nrs2_rate.fits') + rtdata.get_data('nirspec/lamp/jw01125001001_03104_00010_nrs2_rate.fits') # Run the calwebb_spec2 pipeline; save results from intermediate steps args = ["jwst.pipeline.Spec2Pipeline", rtdata.input, @@ -30,8 +33,7 @@ def run_pipeline(rtdata_module): @pytest.mark.parametrize("suffix", [ "assign_wcs", "extract_2d", "flat_field", "cal", "s2d", "x1d"]) def test_nirspec_lamp_fs_spec2(run_pipeline, fitsdiff_default_kwargs, suffix): - """Regression test of the calwebb_spec2 pipeline on a - NIRSpec lamp exposure in Fixed-Slit mode.""" + """Regression test for calwebb_spec2 on NIRSpec lamp in Fixed-Slit mode.""" # Run the pipeline and retrieve outputs rtdata = run_pipeline diff --git a/jwst/regtest/test_nirspec_lamp_ifu_spec2.py b/jwst/regtest/test_nirspec_lamp_ifu_spec2.py index 20c6398ca8..3bb9209cd4 100644 --- a/jwst/regtest/test_nirspec_lamp_ifu_spec2.py +++ b/jwst/regtest/test_nirspec_lamp_ifu_spec2.py @@ -10,11 +10,14 @@ @pytest.fixture(scope="module") def run_pipeline(rtdata_module): - """Run the calwebb_spec2 pipeline on a NIRSpec lamp exposure.""" + """Run the calwebb_spec2 pipeline on a NIRSpec lamp exposure. + + Input data has EXP_TYPE = NRS_AUTOWAVE, OPMODE = IFU + """ rtdata = rtdata_module # Get the input exposure - rtdata.get_data('nirspec/lamp/jw00626030001_02103_00005_nrs1_rate.fits') + rtdata.get_data('nirspec/lamp/jw01125006001_03104_00003_nrs1_rate.fits') # Run the calwebb_spec2 pipeline; save results from intermediate steps args = ["jwst.pipeline.Spec2Pipeline", rtdata.input, @@ -30,8 +33,7 @@ def run_pipeline(rtdata_module): @pytest.mark.parametrize("suffix", [ "assign_wcs", "msa_flagging", "flat_field", "cal", "s3d", "x1d"]) def test_nirspec_lamp_ifu_spec2(run_pipeline, fitsdiff_default_kwargs, suffix): - """Regression test of the calwebb_spec2 pipeline on a - NIRSpec lamp exposure in IFU mode.""" + """Regression test for calwebb_spec2 on NIRSpec lamp in IFU mode.""" # Run the pipeline and retrieve outputs rtdata = run_pipeline From 37c4ca2f4c543ca8f4c92910ed13085269fc78d3 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 17 Oct 2024 12:28:53 -0400 Subject: [PATCH 543/687] Add CRDS cache information for developers --- .../reference_files_crds.rst | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/docs/jwst/user_documentation/reference_files_crds.rst b/docs/jwst/user_documentation/reference_files_crds.rst index 64436f602a..b18568771f 100644 --- a/docs/jwst/user_documentation/reference_files_crds.rst +++ b/docs/jwst/user_documentation/reference_files_crds.rst @@ -121,6 +121,33 @@ To setup to use the server, use the following settings: export CRDS_PATH=$HOME/crds_cache/ export CRDS_SERVER_URL=https://jwst-crds.stsci.edu +CRDS Cache Configuration for Developers +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +For most pipeline users, the above settings will suffice for establishing a consistent +local cache. For pipeline developers or testers, however, it is important to be aware +that if you need to switch between CRDS servers (e.g. the `ops` and `test` servers), you +will need to establish a separate cache for each server. Using the same cache for +more than one server will lead to a corrupted local cache. + +For example, the recommended configuration for developers while using the `ops` server is : + +:: + + export CRDS_PATH=$HOME/crds_cache/jwst_ops + export CRDS_SERVER_URL=https://jwst-crds.stsci.edu + +and while using the `test` server: + +:: + + export CRDS_PATH=$HOME/crds_cache/jwst_test + export CRDS_SERVER_URL=https://jwst-test-crds.stsci.edu + +For more information on CRDS configuration, see the +`CRDS user guide +`__ +posted to the JWST CRDS server. .. _python_crds_variables: From 5243d0be625c476f010ff25a02f76168e9b7587d Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 17 Oct 2024 12:53:39 -0400 Subject: [PATCH 544/687] Add note about removing cache if needed --- docs/jwst/user_documentation/reference_files_crds.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/jwst/user_documentation/reference_files_crds.rst b/docs/jwst/user_documentation/reference_files_crds.rst index b18568771f..9515c7891a 100644 --- a/docs/jwst/user_documentation/reference_files_crds.rst +++ b/docs/jwst/user_documentation/reference_files_crds.rst @@ -144,6 +144,14 @@ and while using the `test` server: export CRDS_PATH=$HOME/crds_cache/jwst_test export CRDS_SERVER_URL=https://jwst-test-crds.stsci.edu +If your cache does become corrupted, the best way to fix it is simply to remove +the local cache and allow subsequent pipeline runs to repopulate it as needed. +For example: + +:: + + rm -rf $CRDS_PATH/* + For more information on CRDS configuration, see the `CRDS user guide `__ From d4c7f6b4aff83b19f1063dfa00e87ebfe2f502f5 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 17 Oct 2024 13:42:58 -0400 Subject: [PATCH 545/687] JP-3347: Improve spectral outlier detection (#8828) --- changes/8828.outlier_detection.rst | 1 + docs/jwst/outlier_detection/arguments.rst | 2 +- .../outlier_detection_spec.rst | 56 +++--- jwst/outlier_detection/_fileio.py | 98 +++++++++- .../outlier_detection_step.py | 2 +- jwst/outlier_detection/spec.py | 20 +- .../tests/test_outlier_detection.py | 1 + jwst/outlier_detection/utils.py | 171 +++++++++++++++--- jwst/resample/resample.py | 39 +++- 9 files changed, 315 insertions(+), 75 deletions(-) create mode 100644 changes/8828.outlier_detection.rst diff --git a/changes/8828.outlier_detection.rst b/changes/8828.outlier_detection.rst new file mode 100644 index 0000000000..e0edcecb80 --- /dev/null +++ b/changes/8828.outlier_detection.rst @@ -0,0 +1 @@ +For slit spectra, threshold outliers with a median error across exposures instead of input error from the exposure itself. diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index 8d551122c0..e44d271e84 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -23,7 +23,7 @@ that control the behavior of the processing: The value to assign to resampled image pixels that have zero weight or do not receive any flux from any input pixels during drizzling. Any floating-point value, given as a string, is valid. - A value of 'INDEF' will use the last zero weight flux. + The default value of 'NAN' sets NaN values. ``--maskpt`` The percent of maximum weight to use as lower-limit for valid data; diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index 6d5b432b2f..4f1d1ce866 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -13,38 +13,50 @@ Specifically, this routine performs the following operations (modified from the #. Extract parameter settings from input model and merge them with any user-provided values - - the same set of parameters available to: + - The same set of parameters available to: ref:`Default Outlier Detection Algorithm ` - also applies to this code + also applies to this code. + #. Convert input data, as needed, to make sure it is in a format that can be processed - A :py:class:`~jwst.datamodels.ModelContainer` serves as the basic format for all processing performed by this step, as each entry will be treated as an element of a stack of images - to be processed to identify bad pixels, cosmic-rays and other artifacts - - If the input data is a :py:class:`~jwst.datamodels.CubeModel`, convert it into a - :py:class:`~jwst.datamodels.ModelContainer`. - This allows each plane of the cube to be treated as a separate 2D image - for resampling (if done) and for combining into a median image. -#. Resample all input images into a :py:class:`~jwst.datamodels.ModelContainer` using - :py:class:`~jwst.resample.resample_spec.ResampleSpecData` - - - Resampled images are written out to disk with suffix "outlier_s2d" + to be processed to identify bad pixels, cosmic rays and other artifacts. + +#. If the ``resample_data`` parameter is set to True, resample all input images + using :py:class:`~jwst.resample.resample_spec.ResampleSpecData`. + + - Error images are resampled alongside the science data to create + approximate error arrays for each resampled exposure. + - The resampled data are written out to disk with suffix "outlier_s2d" if the ``save_intermediate_results`` parameter is set to `True`. - - **If resampling is turned off**, the original unrectified inputs are used to create - the median image for cosmic-ray detection. -#. Create a median image from (possibly) resampled :py:class:`~jwst.datamodels.ModelContainer` - - The median image is written out to disk with suffix "median" - if the ``save_intermediate_results`` parameter is set to `True` -#. Blot median image to match each original input image +#. Create a median image from the resampled exposures, or directly from + the input exposures if ``resample_data`` is set to False. + + - The error images for each exposure are also median-combined. + - The median datamodel, with ``data`` and ``err`` extensions, is + written to disk with suffix "median" if the ``save_intermediate_results`` + parameter is set to `True`. +#. If ``resample_data`` is set to True, blot the median image to match + each original input image. + + - The median error image is also blotted to match the input. - Resampled/blotted images are written out to disk if the ``save_intermediate_results`` - parameter is set to `True` - - **If resampling is turned off**, the median image is used for comparison - with the original input models for detecting outliers -#. Perform statistical comparison between blotted image and original image to identify outliers -#. Update input data model DQ arrays with mask of detected outliers + parameter is set to `True`. + +#. Perform statistical comparison between the median image and the original image + to identify outliers. + + - Signal-to-noise thresholding uses the median error image, rather than the + input error image, in order to better identify outliers that have both + high data values and high error values in the input exposures. + +#. Update input data model DQ arrays with mask of detected outliers. + Update the SCI, ERR, and VAR arrays with NaN values at the outlier + locations. .. automodapi:: jwst.outlier_detection.spec diff --git a/jwst/outlier_detection/_fileio.py b/jwst/outlier_detection/_fileio.py index 25dc3a26bd..3cc2e960c5 100644 --- a/jwst/outlier_detection/_fileio.py +++ b/jwst/outlier_detection/_fileio.py @@ -4,45 +4,123 @@ def save_median(median_model, make_output_path): - ''' - Save median if requested by user + """Save a median model. + + Output suffix is 'median'. Parameters ---------- median_model : ~jwst.datamodels.ImageModel The median ImageModel or CubeModel to save - ''' + + make_output_path : function + A function to be used to create an output path from + an input path and an optional suffix. + + """ _save_intermediate_output(median_model, "median", make_output_path) def save_drizzled(drizzled_model, make_output_path): + """Save a drizzled model. + + Input model is expected to have a filename that ends with + either 'outlier_i2d.fits' or 'outlier_s2d.fits'. + + Parameters + ---------- + drizzled_model : ~jwst.datamodels.ImageModel + The median ImageModel or CubeModel to save. + + make_output_path : function + A function to be used to create an output path from + an input path and an optional suffix. + + """ expected_tail = "outlier_?2d.fits" suffix = drizzled_model.meta.filename[-len(expected_tail):-5] _save_intermediate_output(drizzled_model, suffix, make_output_path) -def save_blot(input_model, blot, make_output_path): - blot_model = _make_blot_model(input_model, blot) +def save_blot(input_model, blot, blot_err, make_output_path): + """Save a blotted model. + + Output suffix is 'blot'. + + Parameters + ---------- + input_model : ~jwst.datamodels.ImageModel + An input model corresponding to the blotted data, + containing metadata to copy. + + blot : array-like + The blotted science data. + + blot_err : array-like or None + The blotted error data, if available. + + make_output_path : function + A function to be used to create an output path from + an input path and an optional suffix. + + """ + blot_model = _make_blot_model(input_model, blot, blot_err) _save_intermediate_output(blot_model, "blot", make_output_path) -def _make_blot_model(input_model, blot): +def _make_blot_model(input_model, blot, blot_err): + """Assemble a blot model. + + Parameters + ---------- + input_model : ~jwst.datamodels.ImageModel + An input model corresponding to the blotted data, + containing metadata to copy. + + blot : array-like + The blotted science data. + + blot_err : array-like or None + The blotted error data, if available. + + Returns + ------- + blot_model : ~jwst.datamodels.ImageModel + An image model containing the blotted data. + + """ blot_model = type(input_model)() blot_model.data = blot + if blot_err is not None: + blot_model.err = blot_err blot_model.update(input_model) return blot_model def _save_intermediate_output(model, suffix, make_output_path): - """ - Ensure all intermediate outputs from OutlierDetectionStep have consistent file naming conventions - + """Save an intermediate output from outlier detection. + + Ensure all intermediate outputs from OutlierDetectionStep have + consistent file naming conventions. + + Parameters + ---------- + model : ~jwst.datamodels.ImageModel + The intermediate datamodel to save. + + suffix : str + A suffix to add to the output file name. + + make_output_path : function + A function to be used to create an output path from + an input path and an optional suffix. + Notes ----- self.make_output_path() is updated globally for the step in the main pipeline to include the asn_id in the output path, so no need to handle it here. - """ + """ # outlier_?2d is not a known suffix, and make_output_path cannot handle an # underscore in an unknown suffix, so do a manual string replacement input_path = model.meta.filename.replace("_outlier_", "_") diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index e44afee505..c0c4e0bfa9 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -43,7 +43,7 @@ class OutlierDetectionStep(Step): weight_type = option('ivm','exptime',default='ivm') pixfrac = float(default=1.0) kernel = string(default='square') # drizzle kernel - fillval = string(default='INDEF') + fillval = string(default='NAN') maskpt = float(default=0.7) snr = string(default='5.0 4.0') scale = string(default='1.2 0.7') diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index a0d6f56d2c..e019f456fe 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -1,6 +1,5 @@ -""" -Submodule for performing outlier detection on spectra. -""" +"""Perform outlier detection on spectra.""" + from jwst.datamodels import ModelContainer, ModelLibrary from jwst.stpipe.utilities import record_step_status @@ -36,8 +35,7 @@ def detect_outliers( in_memory, make_output_path, ): - """ - Flag outliers in spec data. + """Flag outliers in spec data. See `OutlierDetectionStep.spec` for documentation of these arguments. """ @@ -68,20 +66,22 @@ def detect_outliers( good_bits=good_bits, ) - median_data, median_wcs = median_with_resampling( + median_data, median_wcs, median_err = median_with_resampling( library, resamp, maskpt, save_intermediate_results=save_intermediate_results, - make_output_path=make_output_path,) + make_output_path=make_output_path, + return_error=True) else: - median_data, median_wcs = median_without_resampling( + median_data, median_wcs, median_err = median_without_resampling( library, maskpt, weight_type, good_bits, save_intermediate_results=save_intermediate_results, make_output_path=make_output_path, + return_error=True ) # Perform outlier detection using statistical comparisons between @@ -96,11 +96,13 @@ def detect_outliers( scale1, scale2, backg, + median_err=median_err, save_blot=save_intermediate_results, make_output_path=make_output_path ) else: flag_crs_in_models(input_models, median_data, - snr1) + snr1, + median_err=median_err) return input_models diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index bca8e6361f..b6dfe1c3eb 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -83,6 +83,7 @@ def test_flag_cr(sci_blot_image_pair): _flag_resampled_model_crs( sci, blot.data, + None, 5.0, 4.0, 1.2, diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index ecebe8fd2b..d0de3bf8b8 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -1,6 +1,4 @@ -""" -The ever-present utils sub-module. A home for all... -""" +"""Utilities for outlier detection methods.""" import copy from functools import partial @@ -24,6 +22,21 @@ def create_cube_median(cube_model, maskpt): + """Compute the median over a cube of data. + + Parameters + ---------- + cube_model : ~jwst.datamodels.CubeModel + The input cube model. + maskpt : float + The percent threshold for masking bad data. + + Returns + ------- + np.ndarray + The median over the zeroth axis of the input cube. + + """ log.info("Computing median") weight_threshold = compute_weight_threshold(cube_model.wht, maskpt) @@ -40,9 +53,12 @@ def median_without_resampling(input_models, good_bits, save_intermediate_results=False, make_output_path=None, - buffer_size=None): - """ - Shared code between imaging and spec modes for resampling and median computation + buffer_size=None, + return_error=False): + """Compute a median image without resampling. + + The median is performed across input exposures, for both + imaging and spectral modes. Parameters ---------- @@ -64,13 +80,29 @@ def median_without_resampling(input_models, if True, save the drizzled models and median model to fits. make_output_path : function - The functools.partial instance to pass to save_median. Must be + The functools.partial instance to pass to save_median. Must be specified if save_intermediate_results is True. Default None. buffer_size : int - The size of chunk in bytes that will be read into memory when computing the median. - This parameter has no effect if the input library has its on_disk attribute - set to False. + The size of chunk in bytes that will be read into memory when + computing the median. This parameter has no effect if the input + library has its on_disk attribute set to False. + + return_error : bool, optional + If True, an approximate median error is computed alongside the + median science image. + + Returns + ------- + median_data : np.ndarray + The median data array. + + median_wcs : gwcs.WCS + A WCS corresponding to the median data. + + median_error : np.ndarray, optional + A median error estimate, returned only if `return_error` is True. + """ in_memory = not input_models._on_disk ngroups = len(input_models) @@ -80,6 +112,10 @@ def median_without_resampling(input_models, drizzled_model = input_models.borrow(i) drizzled_data = drizzled_model.data.copy() + if return_error: + drizzled_err = drizzled_model.err.copy() + else: + drizzled_err = None weight = build_driz_weight(drizzled_model, weight_type=weight_type, good_bits=good_bits) @@ -88,25 +124,41 @@ def median_without_resampling(input_models, input_shape = (ngroups,) + drizzled_data.shape dtype = drizzled_data.dtype computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) + if return_error: + err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) + else: + err_computer = None weight_threshold = compute_weight_threshold(weight, maskpt) drizzled_data[weight < weight_threshold] = np.nan computer.append(drizzled_data, i) + if return_error: + drizzled_err[weight < weight_threshold] = np.nan + err_computer.append(drizzled_err, i) input_models.shelve(drizzled_model, i, modify=False) # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() + if return_error: + median_err = err_computer.evaluate() + else: + median_err = None if save_intermediate_results: # Save median model to fits median_model = datamodels.ImageModel(median_data) + if return_error: + median_model.err = median_err median_model.update(drizzled_model) median_model.meta.wcs = median_wcs _fileio.save_median(median_model, make_output_path) del drizzled_model - return median_data, median_wcs + if return_error: + return median_data, median_wcs, median_err + else: + return median_data, median_wcs def median_with_resampling(input_models, @@ -114,9 +166,12 @@ def median_with_resampling(input_models, maskpt, save_intermediate_results=False, make_output_path=None, - buffer_size=None): - """ - Shared code between imaging and spec modes for resampling and median computation + buffer_size=None, + return_error=False): + """Compute a median image with resampling. + + The median is performed across resampled groups, for both imaging + and spectral modes. Parameters ---------- @@ -133,17 +188,33 @@ def median_with_resampling(input_models, if True, save the drizzled models and median model to fits. make_output_path : function - The functools.partial instance to pass to save_median. Must be + The functools.partial instance to pass to save_median. Must be specified if save_intermediate_results is True. Default None. buffer_size : int - The size of chunk in bytes that will be read into memory when computing the median. - This parameter has no effect if the input library has its on_disk attribute - set to False. + The size of chunk in bytes that will be read into memory when + computing the median. This parameter has no effect if the input + library has its on_disk attribute set to False. + + return_error : bool, optional + If True, an approximate median error is computed alongside the + median science image. + + Returns + ------- + median_data : np.ndarray + The median data array. + + median_wcs : gwcs.WCS + A WCS corresponding to the median data. + + median_error : np.ndarray, optional + A median error estimate, returned only if `return_error` is True. + """ if not resamp.single: raise ValueError("median_with_resampling should only be used for resample_many_to_many") - + in_memory = not input_models._on_disk indices_by_group = list(input_models.group_indices.values()) ngroups = len(indices_by_group) @@ -151,7 +222,8 @@ def median_with_resampling(input_models, with input_models: for i, indices in enumerate(indices_by_group): - drizzled_model = resamp.resample_group(input_models, indices) + drizzled_model = resamp.resample_group( + input_models, indices, compute_error=return_error) if save_intermediate_results: # write the drizzled model to file @@ -162,17 +234,30 @@ def median_with_resampling(input_models, input_shape = (ngroups,)+drizzled_model.data.shape dtype = drizzled_model.data.dtype computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) + if return_error: + err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) + else: + err_computer = None weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan computer.append(drizzled_model.data, i) + if return_error: + drizzled_model.err[drizzled_model.wht < weight_threshold] = np.nan + err_computer.append(drizzled_model.err, i) # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() + if return_error: + median_err = err_computer.evaluate() + else: + median_err = None if save_intermediate_results: # Save median model to fits median_model = datamodels.ImageModel(median_data) + if return_error: + median_model.err = median_err median_model.update(drizzled_model) median_model.meta.wcs = median_wcs # drizzled model already contains asn_id @@ -180,18 +265,23 @@ def median_with_resampling(input_models, _fileio.save_median(median_model, make_output_path) del drizzled_model - return median_data, median_wcs + if return_error: + return median_data, median_wcs, median_err + else: + return median_data, median_wcs def flag_crs_in_models( input_models, median_data, snr1, + median_err=None ): + """Flag outliers in all input models without resampling.""" for image in input_models: # dq flags will be updated in-place - flag_model_crs(image, median_data, snr1) - + flag_model_crs(image, median_data, snr1, median_err=median_err) + def flag_resampled_model_crs( input_model, @@ -202,9 +292,11 @@ def flag_resampled_model_crs( scale1, scale2, backg, + median_err=None, save_blot=False, make_output_path=None, ): + """Flag outliers in a resampled model.""" if 'SPECTRAL' not in input_model.meta.wcs.output_frame.axes_type: input_pixflux_area = input_model.meta.photometry.pixelarea_steradians # Set array shape, needed to compute image pixel area @@ -214,22 +306,31 @@ def flag_resampled_model_crs( else: pix_ratio = 1.0 - blot = gwcs_blot(median_data, median_wcs, input_model.data.shape, input_model.meta.wcs, pix_ratio) + blot = gwcs_blot(median_data, median_wcs, input_model.data.shape, + input_model.meta.wcs, pix_ratio, fillval=np.nan) + if median_err is not None: + blot_err = gwcs_blot(median_err, median_wcs, input_model.data.shape, + input_model.meta.wcs, pix_ratio, fillval=np.nan) + else: + blot_err = None if save_blot: - _fileio.save_blot(input_model, blot, make_output_path) + _fileio.save_blot(input_model, blot, blot_err, make_output_path) + # dq flags will be updated in-place - _flag_resampled_model_crs(input_model, blot, snr1, snr2, scale1, scale2, backg) + _flag_resampled_model_crs(input_model, blot, blot_err, snr1, snr2, scale1, scale2, backg) def _flag_resampled_model_crs( input_model, blot, + blot_err, snr1, snr2, scale1, scale2, backg, ): + """Flag outliers via comparison to a blotted image.""" # If the datamodel has a measured background that has not been subtracted # use it instead of the user provided backg. # Get background level of science data if it has not been subtracted, so it @@ -240,7 +341,11 @@ def _flag_resampled_model_crs( backg = input_model.meta.background.level log.debug(f"Adding background level {backg} to blotted image") - cr_mask = flag_resampled_crs(input_model.data, input_model.err, blot, snr1, snr2, scale1, scale2, backg) + if blot_err is not None: + err_to_use = blot_err + else: + err_to_use = input_model.err + cr_mask = flag_resampled_crs(input_model.data, err_to_use, blot, snr1, snr2, scale1, scale2, backg) # update the dq flags in-place input_model.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) @@ -260,9 +365,11 @@ def flag_crs_in_models_with_resampling( scale1, scale2, backg, + median_err=None, save_blot=False, make_output_path=None, ): + """Flag outliers in all input models, with resampling.""" for image in input_models: flag_resampled_model_crs(image, median_data, @@ -272,12 +379,18 @@ def flag_crs_in_models_with_resampling( scale1, scale2, backg, + median_err=median_err, save_blot=save_blot, make_output_path=make_output_path) -def flag_model_crs(image, blot, snr): - cr_mask = flag_crs(image.data, image.err, blot, snr) +def flag_model_crs(image, blot, snr, median_err=None): + """Flag outliers in a model.""" + if median_err is not None: + error_to_use = median_err + else: + error_to_use = image.err + cr_mask = flag_crs(image.data, error_to_use, blot, snr) # Update dq array in-place image.dq |= cr_mask * np.uint32(DO_NOT_USE | OUTLIER) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 7d70ae840d..b3e7e7f485 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -252,7 +252,7 @@ def _get_intensity_scale(self, img): iscale = 1.0 return iscale - def resample_group(self, input_models, indices): + def resample_group(self, input_models, indices, compute_error=False): """Apply resample_many_to_many for one group Parameters @@ -260,6 +260,10 @@ def resample_group(self, input_models, indices): input_models : ModelLibrary indices : list + + compute_error : bool, optional + If True, an approximate error image will be resampled + alongside the science image. """ output_model = self.blank_output.copy() @@ -284,6 +288,14 @@ def resample_group(self, input_models, indices): driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, kernel=self.kernel, fillval=self.fillval) + # Also make a temporary model to hold error data + error_model, driz_error = None, None + if compute_error: + error_model = output_model.copy() + driz_error = gwcs_drizzle.GWCSDrizzle( + error_model, pixfrac=self.pixfrac, + kernel=self.kernel, fillval=self.fillval) + log.info(f"{len(indices)} exposures to drizzle together") for index in indices: img = input_models.borrow(index) @@ -323,9 +335,29 @@ def resample_group(self, input_models, indices): ymax=ymax ) del data + + # make an approximate error image by drizzling it + # in the same way the image is handled + if compute_error: + driz_error.add_image( + img.err, + img.meta.wcs, + iscale=iscale, + inwht=inwht, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax + ) + input_models.shelve(img, index, modify=False) del img + # copy the drizzled error into the output model + if compute_error: + output_model.err = error_model.data + del error_model + return output_model def resample_many_to_many(self, input_models): @@ -359,6 +391,7 @@ def resample_many_to_many(self, input_models): asn = asn_from_list(output_models, product_name='outlier_i2d') asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict return ModelLibrary(asn_dict, on_disk=True) + # otherwise just build it as a list of in-memory models return ModelLibrary(output_models, on_disk=False) @@ -435,7 +468,7 @@ def resample_many_to_one(self, input_models): output_model.var_poisson, output_model.var_flat ] - output_model.err = np.sqrt(np.nansum(var_components,axis=0)) + output_model.err = np.sqrt(np.nansum(var_components, axis=0)) # nansum returns zero for input that is all NaN - # set those values to NaN instead @@ -603,7 +636,7 @@ def _resample_one_variance_array(self, name, input_model, output_model): iscale=iscale, pixfrac=self.pixfrac, kernel=self.kernel, - fillval=np.nan, + fillval='NAN', xmin=xmin, xmax=xmax, ymin=ymin, From 62cd2e6850bd7ac125f1e7705eb6dce5337807cd Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 17 Oct 2024 14:14:03 -0400 Subject: [PATCH 546/687] Fix dangerous 'rm' suggestion Co-authored-by: Brett Graham --- docs/jwst/user_documentation/reference_files_crds.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/user_documentation/reference_files_crds.rst b/docs/jwst/user_documentation/reference_files_crds.rst index 9515c7891a..f4b1bdd706 100644 --- a/docs/jwst/user_documentation/reference_files_crds.rst +++ b/docs/jwst/user_documentation/reference_files_crds.rst @@ -150,7 +150,7 @@ For example: :: - rm -rf $CRDS_PATH/* + rm -r $CRDS_PATH For more information on CRDS configuration, see the `CRDS user guide From c275fa6c8197322f84fe7ef882fb9535dc41feec Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 18 Oct 2024 06:37:08 -0700 Subject: [PATCH 547/687] JP-3781: Use center=False for compute_s_region_imaging (#8897) Co-authored-by: Nadia Dencheva --- changes/8897.assign_wcs.rst | 1 + jwst/assign_wcs/tests/test_nircam.py | 29 ++++++++++++++++++++++++++++ jwst/assign_wcs/util.py | 2 +- 3 files changed, 31 insertions(+), 1 deletion(-) create mode 100644 changes/8897.assign_wcs.rst diff --git a/changes/8897.assign_wcs.rst b/changes/8897.assign_wcs.rst new file mode 100644 index 0000000000..fb209a0fdc --- /dev/null +++ b/changes/8897.assign_wcs.rst @@ -0,0 +1 @@ +Use pixel vertices to define s_region instead of pixel centers." diff --git a/jwst/assign_wcs/tests/test_nircam.py b/jwst/assign_wcs/tests/test_nircam.py index 3ee141f0f9..839e3f2e56 100644 --- a/jwst/assign_wcs/tests/test_nircam.py +++ b/jwst/assign_wcs/tests/test_nircam.py @@ -10,6 +10,7 @@ """ import numpy as np import pytest +import re from astropy.io import fits @@ -244,3 +245,31 @@ def test_wfss_sip(): util.wfss_imaging_wcs(wfss_model, nircam.imaging, bbox=((1, 1024), (1, 1024))) for key in ['a_order', 'b_order', 'crpix1', 'crpix2', 'crval1', 'crval2', 'cd1_1']: assert key in wfss_model.meta.wcsinfo.instance + + +def _sregion_to_footprint(s_region): + """ + Parameters + ---------- + s_region : str + The S_REGION header keyword + + Returns + ------- + footprint : np.array + A 2D array of the footprint of the region, shape (N, 2) + """ + no_prefix = re.sub(r"[a-zA-Z]", "", s_region) + return np.array(no_prefix.split(), dtype=float).reshape(-1, 2) + + +def test_update_s_region_imaging(): + """Ensure the s_region keyword matches output of wcs.footprint()""" + model = ImageModel(create_hdul()) + model.meta.wcs = create_imaging_wcs() + util.update_s_region_imaging(model) + + s_region = model.meta.wcsinfo.s_region + footprint = model.meta.wcs.footprint().flatten() + footprint_sregion = _sregion_to_footprint(s_region).flatten() + assert np.allclose(footprint, footprint_sregion, rtol=1e-9) diff --git a/jwst/assign_wcs/util.py b/jwst/assign_wcs/util.py index b33620ed8b..d8e492587f 100644 --- a/jwst/assign_wcs/util.py +++ b/jwst/assign_wcs/util.py @@ -795,7 +795,7 @@ def update_s_region_imaging(model): """ Update the ``S_REGION`` keyword using ``WCS.footprint``. """ - s_region = compute_s_region_imaging(model.meta.wcs, shape=model.data.shape) + s_region = compute_s_region_imaging(model.meta.wcs, shape=model.data.shape, center=False) if s_region is not None: model.meta.wcsinfo.s_region = s_region From 6d4726b7f94ddbcd2530d0fb5b73120ce9692644 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 18 Oct 2024 16:06:22 -0400 Subject: [PATCH 548/687] modifications to update step params for mos pipe --- .../master_background_mos_step.py | 13 ++++++++++++- jwst/master_background/nirspec_utils.py | 19 +++++++++++++++---- 2 files changed, 27 insertions(+), 5 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 7514532a4e..3d841c16a2 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -180,6 +180,16 @@ def set_pars_from_parent(self): del pars[par] getattr(self, step).update_pars(pars) + def _get_steps_params(self): + """Get substep parameters to pass on""" + pixrep_pars = getattr(self, 'pixel_replace').get_pars() + resamp_pars = getattr(self, 'resample_spec').get_pars() + extr1d_pars = getattr(self, 'extract_1d').get_pars() + params = {'pixel_replace': pixrep_pars, + 'resample_spec': resamp_pars, + 'extract_1d': extr1d_pars} + return params + def _classify_slits(self, data): """Determine how many Slits are background and source types @@ -252,7 +262,8 @@ def _calc_master_background(self, data, user_background=None): master_background = user_background else: self.log.debug('Calculating 1D master background') - master_background = nirspec_utils.create_background_from_multislit(pre_calibrated) + params = self._get_steps_params() + master_background = nirspec_utils.create_background_from_multislit(pre_calibrated, params) if master_background is None: self.log.debug('No master background could be calculated. Returning None') return None, None diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index af48c81603..b8ada14c31 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -76,7 +76,7 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(input_model): +def create_background_from_multislit(input_model, params): """Create a 1D master background spectrum from a set of calibrated background MOS slitlets in the input MultiSlitModel. @@ -86,6 +86,9 @@ def create_background_from_multislit(input_model): input_model : `~jwst.datamodels.MultiSlitModel` The input data model containing all slit instances. + params : dict + Dictionary containing a dictionary of the step parameters to set + Returns ------- master_bkg: `~jwst.datamodels.CombinedSpecModel` @@ -113,11 +116,19 @@ def create_background_from_multislit(input_model): bkg_model.slits.extend(slits) + # update step parameters + pr = pixel_replace_step.PixelReplaceStep() + pr.update_pars(params['pixel_replace']) + rs = resample_spec_step.ResampleSpecStep() + rs.update_pars(params['resample_spec']) + e1d = extract_1d_step.Extract1dStep() + e1d.update_pars(params['extract_1d']) + # Apply pixel_replace, resample_spec and extract_1d to all background slitlets log.info('Applying pixel_replace, resampling and 1D extraction to background slits') - pixrepl = pixel_replace_step.PixelReplaceStep.call(bkg_model) - resamp = resample_spec_step.ResampleSpecStep.call(pixrepl) - x1d = extract_1d_step.Extract1dStep.call(resamp) + pixrepl = pr.call(bkg_model) + resamp = rs.call(pixrepl) + x1d = e1d.call(resamp) # Call combine_1d to combine the 1D background spectra log.info('Combining 1D background spectra into master background') From a617dfa700c5f318a42dadedc69a239950c075fb Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 11 Oct 2024 13:16:05 -0400 Subject: [PATCH 549/687] Update regression test data for NIRCam subarray --- jwst/regtest/test_nircam_subarray_4amp.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/jwst/regtest/test_nircam_subarray_4amp.py b/jwst/regtest/test_nircam_subarray_4amp.py index ef9a0932cb..0d91b0fb14 100644 --- a/jwst/regtest/test_nircam_subarray_4amp.py +++ b/jwst/regtest/test_nircam_subarray_4amp.py @@ -6,10 +6,9 @@ from jwst.stpipe import Step """ -jw00617196001_02102_00001_nrca4_uncal.fits the input (uncal) file -jw00617196001_02102_00001_nrca4_rate.fits rate file -jw00617196001_02102_00001_nrca4_rateints.fits rateints file -jw00617196001_02102_00001_nrca4_trapsfilled.fits trapsfilled file +jw01409034001_02102_00001_nrca3_uncal.fits the input (uncal) file +jw01409034001_02102_00001_nrca3_rate.fits rate file +jw01409034001_02102_00001_nrca3_rateints.fits rateints file """ @@ -17,7 +16,7 @@ def run_pipeline(rtdata_module): """Run calwebb_detector1 pipeline on NIRCAM subarray data.""" rtdata = rtdata_module - rtdata.get_data("nircam/subarray/jw00617196001_02102_00001_nrca4_uncal.fits") + rtdata.get_data("nircam/subarray/jw01409034001_02102_00001_nrca3_uncal.fits") args = ["jwst.pipeline.Detector1Pipeline", rtdata.input] Step.from_cmdline(args) @@ -27,8 +26,8 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("output", [ - 'jw00617196001_02102_00001_nrca4_rate.fits', - 'jw00617196001_02102_00001_nrca4_rateints.fits'], + 'jw01409034001_02102_00001_nrca3_rate.fits', + 'jw01409034001_02102_00001_nrca3_rateints.fits'], ids=['rate', 'rateints']) def test_nircam_detector1_subarray(run_pipeline, fitsdiff_default_kwargs, output): """ From 19f78091e3366fe1adf389daebae456e4ef57646 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 18 Oct 2024 16:06:22 -0400 Subject: [PATCH 550/687] Switch to dataset with noutputs=4 --- jwst/regtest/test_nircam_subarray_4amp.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/jwst/regtest/test_nircam_subarray_4amp.py b/jwst/regtest/test_nircam_subarray_4amp.py index 0d91b0fb14..53398b2880 100644 --- a/jwst/regtest/test_nircam_subarray_4amp.py +++ b/jwst/regtest/test_nircam_subarray_4amp.py @@ -6,9 +6,9 @@ from jwst.stpipe import Step """ -jw01409034001_02102_00001_nrca3_uncal.fits the input (uncal) file -jw01409034001_02102_00001_nrca3_rate.fits rate file -jw01409034001_02102_00001_nrca3_rateints.fits rateints file +jw02459005001_03103_00001-seg001_nrca3_uncal.fits the input (uncal) file +jw02459005001_03103_00001-seg001_nrca3_rate.fits rate file +jw02459005001_03103_00001-seg001_nrca3_rateints.fits rateints file """ @@ -16,7 +16,7 @@ def run_pipeline(rtdata_module): """Run calwebb_detector1 pipeline on NIRCAM subarray data.""" rtdata = rtdata_module - rtdata.get_data("nircam/subarray/jw01409034001_02102_00001_nrca3_uncal.fits") + rtdata.get_data("nircam/subarray/jw02459005001_03103_00001-seg001_nrca3_uncal.fits") args = ["jwst.pipeline.Detector1Pipeline", rtdata.input] Step.from_cmdline(args) @@ -26,8 +26,8 @@ def run_pipeline(rtdata_module): @pytest.mark.bigdata @pytest.mark.parametrize("output", [ - 'jw01409034001_02102_00001_nrca3_rate.fits', - 'jw01409034001_02102_00001_nrca3_rateints.fits'], + 'jw02459005001_03103_00001-seg001_nrca3_rate.fits', + 'jw02459005001_03103_00001-seg001_nrca3_rateints.fits'], ids=['rate', 'rateints']) def test_nircam_detector1_subarray(run_pipeline, fitsdiff_default_kwargs, output): """ From 4d6fe063fc92650ea51614cf55285eb115e29de9 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Mon, 21 Oct 2024 16:57:38 -0400 Subject: [PATCH 551/687] addressing PR comments --- .../master_background_mos_step.py | 41 ++++++++++++----- jwst/master_background/nirspec_utils.py | 46 +------------------ 2 files changed, 32 insertions(+), 55 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 3d841c16a2..e91815bcca 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -180,15 +180,28 @@ def set_pars_from_parent(self): del pars[par] getattr(self, step).update_pars(pars) - def _get_steps_params(self): + def _set_steps_params(self): """Get substep parameters to pass on""" - pixrep_pars = getattr(self, 'pixel_replace').get_pars() - resamp_pars = getattr(self, 'resample_spec').get_pars() - extr1d_pars = getattr(self, 'extract_1d').get_pars() - params = {'pixel_replace': pixrep_pars, - 'resample_spec': resamp_pars, - 'extract_1d': extr1d_pars} - return params + steps = ['pixel_replace', 'resample_spec', 'extract_1d'] + + for step in steps: + pars = getattr(self, step).get_pars() + getattr(self, step).update_pars(pars) + + def _extend_bg_slits(self, pre_calibrated): + # Copy dedicated background slitlets to a temporary model + bkg_model = datamodels.MultiSlitModel() + bkg_model.update(pre_calibrated) + slits = [] + for slit in pre_calibrated.slits: + if nirspec_utils.is_background_msa_slit(slit): + self.log.info(f'Using background slitlet {slit.source_name}') + slits.append(slit) + if len(slits) == 0: + self.log.warning('No background slitlets found; skipping master bkg correction') + return None + bkg_model.slits.extend(slits) + return bkg_model def _classify_slits(self, data): """Determine how many Slits are background and source types @@ -261,9 +274,15 @@ def _calc_master_background(self, data, user_background=None): self.log.debug(f'User background provided {user_background}') master_background = user_background else: - self.log.debug('Calculating 1D master background') - params = self._get_steps_params() - master_background = nirspec_utils.create_background_from_multislit(pre_calibrated, params) + self.log.info('Creating MOS master background from background slitlets') + self._set_steps_params() + bkg_model = self._extend_bg_slits(pre_calibrated) + bkg_model = self.pixel_replace(bkg_model) + bkg_model = self.resample_spec(bkg_model) + bkg_model = self.extract_1d(bkg_model) + # Call combine_1d to combine the 1D background spectra + master_background = nirspec_utils.create_background_from_multislit(bkg_model) + del bkg_model if master_background is None: self.log.debug('No master background could be calculated. Returning None') return None, None diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index b8ada14c31..354b99cac5 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -76,7 +76,7 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(input_model, params): +def create_background_from_multislit(input_model): """Create a 1D master background spectrum from a set of calibrated background MOS slitlets in the input MultiSlitModel. @@ -86,58 +86,16 @@ def create_background_from_multislit(input_model, params): input_model : `~jwst.datamodels.MultiSlitModel` The input data model containing all slit instances. - params : dict - Dictionary containing a dictionary of the step parameters to set - Returns ------- master_bkg: `~jwst.datamodels.CombinedSpecModel` The 1D master background spectrum created from the inputs. """ - from ..pixel_replace import pixel_replace_step - from ..resample import resample_spec_step - from ..extract_1d import extract_1d_step from ..combine_1d.combine1d import combine_1d_spectra - log.info('Creating MOS master background from background slitlets') - - # Copy dedicated background slitlets to a temporary model - bkg_model = datamodels.MultiSlitModel() - bkg_model.update(input_model) - slits = [] - for slit in input_model.slits: - if is_background_msa_slit(slit): - log.info(f'Using background slitlet {slit.source_name}') - slits.append(slit) - - if len(slits) == 0: - log.warning('No background slitlets found; skipping master bkg correction') - return None - - bkg_model.slits.extend(slits) - - # update step parameters - pr = pixel_replace_step.PixelReplaceStep() - pr.update_pars(params['pixel_replace']) - rs = resample_spec_step.ResampleSpecStep() - rs.update_pars(params['resample_spec']) - e1d = extract_1d_step.Extract1dStep() - e1d.update_pars(params['extract_1d']) - - # Apply pixel_replace, resample_spec and extract_1d to all background slitlets - log.info('Applying pixel_replace, resampling and 1D extraction to background slits') - pixrepl = pr.call(bkg_model) - resamp = rs.call(pixrepl) - x1d = e1d.call(resamp) - # Call combine_1d to combine the 1D background spectra log.info('Combining 1D background spectra into master background') - master_bkg = combine_1d_spectra(x1d, exptime_key='exposure_time') - - del bkg_model - del pixrepl - del resamp - del x1d + master_bkg = combine_1d_spectra(input_model, exptime_key='exposure_time') return master_bkg From 8893b48345ba7bb2763e942a5b2e22fe2e6ddaf1 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Mon, 21 Oct 2024 17:28:18 -0400 Subject: [PATCH 552/687] adding a catch in case of None --- .../master_background_mos_step.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index e91815bcca..5ab00291e1 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -277,12 +277,15 @@ def _calc_master_background(self, data, user_background=None): self.log.info('Creating MOS master background from background slitlets') self._set_steps_params() bkg_model = self._extend_bg_slits(pre_calibrated) - bkg_model = self.pixel_replace(bkg_model) - bkg_model = self.resample_spec(bkg_model) - bkg_model = self.extract_1d(bkg_model) - # Call combine_1d to combine the 1D background spectra - master_background = nirspec_utils.create_background_from_multislit(bkg_model) - del bkg_model + if bkg_model is not None: + bkg_model = self.pixel_replace(bkg_model) + bkg_model = self.resample_spec(bkg_model) + bkg_model = self.extract_1d(bkg_model) + # Call combine_1d to combine the 1D background spectra + master_background = nirspec_utils.create_background_from_multislit(bkg_model) + del bkg_model + else: + master_background = None if master_background is None: self.log.debug('No master background could be calculated. Returning None') return None, None From 7f54cae4eca3dcb07c8210cf390ac02ee2729355 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Tue, 22 Oct 2024 11:05:58 -0400 Subject: [PATCH 553/687] Store NIRISS sourcefind regression test catalog in rtdata (#8902) --- jwst/regtest/test_niriss_sourcefind.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jwst/regtest/test_niriss_sourcefind.py b/jwst/regtest/test_niriss_sourcefind.py index e0c7c9b496..6f8e21d7ba 100644 --- a/jwst/regtest/test_niriss_sourcefind.py +++ b/jwst/regtest/test_niriss_sourcefind.py @@ -24,6 +24,9 @@ def test_tweakreg_catalog_starfinder_alternatives(rtdata, starfinder): 'minsep_fwhm': 2.5, 'sigma_radius': 2.5, }) + output_name = f"{stem}_{starfinder}_cat.ecsv" + catalog.write(output_name, format='ascii.ecsv') + rtdata.output = output_name rtdata.get_truth(f"truth/test_niriss_sourcefind/{stem}_{starfinder}_cat.ecsv") catalog_truth = ascii.read(rtdata.truth) From 3be9992701027c82f2a03a22791f7400bd2ec012 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 22 Oct 2024 08:16:46 -0700 Subject: [PATCH 554/687] JP-3342: Reorganize outlier detection documentation (#8880) --- changes/8880.outlier_detection.rst | 1 + docs/jwst/outlier_detection/arguments.rst | 108 +++++---- docs/jwst/outlier_detection/index.rst | 4 +- docs/jwst/outlier_detection/main.rst | 94 +++---- .../outlier_detection_coron.rst | 25 +- .../outlier_detection_ifu.rst | 66 +++-- .../outlier_detection_imaging.rst | 229 +++++++----------- .../outlier_detection_spec.rst | 82 ++----- .../outlier_detection_step.rst | 53 ---- .../outlier_detection_tso.rst | 50 ++-- .../outlier_detection/reference_files.rst | 4 + jwst/outlier_detection/ifu.py | 5 + .../outlier_detection_step.py | 7 +- 13 files changed, 310 insertions(+), 418 deletions(-) create mode 100644 changes/8880.outlier_detection.rst delete mode 100644 docs/jwst/outlier_detection/outlier_detection_step.rst create mode 100644 docs/jwst/outlier_detection/reference_files.rst diff --git a/changes/8880.outlier_detection.rst b/changes/8880.outlier_detection.rst new file mode 100644 index 0000000000..f5642b4e30 --- /dev/null +++ b/changes/8880.outlier_detection.rst @@ -0,0 +1 @@ +reorganize outlier detection documentation diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index e44d271e84..4deae0e47b 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -1,19 +1,53 @@ .. _outlier_detection_step_args: +Step Arguments +============== + +The outlier detection step has the following optional arguments +that control the behavior of the processing. For more details about step arguments (including datatypes, possible values and defaults) see :py:obj:`jwst.outlier_detection.OutlierDetectionStep.spec`. -Step Arguments for Non-IFU data -=============================== -The `outlier_detection` step for non-IFU data has the following optional arguments -that control the behavior of the processing: + +General Step Arguments +---------------------- +The following arguments apply to all modes unless otherwise specified: + +``--save_intermediate_results`` + Specifies whether or not to save any intermediate products created + during step processing. + +``--good_bits`` + The DQ bit values from the input image DQ arrays + that should be considered 'good'. Any pixel with a DQ value not included + in this value (or list of values) will be ignored when resampling and flagged + when building the weight mask. See DQ flag :ref:`dq_parameter_specification` for details. + Has no effect for IFU data. + +``--snr`` + The signal-to-noise values to use for bad pixel identification. + Since cosmic rays often extend across several pixels, the user + must specify two cut-off values for determining whether a pixel should + be masked: the first for detecting the primary cosmic ray, and the + second (typically lower threshold) for masking lower-level bad pixels + adjacent to those found in the first pass. Valid values are a pair of + floating-point values in a single string (for example "5.0 4.0"). + Has no effect for IFU data. + + +Step Arguments for Imaging and Slit-like Spectroscopic data +----------------------------------------------------------- ``--weight_type`` - The type of data weighting to use during resampling. + The type of data weighting to apply to the resampled data. Available options are ``ivm`` + (default) to compute and use an inverse-variance map, and ``exptime`` to + weight by the exposure time. ``--pixfrac`` - The pixel fraction used during resampling; - valid values go from 0.0 to 1.0. + The pixel fraction used during resampling; valid values go from 0.0 to 1.0. + Indicates the fraction by which input pixels are "shrunk" before being drizzled onto the + output image grid. This specifies the size of the footprint, or "dropsize", of a pixel + in units of the input pixel size. ``--kernel`` The form of the kernel function used to distribute flux onto a @@ -29,15 +63,6 @@ that control the behavior of the processing: The percent of maximum weight to use as lower-limit for valid data; valid values go from 0.0 to 1.0. -``--snr`` - The signal-to-noise values to use for bad pixel identification. - Since cosmic rays often extend across several pixels the user - must specify two cut-off values for determining whether a pixel should - be masked: the first for detecting the primary cosmic ray, and the - second (typically lower threshold) for masking lower-level bad pixels - adjacent to those found in the first pass. Valid values are a pair of - floating-point values in a single string (for example "5.0 4.0"). - ``--scale`` The scaling factor applied to derivative used to identify bad pixels. Since cosmic rays often extend across several pixels the user @@ -50,30 +75,16 @@ that control the behavior of the processing: ``--backg`` User-specified background value to apply to the median image. -``--rolling_window_width`` - Number of integrations over which to take the median when using rolling-window - median for TSO observations. - -``--save_intermediate_results`` - Specifies whether or not to save any intermediate products created - during step processing. - ``--resample_data`` Specifies whether or not to resample the input images when performing outlier detection. -``--good_bits`` - The DQ bit values from the input image DQ arrays - that should be considered 'good' when building the weight mask. See - DQ flag :ref:`dq_parameter_specification` for details. - ``--allowed_memory`` Specifies the fractional amount of free memory to allow when creating the resampled image. If ``None``, the environment variable ``DMODEL_ALLOWED_MEMORY`` is used. If not defined, no check is made. If the resampled image would be larger than specified, an ``OutputTooLargeError`` exception will be generated. - For example, if set to ``0.5``, only resampled images that use less than half the available memory can be created. @@ -81,28 +92,37 @@ that control the behavior of the processing: Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when needed and all intermediate files are stored on disk, rather than in memory. - This flag is superseded by the pipeline-level ``--in-memory`` flag, and thus - has no effect when running the full level 3 pipeline. + Has no effect for spectroscopic data. For imaging data this parameter is + superseded by the pipeline-level ``in_memory`` parameter set by + ``calwebb_image3``. + Step Arguments for IFU data -=========================== -The `outlier_detection` step for IFU data has the following optional arguments -that control the behavior of the processing: +--------------------------- ``--kernel_size`` The size of the kernel to use to normalize the pixel differences. The kernel size must only contain odd values. Valid values are a pair of ints in a single string - (for example "7 7"). + (for example "7 7", the default). ``--threshold_percent`` The threshold (in percent) of the normalized minimum pixel difference used to identify bad pixels. - Pixels with a normalized minimum pixel difference above this percentage are flagged as a outlier. + Pixels with a normalized minimum difference above this percentage are flagged as outliers. -``--save_intermediate_results`` - Specifies whether or not to save any intermediate products created - during step processing. +``--ifu_second_check`` + Perform a secondary check searching for outliers. This will set outliers + where ever the difference array of adjacent pixels is a Nan. -``--in_memory`` - Specifies whether or not to load and create all images that are used during - processing into memory. If ``False``, input files are loaded from disk when - needed and all intermediate files are stored on disk, rather than in memory. + +Step Arguments for TSO data +--------------------------- + +``--rolling_window_width`` + Number of integrations over which to take the median when using rolling-window + median for TSO observations. The default is 25. If the number of integrations + is less than or equal to ``rolling_window_width``, a simple median is used instead. + + +Step Arguments for Coronagraphic data +------------------------------------- +General step arguments apply to coronagraphic data. No additional arguments are used. \ No newline at end of file diff --git a/docs/jwst/outlier_detection/index.rst b/docs/jwst/outlier_detection/index.rst index e78ff7c57f..649c35a4e7 100644 --- a/docs/jwst/outlier_detection/index.rst +++ b/docs/jwst/outlier_detection/index.rst @@ -8,12 +8,12 @@ Outlier Detection :maxdepth: 2 main.rst + reference_files.rst arguments.rst - outlier_detection_step.rst outlier_detection_imaging.rst + outlier_detection_spec.rst outlier_detection_coron.rst outlier_detection_tso.rst outlier_detection_ifu.rst - outlier_detection_spec.rst .. automodapi:: jwst.outlier_detection diff --git a/docs/jwst/outlier_detection/main.rst b/docs/jwst/outlier_detection/main.rst index 4e53369f27..f227f9f5bc 100644 --- a/docs/jwst/outlier_detection/main.rst +++ b/docs/jwst/outlier_detection/main.rst @@ -1,58 +1,42 @@ -Description -=========== +.. _outlier_design: -:Classes: `jwst.outlier_detection.OutlierDetectionStep` -:Aliases: outlier_detection +Overview +======== + +This module provides the sole interface to all methods of performing outlier +detection on JWST observations. Processing multiple datasets together allows for the identification of bad pixels -or cosmic-rays that remain in each of the input images, many times at levels which -were not detectable by the :ref:`jump ` step. The ``outlier_detection`` step -implements the following algorithm to identify and flag any remaining cosmic-rays or -other artifacts left over from previous calibrations: - -#. build a stack of input data - - - all inputs will need to have the same WCS since outlier detection assumes - the same flux for each point on the sky, and variations from one image to - the next would indicate a problem with the detector during readout of that pixel - - if needed, each input will be resampled to a common output WCS - -#. create a median image from the stack of input data - - - this median operation will ignore any input pixels which have a weight - which is too low (<70% max weight) - -#. create "blotted" data from the median image to exactly match each original - input dataset - -#. perform a statistical comparison (pixel-by-pixel) between the median blotted - data with the original input data to look for pixels with values that are - different from the mean value by more than some specified sigma - based on the noise model - - - the noise model used relies on the error array computed by previous - calibration steps based on the readnoise and calibration errors - -#. flag the DQ array for the input data for any pixel (or affected neighboring - pixels) identified as a statistical outlier - -The outlier detection step serves as a single interface to apply this general -process to any JWST data, with specific variations of this algorithm for each -type of data. Sub-classes of the outlier detection algorithm have been developed -specifically for: - -#. Imaging data -#. IFU spectroscopic data -#. TSO data -#. coronagraphic data -#. spectroscopic data - -This allows the outlier_detection step to be tuned to the variations in each type -of JWST data. - -Reference Files -=============== - -The ``outlier_detection`` step uses the PARS-OUTLIERDETECTIONSTEP parameter reference file. - -.. include:: ../references_general/pars-outlierdetectionstep_reffile.inc +or cosmic rays that remain in each of the input images, often at levels which +were not detectable by the :ref:`jump ` step. +The ``outlier_detection`` step supports multiple +algorithms and determines the appropriate algorithm for the type of observation +being processed. This step supports: + +* **Image modes**: 'FGS_IMAGE', 'MIR_IMAGE', 'NRC_IMAGE', 'NIS_IMAGE' + - See :ref:`outlier-detection-imaging` for algorithm details +* **Slit-like Spectroscopic modes**: 'MIR_LRS-FIXEDSLIT', 'NRS_FIXEDSLIT', 'NRS_MSASPEC' + - See :ref:`outlier-detection-spec` for algorithm details +* **Time-Series-Observation (TSO) modes**: 'MIR_LRS-SLITLESS', 'NRC_TSGRISM', 'NIS_SOSS', 'NRS_BRIGHTOBJ', 'NRC_TSIMAGE' + - See :ref:`outlier-detection-tso` for algorithm details +* **IFU Spectroscopic modes**: 'MIR_MRS', 'NRS_IFU' + - See :ref:`outlier-detection-ifu` for algorithm details +* **Coronagraphic Image modes**: 'MIR_LYOT', 'MIR_4QPM', 'NRC_CORON' + - See :ref:`outlier-detection-coron` for algorithm details + +This step uses the following logic to apply the appropriate algorithm to the +input data: + +#. Interpret inputs (Association, ModelContainer, ModelLibrary, or CubeModel) + to identify all input observations to be processed + +#. Read in parameters set by user. See :ref:`outlier_detection_step_args` for the full list + of parameters. + +#. Select outlier detection algorithm based on exposure type in input model ``meta.exposure.type``. + +#. Instantiate and run outlier detection class determined for the exposure type + using parameter values interpreted from inputs. + +#. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location + of identified outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_coron.rst b/docs/jwst/outlier_detection/outlier_detection_coron.rst index 72cbd79b80..7580107e9b 100644 --- a/docs/jwst/outlier_detection/outlier_detection_coron.rst +++ b/docs/jwst/outlier_detection/outlier_detection_coron.rst @@ -1,26 +1,24 @@ .. _outlier-detection-coron: -Outlier Detection for Coronagraphic Data -======================================== +Coronagraphic Data +================== This module serves as the interface for applying ``outlier_detection`` to coronagraphic -image observations. - -Specifically, this routine performs the following operations: +image observations. A :py:class:`~jwst.datamodels.CubeModel` serves as the basic format +for all processing performed by this step. This routine performs the following operations: #. Extract parameter settings from input model and merge them with any user-provided values. See :ref:`outlier detection arguments ` for the full list of parameters. -#. Convert input data, as needed, to make sure it is in a format that can be processed. - A :py:class:`~jwst.datamodels.CubeModel` serves as the basic format for all processing - performed by this step. +#. Do not attempt resampling; data are assumed to be aligned and have an identical WCS. + This is true automatically for a CubeModel. -#. Create a median image preserving the spatial dimensions of the cube. +#. Create a median image over the `groups` (exposures, planes of cube) axis, + preserving the spatial (x,y) dimensions of the cube. * The ``maskpt`` parameter sets the percentage of the weight image values to - use, and any pixel with a weight below this value gets flagged as "bad" and - ignored when resampled. + use, and any pixel with a weight below this value gets flagged as "bad". #. Perform statistical comparison between median image and original image to identify outliers. @@ -28,6 +26,5 @@ Specifically, this routine performs the following operations: .. math:: | image\_input - image\_median | > SNR*input\_err -#. Update input data model DQ arrays with mask of detected outliers. - -.. automodapi:: jwst.outlier_detection.coron +#. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location + of identified outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_ifu.rst b/docs/jwst/outlier_detection/outlier_detection_ifu.rst index f8a4c4d66e..e12fd7afef 100644 --- a/docs/jwst/outlier_detection/outlier_detection_ifu.rst +++ b/docs/jwst/outlier_detection/outlier_detection_ifu.rst @@ -1,34 +1,48 @@ .. _outlier-detection-ifu: -Outlier Detection for IFU Data +Integral Field Unit (IFU) Data ============================== This module serves as the interface for applying ``outlier_detection`` to IFU -observations, like those taken with NIRSpec and MIRI. The code implements the -basic outlier detection algorithm searching for pixels that are consistent outliers -in the calibrated images created by the :ref:`calwebb_spec2 ` pipeline. -After launch it was discovered the -bad pixels on the MIRI detectors vary with time. The pixels varied from usable to unusable, and at times, -back to usable on a time frame that was too short (sometimes as short as 2 days) to fold into the bad pixel mask applied -in the :ref:`calwebb_detector1 ` pipeline. -At this time it is believed that NIRSpec IFU data also have bad pixels that vary with time, though the time variation is -still under study. +observations, like those taken with NIRSpec and MIRI. A :ref:`Stage 3 association `, +which is loaded into a :py:class:`~jwst.datamodels.ModelContainer` object, +serves as the basic format for all processing performed by this step. + +After launch it was discovered that the bad pixels on the MIRI detectors vary with time. +The pixels varied from usable to unusable, and at times, back to usable on a time frame that was too short +(sometimes as short as 2 days) to fold into the bad pixel mask applied in the +:ref:`calwebb_detector1 ` pipeline. At this time it is believed that NIRSpec IFU data +also have bad pixels that vary with time, though the time variation is still under study. +The outlier detection step is designed to flag these pixels as outliers, in addition +to cosmic ray hits that were not flagged by the jump step. The basis of the outlier detection flagging for IFU data is to look for pixels on the detector that are regularly discrepant from their neighbors, with a sharper division than could be explained -by the detector PSF. The algorithm flags pixels that are outliers when compared to their neighbors for a set of -input files contained in an association. The neighbor pixel differences are the neighbors in the spatial direction. -For MIRI data neighbor differences are found to the left and right of every -science pixel, while for NIRSpec data the neighbor differences are -determined from pixels above and below every science pixel. The difference between the MIRI MRS and NIRPSpec algorithm for finding the -spatial pixel differences is due to the opposite dispersion directions between the two instruments. -The pixel differences for each input model -in the association are determined and stored in a stack of pixel differences. -For each pixel the minimum difference -through this stack is determined and normalized. The normalization uses a local median of the difference array -(set by the kernel size). A pixel is flagged as an outlier if this normalized minimum difference -is greater than the input threshold percentage. Pixels that are found to be outliers are flaged in in the DQ array. - - - -.. automodapi:: jwst.outlier_detection.ifu +by the detector PSF. +This routine performs the following operations: + +#. Extract parameter settings for the input ModelContainer and merge them with any user-provided values. + See :ref:`outlier detection arguments ` for the full list of parameters. + +#. Loop over cal files, computing nearest-neighbor differences for each pixel + in the along-dispersion direction. + For MIRI, with the dispersion axis along the y axis, the neighbors that are used to + to find the differences are to the left and right of each pixel being examined. + For NIRSpec, with the dispersion along the x axis, the neighbors that are used to + find the differences are above and below the pixel being examined. + The smaller of the two (left, right or up, down) differences is stored as the difference value for each + pixel. This avoids artifacts from bright edges. + +#. Compare the nearest-neighbor differences across science exposures to find the minimum + neighbor difference at each detector pixel. + +#. Determine a local spatial median of the minimum difference array using a median filter with a kernel size + set by the user according to the ``kernel_size`` parameter. + +#. Normalize the minimum difference array by the local median. + +#. Select outliers by flagging those normalized minimum values larger than the ``threshold_percent`` + parameter. + +#. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location + of identified outliers. diff --git a/docs/jwst/outlier_detection/outlier_detection_imaging.rst b/docs/jwst/outlier_detection/outlier_detection_imaging.rst index 5bee8c30ef..bbcc8bbc91 100644 --- a/docs/jwst/outlier_detection/outlier_detection_imaging.rst +++ b/docs/jwst/outlier_detection/outlier_detection_imaging.rst @@ -1,180 +1,127 @@ .. _outlier-detection-imaging: -Outlier Detection Algorithm for Imaging Data -============================================ +Imaging Data +============ This module serves as the interface for applying ``outlier_detection`` to direct -image observations, like those taken with MIRI, NIRCam and NIRISS. The code implements the -basic outlier detection algorithm used with HST data, as adapted to JWST. - -Specifically, this routine performs the following operations: - -#. Extract parameter settings from input model and merge them with any user-provided values. - See :ref:`outlier detection arguments ` for the full list - of parameters. - -#. Convert input data, as needed, to make sure it is in a format that can be processed. - - * A :py:class:`~jwst.datamodels.ModelLibrary` serves as the basic format for - all processing performed by - this step, as each entry will be treated as an element of a stack of images - to be processed to identify bad-pixels/cosmic-rays and other artifacts. - * If the input data is a :py:class:`~jwst.datamodels.CubeModel`, convert it into a ModelLibrary. - This allows each plane of the cube to be treated as a separate 2D image - for resampling (if done) and for combining into a median image. - -#. By default, resample all input images. - - * The resampling step starts by computing an output WCS that is large enoug - to encompass all the input images. - * All images from the *same exposure* will get resampled onto this output - WCS to create a mosaic of all the chips for that exposure. This product - is referred to as a "grouped mosaic" since it groups all the chips from - the same exposure into a single image. - * Each dither position will result in a separate grouped mosaic, so only - a single exposure ever contributes to each pixel in these mosaics. - * An explanation of how all NIRCam multiple detector group mosaics are +image observations, like those taken with MIRI, NIRCam, and NIRISS. +A :ref:`Stage 3 association `, +which is loaded into a :py:class:`~jwst.datamodels.ModelLibrary` object, +serves as the basic format for all processing performed by this step. +This routine performs the following operations: + +#. Extract parameter settings for the input models and merge them with any user-provided values. + See :ref:`outlier detection arguments ` for the full list of parameters. + +#. By default, resample all input images to the same output WCS. The resample process is + controlled by the ``fillval``, ``pixfrac``, ``kernel``, and ``good_bits`` parameters; + see the :ref:`outlier detection arguments ` for more information. + Resampling can be turned off with the ``resample_data`` parameter. + + * Compute an output WCS that is large enough to encompass all the input images. + * Resample all images from the *same exposure* onto this output WCS to create a mosaic of all the detectors + for that exposure. This product is referred to as a "grouped mosaic" since it groups all the detectors + from the same exposure into a single image. Each dither position will result in + a separate grouped mosaic, so only a single exposure ever contributes to each pixel in these mosaics. + An explanation of how all NIRCam multiple detector group mosaics are defined from `a single exposure or from a dithered set of exposures `_ can be found here. - * The ``fillval`` parameter specifies what value to use in the ouptut - resampled image for any pixel which has no valid contribution from any - input exposure. The default value of ``INDEF`` indicates that the value - from the last exposure will be used, while a value of 0 would result in - holes. - * The resampling can be controlled with the ``pixfrac``, ``kernel`` and - ``weight_type`` parameters. - * The ``pixfrac`` indicates the fraction by - which input pixels are "shrunk" before being drizzled onto the - output image grid, given as a real number between 0 and 1. This specifies - the size of the footprint, or "dropsize", of a pixel in units of the input - pixel size. - * The ``kernel`` specifies the form of the kernel function used to distribute flux onto - the separate output images. - * The ``weight_type`` indicates the type of weighting image to apply with the bad pixel mask. - Available options are ``ivm`` (default) for computing and using an inverse-variance map - and ``exptime`` for weighting by the exposure time. - * The ``good_bits`` parameter specifies what DQ values from the input exposure - should be used when resampling to create the output mosaic. Any pixel with a - DQ value not included in this value (or list of values) will be ignored when - resampling. - * When the ``save_intermediate_results`` parameter is set to True, - resampled images will be written out to disk with the suffix ``__outlier_i2d.fits`` - if the input model container has an , otherwise the suffix will be ``_outlier_i2d.fits`` - by default. - * **If resampling is turned off** through the use of the ``resample_data`` parameter, - a copy of the unrectified input images (as a ModelLibrary) - will be used for subsequent processing. - -#. Create a median image from all grouped observation mosaics. - - * The median image is created by combining all grouped mosaic images or - non-resampled input data (as planes in a ModelLibrary) pixel-by-pixel. - * The ``maskpt`` parameter sets the percentage of the weight image values to - use, and any pixel with a weight below this value gets flagged as "bad" and - ignored when resampled. - * The median image is written out to disk as `__median.fits` by default. - -#. By default, the median image is blotted back (inverse of resampling) to - match each original input image. Resampled/blotted images are written out to disk if - the ``save_intermediate_results`` parameter is set to `True`. - - * **If resampling is turned off**, the median image is compared directly to - each input image. + * Fill in pixels that have no valid contribution from any input exposure with the value + specified by the ``fillval`` parameter. + +#. If the ``save_intermediate_results`` parameter is set to True, write the resampled images to disk + with the suffix ``_outlier_i2d.fits``. These are not saved if ``resample_data`` is set to False because + they would be copies of the input models. + +#. If resampling is turned off, use the input data itself in place of the resampled data + for subsequent processing. + +#. Construct and apply a bad pixel mask to the resampled data based on the drizzled weights. + The ``weight_type`` parameter indicates the type of weighting image to apply with the bad pixel mask. + The ``maskpt`` parameter sets the threshold weight value such that any pixel + with a weight below this value gets flagged as bad. + +#. Create a median image from all grouped observation mosaics pixel-by-pixel, i.e., averaging over groups. + If ``save_intermediate_results`` is set to True, the median image is written out to disk with the + suffix ``_median.fits``. + +#. Blot (inverse of resampling) the median image back to match each original input image, and write + the blotted images to disk with the suffix ``_blot.fits`` if ``save_intermediate_results`` is `True`. #. Perform statistical comparison between blotted image and original image to identify outliers. - * If resampling is disabled (``resample_data == False``) a large number of parameters - will be ignored and instead the outlier mask will be computed using the following - formula: + * If resampling is disabled (``resample_data == False``), compare the median image directly + to each input image and ignore all sub-bullets below this one. + In this case, compute the outlier mask using the following formula: .. math:: | image\_input - image\_median | > SNR * input\_err - * When resampling is enabled, the comparison uses the original input images, the blotted - median image, and the derivative of the blotted image to - create a cosmic ray mask for each input image. - * The derivative of the blotted image gets created using the blotted - median image to compute the absolute value of the difference between each pixel and - its four surrounding neighbors with the largest value being the recorded derivative. - * These derivative images are used to flag cosmic rays - and other blemishes, such as satellite trails. Where the difference is larger - than can be explained by noise statistics, the flattening effect of taking the - median, or an error in the shift (the latter two effects are estimated using - the image derivative), the suspect pixel is masked. - * The ``backg`` parameter specifies a user-provided value to be used as the - background estimate. This gets added to the background-subtracted - blotted image to attempt to match the original background levels of the - original input mosaic so that cosmic-rays (bad pixels) from the input - mosaic can be identified more easily as outliers compared to the blotted - mosaic. - * Cosmic rays are flagged using the following rule: + * Add a user-specified background value to the median image to match the original background levels + of the input mosaic. This is controlled by the ``backg`` parameter. + * Compute the spatial derivative of each pixel in the blotted median images by computing the absolute value + of the difference between each pixel and its four surrounding neighbors, recording the largest + absolute difference as the derivative. The derivative is multiplied by the ``scale`` parameter, + allowing user flexibility in determining how aggressively to flag outliers at this stage. + * Flag cosmic rays and other blemishes (such as satellite trails) based on the derivative image. + Where the difference is larger than can be explained by a combination of noise statistics, + the flattening effect of taking the median, and an error in the shift + (the latter two effects are estimated using the image derivative), the suspect pixel is considered + an outlier. The following rule is used: .. math:: | image\_input - image\_blotted | > scale*image\_deriv + SNR*noise - * The ``scale`` is defined as the multiplicative factor applied to the - derivative which is used to determine if the difference between the data - image and the blotted image is large enough to require masking. - * The ``noise`` is calculated using a combination of the detector read - noise and the poisson noise of the blotted median image plus the sky background. - * The user must specify two cut-off signal-to-noise values using the - ``snr`` parameter for determining whether a pixel should be masked: - the first for detecting the primary cosmic ray, and the second for masking + The noise in this equation is the value of the input model's ``err`` extension. + The SNR in this equation is the ``snr`` parameter, which encodes two values that + determine whether a pixel should be masked: + the first value detects the primary cosmic ray, and the second masks lower-level bad pixels adjacent to those found in the first pass. Since cosmic rays often extend across several pixels, the adjacent pixels make use of a slightly lower SNR threshold. -#. Update input data model DQ arrays with mask of detected outliers. +#. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location + of identified outliers. -Memory Model for Outlier Detection Algorithm ---------------------------------------------- -The outlier detection algorithm can end up using massive amounts of memory +Memory Saving Options +--------------------- +The outlier detection algorithm for imaging can require a lot of memory depending on the number of inputs, the size of each input, and the size of the final output product. Specifically, -#. The input :py:class:`~jwst.datamodels.ModelLibrary` or - :py:class:`~jwst.datamodels.CubeModel` - for IFU data, by default, all input exposures would have been kept open in memory to make +#. By default, all input exposures are kept open in memory to make processing more efficient. -#. The initial resample step creates an output product for EACH input that is the - same size as the final - output product, which for imaging modes can span all chips in the detector while - also accounting for all dithers. For some Level 3 products, each resampled image can - be on the order of 2Gb or more. +#. The resample step creates an output product that is the + same size as the final output product, which for imaging modes can span all detectors + while also accounting for all dithers. Although only a single resampled image is needed in + memory at a time, for some Level 3 products, each resampled image can be on the order of several + gigabytes in size. -#. The median combination step then needs to have all pixels at the same position on - the sky in memory in order to perform the median computation. The simplest implementation - for this step requires keeping all resampled outputs fully in memory at the same time. +#. The median combination step needs to have all pixels at the same position on + the sky in memory in order to perform the median computation. The simplest (and fastest) implementation + requires keeping all resampled outputs fully in memory at the same time. -Many Level 3 products only include a modest number of input exposures that can be -processed using less than 32Gb of memory at a time. However, there are a number of -ways this memory limit can be exceeded. This has been addressed by implementing an -overall memory model for the outlier detection that includes options to minimize the -memory usage at the expense of file I/O. The control over this memory model happens -with the use of the ``in_memory`` parameter. The full impact of this parameter -during processing includes: +These concerns have been addressed by implementing an overall memory model for outlier detection that +includes options to minimize memory usage at the expense of temporary file I/O and runtime. +Control over this memory model happens +with the use of the ``in_memory`` parameter. The full impact of setting this parameter +to `False` includes: #. The input :py:class:`~jwst.datamodels.ModelLibrary` object is loaded with `on_disk=True`. This ensures that input models are loaded into memory one at at time, - and saved to a temporary file when not in use; these read-write operations are handled by + and saved to a temporary file when not in use; these read-write operations are handled internally by the :py:class:`~jwst.datamodels.ModelLibrary` object. -#. The ``on_disk`` status of the :py:class:`~jwst.datamodels.ModelLibrary` gets passed to the - :py:class:`~jwst.resample.ResampleStep` as well, to set whether or not to keep the - resampled images in memory or not. - #. Computing the median image works by writing the resampled data frames to appendable files - on disk that are split into sections spatially but contain the entire ngroups (i.e., time) + on disk that are split into sections spatially but contain the entire ``groups`` axis. The section size is set to use roughly the same amount of memory as a single resampled - model, and since the resampled models are discarded from memory after this write operation this - choice avoids increasing the memory usage beyond a single resampled model. + model, and since the resampled models are discarded from memory by the time the median calculation + happens, this choice avoids increasing the overall memory usage of the step. Those sections are then read in one at a time to compute the median image. -These changes result in a minimum amount of memory usage during processing at the obvious -expense of reading and writing the products from disk. Note that if a ModelLibrary object +These changes result in a minimum amount of memory usage during processing, but runtimes are +longer because many read and write operations are needed. Note that if a ModelLibrary object is input to the step, the memory behavior of the step is read from the ``on_disk`` status of the ModelLibrary object, and the ``in_memory`` parameter of the step is ignored. -When running ``calwebb_image3``, the ``in_memory`` flag should therefore be set at the pipeline level, -e.g., ``strun calwebb_image3 asn.json --in-memory=True``; the step-specific flag will be ignored. - -.. automodapi:: jwst.outlier_detection.imaging +When running ``calwebb_image3``, the ``in_memory`` flag should be set at the pipeline level, +e.g., ``strun calwebb_image3 asn.json --in-memory=False``; the step-specific flag will be ignored. diff --git a/docs/jwst/outlier_detection/outlier_detection_spec.rst b/docs/jwst/outlier_detection/outlier_detection_spec.rst index 4f1d1ce866..6b7d62e697 100644 --- a/docs/jwst/outlier_detection/outlier_detection_spec.rst +++ b/docs/jwst/outlier_detection/outlier_detection_spec.rst @@ -1,62 +1,32 @@ .. _outlier-detection-spec: -Outlier Detection for Slit-like Spectroscopic Data -================================================== +Slit-like Spectroscopic Data +============================ This module serves as the interface for applying ``outlier_detection`` to slit-like -spectroscopic observations. The code implements the -basic outlier detection algorithm used with HST data, as adapted to JWST -spectroscopic observations. - -Specifically, this routine performs the following operations (modified from the -:ref:`Default Outlier Detection Algorithm ` ): - -#. Extract parameter settings from input model and merge them with any user-provided values - - - The same set of parameters available to: - ref:`Default Outlier Detection Algorithm ` - also applies to this code. - -#. Convert input data, as needed, to make sure it is in a format that can be processed - - - A :py:class:`~jwst.datamodels.ModelContainer` serves as the basic format - for all processing performed by - this step, as each entry will be treated as an element of a stack of images - to be processed to identify bad pixels, cosmic rays and other artifacts. - -#. If the ``resample_data`` parameter is set to True, resample all input images - using :py:class:`~jwst.resample.resample_spec.ResampleSpecData`. - - - Error images are resampled alongside the science data to create - approximate error arrays for each resampled exposure. - - The resampled data are written out to disk with suffix "outlier_s2d" - if the ``save_intermediate_results`` parameter is set to `True`. - -#. Create a median image from the resampled exposures, or directly from - the input exposures if ``resample_data`` is set to False. - - - The error images for each exposure are also median-combined. - - The median datamodel, with ``data`` and ``err`` extensions, is - written to disk with suffix "median" if the ``save_intermediate_results`` - parameter is set to `True`. - -#. If ``resample_data`` is set to True, blot the median image to match - each original input image. - - - The median error image is also blotted to match the input. - - Resampled/blotted images are written out to disk if the ``save_intermediate_results`` - parameter is set to `True`. - -#. Perform statistical comparison between the median image and the original image - to identify outliers. - - - Signal-to-noise thresholding uses the median error image, rather than the - input error image, in order to better identify outliers that have both - high data values and high error values in the input exposures. - -#. Update input data model DQ arrays with mask of detected outliers. - Update the SCI, ERR, and VAR arrays with NaN values at the outlier - locations. - +spectroscopic observations. The algorithm is very similar to the +:ref:`imaging algorithm `, and much of the same code is used. +Please refer to those docs for more information. +A :ref:`Stage 3 association `, +which is loaded into a :py:class:`~jwst.datamodels.ModelContainer` object, +serves as the input and output to this step, and the :py:class:`~jwst.datamodels.ModelContainer` +is converted into a :py:class:`~jwst.datamodels.ModelLibrary` object to allow sharing code +with the imaging mode. + +This routine performs identical operations to the imaging mode, with the following exceptions: + +#. Error thresholding is handled differently: the error arrays are resampled and median-combined + along with the data arrays, and the median error image is used to identify outliers + instead of the input error images for each exposure. This median error image is included + alongside the median datamodel (in the ``err`` extension) if ``save_intermediate_results`` + is `True`. + +#. Resampling is handled by a different class, :py:class:`~jwst.resample.resample_spec.ResampleSpecData` + instead of :py:class:`~jwst.resample.resample.ResampleData`. + +#. The resampled images are written out to disk with suffix "outlier_s2d" instead of + "outlier_i2d" if the ``save_intermediate_results`` parameter is set to `True`. + +#. The ``in_memory`` parameter has no effect, and all operations are performed in memory. .. automodapi:: jwst.outlier_detection.spec diff --git a/docs/jwst/outlier_detection/outlier_detection_step.rst b/docs/jwst/outlier_detection/outlier_detection_step.rst deleted file mode 100644 index 0713147a31..0000000000 --- a/docs/jwst/outlier_detection/outlier_detection_step.rst +++ /dev/null @@ -1,53 +0,0 @@ -.. _outlier_design: - -Python Step Design: OutlierDetectionStep ------------------------------------------ - -This module provides the sole interface to all methods of performing outlier -detection on JWST observations. The ``outlier_detection`` step supports multiple -algorithms and determines the appropriate algorithm for the type of observation -being processed. This step supports: - -#. **Image modes**: 'FGS_IMAGE', 'MIR_IMAGE', 'NRC_IMAGE', 'NIS_IMAGE' -#. **Spectroscopic modes**: 'MIR_LRS-FIXEDSLIT', 'NRS_FIXEDSLIT', 'NRS_MSASPEC' -#. **Time-Series-Observation(TSO) Spectroscopic modes**: 'MIR_LRS-SLITLESS', 'NRC_TSGRISM', 'NIS_SOSS', 'NRS_BRIGHTOBJ' -#. **IFU Spectroscopic modes**: 'MIR_MRS', 'NRS_IFU' -#. **TSO Image modes**: 'NRC_TSIMAGE' -#. **Coronagraphic Image modes**: 'MIR_LYOT', 'MIR_4QPM', 'NRC_CORON' - - -This step uses the following logic to apply the appropriate algorithm to the -input data: - -#. Interpret inputs (ASN table, ModelContainer or CubeModel) - to identify all input observations to be processed - -#. Read in type of exposures in input by interpreting ``meta.exposure.type`` from inputs - -#. Read in parameters set by user - -#. Select outlier detection algorithm based on exposure type - - - **Images**: like those taken with NIRCam, will use - :py:mod:`~jwst.outlier_detection.outlier_detection.OutlierDetection` as described - in :ref:`outlier-detection-imaging` - - **Coronagraphic observations**: - use :py:mod:`~jwst.outlier_detection.coron` - as described in :ref:`outlier-detection-coron` - - **Time-Series Observations(TSO)**: both imaging and spectroscopic modes, use - :py:mod:`~jwst.outlier_detection.tso` - as described in :ref:`outlier-detection-tso` - - **IFU observations**: use - :py:mod:`~jwst.outlier_detection.ifu` as - described in :ref:`outlier-detection-ifu` - - **Long-slit spectroscopic observations**: use - :py:mod:`~jwst.outlier_detection.spec` as - described in :ref:`outlier-detection-spec` - -#. Instantiate and run outlier detection class determined for the exposure type - using parameter values interpreted from inputs. - -#. Return input models with DQ arrays updated with flags for identified outliers - - -.. automodapi:: jwst.outlier_detection.outlier_detection_step diff --git a/docs/jwst/outlier_detection/outlier_detection_tso.rst b/docs/jwst/outlier_detection/outlier_detection_tso.rst index 843ad95941..6e60d172da 100644 --- a/docs/jwst/outlier_detection/outlier_detection_tso.rst +++ b/docs/jwst/outlier_detection/outlier_detection_tso.rst @@ -1,39 +1,39 @@ .. _outlier-detection-tso: -Outlier Detection for TSO Data -============================== +Time-Series Observations (TSO) Data +=================================== -Time-series observations (TSO) result in input data stored as a 3D CubeModel -where each plane in the cube represents a separate integration without changing the -pointing. Normal imaging data benefit from combining all integrations into a +This module serves as the interface for applying ``outlier_detection`` to time +series observations. + +Normal imaging data benefit from combining all integrations into a single image. TSO data's value, however, comes from looking for variations from one integration to the next. The outlier detection algorithm, therefore, gets run with a few variations to accomodate the nature of these 3D data. +A :py:class:`~jwst.datamodels.CubeModel` object serves as the basic format for all +processing performed by this step. This routine performs the following operations: -#. Input data is converted into a CubeModel (3D data array) if a ModelContainer +#. Convert input data into a CubeModel (3D data array) if a ModelContainer of 2D data arrays is provided. -#. The median image is created without resampling the input data - - * The median image is created by combining all planes in the - CubeModel pixel-by-pixel using a rolling-median algorithm, in order - to flag outliers integration-by-integration but preserve real time variability. - * The ``rolling_window_width`` parameter specifies the number of integrations over - which to compute the median. The default is 25. If the number of integrations - is less than or equal to ``rolling_window_width``, a simple median is used instead. - * The ``maskpt`` parameter sets the percentage of the weight image values to - use, and any pixel with a weight below this value gets flagged as "bad" and - ignored when the median is taken. - * The rolling-median CubeModel (3D data array) is written out to disk as `__median.fits` - if the ``save_intermediate_results`` parameter is set to True. - * All integrations are aligned already, so no resampling or shifting needs to be performed +#. Do not attempt resampling; data are assumed to be aligned and have an identical WCS. + This is true automatically for a CubeModel. -#. A matched median gets created by combining the single median frame with the - noise model for each input integration. +#. Apply a bad pixel mask to the input data based on the input DQ arrays and the ``good_bits`` + parameter. -#. A statistical comparison is performed between the matched median with each input integration. +#. Compute a median cube by combining all planes in the CubeModel pixel-by-pixel using a + rolling-median algorithm, in order to flag outliers integration-by-integration but + preserve real time variability. The ``rolling_window_width`` parameter specifies the + number of integrations over which to compute the median. -#. The input data model DQ arrays are updated with the mask of detected outliers. +#. If the ``save_intermediate_results`` parameter is set to True, write the rolling-median + CubeModel to disk with the suffix ``_median.fits``. +#. Perform a statistical comparison frame-by-frame between the rolling-median cube and + the input data. The formula used is the same as for imaging data without resampling: + + .. math:: | image\_input - image\_median | > SNR * input\_err -.. automodapi:: jwst.outlier_detection.tso +#. Update DQ arrays with flags and set SCI, ERR, and variance arrays to NaN at the location + of identified outliers. diff --git a/docs/jwst/outlier_detection/reference_files.rst b/docs/jwst/outlier_detection/reference_files.rst new file mode 100644 index 0000000000..adedea4452 --- /dev/null +++ b/docs/jwst/outlier_detection/reference_files.rst @@ -0,0 +1,4 @@ +Reference Files +=============== + +The ``outlier_detection`` does not use any reference files. diff --git a/jwst/outlier_detection/ifu.py b/jwst/outlier_detection/ifu.py index 6ab12a0157..2c903ba16a 100644 --- a/jwst/outlier_detection/ifu.py +++ b/jwst/outlier_detection/ifu.py @@ -133,6 +133,8 @@ def flag_outliers(input_models, idet, uq_det, ndet_files, Size of input data on x axis ny : int Since of inut data on y axis + kern_size : tuple + Size of the kernel to use for median filtering threshold_percent : float Percent for flagging outliers. Flags pixels where the minimum difference between adjacent pixels for all the input data for a detector is above this percentage. The @@ -143,6 +145,9 @@ def flag_outliers(input_models, idet, uq_det, ndet_files, ifu_second_check : boolean If True then perform a secondary check searching for outliers. This will set outliers where ever the difference array of adjacent pixels is a Nan. + make_output_path : function + The functools.partial instance to pass to save_median. Has no effect if + save_intermediate_results is False. """ # set up array to hold group differences diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index c0c4e0bfa9..94a1ca7e21 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -26,8 +26,11 @@ class OutlierDetectionStep(Step): """Flag outlier bad pixels and cosmic rays in DQ array of each input image. - Input images can be listed in an input association file or already opened - with a ModelContainer. DQ arrays are modified in place. + Input images can be listed in an input association file or dictionary, + or already opened with a ModelContainer or ModelLibrary. + DQ arrays are modified in place. + SCI, ERR, VAR_RNOISE, VAR_FLAT, and VAR_POISSON arrays are updated with + NaN values matching the DQ flags. Parameters ----------- From 55452ce94cbc2fe2a0dcde630de861d01c2b23eb Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 22 Oct 2024 12:03:01 -0400 Subject: [PATCH 555/687] removing unused import --- jwst/master_background/nirspec_utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 354b99cac5..c2df331fe6 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -1,8 +1,6 @@ import logging import warnings -from stdatamodels.jwst import datamodels - log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) From 8fdb3d87c99274def45ad26f76a78345f9c6b29c Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Mon, 21 Oct 2024 14:23:36 -0400 Subject: [PATCH 556/687] add nirspec tangent plane xy corrections --- jwst/resample/resample_spec.py | 51 +++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 19 deletions(-) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 9316a838c0..2416f35dd1 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -335,32 +335,45 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): raise ValueError("Not enough data to construct output WCS.") # Find the spatial extent in x/y tangent - min_tan, max_tan = self._max_spatial_extent(all_wcs, undist2sky.inverse, swap_xy) - diff = np.abs(max_tan - min_tan) - if swap_xy: - pix_to_tan_slope = np.abs(pix_to_ytan.slope) - slope_sign = np.sign(pix_to_ytan.slope) - else: - pix_to_tan_slope = np.abs(pix_to_xtan.slope) - slope_sign = np.sign(pix_to_xtan.slope) + min_tan_y, max_tan_y = self._max_spatial_extent( + all_wcs, undist2sky.inverse, True) + min_tan_x, max_tan_x = self._max_spatial_extent( + all_wcs, undist2sky.inverse, False) + diff_y = np.abs(max_tan_y - min_tan_y) + diff_x = np.abs(max_tan_x - min_tan_x) + + pix_to_tan_slope_y = np.abs(pix_to_ytan.slope) + slope_sign_y = np.sign(pix_to_ytan.slope) + pix_to_tan_slope_x = np.abs(pix_to_xtan.slope) + slope_sign_x = np.sign(pix_to_xtan.slope) # Image size in spatial dimension from the maximum slope # and tangent offset span, plus one pixel to make sure # we catch all the data - ny = int(np.ceil(diff / pix_to_tan_slope)) + 1 + if swap_xy: + ny = int(np.ceil(diff_y / pix_to_tan_slope_y)) + 1 + else: + ny = int(np.ceil(diff_x / pix_to_tan_slope_x)) + 1 # Correct the intercept for the new minimum value. # Also account for integer pixel size to make sure the # data is centered in the array. - offset = ny/2 * pix_to_tan_slope - diff/2 - if slope_sign > 0: - zero_value = min_tan + offset_y = ny/2 * pix_to_tan_slope_y - diff_y/2 + offset_x = ny/2 * pix_to_tan_slope_x - diff_x/2 + + if slope_sign_y > 0: + zero_value_y = min_tan_y else: - zero_value = max_tan - if swap_xy: - pix_to_ytan.intercept = zero_value - slope_sign * offset + zero_value_y = max_tan_y + + if slope_sign_x > 0: + zero_value_x = min_tan_x else: - pix_to_xtan.intercept = zero_value - slope_sign * offset + zero_value_x = max_tan_x + + + pix_to_ytan.intercept = zero_value_y - slope_sign_y * offset_y + pix_to_xtan.intercept = zero_value_x - slope_sign_x * offset_x # Now set up the final transforms @@ -447,7 +460,7 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): return output_wcs - def _max_spatial_extent(self, wcs_list, transform, swap_xy): + def _max_spatial_extent(self, wcs_list, transform, return_y): """ Compute min & max spatial coordinates for all nods in the "virtual" slit frame. @@ -462,7 +475,7 @@ def _max_spatial_extent(self, wcs_list, transform, swap_xy): dec = dec[good] xtan, ytan = transform(ra, dec) - if swap_xy: + if return_y: tan_all = ytan else: tan_all = xtan @@ -944,4 +957,4 @@ def compute_spectral_pixel_scale(wcs, fiducial=None, disp_axis=1): fiducial = wcs(center_x, center_y) pixel_scale = compute_scale(wcs, fiducial, disp_axis=disp_axis) - return float(pixel_scale) \ No newline at end of file + return float(pixel_scale) From 7aa58e8d6d61420813f927f475ed3b0c1cc77a22 Mon Sep 17 00:00:00 2001 From: William Jamieson Date: Tue, 22 Oct 2024 12:26:38 -0400 Subject: [PATCH 557/687] Fix numpy 2 warnings emitted from skymatch (#8892) --- changes/8892.skymatch.rst | 1 + jwst/skymatch/region.py | 15 +++++++++++---- 2 files changed, 12 insertions(+), 4 deletions(-) create mode 100644 changes/8892.skymatch.rst diff --git a/changes/8892.skymatch.rst b/changes/8892.skymatch.rst new file mode 100644 index 0000000000..7662782814 --- /dev/null +++ b/changes/8892.skymatch.rst @@ -0,0 +1 @@ +Resolve warnings emitted by NumPy 2 when running skymatch. diff --git a/jwst/skymatch/region.py b/jwst/skymatch/region.py index 6e7a596ef0..6cb2c3fb11 100755 --- a/jwst/skymatch/region.py +++ b/jwst/skymatch/region.py @@ -413,18 +413,25 @@ def intersection(self, edge): u = self._stop - self._start v = edge._stop - edge._start w = self._start - edge._start - D = np.cross(u, v) - if np.allclose(np.cross(u, v), 0, rtol=0, + # Find the determinant of the matrix formed by the vectors u and v + # Note: Originally this was computed using a numpy "2D" cross product, + # however, this functionality has been deprecated and slated for + # removal. + D = np.linalg.det([u, v]) + + if np.allclose(D, 0, rtol=0, atol=1e2 * np.finfo(float).eps): return np.array(self._start) - return np.cross(v, w) / D * u + self._start + # See note above + return np.linalg.det([v, w]) / D * u + self._start def is_parallel(self, edge): u = self._stop - self._start v = edge._stop - edge._start - return np.allclose(np.cross(u, v), 0, rtol=0, + # See note in intersection method + return np.allclose(np.linalg.det([u, v]), 0, rtol=0, atol=1e2 * np.finfo(float).eps) From f75c39a324afa30c76c3bd8bb129163f7f6e4fd5 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 13:48:09 -0400 Subject: [PATCH 558/687] refactor to remove unnecessary duplication --- jwst/resample/resample_spec.py | 31 +++++++++++++------------------ 1 file changed, 13 insertions(+), 18 deletions(-) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 2416f35dd1..3e94267e5e 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -335,10 +335,8 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): raise ValueError("Not enough data to construct output WCS.") # Find the spatial extent in x/y tangent - min_tan_y, max_tan_y = self._max_spatial_extent( - all_wcs, undist2sky.inverse, True) - min_tan_x, max_tan_x = self._max_spatial_extent( - all_wcs, undist2sky.inverse, False) + min_tan_x, max_tan_x, min_tan_y, max_tan_y = self._max_spatial_extent( + all_wcs, undist2sky.inverse) diff_y = np.abs(max_tan_y - min_tan_y) diff_x = np.abs(max_tan_x - min_tan_x) @@ -460,12 +458,13 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): return output_wcs - def _max_spatial_extent(self, wcs_list, transform, return_y): + def _max_spatial_extent(self, wcs_list, transform): """ Compute min & max spatial coordinates for all nods in the "virtual" slit frame. """ - min_tan_all, max_tan_all = np.inf, -np.inf + limits_x = [np.inf, -np.inf] + limits_y = [np.inf, -np.inf] for wcs in wcs_list: x, y = wcstools.grid_from_bounding_box(wcs.bounding_box) ra, dec, _ = wcs(x, y) @@ -475,20 +474,16 @@ def _max_spatial_extent(self, wcs_list, transform, return_y): dec = dec[good] xtan, ytan = transform(ra, dec) - if return_y: - tan_all = ytan - else: - tan_all = xtan - - min_tan = np.min(tan_all) - max_tan = np.max(tan_all) + for tan_all, limits in zip([xtan, ytan], [limits_x, limits_y]): + min_tan = np.min(tan_all) + max_tan = np.max(tan_all) - if min_tan < min_tan_all: - min_tan_all = min_tan - if max_tan > max_tan_all: - max_tan_all = max_tan + if min_tan < limits[0]: + limits[0] = min_tan + if max_tan > limits[1]: + limits[1] = max_tan - return min_tan_all, max_tan_all + return *limits_x, *limits_y def build_interpolated_output_wcs(self, input_models): """ From 4d6cf3f8acb8d4b18bd283e70715fccc376f8521 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 13:52:47 -0400 Subject: [PATCH 559/687] add changes news fragment --- changes/8908.resample_spec.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8908.resample_spec.rst diff --git a/changes/8908.resample_spec.rst b/changes/8908.resample_spec.rst new file mode 100644 index 0000000000..2d69d9feee --- /dev/null +++ b/changes/8908.resample_spec.rst @@ -0,0 +1 @@ +Update NIRSpec spectral resampling to add a missing correction factor in resampled WCS tangent plane transformation. \ No newline at end of file From 0938aaf268a183b29aa477ac5c39506467b4277c Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 15:30:08 -0400 Subject: [PATCH 560/687] minor docstring update --- jwst/resample/resample_spec.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 3e94267e5e..0d90d0d891 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -460,8 +460,7 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): def _max_spatial_extent(self, wcs_list, transform): """ - Compute min & max spatial coordinates for all nods in the "virtual" - slit frame. + Compute spatial coordinate limits for all nods in the tangent plane. """ limits_x = [np.inf, -np.inf] limits_y = [np.inf, -np.inf] From faf420d34ba57a2ec32c1d71fe1e1b60c8cf1b64 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 16:43:34 -0400 Subject: [PATCH 561/687] check for even sized median kernels --- .../master_background_step.py | 22 +++++++++++++------ 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index f88b76d2e8..e5739a5602 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -22,11 +22,11 @@ class MasterBackgroundStep(Step): class_alias = "master_background" spec = """ + median_kernel = integer(default=1) # Moving-median boxcar size to filter the master background spectrum user_background = string(default=None) # Path to user-supplied master background save_background = boolean(default=False) # Save computed master background force_subtract = boolean(default=False) # Force subtracting master background output_use_model = boolean(default=True) - median_kernel = integer(default=1) """ def process(self, input): @@ -38,6 +38,10 @@ def process(self, input): input : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`, `~jwst.datamodels.ModelContainer`, association Input target datamodel(s) to which master background subtraction is to be applied + + median_kernel : integer, optional + Optional user-supplied kernel with which to moving-median boxcar filter the master background + spectrum. Must be an odd integer. user_background : None, string, or `~jwst.datamodels.MultiSpecModel` Optional user-supplied master background 1D spectrum, path to file @@ -53,10 +57,6 @@ def process(self, input): has already been applied and, if so, the master background step is skipped. If set to True, the step logic is bypassed and the master background is subtracted. - median_kernel : integer, optional - Optional user-supplied kernel with which to moving-median boxcar filter the master background - spectrum. Must be an odd integer. - Returns ------- result : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`, `~jwst.datamodels.ModelContainer` @@ -147,10 +147,18 @@ def process(self, input): ) # If requested, apply a moving-median boxcar filter to the master background spectrum + # Round down even kernel sizes because only odd kernel sizes are supported. + if self.median_kernal % 2 == 0: + self.median_kernal -= 1 + self.log.info('Even median filter kernels are not supported.' + f'Rounding the median kernel size down to {self.median_kernel}.') + if (self.median_kernel > 1): - self.log.info('Applying moving-median boxcar of width %i',self.median_kernel) + self.log.info(f'Applying moving-median boxcar of width {self.median_kernel}.') master_background.spec[0].spec_table['surf_bright'] = medfilt( - master_background.spec[0].spec_table['surf_bright'], kernel_size=[self.median_kernel]) + master_background.spec[0].spec_table['surf_bright'], + kernel_size=[self.median_kernel] + ) background_data.close() From e0ef7c552e4b66d63e16e419611c59f526162681 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Tue, 22 Oct 2024 13:46:29 -0700 Subject: [PATCH 562/687] JP-3721: Simplify ModelContainer (#8831) --- changes/8831.datamodels.rst | 1 + docs/jwst/stpipe/devel_io_design.rst | 36 +- jwst/badpix_selfcal/badpix_selfcal_step.py | 3 +- jwst/cube_build/data_types.py | 2 +- jwst/datamodels/container.py | 332 +++++------------- jwst/datamodels/tests/data/association.json | 6 +- jwst/datamodels/tests/test_model_container.py | 22 ++ .../datamodels/tests/test_open_association.py | 1 - jwst/extract_1d/extract.py | 5 +- jwst/extract_1d/extract_1d_step.py | 5 +- .../master_background_step.py | 31 +- .../outlier_detection_step.py | 6 +- jwst/outlier_detection/spec.py | 2 +- jwst/pipeline/calwebb_coron3.py | 8 +- jwst/pipeline/calwebb_spec3.py | 6 +- jwst/pipeline/calwebb_tso3.py | 12 +- jwst/pixel_replace/pixel_replace_step.py | 9 +- jwst/stpipe/tests/steps/__init__.py | 9 +- jwst/stpipe/tests/test_input.py | 4 +- jwst/tweakreg/tests/test_tweakreg.py | 4 +- pyproject.toml | 2 +- 21 files changed, 203 insertions(+), 303 deletions(-) create mode 100644 changes/8831.datamodels.rst diff --git a/changes/8831.datamodels.rst b/changes/8831.datamodels.rst new file mode 100644 index 0000000000..22289006df --- /dev/null +++ b/changes/8831.datamodels.rst @@ -0,0 +1 @@ +Remove memory-saving options from ModelContainer \ No newline at end of file diff --git a/docs/jwst/stpipe/devel_io_design.rst b/docs/jwst/stpipe/devel_io_design.rst index af233632f2..56f106734f 100644 --- a/docs/jwst/stpipe/devel_io_design.rst +++ b/docs/jwst/stpipe/devel_io_design.rst @@ -163,8 +163,8 @@ Many of the JWST calibration steps and pipelines expect an :ref:`Association ` file as input. When opened with :meth:`~jwst.stpipe.step.Step.open_model`, a :class:`~jwst.datamodels.ModelContainer` is returned. `ModelContainer` -is, among other features, a list-like object where each element is the -`DataModel` of each member of the association. The `meta.asn_table` is +is a list-like object where each element is the +`DataModel` of each member of the association. The `asn_table` attribute is populated with the association data structure, allowing direct access to the association itself. The association file, as well as the files listed in the association file, must be in the input directory. @@ -172,6 +172,38 @@ listed in the association file, must be in the input directory. To read in a list of files, or an association file, as an association, use the `load_as_level2_asn` or `load_as_level3_asn` methods. +ModelContainer vs ModelLibrary +`````````````````````````````` + +Some steps in the pipeline, namely any steps involved in the Stage 3 Imaging pipeline, +rely on the :class:`~jwst.datamodels.ModelLibrary` class instead of the +:class:`~jwst.datamodels.ModelContainer` class to process association-type data. +The `ModelLibrary` class is purpose-built for enabling memory-saving options in the +image3 pipeline and is only recommended when working with large associations. +Additional documentation on the `ModelLibrary` class can be found in the +`stpipe ModelLibrary documentation `_. + +ModelContainer Changes in JWST 1.17 +``````````````````````````````````` + +In JWST 1.17, the `ModelContainer` class was de-scoped in light of the introduction of the +`ModelLibrary` class in JWST 1.16. The `ModelContainer` class is still the recommended class +for handling association-type data, but it is no longer a subclass of `JWSTDataModel`. The +following changes in behavior are noteworthy: + +* The `ModelContainer` class no longer has a `meta` attribute. The association data is now + stored in the top-level `asn_table` attribute, along with several other association-relevant + attributes including `asn_table_name`, `asn_pool_name`, `asn_exptypes`, `asn_n_members`, + `asn_file_path`. Note that `asn_table` is now a dictionary, not an `ObjectNode`. +* All infrastructure that attempted memory savings in the `ModelContainer` class has been removed. + Use the `ModelLibrary` class if memory-saving options are needed. +* A `ModelContainer` object can no longer hold a list of `ModelContainer` objects. +* The `ModelContainer` class is still list-like, and can be indexed and sliced like a list. +* The `ModelContainer` class is still the default class returned by stdatamodels `open()` + for association-type input data, e.g. a .json file or dict. +* The `ModelContainer` class can still be used as a context manager, such that `with open(asn_file.json)` + still works. + Input Source ------------ diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 7d9ad62c9f..b83aaa0f63 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -35,8 +35,7 @@ class BadpixSelfcalStep(Step): """ def save_model(self, model, *args, **kwargs): - """Override save_model to suppress index 0 when save_model is True - """ + """Override save_model to suppress index 0 when save_model is True""" kwargs["idx"] = None return Step.save_model(self, model, *args, **kwargs) diff --git a/jwst/cube_build/data_types.py b/jwst/cube_build/data_types.py index d7217087a4..711f729bd1 100644 --- a/jwst/cube_build/data_types.py +++ b/jwst/cube_build/data_types.py @@ -89,7 +89,7 @@ def __init__(self, input, single, output_file, output_dir): self.output_name = 'Temp' self.input_models = input_models if not single: # find the name of the output file from the association - self.output_name = input_models.meta.asn_table.products[0].name + self.output_name = input_models.asn_table["products"][0]["name"] else: # close files opened above self.close() diff --git a/jwst/datamodels/container.py b/jwst/datamodels/container.py index 45440bcb17..196c31ca5c 100644 --- a/jwst/datamodels/container.py +++ b/jwst/datamodels/container.py @@ -1,15 +1,10 @@ -import copy from collections import OrderedDict from collections.abc import Sequence +import copy import os.path as op import re import logging - -import numpy as np - -from asdf import AsdfFile from astropy.io import fits -from stdatamodels import properties from stdatamodels.jwst.datamodels.model_base import JwstDataModel from stdatamodels.jwst.datamodels.util import open as datamodel_open @@ -19,15 +14,26 @@ __all__ = ['ModelContainer'] -_ONE_MB = 1 << 20 RECOGNIZED_MEMBER_FIELDS = ['tweakreg_catalog', 'group_id'] +EMPTY_ASN_TABLE = { + "asn_id": None, + "asn_pool": None, + "products": [ + {"name": "", + "members": [ + {"exptype": "", + "expname": ""} + ] + } + ] +} # Configure logging logger = logging.getLogger(__name__) logger.addHandler(logging.NullHandler()) -class ModelContainer(JwstDataModel, Sequence): +class ModelContainer(Sequence): """ A container for holding DataModels. @@ -55,10 +61,6 @@ class ModelContainer(JwstDataModel, Sequence): asn_n_members : int Open only the first N qualifying members. - iscopy : bool - Presume this model is a copy. Members will not be closed - when the model is closed/garbage-collected. - Examples -------- >>> container = ModelContainer('example_asn.json') @@ -80,19 +82,6 @@ class ModelContainer(JwstDataModel, Sequence): Notes ----- - The optional paramters ``save_open`` and ``return_open`` can be - provided to control how the `JwstDataModel` are used by the - :py:class:`ModelContainer`. If ``save_open`` is set to `False`, each input - `JwstDataModel` instance in ``init`` will be written out to disk and - closed, then only the filename for the `JwstDataModel` will be used to - initialize the :py:class:`ModelContainer` object. - Subsequent access of each member will then open the `JwstDataModel` file to - work with it. If ``return_open`` is also `False`, then the `JwstDataModel` - will be closed when access to the `JwstDataModel` is completed. The use of - these parameters can minimize the amount of memory used by this object - during processing, with these parameters being used - by :py:class:`~jwst.outlier_detection.OutlierDetectionStep`. - When ASN table's members contain attributes listed in :py:data:`RECOGNIZED_MEMBER_FIELDS`, :py:class:`ModelContainer` will read those attribute values and update the corresponding attributes @@ -146,53 +135,47 @@ class ModelContainer(JwstDataModel, Sequence): ``models_grouped`` property for more details. """ - schema_url = None - - def __init__(self, init=None, asn_exptypes=None, asn_n_members=None, - iscopy=False, **kwargs): - super().__init__(init=None, **kwargs) + def __init__(self, init=None, asn_exptypes=None, asn_n_members=None, **kwargs): self._models = [] - self._iscopy = iscopy self.asn_exptypes = asn_exptypes self.asn_n_members = asn_n_members - self.asn_table = {} + self.asn_table = copy.deepcopy(EMPTY_ASN_TABLE) self.asn_table_name = None self.asn_pool_name = None self.asn_file_path = None self._memmap = kwargs.get("memmap", False) - self._return_open = kwargs.get('return_open', True) - self._save_open = kwargs.get('save_open', True) if init is None: # Don't populate the container with models pass - elif isinstance(init, fits.HDUList): - if self._save_open: - model = [datamodel_open(init, memmap=self._memmap)] - else: - model = init._file.name - init.close() - self._models.append(model) elif isinstance(init, list): if all(isinstance(x, (str, fits.HDUList, JwstDataModel)) for x in init): - if self._save_open: - init = [datamodel_open(m, memmap=self._memmap) for m in init] + for m in init: + self._models.append(datamodel_open(m, memmap=self._memmap)) + # set asn_table_name and product name to first datamodel stem since they were not provided + fname = self._models[0].meta.filename + if fname is not None: + root = op.basename(fname).split(".")[0] + default_name = "_".join(root.split("_")[:-1]) # remove old suffix + else: + default_name = "" + self.asn_table_name = default_name + self.asn_table["products"][0]["name"] = default_name else: raise TypeError("list must contain items that can be opened " "with jwst.datamodels.open()") - self._models = init elif isinstance(init, self.__class__): - instance = copy.deepcopy(init._instance) - self._schema = init._schema - self._shape = init._shape - self._asdf = AsdfFile(instance) - self._instance = instance - self._ctx = self - self._models = init._models - self._iscopy = True + for m in init: + self._models.append(datamodel_open(m, memmap=self._memmap)) + self.asn_exptypes = init.asn_exptypes + self.asn_n_members = init.asn_n_members + self.asn_table = init.asn_table + self.asn_table_name = init.asn_table_name + self.asn_pool_name = init.asn_pool_name + self.asn_file_path = init.asn_file_path elif is_association(init): self.from_asn(init) elif isinstance(init, str): @@ -207,10 +190,7 @@ def __len__(self): return len(self._models) def __getitem__(self, index): - m = self._models[index] - if not isinstance(m, JwstDataModel) and self._return_open: - m = datamodel_open(m, memmap=self._memmap) - return m + return self._models[index] def __setitem__(self, index, model): self._models[index] = model @@ -220,8 +200,6 @@ def __delitem__(self, index): def __iter__(self): for model in self._models: - if not isinstance(model, JwstDataModel) and self._return_open: - model = datamodel_open(model, memmap=self._memmap) yield model def insert(self, index, model): @@ -236,24 +214,19 @@ def extend(self, model): def pop(self, index=-1): self._models.pop(index) + def __enter__(self): + return self + + def __exit__(self, *args): + self.close() + def copy(self, memo=None): """ Returns a deep copy of the models in this model container. """ - result = self.__class__(init=None, - pass_invalid_values=self._pass_invalid_values, - strict_validation=self._strict_validation) - instance = copy.deepcopy(self._instance, memo=memo) - result._asdf = AsdfFile(instance) - result._instance = instance - result._iscopy = self._iscopy - result._schema = self._schema - result._ctx = result + result = self.__class__(init=None) for m in self._models: - if isinstance(m, JwstDataModel): - result.append(m.copy()) - else: - result.append(m) + result.append(m.copy(memo=memo)) return result @staticmethod @@ -315,37 +288,25 @@ def from_asn(self, asn_data): try: for member in sublist: filepath = op.join(asn_dir, member['expname']) - update_model = any(attr in member for attr in RECOGNIZED_MEMBER_FIELDS) - if update_model or self._save_open: - m = datamodel_open(filepath, memmap=self._memmap) - m.meta.asn.exptype = member['exptype'] - for attr, val in member.items(): - if attr in RECOGNIZED_MEMBER_FIELDS: - if attr == 'tweakreg_catalog': - if val.strip(): - val = op.join(asn_dir, val) - else: - val = None - - setattr(m.meta, attr, val) - - if not self._save_open: - m.save(filepath, overwrite=True) - m.close() - else: - m = filepath - + m = datamodel_open(filepath, memmap=self._memmap) + m.meta.asn.exptype = member['exptype'] + for attr, val in member.items(): + if attr in RECOGNIZED_MEMBER_FIELDS: + if attr == 'tweakreg_catalog': + if val.strip(): + val = op.join(asn_dir, val) + else: + val = None + + setattr(m.meta, attr, val) self._models.append(m) except IOError: self.close() raise - # Pull the whole association table into meta.asn_table - self.meta.asn_table = {} - properties.merge_tree( - self.meta.asn_table._instance, asn_data - ) + # Pull the whole association table into the asn_table attribute + self.asn_table = copy.deepcopy(asn_data) if self.asn_file_path is not None: self.asn_table_name = op.basename(self.asn_file_path) @@ -359,7 +320,6 @@ def from_asn(self, asn_data): def save(self, path=None, - dir_path=None, save_model_func=None, **kwargs): """ @@ -367,53 +327,37 @@ def save(self, Parameters ---------- - path : str or func or None + path : str or None - If None, the `meta.filename` is used for each model. - If a string, the string is used as a root and an index is appended. - - If a function, the function takes the two arguments: - the value of model.meta.filename and the - `idx` index, returning constructed file name. - - dir_path : str - Directory to write out files. Defaults to current working dir. - If directory does not exist, it creates it. Filenames are pulled - from `.meta.filename` of each datamodel in the container. save_model_func: func or None Alternate function to save each model instead of the models `save` method. Takes one argument, the model, and keyword argument `idx` for an index. + kwargs : dict + Additional parameters to be passed to the `save` method of each + model. + Returns ------- output_paths: [str[, ...]] List of output file paths of where the models were saved. """ output_paths = [] - if path is None: - def path(filename, idx=None): - return filename - elif not callable(path): - path = make_file_with_index - for idx, model in enumerate(self): - if len(self) <= 1: - idx = None if save_model_func is None: - outpath, filename = op.split( - path(model.meta.filename, idx=idx) - ) - if dir_path: - outpath = dir_path - save_path = op.join(outpath, filename) - try: - output_paths.append( - model.save(save_path, **kwargs) + if path is None: + save_path = model.meta.filename + else: + if len(self) <= 1: + idx = None + save_path = path+str(idx)+".fits" + output_paths.append( + model.save(save_path, **kwargs) ) - except IOError as err: - raise err - else: output_paths.append(save_model_func(model, idx=idx)) return output_paths @@ -454,8 +398,6 @@ def models_grouped(self): group_dict = OrderedDict() for i, model in enumerate(self._models): params = [] - if not self._save_open: - model = datamodel_open(model, memmap=self._memmap) if (hasattr(model.meta, 'group_id') and model.meta.group_id not in [None, '']): @@ -480,10 +422,6 @@ def models_grouped(self): group_id = model.meta.group_id - if not self._save_open and not self._return_open: - model.close() - model = self._models[i] - if group_id in group_dict: group_dict[group_id].append(model) else: @@ -503,10 +441,9 @@ def group_names(self): def close(self): """Close all datamodels.""" - if not self._iscopy: - for model in self._models: - if isinstance(model, JwstDataModel): - model.close() + for model in self._models: + if isinstance(model, JwstDataModel): + model.close() @property def crds_observatory(self): @@ -518,8 +455,6 @@ def crds_observatory(self): ------- str """ - # Eventually ModelContainer will also be used for Roman, but this - # will work for now: return "jwst" def get_crds_parameters(self): @@ -530,27 +465,15 @@ def get_crds_parameters(self): Returns ------- dict - """ - with self._open_first_science_exposure() as model: - return model.get_crds_parameters() - - def _open_first_science_exposure(self): - """ - Open first model with exptype SCIENCE, or the first model - if none exists. - Returns - ------- - stdatamodels.JwstDataModel + Notes + ----- + stpipe requires ModelContainer to have a crds_observatory attribute in order + to pass through step.run(), but it is never accessed. """ - for exposure in self.meta.asn_table.products[0].members: - if exposure.exptype.upper() == "SCIENCE": - first_exposure = exposure.expname - break - else: - first_exposure = self.meta.asn_table.products[0].members[0].expname - - return datamodel_open(first_exposure) + msg = ("stpipe uses the get_crds_parameters method from the 0th model in the " + "ModelContainer. This method is currently not used.") + raise NotImplementedError(msg) def ind_asn_type(self, asn_exptype): """ @@ -571,98 +494,3 @@ def ind_asn_type(self, asn_exptype): if model.meta.asn.exptype.lower() == asn_exptype: ind.append(i) return ind - - def set_buffer(self, buffer_size, overlap=None): - """Set buffer size for scrolling section-by-section access. - - Parameters - ---------- - buffer_size : float, None - Define size of buffer in MB for each section. - If `None`, a default buffer size of 1MB will be used. - - overlap : int, optional - Define the number of rows of overlaps between sections. - If `None`, no overlap will be used. - """ - self.overlap = 0 if overlap is None else overlap - self.grow = 0 - - with datamodel_open(self._models[0]) as model: - imrows, imcols = model.data.shape - data_item_size = model.data.itemsize - data_item_type = model.data.dtype - model.close() - del model - min_buffer_size = imcols * data_item_size - - self.buffer_size = min_buffer_size if buffer_size is None else (buffer_size * _ONE_MB) - - section_nrows = min(imrows, int(self.buffer_size // min_buffer_size)) - - if section_nrows == 0: - self.buffer_size = min_buffer_size - logger.warning("WARNING: Buffer size is too small to hold a single row." - f"Increasing buffer size to {self.buffer_size / _ONE_MB}MB") - section_nrows = 1 - - nbr = section_nrows - self.overlap - nsec = (imrows - self.overlap) // nbr - if (imrows - self.overlap) % nbr > 0: - nsec += 1 - - self.n_sections = nsec - self.nbr = nbr - self.section_nrows = section_nrows - self.imrows = imrows - self.imcols = imcols - self.imtype = data_item_type - - def get_sections(self): - """Iterator to return the sections from all members of the container.""" - - for k in range(self.n_sections): - e1 = k * self.nbr - e2 = e1 + self.section_nrows - - if k == self.n_sections - 1: # last section - e2 = min(e2, self.imrows) - e1 = min(e1, e2 - self.overlap - 1) - - data_list = np.empty((len(self._models), e2 - e1, self.imcols), - dtype=self.imtype) - wht_list = np.empty((len(self._models), e2 - e1, self.imcols), - dtype=self.imtype) - for i, model in enumerate(self._models): - model = datamodel_open(model, memmap=self._memmap) - - data_list[i, :, :] = model.data[e1:e2].copy() - wht_list[i, :, :] = model.wht[e1:e2].copy() - model.close() - del model - - yield (data_list, wht_list, (e1, e2)) - - -def make_file_with_index(file_path, idx): - """Append an index to a filename - - Parameters - ---------- - file_path: str - The file to append the index to. - idx: int - An index to append - - - Returns - ------- - file_path: str - Path with index appended - """ - # Decompose path - path_head, path_tail = op.split(file_path) - base, ext = op.splitext(path_tail) - if idx is not None: - base = base + str(idx) - return op.join(path_head, base + ext) diff --git a/jwst/datamodels/tests/data/association.json b/jwst/datamodels/tests/data/association.json index 0b3d4378d3..4dfa293f57 100644 --- a/jwst/datamodels/tests/data/association.json +++ b/jwst/datamodels/tests/data/association.json @@ -13,12 +13,12 @@ "members": [ { "exptype": "SCIENCE", - "expname": "test.fits", + "expname": "test.fits" }, { "exptype": "SCIENCE", - "expname": "test.fits", - }, + "expname": "test.fits" + } ] } ], diff --git a/jwst/datamodels/tests/test_model_container.py b/jwst/datamodels/tests/test_model_container.py index 2bf3528a32..2266f7909d 100644 --- a/jwst/datamodels/tests/test_model_container.py +++ b/jwst/datamodels/tests/test_model_container.py @@ -125,3 +125,25 @@ def test_group_id(tmp_path): model_droup_ids.add(m.meta.group_id) assert asn_group_ids == model_droup_ids + + +def test_save(tmp_cwd, container): + + # container pushes us to data/ directory so need to go back to tmp_cwd + # to avoid polluting the data/ directory + with pushdir(tmp_cwd): + + # test default just saves things at model meta filename + container.save() + expected_fnames = [] + for model in container: + expected_fnames.append(model.meta.filename) + for fname in expected_fnames: + assert os.path.exists(fname) + + # test specifying path saves to custom path with indices + path = "foo" + container.save(path) + expected_fnames = [path+str(i)+".fits" for i in range(len(container))] + for fname in expected_fnames: + assert os.path.exists(fname) diff --git a/jwst/datamodels/tests/test_open_association.py b/jwst/datamodels/tests/test_open_association.py index c11c9ec8dc..5ca8c4b930 100644 --- a/jwst/datamodels/tests/test_open_association.py +++ b/jwst/datamodels/tests/test_open_association.py @@ -10,7 +10,6 @@ # Define artificial memory size MEMORY = 100 # 100 bytes - # Utilities def t_path(partial_path): """Construction the full path for test files""" diff --git a/jwst/extract_1d/extract.py b/jwst/extract_1d/extract.py index bd7cda6d8c..fac9dc799f 100644 --- a/jwst/extract_1d/extract.py +++ b/jwst/extract_1d/extract.py @@ -2790,10 +2790,7 @@ def do_extract1d( # Set "meta_source" to either the first model in a container, or the individual input model, for convenience # of retrieving meta attributes in subsequent statements if was_source_model: - if isinstance(input_model, datamodels.SlitModel): # input_model is SourceContainer with a single SlitModel - meta_source = input_model - else: - meta_source = input_model[0] + meta_source = input_model[0] else: meta_source = input_model diff --git a/jwst/extract_1d/extract_1d_step.py b/jwst/extract_1d/extract_1d_step.py index f8b43b0178..3ff620e6c5 100644 --- a/jwst/extract_1d/extract_1d_step.py +++ b/jwst/extract_1d/extract_1d_step.py @@ -197,7 +197,10 @@ def process(self, input): """ # Open the input and figure out what type of model it is - input_model = datamodels.open(input) + if isinstance(input, ModelContainer): + input_model = input + else: + input_model = datamodels.open(input) was_source_model = False # default value if isinstance(input_model, datamodels.CubeModel): diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 1ea83855ee..727b351c47 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -86,9 +86,9 @@ def process(self, input): if self.user_background: if isinstance(input_data, ModelContainer): input_data, _ = split_container(input_data) + asn_id = input_data.asn_table["asn_id"] del _ result = ModelContainer() - result.update(input_data) background_2d_collection = ModelContainer() background_2d_collection.update(input_data) for model in input_data: @@ -100,6 +100,7 @@ def process(self, input): model.meta.background.master_background_file = basename(self.user_background) # Use user-supplied master background and subtract it else: + asn_id = None background_2d = expand_to_2d(input_data, self.user_background) background_2d_collection = background_2d result = subtract_2d_background(input_data, background_2d) @@ -109,14 +110,20 @@ def process(self, input): # Save the computed 2d background if requested by user. The user has supplied # the master background so just save the expanded 2d background if self.save_background: - asn_id = input_data.meta.asn_table.asn_id - self.save_model(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) + self.save_container(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) # Compute master background and subtract it else: if isinstance(input_data, ModelContainer): input_data, background_data = split_container(input_data) - asn_id = input_data.meta.asn_table.asn_id + if len(background_data) == 0: + msg = ("No background data found in input container, " + "and no user-supplied background provided. Skipping step.") + self.log.warning(msg) + result = input_data.copy() + record_step_status(result, 'master_background', success=False) + return result + asn_id = input_data.asn_table["asn_id"] for model in background_data: # Check if the background members are nodded x1d extractions @@ -143,9 +150,7 @@ def process(self, input): background_data.close() result = ModelContainer() - result.update(input_data) background_2d_collection = ModelContainer() - background_2d_collection.update(input_data) for model in input_data: background_2d = expand_to_2d(model, master_background) result.append(subtract_2d_background(model, background_2d)) @@ -166,7 +171,7 @@ def process(self, input): # Save the computed background if requested by user if self.save_background: self.save_model(master_background, suffix='masterbg1d', force=True, asn_id=asn_id) - self.save_model(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) + self.save_container(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) record_step_status(result, 'master_background', success=True) @@ -224,6 +229,11 @@ def _do_sub(self): "run again and set force_subtract = True.") return do_sub + + def save_container(self, container, suffix="", asn_id="", force=True): + """Save all models in container for intermediate background subtraction""" + for i, model in enumerate(container): + self.save_model(model, suffix=suffix, force=force, asn_id=asn_id, idx=i) def copy_background_to_surf_bright(spectrum): @@ -240,8 +250,6 @@ def copy_background_to_surf_bright(spectrum): def split_container(container): """Divide a ModelContainer with science and background into one of each """ - asn = container.meta.asn_table.instance - background = ModelContainer() science = ModelContainer() @@ -252,12 +260,11 @@ def split_container(container): background.append(container._models[ind_bkgd]) # Pass along the association table to the output science container - science.meta.asn_table = {} science.asn_pool_name = container.asn_pool_name science.asn_table_name = container.asn_table_name - merge_tree(science.meta.asn_table.instance, asn) + merge_tree(science.asn_table, container.asn_table) # Prune the background members from the table - for p in science.meta.asn_table.instance['products']: + for p in science.asn_table['products']: p['members'] = [m for m in p['members'] if m['exptype'].lower() != 'background'] return science, background diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 94a1ca7e21..1b6b42f733 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -159,7 +159,7 @@ def _guess_mode(self, input_models): return self.mode # guess mode from input type - if isinstance(input_models, (str, dict)): + if isinstance(input_models, (str, dict, list)): input_models = datamodels.open(input_models, asn_n_members=1) # Select which version of OutlierDetection @@ -202,6 +202,8 @@ def _get_asn_id(self, input_models): try: if isinstance(input_models, ModelLibrary): asn_id = input_models.asn["asn_id"] + elif isinstance(input_models, ModelContainer): + asn_id = input_models.asn_table["asn_id"] else: asn_id = input_models.meta.asn_table.asn_id except (AttributeError, KeyError): @@ -223,7 +225,7 @@ def _get_asn_id(self, input_models): def _set_status(self, input_models, status): # this might be called with the input which might be a filename or path - if not isinstance(input_models, (datamodels.JwstDataModel, ModelLibrary)): + if not isinstance(input_models, (datamodels.JwstDataModel, ModelLibrary, ModelContainer)): input_models = datamodels.open(input_models) record_step_status(input_models, "outlier_detection", status) diff --git a/jwst/outlier_detection/spec.py b/jwst/outlier_detection/spec.py index e019f456fe..6467daadd4 100644 --- a/jwst/outlier_detection/spec.py +++ b/jwst/outlier_detection/spec.py @@ -40,7 +40,7 @@ def detect_outliers( See `OutlierDetectionStep.spec` for documentation of these arguments. """ if not isinstance(input_models, ModelContainer): - input_models = ModelContainer(input_models, save_open=in_memory) + input_models = ModelContainer(input_models) if len(input_models) < 2: log.warning(f"Input only contains {len(input_models)} exposures") diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index b35cceecf8..ac5bb28cd3 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -90,14 +90,14 @@ def process(self, user_input): # This asn_id assignment is important as it allows outlier detection # to know the asn_id since that step receives the cube as input. - self.asn_id = input_models.meta.asn_table.asn_id + self.asn_id = input_models.asn_table["asn_id"] # Store the output file for future use - self.output_file = input_models.meta.asn_table.products[0].name + self.output_file = input_models.asn_table["products"][0]["name"] # Find all the member types in the product members_by_type = defaultdict(list) - prod = input_models.meta.asn_table.products[0].instance + prod = input_models.asn_table["products"][0] for member in prod['members']: members_by_type[member['exptype'].lower()].append(member['expname']) @@ -218,7 +218,7 @@ def process(self, user_input): model_blender.finalize_model(result) try: - result.meta.asn.pool_name = input_models.meta.asn_table.asn_pool + result.meta.asn.pool_name = input_models.asn_pool_name result.meta.asn.table_name = op.basename(user_input) except AttributeError: self.log.debug('Cannot set association information on final') diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index 2644bee233..d62bcc926f 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -128,12 +128,12 @@ def process(self, input): # products until the individual tasks work and do it themselves exptype = input_models[0].meta.exposure.type model_type = input_models[0].meta.model_type - output_file = input_models.meta.asn_table.products[0].name + output_file = input_models.asn_table["products"][0]["name"] self.output_file = output_file # Find all the member types in the product members_by_type = defaultdict(list) - product = input_models.meta.asn_table.products[0].instance + product = input_models.asn_table["products"][0] for member in product['members']: members_by_type[member['exptype'].lower()].append(member['expname']) @@ -145,7 +145,7 @@ def process(self, input): # If background data are present, call the master background step if members_by_type['background']: source_models = self.master_background(input_models) - source_models.meta.asn_table = input_models.meta.asn_table + source_models.asn_table = input_models.asn_table # If the step is skipped, do the container splitting that # would've been done in master_background diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index 2b9e14ae86..f5d974f48b 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -72,11 +72,11 @@ def process(self, input): return if self.output_file is None: - self.output_file = input_models.meta.asn_table.products[0].name + self.output_file = input_models.asn_table["products"][0]["name"] # This asn_id assignment is important as it allows outlier detection # to know the asn_id since that step receives the cube as input. - self.asn_id = input_models.meta.asn_table.asn_id + self.asn_id = input_models.asn_table["asn_id"] self.outlier_detection.mode = 'tso' # Input may consist of multiple exposures, so loop over each of them @@ -100,13 +100,13 @@ def process(self, input): original_filename = cube.meta.filename # ensure output filename will not have duplicate asn_id - if "_"+input_models.meta.asn_table.asn_id in original_filename: + if "_"+self.asn_id in original_filename: original_filename = original_filename.replace( - "_"+input_models.meta.asn_table.asn_id, '' + "_"+self.asn_id, '' ) self.save_model( cube, output_file=original_filename, suffix='crfints', - asn_id=input_models.meta.asn_table.asn_id + asn_id=self.asn_id ) cube.meta.filename = original_filename input_models[i] = cube @@ -173,7 +173,7 @@ def process(self, input): phot_result_list.append(self.white_light(result)) # Update some metadata from the association - x1d_result.meta.asn.pool_name = input_models.meta.asn_table.asn_pool + x1d_result.meta.asn.pool_name = input_models.asn_table["asn_pool"] x1d_result.meta.asn.table_name = op.basename(input) # Save the final x1d Multispec model diff --git a/jwst/pixel_replace/pixel_replace_step.py b/jwst/pixel_replace/pixel_replace_step.py index b76260d0a0..c7695440b8 100644 --- a/jwst/pixel_replace/pixel_replace_step.py +++ b/jwst/pixel_replace/pixel_replace_step.py @@ -52,8 +52,11 @@ def process(self, input): with datamodels.open(input) as input_model: # If more than one 2d spectrum exists in input, call replacement - if input_model.meta.model_type in ['MultiSlitModel', 'SlitModel', - 'ImageModel', 'IFUImageModel', 'CubeModel']: + if isinstance(input_model, (datamodels.MultiSlitModel, + datamodels.SlitModel, + datamodels.ImageModel, + datamodels.IFUImageModel, + datamodels.CubeModel)): self.log.debug('Input is a {input_model.meta.model_type}.') elif isinstance(input_model, datamodels.ModelContainer): self.log.debug('Input is a ModelContainer.') @@ -77,7 +80,7 @@ def process(self, input): # Setup output path naming if associations are involved. asn_id = None try: - asn_id = input_model.meta.asn_table.asn_id + asn_id = input_model.asn_table["asn_id"] except (AttributeError, KeyError): pass if asn_id is None: diff --git a/jwst/stpipe/tests/steps/__init__.py b/jwst/stpipe/tests/steps/__init__.py index 503b3e3d00..eab156a0e5 100644 --- a/jwst/stpipe/tests/steps/__init__.py +++ b/jwst/stpipe/tests/steps/__init__.py @@ -2,6 +2,7 @@ from stdatamodels.jwst import datamodels from stdatamodels.jwst.datamodels import ImageModel +from jwst.datamodels import ModelContainer class StepWithReference(Step): @@ -177,8 +178,12 @@ class StepWithContainer(Step): def process(self, *args): container = [] - model1 = ImageModel(args[0]).copy() - model2 = ImageModel(args[0]).copy() + if isinstance(args[0], ModelContainer): + model = args[0][0] + else: + model = args[0] + model1 = ImageModel(model).copy() + model2 = ImageModel(model).copy() model1.meta.filename = 'swc_model1.fits' model2.meta.filename = 'swc_model2.fits' container.append(model1) diff --git a/jwst/stpipe/tests/test_input.py b/jwst/stpipe/tests/test_input.py index 8586dd37db..a628382458 100644 --- a/jwst/stpipe/tests/test_input.py +++ b/jwst/stpipe/tests/test_input.py @@ -9,7 +9,7 @@ from jwst.stpipe import Step from jwst.datamodels import ModelContainer -from jwst.stpipe.tests.steps import StepWithModel +from jwst.stpipe.tests.steps import StepWithModel, StepWithContainer from jwst.stpipe.tests.util import t_path @@ -18,7 +18,7 @@ def test_default_input_with_container(mk_tmp_dirs): model_path = t_path('data/flat.fits') with ModelContainer([model_path]) as container: - step = StepWithModel() + step = StepWithContainer() step.run(container) assert step._input_filename is None diff --git a/jwst/tweakreg/tests/test_tweakreg.py b/jwst/tweakreg/tests/test_tweakreg.py index 9dc21267c9..57db989e16 100644 --- a/jwst/tweakreg/tests/test_tweakreg.py +++ b/jwst/tweakreg/tests/test_tweakreg.py @@ -290,7 +290,9 @@ def test_custom_catalog(custom_catalog_path, example_input, catfile, asn, meta, example_input[0].meta.tweakreg_catalog = "" # write out the ModelContainer and association (so the association table will be loaded) - example_input.save(dir_path=str(custom_catalog_path.parent)) + for model in example_input: + model.save(model.meta.filename, dir_path=str(custom_catalog_path.parent)) + model.close() asn_data = { 'asn_id': 'foo', 'asn_pool': 'bar', diff --git a/pyproject.toml b/pyproject.toml index 0ef1fc7253..1b980ddb5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ dependencies = [ "scipy>=1.9.3", "spherical-geometry>=1.2.22", "stcal @ git+https://github.com/spacetelescope/stcal.git@main", - "stdatamodels>=2.1.0,<2.2.0", + "stdatamodels @ git+https://github.com/spacetelescope/stdatamodels.git@main", "stpipe>=0.7.0,<0.8.0", "stsci.imagestats>=1.6.3", "synphot>=1.2", From 8c10fad7f5404397808a075a581bec6a4b51bbb5 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 16:48:38 -0400 Subject: [PATCH 563/687] fix typo --- jwst/master_background/master_background_step.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index e5739a5602..9f62a089a2 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -148,8 +148,8 @@ def process(self, input): # If requested, apply a moving-median boxcar filter to the master background spectrum # Round down even kernel sizes because only odd kernel sizes are supported. - if self.median_kernal % 2 == 0: - self.median_kernal -= 1 + if self.median_kernel % 2 == 0: + self.median_kernel -= 1 self.log.info('Even median filter kernels are not supported.' f'Rounding the median kernel size down to {self.median_kernel}.') From eab772aff3ad89f406e31ebabc6f339dbec424e4 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 22 Oct 2024 16:59:41 -0400 Subject: [PATCH 564/687] add median filtering option for master_background_mos --- .../master_background_mos_step.py | 20 +++++++++++++--- .../master_background_step.py | 3 ++- jwst/master_background/nirspec_utils.py | 24 +++++++++++++++++-- 3 files changed, 41 insertions(+), 6 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index a9460d288d..0466df3f67 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -48,6 +48,7 @@ class MasterBackgroundMosStep(Pipeline): spec = """ sigma_clip = float(default=3) # Factor for clipping outliers when combining background spectra + median_kernel = integer(default=1) # Moving-median boxcar size to filter the master background spectrum force_subtract = boolean(default=False) # Force subtracting master background save_background = boolean(default=False) # Save computed master background user_background = string(default=None) # Path to user-supplied master background @@ -85,6 +86,14 @@ def process(self, data): - "masterbkg_2d": `~jwst.datamodels.MultiSlitModel` The 2D slit-based version of the master background. + + sigma_clip : None or float + Optional factor for sigma clipping outliers when combining background spectra. + + median_kernel : integer, optional + Optional user-supplied kernel with which to moving-median boxcar filter the master background + spectrum. Must be an odd integer, even integers will be rounded down to the nearest + odd integer. force_subtract : bool, optional Optional user-supplied flag that overrides step logic to force subtraction of the @@ -134,7 +143,7 @@ def process(self, data): self.log.info('Calculating master background') master_background, mb_multislit, bkg_x1d_spectra = self._calc_master_background( - data_model, sigma_clip=self.sigma_clip) + data_model, sigma_clip=self.sigma_clip, median_kernel=self.median_kernel) # Check that a master background was actually determined. if master_background is None: @@ -202,7 +211,8 @@ def _classify_slits(self, data): return num_bkg, num_src - def _calc_master_background(self, data, user_background=None, sigma_clip=3): + def _calc_master_background( + self, data, user_background=None, sigma_clip=3, median_kernel=1): """Calculate master background from background slits Parameters @@ -214,6 +224,10 @@ def _calc_master_background(self, data, user_background=None, sigma_clip=3): or opened datamodel sigma_clip : None or float Optional factor for sigma clipping outliers when combining background spectra. + median_kernel : integer, optional + Optional user-supplied kernel with which to moving-median boxcar filter the master background + spectrum. Must be an odd integer, even integers will be rounded down to the nearest + odd integer. Returns ------- @@ -255,7 +269,7 @@ def _calc_master_background(self, data, user_background=None, sigma_clip=3): else: self.log.debug('Calculating 1D master background') master_background, bkg_x1d_spectra = nirspec_utils.create_background_from_multislit( - pre_calibrated, sigma_clip=sigma_clip) + pre_calibrated, sigma_clip=sigma_clip, median_kernel=median_kernel) if master_background is None: self.log.debug('No master background could be calculated. Returning None') return None, None, None diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 9f62a089a2..4d01709074 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -41,7 +41,8 @@ def process(self, input): median_kernel : integer, optional Optional user-supplied kernel with which to moving-median boxcar filter the master background - spectrum. Must be an odd integer. + spectrum. Must be an odd integer, even integers will be rounded down to the nearest + odd integer. user_background : None, string, or `~jwst.datamodels.MultiSpecModel` Optional user-supplied master background 1D spectrum, path to file diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 9bff793a99..9395dd432e 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -1,6 +1,8 @@ import logging import warnings +from scipy.signal import medfilt + from stdatamodels.jwst import datamodels log = logging.getLogger(__name__) @@ -76,7 +78,7 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(input_model, sigma_clip=3): +def create_background_from_multislit(input_model, sigma_clip=3, median_kernel=1): """Create a 1D master background spectrum from a set of calibrated background MOS slitlets in the input MultiSlitModel. @@ -85,8 +87,12 @@ def create_background_from_multislit(input_model, sigma_clip=3): ---------- input_model : `~jwst.datamodels.MultiSlitModel` The input data model containing all slit instances. - sigma_clip : None or float + sigma_clip : None or float, optional Optional factor for sigma clipping outliers when combining background spectra. + median_kernel : integer, optional + Optional user-supplied kernel with which to moving-median boxcar filter the master background + spectrum. Must be an odd integer, even integers will be rounded down to the nearest + odd integer. Returns ------- @@ -125,6 +131,20 @@ def create_background_from_multislit(input_model, sigma_clip=3): log.info('Combining 1D background spectra into master background') master_bkg = combine_1d_spectra( x1d, exptime_key='exposure_time', sigma_clip=sigma_clip) + + # If requested, apply a moving-median boxcar filter to the master background spectrum + # Round down even kernel sizes because only odd kernel sizes are supported. + if median_kernel % 2 == 0: + median_kernel -= 1 + log.info('Even median filter kernels are not supported.' + f'Rounding the median kernel size down to {median_kernel}.') + + if (median_kernel > 1): + log.info(f'Applying moving-median boxcar of width {median_kernel}.') + master_bkg.spec[0].spec_table['surf_bright'] = medfilt( + master_bkg.spec[0].spec_table['surf_bright'], + kernel_size=[median_kernel] + ) del bkg_model del resamp From 24b693c2c4fdf4b598257c7b5555b070781c9571 Mon Sep 17 00:00:00 2001 From: David Law Date: Wed, 23 Oct 2024 13:36:36 -0400 Subject: [PATCH 565/687] Tweak spaxel debugging --- jwst/cube_build/ifu_cube.py | 2 ++ jwst/cube_build/src/cube_match_sky_driz.c | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 3512e03542..70d0ee4bad 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -679,6 +679,8 @@ def build_ifucube(self): linear = 0 if self.linear_wavelength: linear = 1 + if debug_cube_index >= 0: + log.info(f"Input filename: {input_model.meta.filename}") result = cube_wrapper_driz(instrument, flag_dq_plane, start_region, end_region, self.overlap_partial, self.overlap_full, diff --git a/jwst/cube_build/src/cube_match_sky_driz.c b/jwst/cube_build/src/cube_match_sky_driz.c index a23e8aae1b..02e509f4c4 100644 --- a/jwst/cube_build/src/cube_match_sky_driz.c +++ b/jwst/cube_build/src/cube_match_sky_driz.c @@ -287,7 +287,7 @@ int match_driz(double *xc, double *yc, double *zc, // Keep print statement in code - used for debugging if (index_cube == debug_cube_index){ printf("spaxel, flux, x, y [count starting at 0] %i %f %f %f \n ", index_cube, - x_det[k], y_det[k], flux[k]); + flux[k], x_det[k], y_det[k]); } From 811a3c4fecaafa9b4829c383d0611ee46e09fbc4 Mon Sep 17 00:00:00 2001 From: David Law Date: Wed, 23 Oct 2024 13:39:55 -0400 Subject: [PATCH 566/687] Add change log --- changes/8913.cube_build.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8913.cube_build.rst diff --git a/changes/8913.cube_build.rst b/changes/8913.cube_build.rst new file mode 100644 index 0000000000..2fd2ff006f --- /dev/null +++ b/changes/8913.cube_build.rst @@ -0,0 +1 @@ +Tweak the cube_build spaxel debugging option to provide filename info and match outputs to the stated ordering. From 6df9a8d8856e43974349b65cc80036170e4e5e01 Mon Sep 17 00:00:00 2001 From: William Jamieson Date: Wed, 16 Oct 2024 09:56:31 -0400 Subject: [PATCH 567/687] Colorize the test results if possible by default This should make reading the test results a little easier. --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 1b980ddb5a..150bd6e376 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -210,6 +210,7 @@ addopts = [ "-p no:legacypath", "--show-capture=no", "--report-crds-context", + "--color=yes", ] filterwarnings = [ "error::ResourceWarning", From 12cf7ce9801811e8f55b69fbc1f7e31a895fde59 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Wed, 23 Oct 2024 13:23:27 -0700 Subject: [PATCH 568/687] JP-3725: Remove DefaultOrderedDict (#8915) --- jwst/exp_to_source/exp_to_source.py | 49 ++--------------------------- 1 file changed, 2 insertions(+), 47 deletions(-) diff --git a/jwst/exp_to_source/exp_to_source.py b/jwst/exp_to_source/exp_to_source.py index c0f385a339..13784a6b86 100644 --- a/jwst/exp_to_source/exp_to_source.py +++ b/jwst/exp_to_source/exp_to_source.py @@ -2,8 +2,7 @@ """ import logging -from collections import OrderedDict -from collections.abc import Callable +from collections import defaultdict from stdatamodels.properties import merge_tree from stdatamodels.jwst.datamodels import MultiExposureModel @@ -31,7 +30,7 @@ def exp_to_source(inputs): instance contains slits belonging to the same source. The key is the ID of each source, i.e. ``source_id``. """ - result = DefaultOrderedDict(MultiExposureModel) + result = defaultdict(MultiExposureModel) for exposure in inputs: log.info(f'Reorganizing data from exposure {exposure.meta.filename}') @@ -111,47 +110,3 @@ def multislit_to_container(inputs): containers[id] = SourceModelContainer(containers[id]) return containers - - -class DefaultOrderedDict(OrderedDict): - # Source http://stackoverflow.com/a/6190500/562769 - def __init__(self, default_factory=None, *a, **kw): - if (default_factory is not None and - not isinstance(default_factory, Callable)): - raise TypeError('first argument must be callable') - OrderedDict.__init__(self, *a, **kw) - self.default_factory = default_factory - - def __getitem__(self, key): - try: - return OrderedDict.__getitem__(self, key) - except KeyError: - return self.__missing__(key) - - def __missing__(self, key): - if self.default_factory is None: - raise KeyError(key) - self[key] = value = self.default_factory() - return value - - def __reduce__(self): - if self.default_factory is None: - args = tuple() - else: - args = self.default_factory, - return type(self), args, None, None, self.items() - - def copy(self): - return self.__copy__() - - def __copy__(self): - return type(self)(self.default_factory, self) - - def __deepcopy__(self, memo): - import copy - return type(self)(self.default_factory, - copy.deepcopy(self.items())) - - def __repr__(self): - return 'OrderedDefaultDict(%s, %s)' % (self.default_factory, - OrderedDict.__repr__(self)) From 48e1557045e7f25eb507af0160599c41dfe4784f Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 23 Oct 2024 14:13:53 -0400 Subject: [PATCH 569/687] use meta.asn.exptype --- jwst/datamodels/library.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/jwst/datamodels/library.py b/jwst/datamodels/library.py index 32e27aa117..72c6a047fc 100644 --- a/jwst/datamodels/library.py +++ b/jwst/datamodels/library.py @@ -20,14 +20,14 @@ class ModelLibrary(AbstractModelLibrary): @property def crds_observatory(self): return "jwst" - + @property def exptypes(self): """ List of exposure types for all members in the library. """ return [member["exptype"] for member in self._members] - + def indices_for_exptype(self, exptype): """ Determine the indices of models corresponding to ``exptype``. @@ -104,6 +104,9 @@ def _filename_to_group_id(self, filename): msg = f"Cannot find header keyword {e} in {filename}" raise NoGroupID(msg) from e + def _model_to_exptype(self, model): + return model.meta.asn.exptype + def _model_to_group_id(self, model): """ Compute a "group_id" from a model using the DataModel interface @@ -123,7 +126,8 @@ def _model_to_group_id(self, model): raise NoGroupID(f"{model} missing group_id") def _assign_member_to_model(self, model, member): - for attr in ("group_id", "tweakreg_catalog", "exptype"): + model.meta.asn.exptype = member["exptype"] + for attr in ("group_id", "tweakreg_catalog"): if attr in member: setattr(model.meta, attr, member[attr]) if not hasattr(model.meta, "asn"): From 0928e07949d26cb9f68c8db44aa6f0ec21064cc7 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 23 Oct 2024 15:51:51 -0400 Subject: [PATCH 570/687] add changelog fragment --- changes/8918.datamodels.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8918.datamodels.rst diff --git a/changes/8918.datamodels.rst b/changes/8918.datamodels.rst new file mode 100644 index 0000000000..2d442399f7 --- /dev/null +++ b/changes/8918.datamodels.rst @@ -0,0 +1 @@ +Update ModelLibrary to use meta.asn.exptype instead of meta.exptype. From 70bdcd0740395db9e6cff7bf1aa8d8c72a954ff1 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 23 Oct 2024 14:06:48 -0400 Subject: [PATCH 571/687] Add regtest for IFU moving target with background spectrum --- .../test_nirspec_ifu_spec3_moving_target.py | 47 +++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 jwst/regtest/test_nirspec_ifu_spec3_moving_target.py diff --git a/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py b/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py new file mode 100644 index 0000000000..e01b1648d0 --- /dev/null +++ b/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py @@ -0,0 +1,47 @@ +from astropy.io.fits.diff import FITSDiff +import pytest +import numpy as np +from gwcs import wcstools + +from jwst.stpipe import Step +from stdatamodels.jwst import datamodels + + +@pytest.fixture(scope="module") +def run_pipeline(rtdata_module): + """ + Run the calwebb_spec3 pipeline on a NIRSpec IFU moving target. + """ + rtdata = rtdata_module + + # Get the ASN file and input exposures + # Note: ASN contains a background spectrum + rtdata.get_asn('nirspec/ifu/jw01252-o001_spec3_00003_asn_with_bg.json') + + # Run the calwebb_spec3 pipeline; save results from intermediate steps + args = ["calwebb_spec3", rtdata.input] + Step.from_cmdline(args) + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["cal", "o001_crf", "s3d", "x1d"]) +def test_nirspec_ifu_spec3_moving_target( + run_pipeline, rtdata_module, fitsdiff_default_kwargs, suffix): + """Test spec3 pipeline on a NIRSpec IFU moving target.""" + rtdata = rtdata_module + + if suffix in {"cal", "o001_crf"}: + output = f"jw01252001001_03101_00001_nrs1_{suffix}.fits" + else: + output = f"jw01252-o001_t004_nirspec_prism-clear_{suffix}.fits" + rtdata.output = output + rtdata.get_truth(f"truth/test_nirspec_ifu_spec3_moving_target/{output}") + + # Adjust tolerance for machine precision for resampled data + if suffix == "s3d": + fitsdiff_default_kwargs["rtol"] = 1e-2 + fitsdiff_default_kwargs["atol"] = 2e-4 + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() From 72380629bc64acec44874c1f3ab7711b58546d99 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Wed, 23 Oct 2024 14:21:34 -0400 Subject: [PATCH 572/687] Code style fix --- jwst/regtest/test_nirspec_ifu_spec3_moving_target.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py b/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py index e01b1648d0..102a1cfed6 100644 --- a/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py +++ b/jwst/regtest/test_nirspec_ifu_spec3_moving_target.py @@ -1,10 +1,7 @@ from astropy.io.fits.diff import FITSDiff import pytest -import numpy as np -from gwcs import wcstools from jwst.stpipe import Step -from stdatamodels.jwst import datamodels @pytest.fixture(scope="module") From c751b4ed3d58babe1ed066fbd59c4957a745803b Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 23 Oct 2024 17:15:31 -0400 Subject: [PATCH 573/687] pin stpipe to main --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 150bd6e376..4015dad8e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ dependencies = [ "spherical-geometry>=1.2.22", "stcal @ git+https://github.com/spacetelescope/stcal.git@main", "stdatamodels @ git+https://github.com/spacetelescope/stdatamodels.git@main", - "stpipe>=0.7.0,<0.8.0", + "stpipe @ git+https://github.com/spacetelescope/stpipe.git@main", "stsci.imagestats>=1.6.3", "synphot>=1.2", "tweakwcs>=0.8.8", From 200b1ab16bc6c52dd159ff26e12281c608b432c6 Mon Sep 17 00:00:00 2001 From: Mihai Cara Date: Thu, 24 Oct 2024 10:33:30 -0400 Subject: [PATCH 574/687] Switch resample to the new drizzle package API (#8866) --- changes/8866.resample.rst | 1 + jwst/outlier_detection/utils.py | 28 +- jwst/regtest/test_nirspec_fs_spec3.py | 2 +- jwst/resample/gwcs_drizzle.py | 408 ------------ jwst/resample/resample.py | 865 ++++++++++++-------------- jwst/resample/resample_spec.py | 26 +- pyproject.toml | 4 +- 7 files changed, 453 insertions(+), 881 deletions(-) create mode 100644 changes/8866.resample.rst delete mode 100644 jwst/resample/gwcs_drizzle.py diff --git a/changes/8866.resample.rst b/changes/8866.resample.rst new file mode 100644 index 0000000000..f31024d0ee --- /dev/null +++ b/changes/8866.resample.rst @@ -0,0 +1 @@ +Updated resample code to support the new ``drizzle`` API, see https://github.com/spacetelescope/drizzle/pull/134 for more details. diff --git a/jwst/outlier_detection/utils.py b/jwst/outlier_detection/utils.py index d0de3bf8b8..ac0d2b6192 100644 --- a/jwst/outlier_detection/utils.py +++ b/jwst/outlier_detection/utils.py @@ -107,6 +107,10 @@ def median_without_resampling(input_models, in_memory = not input_models._on_disk ngroups = len(input_models) + if save_intermediate_results: + # create an empty image model for the median data + median_model = datamodels.ImageModel(None) + with input_models: for i in range(len(input_models)): @@ -128,6 +132,10 @@ def median_without_resampling(input_models, err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) else: err_computer = None + if save_intermediate_results: + # update median model's meta with meta from the first model: + median_model.update(drizzled_model) + median_model.meta.wcs = median_wcs weight_threshold = compute_weight_threshold(weight, maskpt) drizzled_data[weight < weight_threshold] = np.nan @@ -137,6 +145,7 @@ def median_without_resampling(input_models, err_computer.append(drizzled_err, i) input_models.shelve(drizzled_model, i, modify=False) + del drizzled_model # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() @@ -147,13 +156,10 @@ def median_without_resampling(input_models, if save_intermediate_results: # Save median model to fits - median_model = datamodels.ImageModel(median_data) + median_model.data = median_data if return_error: median_model.err = median_err - median_model.update(drizzled_model) - median_model.meta.wcs = median_wcs _fileio.save_median(median_model, make_output_path) - del drizzled_model if return_error: return median_data, median_wcs, median_err @@ -219,6 +225,10 @@ def median_with_resampling(input_models, indices_by_group = list(input_models.group_indices.values()) ngroups = len(indices_by_group) + if save_intermediate_results: + # create an empty image model for the median data + median_model = datamodels.ImageModel(None) + with input_models: for i, indices in enumerate(indices_by_group): @@ -238,6 +248,10 @@ def median_with_resampling(input_models, err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype) else: err_computer = None + if save_intermediate_results: + # update median model's meta with meta from the first model: + median_model.update(drizzled_model) + median_model.meta.wcs = median_wcs weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt) drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan @@ -245,6 +259,7 @@ def median_with_resampling(input_models, if return_error: drizzled_model.err[drizzled_model.wht < weight_threshold] = np.nan err_computer.append(drizzled_model.err, i) + del drizzled_model # Perform median combination on set of drizzled mosaics median_data = computer.evaluate() @@ -255,15 +270,12 @@ def median_with_resampling(input_models, if save_intermediate_results: # Save median model to fits - median_model = datamodels.ImageModel(median_data) + median_model.data = median_data if return_error: median_model.err = median_err - median_model.update(drizzled_model) - median_model.meta.wcs = median_wcs # drizzled model already contains asn_id make_output_path = partial(make_output_path, asn_id=None) _fileio.save_median(median_model, make_output_path) - del drizzled_model if return_error: return median_data, median_wcs, median_err diff --git a/jwst/regtest/test_nirspec_fs_spec3.py b/jwst/regtest/test_nirspec_fs_spec3.py index 4fd2d62cf5..997b121961 100644 --- a/jwst/regtest/test_nirspec_fs_spec3.py +++ b/jwst/regtest/test_nirspec_fs_spec3.py @@ -36,7 +36,7 @@ def test_nirspec_fs_spec3(run_pipeline, rtdata_module, fitsdiff_default_kwargs, rtdata = rtdata_module if suffix == "median": - output = f"jw01309022001_04102_00002_nrs2_{slit_name}_{suffix}.fits" + output = f"jw01309022001_04102_00001_nrs2_{slit_name}_{suffix}.fits" # also ensure drizzled and blot models were created with the correct names assert os.path.isfile(output.replace("median", "outlier_s2d")) assert os.path.isfile(output.replace("median", "blot")) diff --git a/jwst/resample/gwcs_drizzle.py b/jwst/resample/gwcs_drizzle.py deleted file mode 100644 index 9221cbdf01..0000000000 --- a/jwst/resample/gwcs_drizzle.py +++ /dev/null @@ -1,408 +0,0 @@ -import numpy as np - -from drizzle import util -from drizzle import cdrizzle -from . import resample_utils - -import logging -log = logging.getLogger(__name__) -log.setLevel(logging.DEBUG) - - -class GWCSDrizzle: - """ - Combine images using the drizzle algorithm - """ - - def __init__(self, product, outwcs=None, wt_scl=None, - pixfrac=1.0, kernel="square", fillval="NAN"): - """ - Create a new Drizzle output object and set the drizzle parameters. - - Parameters - ---------- - - product : DataModel - A data model containing results from a previous run. The three - extensions SCI, WHT, and CTX contain the combined image, total counts - and image id bitmap, respectively. The WCS of the combined image is - also read from the SCI extension. - - outwcs : `gwcs.WCS` - The world coordinate system (WCS) of the resampled image. If not - provided, the WCS is taken from product. - - wt_scl : str, optional - How each input image should be scaled. The choices are `exptime`, - which scales each image by its exposure time, or `expsq`, which scales - each image by the exposure time squared. If not set, then each - input image is scaled by its own weight map. - - pixfrac : float, optional - The fraction of a pixel that the pixel flux is confined to. The - default value of 1 has the pixel flux evenly spread across the image. - A value of 0.5 confines it to half a pixel in the linear dimension, - so the flux is confined to a quarter of the pixel area when the square - kernel is used. - - kernel : str, optional - The name of the kernel used to combine the inputs. The choice of - kernel controls the distribution of flux over the kernel. The kernel - names are: "square", "gaussian", "point", "turbo", "lanczos2", - and "lanczos3". The square kernel is the default. - - fillval : str, optional - The value a pixel is set to in the output if the input image does - not overlap it. The default value of NAN sets NaN values. - """ - - # Initialize the object fields - self._product = product - self.outsci = None - self.outwht = None - self.outcon = None - self.uniqid = 0 - - if wt_scl is None: - self.wt_scl = "" - else: - self.wt_scl = wt_scl - self.kernel = kernel - self.fillval = fillval - self.pixfrac = pixfrac - - self.sciext = "SCI" - self.whtext = "WHT" - self.conext = "CON" - - out_units = "cps" - - self.outexptime = product.meta.exposure.measurement_time or 0.0 - - self.outsci = product.data - if outwcs: - self.outwcs = outwcs - else: - self.outwcs = product.meta.wcs - - self.outwht = product.wht - self.outcon = product.con - - if self.outcon.ndim == 2: - self.outcon = np.reshape(self.outcon, (1, - self.outcon.shape[0], - self.outcon.shape[1])) - elif self.outcon.ndim != 3: - raise ValueError("Drizzle context image has wrong dimensions: \ - {0}".format(product)) - - # Check field values - if not self.outwcs: - raise ValueError("Either an existing file or wcs must be supplied") - - if out_units == "counts": - np.divide(self.outsci, self.outexptime, self.outsci) - elif out_units != "cps": - raise ValueError("Illegal value for out_units: %s" % out_units) - - # Since the context array is dynamic, it must be re-assigned - # back to the product's `con` attribute. - @property - def outcon(self): - """Return the context array""" - return self._product.con - - @outcon.setter - def outcon(self, value): - """Set new context array""" - self._product.con = value - - def add_image(self, insci, inwcs, inwht=None, xmin=0, xmax=0, ymin=0, ymax=0, - expin=1.0, in_units="cps", wt_scl=1.0, iscale=1.0): - """ - Combine an input image with the output drizzled image. - - Instead of reading the parameters from a fits file, you can set - them by calling this lower level method. `Add_fits_file` calls - this method after doing its setup. - - Parameters - ---------- - - insci : array - A 2d numpy array containing the input image to be drizzled. - it is an error to not supply an image. - - inwcs : wcs - The world coordinate system of the input image. This is - used to convert the pixels to the output coordinate system. - - inwht : array, optional - A 2d numpy array containing the pixel by pixel weighting. - Must have the same dimensions as insci. If none is supplied, - the weighting is set to one. - - xmin : int, optional - This and the following three parameters set a bounding rectangle - on the input image. Only pixels on the input image inside this - rectangle will have their flux added to the output image. Xmin - sets the minimum value of the x dimension. The x dimension is the - dimension that varies quickest on the image. All four parameters - are zero based, counting starts at zero. - - xmax : int, optional - Sets the maximum value of the x dimension on the bounding box - of the input image. If ``xmax = 0``, no maximum will - be set in the x dimension (all pixels in a row of the input image - will be resampled). - - ymin : int, optional - Sets the minimum value in the y dimension on the bounding box. The - y dimension varies less rapidly than the x and represents the line - index on the input image. - - ymax : int, optional - Sets the maximum value in the y dimension. If ``ymax = 0``, - no maximum will be set in the y dimension (all pixels in a column - of the input image will be resampled). - - expin : float, optional - The exposure time of the input image, a positive number. The - exposure time is used to scale the image if the units are counts and - to scale the image weighting if the drizzle was initialized with - wt_scl equal to "exptime" or "expsq." - - in_units : str, optional - The units of the input image. The units can either be "counts" - or "cps" (counts per second.) If the value is counts, before using - the input image it is scaled by dividing it by the exposure time. - - wt_scl : float, optional - If drizzle was initialized with wt_scl left blank, this value will - set a scaling factor for the pixel weighting. If drizzle was - initialized with wt_scl set to "exptime" or "expsq", the exposure time - will be used to set the weight scaling and the value of this parameter - will be ignored. - - iscale : float, optional - A scale factor to be applied to pixel intensities of the - input image before resampling. - - """ - if self.wt_scl == "exptime": - wt_scl = expin - elif self.wt_scl == "expsq": - wt_scl = expin * expin - - wt_scl = 1.0 # hard-coded for JWST count-rate data - self.increment_id() - - dodrizzle(insci, inwcs, inwht, self.outwcs, self.outsci, self.outwht, - self.outcon, expin, in_units, wt_scl, uniqid=self.uniqid, - xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, - iscale=iscale, pixfrac=self.pixfrac, kernel=self.kernel, - fillval=self.fillval) - - def increment_id(self): - """ - Increment the id count and add a plane to the context image if needed - - Drizzle tracks which input images contribute to the output image - by setting a bit in the corresponding pixel in the context image. - The uniqid indicates which bit. So it must be incremented each time - a new image is added. Each plane in the context image can hold 32 bits, - so after each 32 images, a new plane is added to the context. - """ - - # Compute what plane of the context image this input would - # correspond to: - planeid = int(self.uniqid / 32) - - # Add a new plane to the context image if planeid overflows - - if self.outcon.shape[0] == planeid: - plane = np.zeros_like(self.outcon[0]) - plane = plane.reshape((1, plane.shape[0], plane.shape[1])) - self.outcon = np.concatenate((self.outcon, plane)) - - # Increment the id - self.uniqid += 1 - - -def dodrizzle(insci, input_wcs, inwht, output_wcs, outsci, outwht, outcon, - expin, in_units, wt_scl, uniqid=1, xmin=0, xmax=0, ymin=0, ymax=0, - iscale=1.0, pixfrac=1.0, kernel='square', fillval="NAN"): - """ - Low level routine for performing 'drizzle' operation on one image. - - Parameters - ---------- - - insci : 2d array - A 2d numpy array containing the input image to be drizzled. - - input_wcs : gwcs.WCS object - The world coordinate system of the input image. - - inwht : 2d array - A 2d numpy array containing the pixel by pixel weighting. - Must have the same dimensions as insci. If none is supplied, - the weighting is set to one. - - output_wcs : gwcs.WCS object - The world coordinate system of the output image. - - outsci : 2d array - A 2d numpy array containing the output image produced by - drizzling. On the first call it should be set to zero. - Subsequent calls it will hold the intermediate results - - outwht : 2d array - A 2d numpy array containing the output counts. On the first - call it should be set to zero. On subsequent calls it will - hold the intermediate results. - - outcon : 2d or 3d array, optional - A 2d or 3d numpy array holding a bitmap of which image was an input - for each output pixel. Should be integer zero on first call. - Subsequent calls hold intermediate results. - - expin : float - The exposure time of the input image, a positive number. The - exposure time is used to scale the image if the units are counts. - - in_units : str - The units of the input image. The units can either be "counts" - or "cps" (counts per second.) - - wt_scl : float - A scaling factor applied to the pixel by pixel weighting. - - uniqid : int, optional - The id number of the input image. Should be one the first time - this function is called and incremented by one on each subsequent - call. - - xmin : int, optional - This and the following three parameters set a bounding rectangle - on the input image. Only pixels on the input image inside this - rectangle will have their flux added to the output image. Xmin - sets the minimum value of the x dimension. The x dimension is the - dimension that varies quickest on the image. All four parameters - are zero based, counting starts at zero. - - xmax : int, optional - Sets the maximum value of the x dimension on the bounding box - of the input image. If ``xmax = 0``, no maximum will - be set in the x dimension (all pixels in a row of the input image - will be resampled). - - ymin : int, optional - Sets the minimum value in the y dimension on the bounding box. The - y dimension varies less rapidly than the x and represents the line - index on the input image. - - ymax : int, optional - Sets the maximum value in the y dimension. If ``ymax = 0``, - no maximum will be set in the y dimension (all pixels in a column - of the input image will be resampled). - - iscale : float, optional - A scale factor to be applied to pixel intensities of the - input image before resampling. - - pixfrac : float, optional - The fraction of a pixel that the pixel flux is confined to. The - default value of 1 has the pixel flux evenly spread across the image. - A value of 0.5 confines it to half a pixel in the linear dimension, - so the flux is confined to a quarter of the pixel area when the square - kernel is used. - - kernel: str, optional - The name of the kernel used to combine the input. The choice of - kernel controls the distribution of flux over the kernel. The kernel - names are: "square", "gaussian", "point", "turbo", "lanczos2", - and "lanczos3". The square kernel is the default. - - fillval: str, optional - The value a pixel is set to in the output if the input image does - not overlap it. The default value of NAN sets NaN values. - - Returns - ------- - A tuple with three values: a version string, the number of pixels - on the input image that do not overlap the output image, and the - number of complete lines on the input image that do not overlap the - output input image. - - """ - - # Insure that the fillval parameter gets properly interpreted for use with tdriz - if util.is_blank(str(fillval)): - fillval = 'NAN' - else: - fillval = str(fillval) - - if in_units == 'cps': - expscale = 1.0 - else: - expscale = expin - - if insci.dtype > np.float32: - insci = insci.astype(np.float32) - - # Add input weight image if it was not passed in - if inwht is None: - inwht = np.ones_like(insci) - - if xmax is None or xmax == xmin: - xmax = insci.shape[1] - if ymax is None or ymax == ymin: - ymax = insci.shape[0] - - # Compute what plane of the context image this input would - # correspond to: - planeid = int((uniqid - 1) / 32) - - # Check if the context image has this many planes - if outcon.ndim == 3: - nplanes = outcon.shape[0] - elif outcon.ndim == 2: - nplanes = 1 - else: - nplanes = 0 - - if nplanes <= planeid: - raise IndexError("Not enough planes in drizzle context image") - - # Alias context image to the requested plane if 3d - if outcon.ndim == 3: - outcon = outcon[planeid] - - # Compute the mapping between the input and output pixel coordinates - # for use in drizzle.cdrizzle.tdriz - pixmap = resample_utils.calc_gwcs_pixmap(input_wcs, output_wcs, insci.shape) - # inwht[np.isnan(pixmap[:,:,0])] = 0. - - log.debug(f"Pixmap shape: {pixmap[:,:,0].shape}") - log.debug(f"Input Sci shape: {insci.shape}") - log.debug(f"Output Sci shape: {outsci.shape}") - - # Call 'drizzle' to perform image combination - log.info(f"Drizzling {insci.shape} --> {outsci.shape}") - - _vers, nmiss, nskip = cdrizzle.tdriz( - insci.astype(np.float32), inwht.astype(np.float32), pixmap, - outsci, outwht, outcon, - uniqid=uniqid, - xmin=xmin, xmax=xmax, - ymin=ymin, ymax=ymax, - scale=iscale, - pixfrac=pixfrac, - kernel=kernel, - in_units=in_units, - expscale=expscale, - wtscale=wt_scl, - fillstr=fillval - ) - return _vers, nmiss, nskip diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index b3e7e7f485..72b325cbd3 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -5,7 +5,7 @@ import numpy as np import psutil -from drizzle import cdrizzle, util +from drizzle.resample import Drizzle from spherical_geometry.polygon import SphericalPolygon from stdatamodels.jwst import datamodels @@ -14,7 +14,6 @@ from jwst.datamodels import ModelLibrary from jwst.associations.asn_from_list import asn_from_list -from . import gwcs_drizzle from jwst.model_blender.blender import ModelBlender from jwst.resample import resample_utils @@ -115,13 +114,13 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, self.output_wcs.array_shape = output_shape[::-1] if output_wcs.pixel_area is None: - output_pix_area = compute_image_pixel_area(self.output_wcs) - if output_pix_area is None: + self.output_pix_area = compute_image_pixel_area(self.output_wcs) + if self.output_pix_area is None: raise ValueError( "Unable to compute output pixel area from 'output_wcs'." ) else: - output_pix_area = output_wcs.pixel_area + self.output_pix_area = output_wcs.pixel_area else: # Define output WCS based on all inputs, including a reference WCS: @@ -139,18 +138,21 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, # use the same algorithm as for when output_wcs is provided by the # user. tr = self.output_wcs.pipeline[0].transform - output_pix_area = ( + self.output_pix_area = ( np.deg2rad(tr['cdelt1'].factor.value) * np.deg2rad(tr['cdelt2'].factor.value) ) + self.output_array_shape = tuple(self.output_wcs.array_shape) + log.debug(f"Output mosaic size: {self.output_array_shape}") + if pscale is None: - pscale = np.rad2deg(np.sqrt(output_pix_area)) + pscale = np.rad2deg(np.sqrt(self.output_pix_area)) log.info(f'Computed output pixel scale: {3600.0 * pscale} arcsec.') self.pscale = pscale # in deg - log.debug('Output mosaic size: {}'.format(self.output_wcs.array_shape)) + log.debug(f"Output mosaic size: {tuple(self.output_wcs.pixel_shape)}") allowed_memory = kwargs['allowed_memory'] if allowed_memory is None: @@ -164,7 +166,7 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, available_memory = psutil.virtual_memory().available + psutil.swap_memory().total # compute the output array size - required_memory = np.prod(self.output_wcs.array_shape) * dtype.itemsize + required_memory = np.prod(self.output_array_shape) * dtype.itemsize # compare used to available used_fraction = required_memory / available_memory @@ -174,24 +176,25 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, if not can_allocate: raise OutputTooLargeError( - f'Combined ImageModel size {self.output_wcs.array_shape} ' + f'Combined ImageModel size {self.output_array_shape} ' f'requires {bytes2human(required_memory)}. ' f'Model cannot be instantiated.' ) - self.blank_output = datamodels.ImageModel(tuple(self.output_wcs.array_shape)) - # update meta data and wcs - with input_models: - example_model = input_models.borrow(0) - self.blank_output.update(example_model) - input_models.shelve(example_model, 0, modify=False) - del example_model - self.blank_output.meta.wcs = self.output_wcs - self.blank_output.meta.photometry.pixelarea_steradians = output_pix_area - self.blank_output.meta.photometry.pixelarea_arcsecsq = ( - output_pix_area * np.rad2deg(3600)**2 - ) + def _create_output_model(self, ref_input_model=None): + """ Create a new blank model and update it's meta with info from ``ref_input_model``. """ + output_model = datamodels.ImageModel(None) # tuple(self.output_wcs.array_shape)) + # update meta data and wcs + if ref_input_model is not None: + output_model.update(ref_input_model) + output_model.meta.wcs = self.output_wcs + if self.output_pix_area is not None: + output_model.meta.photometry.pixelarea_steradians = self.output_pix_area + output_model.meta.photometry.pixelarea_arcsecsq = ( + self.output_pix_area * np.rad2deg(3600)**2 + ) + return output_model def do_drizzle(self, input_models): """Pick the correct drizzling mode based on self.single @@ -251,10 +254,10 @@ def _get_intensity_scale(self, img): else: iscale = 1.0 return iscale - + def resample_group(self, input_models, indices, compute_error=False): """Apply resample_many_to_many for one group - + Parameters ---------- input_models : ModelLibrary @@ -265,98 +268,118 @@ def resample_group(self, input_models, indices, compute_error=False): If True, an approximate error image will be resampled alongside the science image. """ - output_model = self.blank_output.copy() + output_model = None - copy_asn_info_from_library(input_models, output_model) + # Initialize the output with the wcs + driz = Drizzle( + out_shape=self.output_array_shape, + kernel=self.kernel, + fillval=self.fillval, + disable_ctx=True, + ) + # Also make a temporary model to hold error data + if compute_error: + driz_error = Drizzle( + out_shape=self.output_array_shape, + kernel=self.kernel, + fillval=self.fillval, + disable_ctx=True, + ) + log.info(f"{len(indices)} exposures to drizzle together") + for index in indices: + img = input_models.borrow(index) + if output_model is None: + output_model = self._create_output_model( + ref_input_model=img + ) + # Determine output file type from input exposure filenames + # Use this for defining the output filename + indx = img.meta.filename.rfind('.') + output_type = img.meta.filename[indx:] + output_root = '_'.join(img.meta.filename.replace( + output_type, + '' + ).split('_')[:-1]) + output_model.meta.filename = ( + f'{output_root}_' + f'{self.intermediate_suffix}{output_type}' + ) + copy_asn_info_from_library(input_models, output_model) - with input_models: - example_image = input_models.borrow(indices[0]) - - # Determine output file type from input exposure filenames - # Use this for defining the output filename - indx = example_image.meta.filename.rfind('.') - output_type = example_image.meta.filename[indx:] - output_root = '_'.join(example_image.meta.filename.replace( - output_type, '').split('_')[:-1]) - output_model.meta.filename = ( - f'{output_root}_' - f'{self.intermediate_suffix}{output_type}') - input_models.shelve(example_image, indices[0], modify=False) - del example_image - - # Initialize the output with the wcs - driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, - kernel=self.kernel, fillval=self.fillval) - - # Also make a temporary model to hold error data - error_model, driz_error = None, None - if compute_error: - error_model = output_model.copy() - driz_error = gwcs_drizzle.GWCSDrizzle( - error_model, pixfrac=self.pixfrac, - kernel=self.kernel, fillval=self.fillval) - - log.info(f"{len(indices)} exposures to drizzle together") - for index in indices: - img = input_models.borrow(index) - if isinstance(img, datamodels.SlitModel): - # must call this explicitly to populate area extension - # although the existence of this extension may not be necessary - img.area = img.area - iscale = self._get_intensity_scale(img) - log.debug(f'Using intensity scale iscale={iscale}') + if isinstance(img, datamodels.SlitModel): + # must call this explicitly to populate area extension + # although the existence of this extension may not be necessary + img.area = img.area - inwht = resample_utils.build_driz_weight( - img, - weight_type=self.weight_type, - good_bits=self.good_bits - ) + iscale = self._get_intensity_scale(img) + log.debug(f'Using intensity scale iscale={iscale}') - # apply sky subtraction - blevel = img.meta.background.level - if not img.meta.background.subtracted and blevel is not None: - data = img.data - blevel - else: - data = img.data + inwht = resample_utils.build_driz_weight( + img, + weight_type=self.weight_type, + good_bits=self.good_bits + ) - xmin, xmax, ymin, ymax = resample_utils._resample_range( - data.shape, - img.meta.wcs.bounding_box - ) + # apply sky subtraction + blevel = img.meta.background.level + if not img.meta.background.subtracted and blevel is not None: + data = img.data - blevel + else: + data = img.data - driz.add_image( - data, - img.meta.wcs, - iscale=iscale, - inwht=inwht, + xmin, xmax, ymin, ymax = resample_utils._resample_range( + data.shape, + img.meta.wcs.bounding_box + ) + pixmap = resample_utils.calc_gwcs_pixmap( + img.meta.wcs, + self.output_wcs, + img.data.shape, + ) + + driz.add_image( + data=data, + exptime=img.meta.exposure.exposure_time, # GWCSDrizzle.add_image param default was 1.0 + pixmap=pixmap, + scale=iscale, + weight_map=inwht, + wht_scale=1.0, # hard-coded for JWST count-rate data + pixfrac=self.pixfrac, + in_units="cps", # GWCSDrizzle.add_image param default + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax, + ) + del data + + # make an approximate error image by drizzling it + # in the same way the image is handled + if compute_error: + driz_error.add_image( + data=img.err, + exptime=img.meta.exposure.exposure_time, # GWCSDrizzle.add_image param default + pixmap=pixmap, + scale=iscale, + weight_map=inwht, + wht_scale=1.0, # hard-coded for JWST count-rate data + pixfrac=self.pixfrac, + in_units="cps", # GWCSDrizzle.add_image param default xmin=xmin, xmax=xmax, ymin=ymin, - ymax=ymax + ymax=ymax, ) - del data - - # make an approximate error image by drizzling it - # in the same way the image is handled - if compute_error: - driz_error.add_image( - img.err, - img.meta.wcs, - iscale=iscale, - inwht=inwht, - xmin=xmin, - xmax=xmax, - ymin=ymin, - ymax=ymax - ) - - input_models.shelve(img, index, modify=False) - del img + input_models.shelve(img, index, modify=False) + del img + output_model.data = driz.out_img + output_model.wht = driz.out_wht + del driz # copy the drizzled error into the output model if compute_error: - output_model.err = error_model.data - del error_model + output_model.err = driz_error.out_img + del driz_error return output_model @@ -371,7 +394,7 @@ def resample_many_to_many(self, input_models): """ output_models = [] for group_id, indices in input_models.group_indices.items(): - + output_model = self.resample_group(input_models, indices) if not self.in_memory: @@ -385,28 +408,22 @@ def resample_many_to_many(self, input_models): else: output_models.append(output_model) - if not self.in_memory: + if self.in_memory: + # build ModelLibrary as a list of in-memory models + return ModelLibrary(output_models, on_disk=False) + else: # build ModelLibrary as an association from the output files # this saves memory if there are multiple groups asn = asn_from_list(output_models, product_name='outlier_i2d') - asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict + asn_dict = json.loads(asn.dump()[1]) # serializes the asn and converts to dict return ModelLibrary(asn_dict, on_disk=True) - # otherwise just build it as a list of in-memory models - return ModelLibrary(output_models, on_disk=False) - def resample_many_to_one(self, input_models): """Resample and coadd many inputs to a single output. Used for stage 3 resampling """ - output_model = self.blank_output.copy() - output_model.meta.filename = self.output_filename - output_model.meta.resample.weight_type = self.weight_type - output_model.meta.resample.pointings = len(input_models.group_names) - - # copy over asn information - copy_asn_info_from_library(input_models, output_model) + output_model = None if self.blendheaders: blender = ModelBlender( @@ -417,22 +434,50 @@ def resample_many_to_one(self, input_models): ] ) - # Initialize the output with the wcs - driz = gwcs_drizzle.GWCSDrizzle(output_model, pixfrac=self.pixfrac, - kernel=self.kernel, fillval=self.fillval) + driz = Drizzle( + out_shape=self.output_array_shape, + kernel=self.kernel, + fillval=self.fillval, + max_ctx_id=len(input_models), + disable_ctx=False, + ) + self._init_variance_arrays() + self._init_exptime_counters() + + log.info("Resampling science and variance data") + + leading_group_idx = [v[0] for v in input_models.group_indices.values()] - log.info("Resampling science data") with input_models: - for img in input_models: + for idx, img in enumerate(input_models): + if output_model is None: + output_model = self._create_output_model( + ref_input_model=img + ) + # Determine output file type from input exposure filenames + # Use this for defining the output filename + output_model.meta.filename = self.output_filename + output_model.meta.resample.weight_type = self.weight_type + output_model.meta.resample.pointings = len(input_models.group_names) + + # copy over asn information + copy_asn_info_from_library(input_models, output_model) + + if idx in leading_group_idx: + self._update_exptime(img) + if self.blendheaders: blender.accumulate(img) + iscale = self._get_intensity_scale(img) log.debug(f'Using intensity scale iscale={iscale}') - img.meta.iscale = iscale - inwht = resample_utils.build_driz_weight(img, - weight_type=self.weight_type, - good_bits=self.good_bits) + inwht = resample_utils.build_driz_weight( + img, + weight_type=self.weight_type, + good_bits=self.good_bits, + ) + # apply sky subtraction blevel = img.meta.background.level if not img.meta.background.subtracted and blevel is not None: @@ -440,29 +485,58 @@ def resample_many_to_one(self, input_models): else: data = img.data.copy() - xmin, xmax, ymin, ymax = resample_utils._resample_range( + in_image_limits = resample_utils._resample_range( data.shape, img.meta.wcs.bounding_box ) + xmin, xmax, ymin, ymax = in_image_limits - driz.add_image( - data, + pixmap = resample_utils.calc_gwcs_pixmap( img.meta.wcs, - iscale=iscale, - inwht=inwht, + output_model.meta.wcs, + data.shape, + ) + + driz.add_image( + data=data, + exptime=img.meta.exposure.exposure_time, # GWCSDrizzle.add_image param default + pixmap=pixmap, + scale=iscale, + weight_map=inwht, + wht_scale=1.0, # hard-coded for JWST count-rate data + pixfrac=self.pixfrac, + in_units="cps", # GWCSDrizzle.add_image param default xmin=xmin, xmax=xmax, ymin=ymin, - ymax=ymax + ymax=ymax, + ) + # Resample variance arrays in input_models to output_model + self._resample_variance_arrays( + model=img, + iscale=iscale, + inwht=inwht, + pixmap=pixmap, + in_image_limits=in_image_limits, + output_shape=self.output_array_shape, ) + del data, inwht + input_models.shelve(img) - if self.blendheaders: - blender.finalize_model(output_model) + # Since the context array is dynamic, it must be re-assigned + # back to the product's `con` attribute. + output_model.data = driz.out_img + output_model.wht = driz.out_wht + if driz.out_ctx is not None: + output_model.con = driz.out_ctx + + del driz + + # compute final variances: + self._compute_resample_variance_totals(output_model) - # Resample variance arrays in input_models to output_model - self.resample_variance_arrays(output_model, input_models) var_components = [ output_model.var_rnoise, output_model.var_poisson, @@ -475,115 +549,153 @@ def resample_many_to_one(self, input_models): all_nan = np.all(np.isnan(var_components), axis=0) output_model.err[all_nan] = np.nan - self.update_exposure_times(output_model, input_models) - - return ModelLibrary([output_model,], on_disk=False) - - - def resample_variance_arrays(self, output_model, input_models): - """Resample variance arrays from input_models to the output_model. + if self.blendheaders: + blender.finalize_model(output_model) - Variance images from each input model are resampled individually and - added to a weighted sum. If weight_type is 'ivm', the inverse of the - resampled read noise variance is used as the weight for all the variance - components. If weight_type is 'exptime', the exposure time is used. + self._get_exptime_totals(output_model) - The output_model is modified in place. - """ - log.info("Resampling variance components") - weighted_rn_var = np.full_like(output_model.data, np.nan) - weighted_pn_var = np.full_like(output_model.data, np.nan) - weighted_flat_var = np.full_like(output_model.data, np.nan) - total_weight_rn_var = np.zeros_like(output_model.data) - total_weight_pn_var = np.zeros_like(output_model.data) - total_weight_flat_var = np.zeros_like(output_model.data) - with input_models: - for i, model in enumerate(input_models): - # Do the read noise variance first, so it can be - # used for weights if needed - rn_var = self._resample_one_variance_array( - "var_rnoise", model, output_model) - - # Find valid weighting values in the variance - if rn_var is not None: - mask = (rn_var > 0) & np.isfinite(rn_var) - else: - mask = np.full_like(rn_var, False) - - # Set the weight for the image from the weight type - weight = np.ones(output_model.data.shape) - if self.weight_type == "ivm" and rn_var is not None: - weight[mask] = rn_var[mask] ** -1 - elif self.weight_type == "exptime": - if resample_utils.check_for_tmeasure(model): - weight[:] = model.meta.exposure.measurement_time - else: - weight[:] = model.meta.exposure.exposure_time - - # Weight and add the readnoise variance - # Note: floating point overflow is an issue if variance weights - # are used - it can't be squared before multiplication - if rn_var is not None: - mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0) - weighted_rn_var[mask] = np.nansum( - [weighted_rn_var[mask], - rn_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_rn_var[mask] += weight[mask] - - # Now do poisson and flat variance, updating only valid new values - # (zero is a valid value; negative, inf, or NaN are not) - pn_var = self._resample_one_variance_array( - "var_poisson", model, output_model) - if pn_var is not None: - mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0) - weighted_pn_var[mask] = np.nansum( - [weighted_pn_var[mask], - pn_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_pn_var[mask] += weight[mask] - - flat_var = self._resample_one_variance_array( - "var_flat", model, output_model) - if flat_var is not None: - mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0) - weighted_flat_var[mask] = np.nansum( - [weighted_flat_var[mask], - flat_var[mask] * weight[mask] * weight[mask]], - axis=0 - ) - total_weight_flat_var[mask] += weight[mask] - - del model.meta.iscale - del weight - input_models.shelve(model, i) + return ModelLibrary([output_model,], on_disk=False) - # We now have a sum of the weighted resampled variances. - # Divide by the total weights, squared, and set in the output model. - # Zero weight and missing values are NaN in the output. - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) - warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) + def _init_variance_arrays(self): + shape = self.output_array_shape + self._weighted_rn_var = np.full(shape, np.nan, dtype=np.float32) + self._weighted_pn_var = np.full(shape, np.nan, dtype=np.float32) + self._weighted_flat_var = np.full(shape, np.nan, dtype=np.float32) + self._total_weight_rn_var = np.zeros(shape, dtype=np.float32) + self._total_weight_pn_var = np.zeros(shape, dtype=np.float32) + self._total_weight_flat_var = np.zeros(shape, dtype=np.float32) + + def _resample_variance_arrays(self, model, iscale, inwht, pixmap, + in_image_limits, output_shape): + xmin, xmax, ymin, ymax = in_image_limits + + # Do the read noise variance first, so it can be + # used for weights if needed + rn_var = self._resample_one_variance_array( + "var_rnoise", + input_model=model, + iscale=iscale, + inwht=inwht, + pixmap=pixmap, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax, + ) - output_variance = (weighted_rn_var - / total_weight_rn_var / total_weight_rn_var) - setattr(output_model, "var_rnoise", output_variance) + # Find valid weighting values in the variance + if rn_var is not None: + mask = (rn_var > 0) & np.isfinite(rn_var) + else: + mask = np.full_like(rn_var, False) + + # Set the weight for the image from the weight type + weight = np.ones(output_shape) + if self.weight_type == "ivm" and rn_var is not None: + weight[mask] = rn_var[mask] ** -1 + elif self.weight_type == "exptime": + if resample_utils.check_for_tmeasure(model): + weight[:] = model.meta.exposure.measurement_time + else: + weight[:] = model.meta.exposure.exposure_time + + # Weight and add the readnoise variance + # Note: floating point overflow is an issue if variance weights + # are used - it can't be squared before multiplication + if rn_var is not None: + mask = (rn_var >= 0) & np.isfinite(rn_var) & (weight > 0) + self._weighted_rn_var[mask] = np.nansum( + [ + self._weighted_rn_var[mask], + rn_var[mask] * weight[mask] * weight[mask] + ], + axis=0 + ) + self._total_weight_rn_var[mask] += weight[mask] - output_variance = (weighted_pn_var - / total_weight_pn_var / total_weight_pn_var) - setattr(output_model, "var_poisson", output_variance) + # Now do poisson and flat variance, updating only valid new values + # (zero is a valid value; negative, inf, or NaN are not) + pn_var = self._resample_one_variance_array( + "var_poisson", + input_model=model, + iscale=iscale, + inwht=inwht, + pixmap=pixmap, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax, + ) + if pn_var is not None: + mask = (pn_var >= 0) & np.isfinite(pn_var) & (weight > 0) + self._weighted_pn_var[mask] = np.nansum( + [ + self._weighted_pn_var[mask], + pn_var[mask] * weight[mask] * weight[mask] + ], + axis=0 + ) + self._total_weight_pn_var[mask] += weight[mask] - output_variance = (weighted_flat_var - / total_weight_flat_var / total_weight_flat_var) - setattr(output_model, "var_flat", output_variance) + flat_var = self._resample_one_variance_array( + "var_flat", + input_model=model, + iscale=iscale, + inwht=inwht, + pixmap=pixmap, + xmin=xmin, + xmax=xmax, + ymin=ymin, + ymax=ymax, + ) + if flat_var is not None: + mask = (flat_var >= 0) & np.isfinite(flat_var) & (weight > 0) + self._weighted_flat_var[mask] = np.nansum( + [ + self._weighted_flat_var[mask], + flat_var[mask] * weight[mask] * weight[mask] + ], + axis=0 + ) + self._total_weight_flat_var[mask] += weight[mask] + + def _compute_resample_variance_totals(self, output_model): + # Divide by the total weights, squared, and set in the output model. + # Zero weight and missing values are NaN in the output. + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", "invalid value*", RuntimeWarning) + warnings.filterwarnings("ignore", "divide by zero*", RuntimeWarning) + + output_variance = ( + self._weighted_rn_var / self._total_weight_rn_var / + self._total_weight_rn_var + ) + setattr(output_model, "var_rnoise", output_variance) - del weighted_rn_var, weighted_pn_var, weighted_flat_var - del total_weight_rn_var, total_weight_pn_var, total_weight_flat_var + output_variance = ( + self._weighted_pn_var / self._total_weight_pn_var / + self._total_weight_pn_var + ) + setattr(output_model, "var_poisson", output_variance) + output_variance = ( + self._weighted_flat_var / self._total_weight_flat_var / + self._total_weight_flat_var + ) + setattr(output_model, "var_flat", output_variance) + + del ( + self._weighted_rn_var, + self._weighted_pn_var, + self._weighted_flat_var, + self._total_weight_rn_var, + self._total_weight_pn_var, + self._total_weight_flat_var, + ) - def _resample_one_variance_array(self, name, input_model, output_model): + def _resample_one_variance_array(self, name, input_model, iscale, + inwht, pixmap, + xmin=None, xmax=None, ymin=None, ymax=None): """Resample one variance image from an input model. The error image is passed to drizzle instead of the variance, to @@ -606,242 +718,91 @@ def _resample_one_variance_array(self, name, input_model, output_model): ) return - # Make input weight map - inwht = resample_utils.build_driz_weight( - input_model, - weight_type=self.weight_type, # weights match science - good_bits=self.good_bits - ) - - resampled_error = np.zeros_like(output_model.data) - outwht = np.zeros_like(output_model.data) - outcon = np.zeros_like(output_model.con) + output_shape = self.output_array_shape - xmin, xmax, ymin, ymax = resample_utils._resample_range( - variance.shape, - input_model.meta.wcs.bounding_box + # Resample the error array. Fill "unpopulated" pixels with NaNs. + driz = Drizzle( + out_shape=output_shape, + kernel=self.kernel, + fillval=np.nan, + disable_ctx=True ) - iscale = input_model.meta.iscale + log.debug(f"Pixmap shape: {pixmap[:,:,0].shape}") + log.debug(f"Input Sci shape: {variance.shape}") + log.debug(f"Output Sci shape: {output_shape}") - # Resample the error array. Fill "unpopulated" pixels with NaNs. - self.drizzle_arrays( - np.sqrt(variance), - inwht, - input_model.meta.wcs, - output_model.meta.wcs, - resampled_error, - outwht, - outcon, - iscale=iscale, + # Call 'drizzle' to perform image combination + log.info(f"Drizzling {variance.shape} --> {output_shape}") + + driz.add_image( + data=np.sqrt(variance), + exptime=input_model.meta.exposure.exposure_time, + pixmap=pixmap, + scale=iscale, + weight_map=inwht, + wht_scale=1.0, # hard-coded for JWST count-rate data pixfrac=self.pixfrac, - kernel=self.kernel, - fillval='NAN', + in_units="cps", xmin=xmin, xmax=xmax, ymin=ymin, - ymax=ymax + ymax=ymax, ) - return resampled_error ** 2 - def update_exposure_times(self, output_model, input_models): - """Modify exposure time metadata in-place""" - total_exposure_time = 0. - exposure_times = {'start': [], 'end': []} - duration = 0.0 - total_measurement_time = 0.0 - measurement_time_failures = [] - with input_models: - for _, indices in input_models.group_indices.items(): - model = input_models.borrow(indices[0]) - total_exposure_time += model.meta.exposure.exposure_time - if not resample_utils.check_for_tmeasure(model): - measurement_time_failures.append(1) - else: - total_measurement_time += model.meta.exposure.measurement_time - measurement_time_failures.append(0) - exposure_times['start'].append(model.meta.exposure.start_time) - exposure_times['end'].append(model.meta.exposure.end_time) - duration += model.meta.exposure.duration - input_models.shelve(model, indices[0], modify=False) + return driz.out_img ** 2 + + def _init_exptime_counters(self): + self._total_exposure_time = 0. + self._exposure_times = {'start': [], 'end': []} + self._duration = 0.0 + self._total_measurement_time = 0.0 + self._measurement_time_failures = [] + def _update_exptime(self, model): + self._total_exposure_time += model.meta.exposure.exposure_time + if not resample_utils.check_for_tmeasure(model): + self._measurement_time_failures.append(1) + else: + self._total_measurement_time += model.meta.exposure.measurement_time + self._measurement_time_failures.append(0) + self._exposure_times['start'].append(model.meta.exposure.start_time) + self._exposure_times['end'].append(model.meta.exposure.end_time) + self._duration += model.meta.exposure.duration + + def _get_exptime_totals(self, output_model): # Update some basic exposure time values based on output_model - output_model.meta.exposure.exposure_time = total_exposure_time - if not any(measurement_time_failures): - output_model.meta.exposure.measurement_time = total_measurement_time - output_model.meta.exposure.start_time = min(exposure_times['start']) - output_model.meta.exposure.end_time = max(exposure_times['end']) + output_model.meta.exposure.exposure_time = self._total_exposure_time + if not any(self._measurement_time_failures): + output_model.meta.exposure.measurement_time = self._total_measurement_time + output_model.meta.exposure.start_time = min(self._exposure_times['start']) + output_model.meta.exposure.end_time = max(self._exposure_times['end']) # Update other exposure time keywords: # XPOSURE (identical to the total effective exposure time, EFFEXPTM) - xposure = total_exposure_time + xposure = self._total_exposure_time output_model.meta.exposure.effective_exposure_time = xposure # DURATION (identical to TELAPSE, elapsed time) - output_model.meta.exposure.duration = duration - output_model.meta.exposure.elapsed_exposure_time = duration - - @staticmethod - def drizzle_arrays(insci, inwht, input_wcs, output_wcs, outsci, outwht, - outcon, uniqid=1, xmin=0, xmax=0, ymin=0, ymax=0, - iscale=1.0, pixfrac=1.0, kernel='square', - fillval="NAN", wtscale=1.0): - """ - Low level routine for performing 'drizzle' operation on one image. - - The interface is compatible with STScI code. All images are Python - ndarrays, instead of filenames. File handling (input and output) is - performed by the calling routine. - - Parameters - ---------- - - insci : 2d array - A 2d numpy array containing the input image to be drizzled. - - inwht : 2d array - A 2d numpy array containing the pixel by pixel weighting. - Must have the same dimensions as insci. If none is supplied, - the weighting is set to one. - - input_wcs : gwcs.WCS object - The world coordinate system of the input image. - - output_wcs : gwcs.WCS object - The world coordinate system of the output image. - - outsci : 2d array - A 2d numpy array containing the output image produced by - drizzling. On the first call it should be set to zero. - Subsequent calls it will hold the intermediate results. This - is modified in-place. - - outwht : 2d array - A 2d numpy array containing the output counts. On the first - call it should be set to zero. On subsequent calls it will - hold the intermediate results. This is modified in-place. - - outcon : 2d or 3d array, optional - A 2d or 3d numpy array holding a bitmap of which image was an input - for each output pixel. Should be integer zero on first call. - Subsequent calls hold intermediate results. This is modified - in-place. - - uniqid : int, optional - The id number of the input image. Should be one the first time - this function is called and incremented by one on each subsequent - call. - - xmin : int, optional - This and the following three parameters set a bounding rectangle - on the input image. Only pixels on the input image inside this - rectangle will have their flux added to the output image. Xmin - sets the minimum value of the x dimension. The x dimension is the - dimension that varies quickest on the image. All four parameters - are zero based, counting starts at zero. - - xmax : int, optional - Sets the maximum value of the x dimension on the bounding box - of the input image. If ``xmax = 0``, no maximum will - be set in the x dimension (all pixels in a row of the input image - will be resampled). - - ymin : int, optional - Sets the minimum value in the y dimension on the bounding box. The - y dimension varies less rapidly than the x and represents the line - index on the input image. - - ymax : int, optional - Sets the maximum value in the y dimension. If ``ymax = 0``, - no maximum will be set in the y dimension (all pixels in a column - of the input image will be resampled). - - iscale : float, optional - A scale factor to be applied to pixel intensities of the - input image before resampling. - - pixfrac : float, optional - The fraction of a pixel that the pixel flux is confined to. The - default value of 1 has the pixel flux evenly spread across the image. - A value of 0.5 confines it to half a pixel in the linear dimension, - so the flux is confined to a quarter of the pixel area when the square - kernel is used. - - kernel: str, optional - The name of the kernel used to combine the input. The choice of - kernel controls the distribution of flux over the kernel. The kernel - names are: "square", "gaussian", "point", "turbo", "lanczos2", - and "lanczos3". The square kernel is the default. - - fillval: str, optional - The value a pixel is set to in the output if the input image does - not overlap it. The default value of NAN sets NaN values. - - Returns - ------- - A tuple with three values: a version string, the number of pixels - on the input image that do not overlap the output image, and the - number of complete lines on the input image that do not overlap the - output input image. - - """ - - # Insure that the fillval parameter gets properly interpreted for use with tdriz - if util.is_blank(str(fillval)): - fillval = 'NAN' - else: - fillval = str(fillval) - - if insci.dtype > np.float32: - insci = insci.astype(np.float32) - - # Add input weight image if it was not passed in - if inwht is None: - inwht = np.ones_like(insci) - - # Compute what plane of the context image this input would - # correspond to: - planeid = int((uniqid - 1) / 32) - - # Check if the context image has this many planes - if outcon.ndim == 3: - nplanes = outcon.shape[0] - elif outcon.ndim == 2: - nplanes = 1 - else: - nplanes = 0 - - if nplanes <= planeid: - raise IndexError("Not enough planes in drizzle context image") - - # Alias context image to the requested plane if 3d - if outcon.ndim == 3: - outcon = outcon[planeid] - - # Compute the mapping between the input and output pixel coordinates - # for use in drizzle.cdrizzle.tdriz - pixmap = resample_utils.calc_gwcs_pixmap(input_wcs, output_wcs, insci.shape) - - log.debug(f"Pixmap shape: {pixmap[:,:,0].shape}") - log.debug(f"Input Sci shape: {insci.shape}") - log.debug(f"Output Sci shape: {outsci.shape}") - - log.info(f"Drizzling {insci.shape} --> {outsci.shape}") - - _vers, _nmiss, _nskip = cdrizzle.tdriz( - insci, inwht, pixmap, - outsci, outwht, outcon, - uniqid=uniqid, - xmin=xmin, xmax=xmax, - ymin=ymin, ymax=ymax, - scale=iscale, - pixfrac=pixfrac, - kernel=kernel, - in_units="cps", - expscale=1.0, - wtscale=wtscale, - fillstr=fillval + output_model.meta.exposure.duration = self._duration + output_model.meta.exposure.elapsed_exposure_time = self._duration + + del ( + self._total_exposure_time, + self._exposure_times, + self._duration, + self._total_measurement_time, + self._measurement_time_failures, ) + def update_exposure_times(self, output_model, input_models): + """Modify exposure time metadata in-place""" + self._init_exptime_counters() + with input_models: + for _, indices in input_models.group_indices.items(): + model = input_models.borrow(indices[0]) + self._update_exptime(model) + self._get_exptime_totals(output_model) + def _get_boundary_points(xmin, xmax, ymin, ymax, dx=None, dy=None, shrink=0): """ diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 0d90d0d891..5160034ddb 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -136,7 +136,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, self.pscale_ratio = nominal_area / output_pix_area else: self.pscale_ratio = 1.0 - # Set the output shape if specified if output_shape is not None: self.output_wcs.array_shape = output_shape[::-1] @@ -174,23 +173,30 @@ def __init__(self, input_models, output=None, single=False, blendheaders=False, else: output_pix_area = None + self.output_pix_area = output_pix_area + self.output_array_shape = tuple(self.output_wcs.array_shape) + log.debug(f"Output mosaic size: {self.output_array_shape}") + if pscale is None: log.info(f'Specified output pixel scale ratio: {self.pscale_ratio}.') pscale = compute_spectral_pixel_scale( self.output_wcs, disp_axis=disp_axis) log.info(f'Computed output pixel scale: {3600.0 * pscale:.5g} arcsec.') - # Output model - self.blank_output = datamodels.SlitModel(tuple(self.output_wcs.array_shape)) + def _create_output_model(self, ref_input_model=None): + """ Create a new blank model and update it's meta with info from ``ref_input_model``. """ + output_model = datamodels.SlitModel(None) # update meta data and wcs - self.blank_output.update(input_models[0]) - self.blank_output.meta.wcs = self.output_wcs - if output_pix_area is not None: - self.blank_output.meta.photometry.pixelarea_steradians = output_pix_area - self.blank_output.meta.photometry.pixelarea_arcsecsq = ( - output_pix_area * np.rad2deg(3600)**2) - + if ref_input_model is not None: + output_model.update(ref_input_model) + output_model.meta.wcs = self.output_wcs + if self.output_pix_area is not None: + output_model.meta.photometry.pixelarea_steradians = self.output_pix_area + output_model.meta.photometry.pixelarea_arcsecsq = ( + self.output_pix_area * np.rad2deg(3600)**2 + ) + return output_model def build_nirspec_output_wcs(self, input_models, refmodel=None): """ diff --git a/pyproject.toml b/pyproject.toml index 4015dad8e9..c1343788a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ dependencies = [ "astropy>=5.3", "BayesicFitting>=3.0.1", "crds>=11.17.14", - "drizzle>=1.15.0", + "drizzle>=2.0.0", "gwcs>=0.21.0,<0.23.0", "numpy>=1.22,<2.0", "opencv-python-headless>=4.6.0.66", @@ -266,7 +266,7 @@ lint.ignore = [ "E741", # ambiguous variable name (O/0, l/I, etc.) "E722", # Do not use bare `except` "ANN101", # Missing type annotation for self in method - "ANN102", # Missing type annotation for cls in classmethod + "ANN102", # Missing type annotation for cls in classmethod ] [tool.ruff.lint.per-file-ignores] From a2d6ba8b0a0eface87d6473738aa1c208f0a8e85 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Thu, 24 Oct 2024 08:20:31 -0700 Subject: [PATCH 575/687] JP-3750: Fix IFU cube moving target RA, Dec metadata (#8911) --- changes/8911.cube_build.rst | 1 + jwst/cube_build/ifu_cube.py | 8 ++++++++ 2 files changed, 9 insertions(+) create mode 100644 changes/8911.cube_build.rst diff --git a/changes/8911.cube_build.rst b/changes/8911.cube_build.rst new file mode 100644 index 0000000000..e6de4d2827 --- /dev/null +++ b/changes/8911.cube_build.rst @@ -0,0 +1 @@ +For moving-target IFU data, set RA, Dec header information of s3d products according to the mean of input models instead of the first input model. diff --git a/jwst/cube_build/ifu_cube.py b/jwst/cube_build/ifu_cube.py index 70d0ee4bad..84d56a8540 100644 --- a/jwst/cube_build/ifu_cube.py +++ b/jwst/cube_build/ifu_cube.py @@ -2436,6 +2436,14 @@ def blend_output_metadata(self, IFUCube): "meta.filename", ], ) + # For moving targets, set RA, Dec equal to the average + mt_avra = getattr(self.input_models_this_cube[0].meta.wcsinfo, "mt_avra", None) + mt_avdec = getattr(self.input_models_this_cube[0].meta.wcsinfo, "mt_avdec", None) + if mt_avra is not None: + IFUCube.meta.wcsinfo.mt_ra = mt_avra + IFUCube.meta.wcsinfo.mt_dec = mt_avdec + IFUCube.meta.target.ra = mt_avra + IFUCube.meta.target.dec = mt_avdec # ******************************************************************************** def find_ra_dec_offset(self, filename): From e50a748604b12ae2d89518f2d0eb11d877a1777f Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Thu, 24 Oct 2024 12:44:27 -0400 Subject: [PATCH 576/687] clean formatting --- jwst/combine_1d/combine1d.py | 14 +++++++------- jwst/master_background/master_background_step.py | 6 +++--- jwst/master_background/nirspec_utils.py | 6 +++--- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index 9f7f9de4ef..ec6dbfc80e 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -242,9 +242,9 @@ def accumulate_sums(self, input_spectra, sigma_clip=None): weight[s, k] = in_spec.weight[i] count[s, k] = 1. - (flux, flux_error, surf_bright, sb_error, - weight, count) = self.combine_spectra(flux, flux_error, - surf_bright, sb_error, + (flux, flux_error, surf_bright, sb_error, + weight, count) = self.combine_spectra(flux, flux_error, + surf_bright, sb_error, weight, count, sigma_clip=sigma_clip) if n_nan > 0: @@ -278,7 +278,7 @@ def accumulate_sums(self, input_spectra, sigma_clip=None): self.count = self.count[index] del index - def combine_spectra(self, flux, flux_error, surf_bright, sb_error, + def combine_spectra(self, flux, flux_error, surf_bright, sb_error, weight, count, sigma_clip=None): """Combine accumulated spectra. @@ -333,7 +333,7 @@ def combine_spectra(self, flux, flux_error, surf_bright, sb_error, med_flux = np.nanmedian(flux_2d, axis=0) mad = np.nanmedian(np.abs(flux_2d - med_flux)) - # Clip any outlier pixels in the input spectra + # Clip any outlier pixels in the input spectra clipped = np.abs(flux * weight - med_flux) > sigma_clip * mad flux[clipped] = np.nan flux_error[clipped] = np.nan @@ -353,9 +353,9 @@ def combine_spectra(self, flux, flux_error, surf_bright, sb_error, count = np.nansum(count, axis=0) self.normalized = True - + return flux, flux_error, surf_bright, sb_error, sum_weight, count - + def create_output_data(self): """Create the output data. diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 4d01709074..0c799ef4c4 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -118,7 +118,7 @@ def process(self, input): if self.save_background: asn_id = input_data.meta.asn_table.asn_id self.save_model(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) - + # Compute master background and subtract it else: if isinstance(input_data, ModelContainer): @@ -153,11 +153,11 @@ def process(self, input): self.median_kernel -= 1 self.log.info('Even median filter kernels are not supported.' f'Rounding the median kernel size down to {self.median_kernel}.') - + if (self.median_kernel > 1): self.log.info(f'Applying moving-median boxcar of width {self.median_kernel}.') master_background.spec[0].spec_table['surf_bright'] = medfilt( - master_background.spec[0].spec_table['surf_bright'], + master_background.spec[0].spec_table['surf_bright'], kernel_size=[self.median_kernel] ) diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 9395dd432e..272d1fc088 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -131,18 +131,18 @@ def create_background_from_multislit(input_model, sigma_clip=3, median_kernel=1) log.info('Combining 1D background spectra into master background') master_bkg = combine_1d_spectra( x1d, exptime_key='exposure_time', sigma_clip=sigma_clip) - + # If requested, apply a moving-median boxcar filter to the master background spectrum # Round down even kernel sizes because only odd kernel sizes are supported. if median_kernel % 2 == 0: median_kernel -= 1 log.info('Even median filter kernels are not supported.' f'Rounding the median kernel size down to {median_kernel}.') - + if (median_kernel > 1): log.info(f'Applying moving-median boxcar of width {median_kernel}.') master_bkg.spec[0].spec_table['surf_bright'] = medfilt( - master_bkg.spec[0].spec_table['surf_bright'], + master_bkg.spec[0].spec_table['surf_bright'], kernel_size=[median_kernel] ) From 476283a6e5024bc557fb6455f8cb9b36da13cb0f Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Thu, 24 Oct 2024 15:42:40 -0400 Subject: [PATCH 577/687] JP-3102 follow-up: Catch source_pos nans from msa file (#8874) --- changes/8874.assign_wcs.rst | 1 + jwst/assign_wcs/nirspec.py | 9 ++++---- jwst/assign_wcs/tests/test_nirspec.py | 32 +++++++++++++++++++++++++++ 3 files changed, 38 insertions(+), 4 deletions(-) create mode 100644 changes/8874.assign_wcs.rst diff --git a/changes/8874.assign_wcs.rst b/changes/8874.assign_wcs.rst new file mode 100644 index 0000000000..d27218f8d4 --- /dev/null +++ b/changes/8874.assign_wcs.rst @@ -0,0 +1 @@ +Catch NaN values in msa tables for source positions in slit and replace with slit center. \ No newline at end of file diff --git a/jwst/assign_wcs/nirspec.py b/jwst/assign_wcs/nirspec.py index 2168804bee..7902d6c3bc 100644 --- a/jwst/assign_wcs/nirspec.py +++ b/jwst/assign_wcs/nirspec.py @@ -674,8 +674,9 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, if n_main_shutter == 1: # Source is marked primary source_id = slitlet['source_id'] - source_xpos = slitlet['estimated_source_in_shutter_x'] - source_ypos = slitlet['estimated_source_in_shutter_y'] + source_xpos = np.nan_to_num(slitlet['estimated_source_in_shutter_x'], nan=0.5) + source_ypos = np.nan_to_num(slitlet['estimated_source_in_shutter_y'], nan=0.5) + log.info(f'Found fixed slit {slitlet_id} with source_id = {source_id}.') # Get source info for this slitlet: @@ -746,8 +747,8 @@ def get_open_msa_slits(prog_id, msa_file, msa_metadata_id, dither_position, elif n_main_shutter == 1: xcen, ycen, quadrant, source_xpos, source_ypos = [ (s['shutter_row'], s['shutter_column'], s['shutter_quadrant'], - s['estimated_source_in_shutter_x'], - s['estimated_source_in_shutter_y']) + np.nan_to_num(s['estimated_source_in_shutter_x'], nan=0.5), + np.nan_to_num(s['estimated_source_in_shutter_y'], nan=0.5)) for s in slitlet_rows if s['background'] == 'N'][0] shutter_id = xcen + (ycen - 1) * 365 # shutter numbers in MSA file are 1-indexed diff --git a/jwst/assign_wcs/tests/test_nirspec.py b/jwst/assign_wcs/tests/test_nirspec.py index 3c6d3591f5..fc7a7032c3 100644 --- a/jwst/assign_wcs/tests/test_nirspec.py +++ b/jwst/assign_wcs/tests/test_nirspec.py @@ -446,6 +446,38 @@ def test_msa_missing_source(tmp_path): _compare_slits(slitlet_info[1], ref_slit) +def test_msa_nan_source_posn(tmp_path): + """ + Test the get_open_msa_slits function with nan values for source position. + """ + # modify an existing MSA file to remove source info + msaconfl = get_file_path('msa_fs_configuration.fits') + bad_confl = str(tmp_path / 'nan_msa_fs_configuration.fits') + shutil.copy(msaconfl, bad_confl) + + with fits.open(bad_confl) as msa_hdu_list: + shutter_table = table.Table(msa_hdu_list['SHUTTER_INFO'].data) + shutter_table[-5:]['estimated_source_in_shutter_x'] = np.nan + msa_hdu_list['SHUTTER_INFO'] = fits.table_to_hdu(shutter_table) + msa_hdu_list[2].name = 'SHUTTER_INFO' + msa_hdu_list.writeto(bad_confl, overwrite=True) + + prog_id = '1234' + msa_meta_id = 12 + dither_position = 1 + + slitlet_info = nirspec.get_open_msa_slits( + prog_id, bad_confl, msa_meta_id, dither_position, slit_y_range=[-.5, .5]) + + # MSA slit: virtual source name assigned + ref_slit = trmodels.Slit(name='S200A1', shutter_id=0, dither_position=1, xcen=0, ycen=0, + ymin=-0.5, ymax=0.5, quadrant=5, source_id=3, shutter_state='x', + source_name='95065_3', source_alias='3', stellarity=1.0, + source_xpos=0.0, source_ypos=-0.2290000021457672, + source_ra=53.139904, source_dec=-27.805002) + _compare_slits(slitlet_info[1], ref_slit) + + open_shutters = [[24], [23, 24], [22, 23, 25, 27], [22, 23, 25, 27, 28]] main_shutter = [24, 23, 25, 28] result = ["x", "x1", "110x01", "110101x"] From b4c09b758fb75897906c3fcdbe0730bc65286d0c Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Fri, 25 Oct 2024 11:50:56 -0700 Subject: [PATCH 578/687] JP-3782: Fix improper setting of AREA extension in coron3 pipeline (#8907) --- changes/8907.pipeline.rst | 1 + jwst/pipeline/calwebb_coron3.py | 9 ++++++++- jwst/pipeline/tests/test_calwebb_coron3.py | 21 +++++++++++++++++++++ 3 files changed, 30 insertions(+), 1 deletion(-) create mode 100644 changes/8907.pipeline.rst diff --git a/changes/8907.pipeline.rst b/changes/8907.pipeline.rst new file mode 100644 index 0000000000..c6d1759f93 --- /dev/null +++ b/changes/8907.pipeline.rst @@ -0,0 +1 @@ +Fixed a bug leading to incorrect area extensions, and sometimes crashes, in the coron3 pipeline diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index ac5bb28cd3..26310a5e7a 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -30,7 +30,14 @@ def to_container(model): 'var_poisson', 'var_rnoise', 'var_flat' ]: try: - setattr(image, attribute, model.getarray_noinit(attribute)[plane]) + arr = model.getarray_noinit(attribute) + if arr.ndim == 3: + setattr(image, attribute, arr[plane]) + elif arr.ndim == 2: + setattr(image, attribute, arr) + else: + msg = f"Unexpected array shape for extension {attribute}: {arr.shape}" + raise ValueError(msg) except AttributeError: pass image.update(model) diff --git a/jwst/pipeline/tests/test_calwebb_coron3.py b/jwst/pipeline/tests/test_calwebb_coron3.py index 3a8c4b34c4..9dc9756fe0 100644 --- a/jwst/pipeline/tests/test_calwebb_coron3.py +++ b/jwst/pipeline/tests/test_calwebb_coron3.py @@ -3,6 +3,7 @@ from stdatamodels.jwst.datamodels import CubeModel from jwst.pipeline.calwebb_coron3 import to_container +import numpy as np # Generate data @@ -22,6 +23,26 @@ def make_container(): ] +def test_to_container(): + """Cover bug where IndexError would be raised when area extension of CubeModel + has shape (x,y) instead of (nints,x,y). In this case area extension should + be copied to each ImageModel in the ModelContainer. + """ + shp = (10,5,5) + cube = CubeModel(shp) + extensions_3d = ['data', 'dq', 'err',] + for extension in extensions_3d: + setattr(cube, extension, np.random.rand(*shp)) + cube.area = np.random.rand(5,5) + + container = to_container(cube) + for i, model in enumerate(container): + for extension in extensions_3d: + assert np.all(getattr(model, extension) == getattr(cube, extension)[i]) + assert hasattr(model, 'area') + assert np.all(model.area == cube.area) + + @pytest.mark.parametrize('cube, container', [(cube, container)]) def test_container_shape(cube, container): """Test container shape""" From daf08d7f1f80216c1192d4d1fa3f496e1002d8bb Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Fri, 25 Oct 2024 18:12:36 -0400 Subject: [PATCH 579/687] also median filter fluxes for consistency --- jwst/master_background/master_background_step.py | 6 +++++- jwst/master_background/nirspec_utils.py | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 0c799ef4c4..66dd2ffd9c 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -152,7 +152,7 @@ def process(self, input): if self.median_kernel % 2 == 0: self.median_kernel -= 1 self.log.info('Even median filter kernels are not supported.' - f'Rounding the median kernel size down to {self.median_kernel}.') + f' Rounding the median kernel size down to {self.median_kernel}.') if (self.median_kernel > 1): self.log.info(f'Applying moving-median boxcar of width {self.median_kernel}.') @@ -160,6 +160,10 @@ def process(self, input): master_background.spec[0].spec_table['surf_bright'], kernel_size=[self.median_kernel] ) + master_background.spec[0].spec_table['flux'] = medfilt( + master_background.spec[0].spec_table['flux'], + kernel_size=[self.median_kernel] + ) background_data.close() diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 272d1fc088..862e6b82af 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -137,7 +137,7 @@ def create_background_from_multislit(input_model, sigma_clip=3, median_kernel=1) if median_kernel % 2 == 0: median_kernel -= 1 log.info('Even median filter kernels are not supported.' - f'Rounding the median kernel size down to {median_kernel}.') + f' Rounding the median kernel size down to {median_kernel}.') if (median_kernel > 1): log.info(f'Applying moving-median boxcar of width {median_kernel}.') @@ -145,6 +145,10 @@ def create_background_from_multislit(input_model, sigma_clip=3, median_kernel=1) master_bkg.spec[0].spec_table['surf_bright'], kernel_size=[median_kernel] ) + master_bkg.spec[0].spec_table['flux'] = medfilt( + master_bkg.spec[0].spec_table['flux'], + kernel_size=[median_kernel] + ) del bkg_model del resamp From 1d172753fd777a59be3969e9ee83d9772b903e2d Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 28 Oct 2024 09:05:47 -0400 Subject: [PATCH 580/687] mention strun package name usage in docs --- .../user_documentation/running_pipeline_command_line.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/jwst/user_documentation/running_pipeline_command_line.rst b/docs/jwst/user_documentation/running_pipeline_command_line.rst index 3c87dd3dc9..70465b1b6c 100644 --- a/docs/jwst/user_documentation/running_pipeline_command_line.rst +++ b/docs/jwst/user_documentation/running_pipeline_command_line.rst @@ -48,6 +48,14 @@ has the alias ``calwebb_detector1`` and can be run as A full list of pipeline aliases can be found in :ref:`Pipeline Stages `. + +.. note:: + + When using an **alias** with strun (for example ``strun resample``) you may + need to provide the ``jwst`` package name if you have other packages installed + that also use the same **alias**. The package name is provided prior to the + **alias** separated by ``::`` (for example ``strun jwst::resample``). + .. _exit_status: Exit Status From 8d2b64c73a50f11cfd27026e2f21592a3c9e8e40 Mon Sep 17 00:00:00 2001 From: Brett Date: Mon, 28 Oct 2024 09:18:01 -0400 Subject: [PATCH 581/687] add changelog fragment --- changes/8926.docs.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8926.docs.rst diff --git a/changes/8926.docs.rst b/changes/8926.docs.rst new file mode 100644 index 0000000000..621c1a9254 --- /dev/null +++ b/changes/8926.docs.rst @@ -0,0 +1 @@ +Mention possible need to provide package name to strun when using aliases. From 12a8b94781f76d11da3ef3ff653a4f506a664725 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 10:58:18 -0400 Subject: [PATCH 582/687] Update FGS guider data, add image3 test --- jwst/regtest/test_fgs_guider.py | 34 ++++++------------ jwst/regtest/test_fgs_image3.py | 62 +++++++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 24 deletions(-) create mode 100644 jwst/regtest/test_fgs_image3.py diff --git a/jwst/regtest/test_fgs_guider.py b/jwst/regtest/test_fgs_guider.py index af1e2b2a90..36841d14b3 100644 --- a/jwst/regtest/test_fgs_guider.py +++ b/jwst/regtest/test_fgs_guider.py @@ -1,16 +1,19 @@ """Regression tests for FGS Guidestar in ID and FINEGUIDE modes""" import pytest -from jwst.resample.resample import OutputTooLargeError -from jwst.stpipe import Step - from jwst.regtest import regtestdata as rt +from jwst.stpipe import Step -file_roots = ['exptype_fgs_acq1', 'exptype_fgs_fineguide', 'exptype_fgs_id_image', 'exptype_fgs_id_stack'] +EXP_TYPES = ['fgs_acq1', 'fgs_fineguide', 'fgs_id-image', 'fgs_id-stack'] +FILE_ROOTS = ['jw01029001001_gs-acq1_2022142180746', + 'jw01029001001_gs-fg_2022142181502', + 'jw01029001001_gs-id_1_image', + 'jw01029001001_gs-id_1_stacked'] +GUIDER_SUFFIXES = ['cal', 'dq_init', 'guider_cds'] -@pytest.fixture(scope='module', params=file_roots, ids=file_roots) +@pytest.fixture(scope='module', params=FILE_ROOTS, ids=FILE_ROOTS) def run_guider_pipelines(rtdata_module, request): """Run pipeline for guider data""" rtdata = rtdata_module @@ -27,26 +30,9 @@ def run_guider_pipelines(rtdata_module, request): return rtdata -guider_suffixes = ['cal', 'dq_init', 'guider_cds'] - - @pytest.mark.bigdata -@pytest.mark.parametrize('suffix', guider_suffixes, ids=guider_suffixes) +@pytest.mark.parametrize('suffix', GUIDER_SUFFIXES, ids=GUIDER_SUFFIXES) def test_fgs_guider(run_guider_pipelines, fitsdiff_default_kwargs, suffix): """Regression for FGS Guider data""" rt.is_like_truth(run_guider_pipelines, fitsdiff_default_kwargs, suffix, - 'truth/fgs/test_fgs_guider', is_suffix=True) - - -@pytest.mark.bigdata -def test_fgs_toobig(rtdata, fitsdiff_default_kwargs, caplog, monkeypatch): - """Test for the situation where the combined mosaic is too large""" - - # Set the environment to not allow the resultant too-large image. - monkeypatch.setenv('DMODEL_ALLOWED_MEMORY', "0.9") - - rtdata.get_asn('fgs/image3/image3_asn.json') - - args = ['calwebb_image3', rtdata.input] - with pytest.raises(OutputTooLargeError): - Step.from_cmdline(args) + 'truth/test_fgs_guider', is_suffix=True) diff --git a/jwst/regtest/test_fgs_image3.py b/jwst/regtest/test_fgs_image3.py new file mode 100644 index 0000000000..77488afdb7 --- /dev/null +++ b/jwst/regtest/test_fgs_image3.py @@ -0,0 +1,62 @@ +import pytest +from astropy.io.fits.diff import FITSDiff + +from jwst.resample.resample import OutputTooLargeError +from jwst.stpipe import Step + + +@pytest.fixture(scope="module") +def run_fgs_image3(rtdata_module): + rtdata = rtdata_module + rtdata.get_asn('fgs/image3/jw01029-o001_20240716t172128_image3_00001_asn.json') + + args = ["calwebb_image3", rtdata.input] + Step.from_cmdline(args) + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ['i2d', 'segm']) +def test_fgs_image3(run_fgs_image3, rtdata_module, fitsdiff_default_kwargs, suffix): + """Regression test for FGS data in the image3 pipeline""" + rtdata = rtdata_module + output = f"jw01029-o001_t009_fgs_clear_{suffix}.fits" + rtdata.output = output + + rtdata.get_truth(f"truth/test_fgs_image3/{output}") + + # Adjust tolerance for machine precision with float32 drizzle code + if suffix == "i2d": + fitsdiff_default_kwargs["rtol"] = 3e-3 + fitsdiff_default_kwargs["atol"] = 2e-2 + + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + +@pytest.mark.bigdata +def test_fgs_image3_catalog(run_fgs_image3, rtdata_module, diff_astropy_tables): + rtdata = rtdata_module + rtdata.output = "jw01029-o001_t009_fgs_clear_cat.ecsv" + rtdata.get_truth("truth/test_fgs_image3/jw01029-o001_t009_fgs_clear_cat.ecsv") + + assert diff_astropy_tables(rtdata.output, rtdata.truth, rtol=1e-3, atol=1e-4) + + +@pytest.mark.bigdata +def test_fgs_toobig(rtdata, fitsdiff_default_kwargs, caplog, monkeypatch): + """Test for the situation where the combined mosaic is too large""" + + # Set the environment to not allow the resultant too-large image. + # Note: this test was originally run on two pre-flight images + # with WCSs from very different parts of the sky. + # This condition should hopefully never be encountered in reductions + # of in-flight data. To test the software failsafe, we now use real data + # that makes a reasonable sized mosaic, but set the allowed memory to a + # small value. + monkeypatch.setenv('DMODEL_ALLOWED_MEMORY', "0.0001") + + rtdata.get_asn('fgs/image3/jw01029-o001_20240716t172128_image3_00001_asn.json') + + args = ['jwst.resample.ResampleStep', rtdata.input] + with pytest.raises(OutputTooLargeError): + Step.from_cmdline(args) From 9000e5f1093f5f6bd9150651874f04214da9229d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 28 Oct 2024 22:41:19 +0000 Subject: [PATCH 583/687] Bump actions/checkout from 4.2.1 to 4.2.2 Bumps [actions/checkout](https://github.com/actions/checkout) from 4.2.1 to 4.2.2. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871...11bd71901bbe5b1630ceea73d27597364c9af683) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/build.yml | 2 +- .github/workflows/changelog.yml | 4 ++-- .github/workflows/ci.yml | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index e563348d7b..f9f78fea0d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -16,7 +16,7 @@ jobs: name: check that `requirements-sdp.txt` is populated runs-on: ubuntu-latest steps: - - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - run: grep -v '^ *#' requirements-sdp.txt build: needs: [ check ] diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index dd09228b0d..fcbbc1e2ed 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -21,7 +21,7 @@ jobs: - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 with: python-version: 3 - - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - run: pip install . @@ -32,7 +32,7 @@ jobs: if: ${{ !contains(github.event.pull_request.labels.*.name, 'allow-manual-changelog-edit') }} runs-on: ubuntu-latest steps: - - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 with: fetch-depth: 0 - name: prevent direct changes to `CHANGES.rst` diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 161b70a1db..a48ae81918 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,7 +34,7 @@ jobs: pre-commit: runs-on: ubuntu-latest steps: - - uses: actions/checkout@eef61447b9ff4aafe5dcd4e0bbf5d482be7e7871 # v4.2.1 + - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 with: python-version: '3.12' From 3f240530c9a4ca0b2c267399c3532c0a0405d4f2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 29 Oct 2024 12:37:02 +0000 Subject: [PATCH 584/687] Bump actions/setup-python from 5.2.0 to 5.3.0 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5.2.0 to 5.3.0. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/f677139bbe7f9c59b41e40162b753c062f5d49a3...0b93645e9fea7318ecaed2b359559ac225c90a2b) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/changelog.yml | 2 +- .github/workflows/ci.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/changelog.yml b/.github/workflows/changelog.yml index fcbbc1e2ed..7404fe9d8b 100644 --- a/.github/workflows/changelog.yml +++ b/.github/workflows/changelog.yml @@ -18,7 +18,7 @@ jobs: if: ${{ !contains(github.event.pull_request.labels.*.name, 'no-changelog-entry-needed') }} runs-on: ubuntu-latest steps: - - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 + - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: 3 - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a48ae81918..3185d02aea 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -35,7 +35,7 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0 + - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0 with: python-version: '3.12' - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 From ab4795d6b6957627af27fcf01ad033bffccd91fc Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Tue, 29 Oct 2024 10:52:43 -0400 Subject: [PATCH 585/687] add test for master background medfilt --- .../tests/test_master_background.py | 163 +++++++++++++++++- 1 file changed, 162 insertions(+), 1 deletion(-) diff --git a/jwst/master_background/tests/test_master_background.py b/jwst/master_background/tests/test_master_background.py index bf3fb2e136..c11fa704ce 100644 --- a/jwst/master_background/tests/test_master_background.py +++ b/jwst/master_background/tests/test_master_background.py @@ -6,15 +6,18 @@ import json from stdatamodels.jwst import datamodels +from jwst.stpipe import query_step_status from jwst.assign_wcs import AssignWcsStep +from jwst.extract_1d import Extract1dStep +from jwst.extract_2d import Extract2dStep from jwst.master_background import MasterBackgroundStep from jwst.master_background.create_master_bkg import create_background from jwst.master_background.master_background_step import ( copy_background_to_surf_bright, split_container, ) - +from jwst.srctype import SourceTypeStep @pytest.fixture(scope='module') def user_background(tmp_path_factory): @@ -29,6 +32,133 @@ def user_background(tmp_path_factory): # filename must be string not Path to validate with current stpipe version return str(filename) +@pytest.fixture +def nirspec_rate(): + ysize = 2048 + xsize = 2048 + shape = (ysize, xsize) + im = datamodels.ImageModel(shape) + im.var_rnoise += 1 + im.meta.target = { + 'ra': 100.1237, + 'dec': 39.86, + 'source_type_apt': 'EXTENDED'} + im.meta.wcsinfo = { + 'dec_ref': 40, + 'ra_ref': 100, + 'roll_ref': 0, + 'v2_ref': -453.5134, + 'v3_ref': -373.4826, + 'v3yangle': 0.0, + 'vparity': -1} + im.meta.instrument = { + 'detector': 'NRS1', + 'filter': 'CLEAR', + 'grating': 'PRISM', + 'name': 'NIRSPEC', + 'gwa_tilt': 37.0610, + 'gwa_xtilt': 0.0001, + 'gwa_ytilt': 0.0001, + 'fixed_slit': 'S200A1'} + im.meta.subarray = { + 'fastaxis': 1, + 'name': 'SUBS200A1', + 'slowaxis': 2, + 'xsize': 72, + 'xstart': 1, + 'ysize': 416, + 'ystart': 529} + im.meta.observation = { + 'program_number': '1234', + 'date': '2016-09-05', + 'time': '8:59:37'} + im.meta.exposure = { + 'duration': 11.805952, + 'end_time': 58119.85416, + 'exposure_time': 11.776, + 'measurement_time': 11.65824, + 'frame_time': 0.11776, + 'group_time': 0.11776, + 'groupgap': 0, + 'integration_time': 11.776, + 'nframes': 1, + 'ngroups': 100, + 'nints': 1, + 'nresets_between_ints': 0, + 'nsamples': 1, + 'readpatt': 'NRSRAPID', + 'sample_time': 10.0, + 'start_time': 58119.8333, + 'type': 'NRS_FIXEDSLIT', + 'zero_frame': False} + + yield im + im.close() + + +@pytest.fixture +def nirspec_cal_pair(nirspec_rate): + # copy the rate model to make files with different filters + rate2 = nirspec_rate.copy() + + im1 = AssignWcsStep.call(nirspec_rate) + im2 = AssignWcsStep.call(rate2) + + # We want to test the background subtraction, so set the + # data to a constant. + im1.data += 1.0 + im2.data += 1.0 + + im1 = Extract2dStep.call(im1) + im2 = Extract2dStep.call(im2) + im1 = SourceTypeStep.call(im1) + im2 = SourceTypeStep.call(im2) + im1.meta.filename = 'foo1_cal.fits' + im2.meta.filename = 'foo2_cal.fits' + yield im1, im2 + im1.close() + im2.close() + + +@pytest.fixture +def nirspec_asn(nirspec_cal_pair, tmp_cwd): + """Create an association with the mock data""" + sci, im2 = nirspec_cal_pair + + sci_filename = sci.meta.filename + sci.save(sci_filename) + + x1d = Extract1dStep.call(im2) + + # Add a CR to the x1d + x1d.spec[0].spec_table['SURF_BRIGHT'][10] += 10 + + x1d_filename = im2.meta.filename.replace('cal', 'x1d') + x1d.meta.filename = x1d_filename + x1d.save(x1d_filename) + + # Make a basic association with the science image + # and the x1d as a background member + asn = { + 'asn_type': 'test', + 'asn_id': 'o001', + 'asn_pool': 'test', + 'products': [ + { + 'name': 'product_a', + 'members': [ + {'expname': sci_filename, 'exptype': 'science'}, + {'expname': x1d_filename, 'exptype': 'background'}, + ] + }, + ]} + + # Save the association + new_data = json.dumps(asn) + asn_file = 'asn.json' + with open(asn_file, "w") as file: + file.write(new_data) + return asn_file @pytest.fixture(scope='function') def science_image(): @@ -67,6 +197,37 @@ def test_master_background_userbg(tmp_cwd, user_background, science_image): assert result.meta.cal_step.master_background == 'COMPLETE' assert result.meta.background.master_background_file == 'user_background.fits' +def test_master_background_medfilt(tmp_cwd, nirspec_asn): + """Verify data can run through the step with a median filter""" + + # Load in the example association containing a cal science member + # and a background x1d member that has a CR in it + container = datamodels.open(nirspec_asn) + + # Run without the median filter + result = MasterBackgroundStep.call( + container, + ) + + # Run with a median filter using kernel size 4. This should be rounded + # down to 3 otherwise medfilt will throw an error. + result_medfilt = MasterBackgroundStep.call( + container, median_kernel=4 + ) + + # Check that the background correction was run in both cases + assert query_step_status(result, "master_background") == 'COMPLETE' + assert query_step_status(result_medfilt, "master_background") == 'COMPLETE' + + # Check that there is a difference between the median filter on and off + data_nofilt = result[0].slits[0].data + data_medfilt = result_medfilt[0].slits[0].data + assert not np.all(data_nofilt == data_medfilt) + + # Check that once filtered any pixels where the background correction + # was applied have zero flux + bkg_sub_pixels = data_medfilt[data_medfilt != 1] + assert np.allclose(bkg_sub_pixels, 0) def test_master_background_logic(tmp_cwd, user_background, science_image): """Verify if calspec 2 background step was run the master background step will be skipped""" From 1cb213aedd9c09d05f08dd48a828b0b778de89d9 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 29 Oct 2024 11:15:43 -0400 Subject: [PATCH 586/687] Tighten tolerance on i2d product --- jwst/regtest/test_fgs_image3.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/jwst/regtest/test_fgs_image3.py b/jwst/regtest/test_fgs_image3.py index 77488afdb7..b9f574e5dc 100644 --- a/jwst/regtest/test_fgs_image3.py +++ b/jwst/regtest/test_fgs_image3.py @@ -21,14 +21,8 @@ def test_fgs_image3(run_fgs_image3, rtdata_module, fitsdiff_default_kwargs, suff rtdata = rtdata_module output = f"jw01029-o001_t009_fgs_clear_{suffix}.fits" rtdata.output = output - rtdata.get_truth(f"truth/test_fgs_image3/{output}") - # Adjust tolerance for machine precision with float32 drizzle code - if suffix == "i2d": - fitsdiff_default_kwargs["rtol"] = 3e-3 - fitsdiff_default_kwargs["atol"] = 2e-2 - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From 8d9e2159a218bb6a00cfab88a9d544d7cc1175fa Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Tue, 29 Oct 2024 12:36:35 -0400 Subject: [PATCH 587/687] JP-2928: Update regression tests to use flight data - set_telescope_pointing.add_wcs edition (#8887) --- jwst/engdblog/engdblog.py | 12 +- jwst/engdblog/tests/test_engdblog.py | 14 +- jwst/lib/engdb_mast.py | 2 - jwst/lib/tests/data/add_wcs_default.fits | 35 +- .../data/add_wcs_method_full_nosiafdb.fits | 35 +- jwst/lib/tests/data/add_wcs_with_db.fits | 34 +- .../engdb/inrsi_gwa_y_tilt_avged_data.json | 1 - jwst/lib/tests/data/engdb/meta.json | 1 - .../tests/data/engdb/sa_zaducmdx_data.json | 1 - .../tests/data/engdb/sa_zaducmdy_data.json | 1 - .../tests/data/engdb/sa_zattest1_data.json | 1 - .../tests/data/engdb/sa_zattest2_data.json | 1 - .../tests/data/engdb/sa_zattest3_data.json | 1 - .../tests/data/engdb/sa_zattest4_data.json | 1 - .../tests/data/engdb/sa_zfgdetid_data.json | 1 - .../tests/data/engdb/sa_zfggscmdx_data.json | 1 - .../tests/data/engdb/sa_zfggscmdy_data.json | 1 - 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jwst/lib/tests/test_pointing_summary.py | 4 +- jwst/lib/tests/test_set_telescope_pointing.py | 222 ++++-------- jwst/lib/tests/test_siafdb.py | 19 +- jwst/lib/tests/test_v1_calculate.py | 48 +-- jwst/regtest/test_fgs_pointing.py | 50 +-- jwst/regtest/test_miri_setpointing.py | 27 +- jwst/regtest/test_nircam_tsgrism.py | 13 +- jwst/regtest/test_nircam_tsimg.py | 30 +- jwst/regtest/test_stp_jw00697013.py | 144 -------- pyproject.toml | 1 + 86 files changed, 179 insertions(+), 1358 deletions(-) delete mode 100644 jwst/lib/tests/data/engdb/inrsi_gwa_y_tilt_avged_data.json delete mode 100644 jwst/lib/tests/data/engdb/meta.json delete mode 100644 jwst/lib/tests/data/engdb/sa_zaducmdx_data.json delete mode 100644 jwst/lib/tests/data/engdb/sa_zaducmdy_data.json delete mode 100644 jwst/lib/tests/data/engdb/sa_zattest1_data.json delete mode 100644 jwst/lib/tests/data/engdb/sa_zattest2_data.json delete mode 100644 jwst/lib/tests/data/engdb/sa_zattest3_data.json delete mode 100644 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delete mode 100644 jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j31_data.json delete mode 100644 jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j32_data.json delete mode 100644 jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j33_data.json delete mode 100755 jwst/lib/tests/data/xml_data_no_siafxml/prd.db delete mode 100644 jwst/lib/tests/engdb_mock.py delete mode 100644 jwst/lib/tests/test_engdb_mock.py delete mode 100644 jwst/regtest/test_stp_jw00697013.py diff --git a/jwst/engdblog/engdblog.py b/jwst/engdblog/engdblog.py index 5eb470703d..69cf025832 100644 --- a/jwst/engdblog/engdblog.py +++ b/jwst/engdblog/engdblog.py @@ -1,4 +1,5 @@ from ..lib.engdb_tools import ENGDB_Service +from ..lib.engdb_mast import EngdbMast from ..stpipe import Step @@ -21,10 +22,10 @@ class EngDBLogStep(Step): """ spec = """ - stime = string(default='2021-01-25') # Start time - etime = string(default='2021-01-27') # End time + stime = string(default='2022-01-25 02:00:00') # Start time + etime = string(default='2022-01-26 02:10:00') # End time verbosity = option('initial', 'all', default='initial') # How much to report. - engdb_url = string(default='http://localhost') # Mock url + engdb_url = string(default=None) # Mock url """ def process(self, mnemonics): @@ -46,7 +47,10 @@ def process(self, mnemonics): stime = self.stime etime = self.etime verbosity = self.verbosity - edb = ENGDB_Service(base_url=self.engdb_url) + if self.engdb_url is not None: + edb = ENGDB_Service(base_url=self.engdb_url) + else: + edb = EngdbMast() if isinstance(mnemonics, str): mnemonics = [mnemonics] diff --git a/jwst/engdblog/tests/test_engdblog.py b/jwst/engdblog/tests/test_engdblog.py index 681b1597c4..3b3656f657 100644 --- a/jwst/engdblog/tests/test_engdblog.py +++ b/jwst/engdblog/tests/test_engdblog.py @@ -4,9 +4,8 @@ import os import pytest from tempfile import TemporaryDirectory - +from jwst.lib import engdb_mast from jwst.engdblog import EngDBLogStep -from jwst.lib.tests.engdb_mock import EngDB_Mocker def test_engdblogstep(caplog, engdb): @@ -17,7 +16,7 @@ def test_engdblogstep(caplog, engdb): assert 'EngDBLogStep instance created' in caplog.text assert mnemonic in caplog.text assert "Step EngDBLogStep running with args (['{}'],)".format(mnemonic) in caplog.text - assert '{}[2021-01-25:2021-01-27] = '.format(mnemonic) in caplog.text + assert '{}[2022-01-25 02:00:00:2022-01-26 02:10:00] = '.format(mnemonic) in caplog.text assert 'Step EngDBLogStep done' in caplog.text @@ -29,7 +28,7 @@ def test_barestring(caplog, engdb): assert 'EngDBLogStep instance created' in caplog.text assert mnemonic in caplog.text assert f"Step EngDBLogStep running with args ('{mnemonic}',)." in caplog.text - assert '{}[2021-01-25:2021-01-27] = '.format(mnemonic) in caplog.text + assert '{}[2022-01-25 02:00:00:2022-01-26 02:10:00] = '.format(mnemonic) in caplog.text assert 'Step EngDBLogStep done' in caplog.text @@ -38,7 +37,7 @@ def test_badmnemonic(caplog, engdb): result = EngDBLogStep.call([mnemonic]) assert isinstance(result, dict) assert len(result) == 0 - assert 'Cannot retrieve info for {}'.format(mnemonic) in caplog.text + assert '{} has no entries in time range'.format(mnemonic) in caplog.text def test_novalues(caplog, engdb): @@ -65,7 +64,7 @@ def test_multi_mnemonics(caplog, engdb): result = EngDBLogStep.call(mnemonics) assert len(result) == 2 for mnemonic in mnemonics: - assert '{}[2021-01-25:2021-01-27] = '.format(mnemonic) in caplog.text + assert '{}[2022-01-25 02:00:00:2022-01-26 02:10:00] = '.format(mnemonic) in caplog.text # ##################### @@ -73,5 +72,4 @@ def test_multi_mnemonics(caplog, engdb): # ##################### @pytest.fixture def engdb(): - with EngDB_Mocker() as mocker: # noqa: F841 - yield + yield engdb_mast.EngdbMast() diff --git a/jwst/lib/engdb_mast.py b/jwst/lib/engdb_mast.py index afd5603eb1..796f98626c 100644 --- a/jwst/lib/engdb_mast.py +++ b/jwst/lib/engdb_mast.py @@ -187,8 +187,6 @@ def configure(self, base_url=None, token=None): # Get the token if token is None: token = getenv('MAST_API_TOKEN', None) - if token is None: - raise RuntimeError('No MAST token provided but is required. See https://auth.mast.stsci.edu/ for more information.') self.token = token # Get various timeout parameters diff --git a/jwst/lib/tests/data/add_wcs_default.fits b/jwst/lib/tests/data/add_wcs_default.fits index c913ed225a..6c1c5470fc 100644 --- a/jwst/lib/tests/data/add_wcs_default.fits +++ b/jwst/lib/tests/data/add_wcs_default.fits @@ -1,36 +1,39 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2022-07-29T03:34:20.552' / UTC date file created FILENAME= 'add_wcs_default.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'PLANNED ' / Quality of pointing information from EngDB Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 56660.0 / UTC exposure start time EXPEND = 56661.0 / UTC exposure end time Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 0.0 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 345.0 / [deg] RA of telescope V1 axis DEC_V1 = -87.0 / [deg] Dec of telescope V1 axis PA_V3 = 0.0 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 345.0 / first axis value at the reference pixel CRVAL2 = -87.0 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / first axis coordinate type CTYPE2 = 'DEC--TAN' / second axis coordinate type CUNIT1 = 'deg ' / first axis units CUNIT2 = 'deg ' / second axis units CDELT1 = 3.06939222222222E-05 / first axis increment per pixel CDELT2 = 3.09266472222222E-05 / second axis increment per pixel PC1_1 = -1.0 / linear transformation matrix element PC1_2 = 0.0 / linear transformation matrix element PC2_1 = 0.0 / linear transformation matrix element PC2_2 = 1.0 / linear transformation matrix element S_REGION= 'POLYGON ICRS 345.408068315 -87.015479009 344.799106279 &' CONTINUE '-87.015659106 344.804553622 -86.984328015 345.401695961 &' CONTINUE '-86.984200751&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 345.0 / [deg] Right ascension of the reference point DEC_REF = -87.0 / [deg] Declination of the reference point ROLL_REF= -4.83544897 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 1615 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '1615B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2024-10-18T15:17:04.909' / UTC date file created FILENAME= 'add_wcs_default.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'CALCULATED_COARSE_TR_202111' / Quality of pointing information from E Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 59733.72615740741 / [d] exposure start time in MJD EXPEND = 59733.72634259259 / [d] exposure end time in MJD Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 116.78328104798904 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 151.9058983478287 / [deg] RA of telescope V1 axis DEC_V1 = -40.51600163033943 / [deg] Dec of telescope V1 axis PA_V3 = 111.90593481429273 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 151.8411287953228 / first axis value at the reference pixel CRVAL2 = -40.36039857194743 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / Axis 1 type CTYPE2 = 'DEC--TAN' / Axis 2 type CUNIT1 = 'deg ' / Axis 1 units CUNIT2 = 'deg ' / Axis 2 units CDELT1 = 3.06939222222222E-05 / Axis 1 coordinate increment at reference point CDELT2 = 3.09266472222222E-05 / Axis 2 coordinate increment at reference point PC1_1 = 0.4506170642772821 / linear transformation matrix element PC1_2 = 0.8927173468585249 / linear transformation matrix element PC2_1 = 0.8927173468585249 / linear transformation matrix element PC2_2 = -0.4506170642772821 / linear transformation matrix element S_REGION= 'POLYGON ICRS 151.810335621 -40.372352342 151.828949847 &' CONTINUE '-40.343996389 151.865548906 -40.358271042 151.847056469 &' CONTINUE '-40.386350812&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 151.8411287953228 / [deg] Right ascension of the reference point DEC_REF = -40.36039857194743 / [deg] Declination of the reference point ROLL_REF= 111.94783207798903 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 1991 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '1991B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 #ASDF_STANDARD 1.5.0 %YAML 1.1 %TAG ! tag:stsci.edu:asdf/ --- !core/asdf-1.1.0 asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf', - name: asdf, version: 2.12.0} + name: asdf, version: 3.4.0} history: extensions: - !core/extension_metadata-1.0.0 - extension_class: asdf.extension.BuiltinExtension - software: !core/software-1.0.0 {name: asdf, version: 2.12.0} -_fits_hash: d13be29212007febef7528ef6667d2c6da94b8041a90358fdd835059a0ac55ed + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0 + manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1} + software: !core/software-1.0.0 {name: asdf, version: 3.4.0} +_fits_hash: 37ddbe49859987843a596dd1f321892087e6a978cc717cdba6f3b8585d77a036 meta: - aperture: {name: MIRIM_FULL, position_angle: 0.0} + aperture: {name: MIRIM_FULL, position_angle: 116.78328104798904} coordinates: {reference_frame: ICRS} - date: '2022-07-29T03:34:20.552' + date: '2024-10-18T15:17:04.909' ephemeris: {velocity_x: -25.021, velocity_y: -16.507, velocity_z: -7.187} - exposure: {end_time: 56661.0, start_time: 56660.0, type: MIR_IMAGE} + exposure: {end_time: 59733.72634259259, start_time: 59733.72615740741, type: MIR_IMAGE} filename: add_wcs_default.fits guidestar: {gs_dec: -86.9999, gs_ra: 345.0001} instrument: {} model_type: Level1bModel observation: {date: '2017-01-01'} - pointing: {dec_v1: -87.0, pa_v3: 0.0, ra_v1: 345.0} + pointing: {dec_v1: -40.51600163033943, pa_v3: 111.90593481429273, ra_v1: 151.9058983478287} target: {dec: -87.0, ra: 345.0} - visit: {engdb_pointing_quality: PLANNED} + visit: {engdb_pointing_quality: CALCULATED_COARSE_TR_202111} wcsinfo: {cdelt1: 3.0693922222222226e-05, cdelt2: 3.092664722222222e-05, crpix1: 693.5, - crpix2: 512.5, crval1: 345.0, crval2: -87.0, ctype1: RA---TAN, ctype2: DEC--TAN, - cunit1: deg, cunit2: deg, dec_ref: -87.0, pc1_1: -1.0, pc1_2: 0.0, pc2_1: 0.0, - pc2_2: 1.0, ra_ref: 345.0, roll_ref: -4.83544897, s_region: POLYGON ICRS 345.408068315 - -87.015479009 344.799106279 -87.015659106 344.804553622 -86.984328015 345.401695961 - -86.984200751, v2_ref: -453.37849, v3_ref: -373.810549, v3yangle: 4.83544897, + crpix2: 512.5, crval1: 151.8411287953228, crval2: -40.36039857194743, ctype1: RA---TAN, + ctype2: DEC--TAN, cunit1: deg, cunit2: deg, dec_ref: -40.36039857194743, pc1_1: 0.4506170642772821, + pc1_2: 0.8927173468585249, pc2_1: 0.8927173468585249, pc2_2: -0.4506170642772821, + ra_ref: 151.8411287953228, roll_ref: 111.94783207798903, s_region: POLYGON ICRS 151.810335621 + -40.372352342 151.828949847 -40.343996389 151.865548906 -40.358271042 151.847056469 + -40.386350812, v2_ref: -453.37849, v3_ref: -373.810549, v3yangle: 4.83544897, vparity: -1, wcsaxes: 2} ... - \ No newline at end of file + \ No newline at end of file diff --git a/jwst/lib/tests/data/add_wcs_method_full_nosiafdb.fits b/jwst/lib/tests/data/add_wcs_method_full_nosiafdb.fits index 75b75dc227..be33748324 100644 --- a/jwst/lib/tests/data/add_wcs_method_full_nosiafdb.fits +++ b/jwst/lib/tests/data/add_wcs_method_full_nosiafdb.fits @@ -1,36 +1,39 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2022-06-13T13:14:15.915' / UTC date file created FILENAME= 'add_wcs_method_full_nosiafdb.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'CALCULATED_COARSE_TR_202111' / Quality of pointing information from E Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 56660.0 / UTC exposure start time EXPEND = 56661.0 / UTC exposure end time Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 224.5546184370296 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 115.9096620762875 / [deg] RA of telescope V1 axis DEC_V1 = -37.10303105881081 / [deg] Dec of telescope V1 axis PA_V3 = 219.8343148311268 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 116.1143186105138 / first axis value at the reference pixel CRVAL2 = -37.10379155240744 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / first axis coordinate type CTYPE2 = 'DEC--TAN' / second axis coordinate type CUNIT1 = 'deg ' / first axis units CUNIT2 = 'deg ' / second axis units CDELT1 = 3.06939222222222E-05 / first axis increment per pixel CDELT2 = 3.09266472222222E-05 / second axis increment per pixel PC1_1 = 0.7126851741563699 / linear transformation matrix element PC1_2 = -0.7014840287117766 / linear transformation matrix element PC2_1 = -0.7014840287117766 / linear transformation matrix element PC2_2 = -0.7126851741563699 / linear transformation matrix element S_REGION= 'POLYGON ICRS 116.109015775 -37.077801934 116.137452855 &' CONTINUE '-37.099953144 116.109736734 -37.122161001 116.081603965 &' CONTINUE '-37.100169352&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 116.1143186105138 / [deg] Right ascension of the reference point DEC_REF = -37.10379155240744 / [deg] Declination of the reference point ROLL_REF= 219.7107404549584 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 1813 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '1813B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2024-10-18T14:59:24.523' / UTC date file created FILENAME= 'add_wcs_method_full_nosiafdb.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'CALCULATED_COARSE_TR_202111' / Quality of pointing information from E Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 59733.72615740741 / [d] exposure start time in MJD EXPEND = 59733.72634259259 / [d] exposure end time in MJD Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 116.78328104798904 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 151.9058983478287 / [deg] RA of telescope V1 axis DEC_V1 = -40.51600163033943 / [deg] Dec of telescope V1 axis PA_V3 = 111.90593481429273 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 151.8411287953228 / first axis value at the reference pixel CRVAL2 = -40.36039857194743 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / Axis 1 type CTYPE2 = 'DEC--TAN' / Axis 2 type CUNIT1 = 'deg ' / Axis 1 units CUNIT2 = 'deg ' / Axis 2 units CDELT1 = 3.06939222222222E-05 / Axis 1 coordinate increment at reference point CDELT2 = 3.09266472222222E-05 / Axis 2 coordinate increment at reference point PC1_1 = 0.4506170642772821 / linear transformation matrix element PC1_2 = 0.8927173468585249 / linear transformation matrix element PC2_1 = 0.8927173468585249 / linear transformation matrix element PC2_2 = -0.4506170642772821 / linear transformation matrix element S_REGION= 'POLYGON ICRS 151.810335621 -40.372352342 151.828949847 &' CONTINUE '-40.343996389 151.865548906 -40.358271042 151.847056469 &' CONTINUE '-40.386350812&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 151.8411287953228 / [deg] Right ascension of the reference point DEC_REF = -40.36039857194743 / [deg] Declination of the reference point ROLL_REF= 111.94783207798903 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 2004 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '2004B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 #ASDF_STANDARD 1.5.0 %YAML 1.1 %TAG ! tag:stsci.edu:asdf/ --- !core/asdf-1.1.0 asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf', - name: asdf, version: 2.11.0} + name: asdf, version: 3.4.0} history: extensions: - !core/extension_metadata-1.0.0 - extension_class: asdf.extension.BuiltinExtension - software: !core/software-1.0.0 {name: asdf, version: 2.11.0} -_fits_hash: 9c45ea1f0cd74a64081b8c59085a0b393fcdbcfb4558f70458d92069618eb6e4 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0 + manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1} + software: !core/software-1.0.0 {name: asdf, version: 3.4.0} +_fits_hash: 5df486be779482a08c8faa22fd680877e726b43a6af5f816f71f90227c1643b3 meta: - aperture: {name: MIRIM_FULL, position_angle: 224.5546184370296} + aperture: {name: MIRIM_FULL, position_angle: 116.78328104798904} coordinates: {reference_frame: ICRS} - date: '2022-06-13T13:14:15.915' + date: '2024-10-18T14:59:24.523' ephemeris: {velocity_x: -25.021, velocity_y: -16.507, velocity_z: -7.187} - exposure: {end_time: 56661.0, start_time: 56660.0, type: MIR_IMAGE} + exposure: {end_time: 59733.72634259259, start_time: 59733.72615740741, type: MIR_IMAGE} filename: add_wcs_method_full_nosiafdb.fits guidestar: {gs_dec: -86.9999, gs_ra: 345.0001} + instrument: {} model_type: Level1bModel observation: {date: '2017-01-01'} - pointing: {dec_v1: -37.10303105881081, pa_v3: 219.83431483112676, ra_v1: 115.90966207628749} + pointing: {dec_v1: -40.51600163033943, pa_v3: 111.90593481429273, ra_v1: 151.9058983478287} target: {dec: -87.0, ra: 345.0} visit: {engdb_pointing_quality: CALCULATED_COARSE_TR_202111} wcsinfo: {cdelt1: 3.0693922222222226e-05, cdelt2: 3.092664722222222e-05, crpix1: 693.5, - crpix2: 512.5, crval1: 116.11431861051379, crval2: -37.103791552407436, ctype1: RA---TAN, - ctype2: DEC--TAN, cunit1: deg, cunit2: deg, dec_ref: -37.103791552407436, pc1_1: 0.7126851741563699, - pc1_2: -0.7014840287117766, pc2_1: -0.7014840287117766, pc2_2: -0.7126851741563699, - ra_ref: 116.11431861051379, roll_ref: 219.71074045495837, s_region: POLYGON ICRS 116.109015775 - -37.077801934 116.137452855 -37.099953144 116.109736734 -37.122161001 116.081603965 - -37.100169352, v2_ref: -453.37849, v3_ref: -373.810549, v3yangle: 4.83544897, + crpix2: 512.5, crval1: 151.8411287953228, crval2: -40.36039857194743, ctype1: RA---TAN, + ctype2: DEC--TAN, cunit1: deg, cunit2: deg, dec_ref: -40.36039857194743, pc1_1: 0.4506170642772821, + pc1_2: 0.8927173468585249, pc2_1: 0.8927173468585249, pc2_2: -0.4506170642772821, + ra_ref: 151.8411287953228, roll_ref: 111.94783207798903, s_region: POLYGON ICRS 151.810335621 + -40.372352342 151.828949847 -40.343996389 151.865548906 -40.358271042 151.847056469 + -40.386350812, v2_ref: -453.37849, v3_ref: -373.810549, v3yangle: 4.83544897, vparity: -1, wcsaxes: 2} ... - \ No newline at end of file + \ No newline at end of file diff --git a/jwst/lib/tests/data/add_wcs_with_db.fits b/jwst/lib/tests/data/add_wcs_with_db.fits index 488b6bc220..0de4707e56 100644 --- a/jwst/lib/tests/data/add_wcs_with_db.fits +++ b/jwst/lib/tests/data/add_wcs_with_db.fits @@ -1,37 +1,39 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2022-07-29T03:35:16.299' / UTC date file created FILENAME= 'add_wcs_with_db.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'CALCULATED_COARSE_TR_202111' / Quality of pointing information from E Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 56660.0 / UTC exposure start time EXPEND = 56661.0 / UTC exposure end time Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 224.5461894249584 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 115.9096620762875 / [deg] RA of telescope V1 axis DEC_V1 = -37.10303105881081 / [deg] Dec of telescope V1 axis PA_V3 = 219.8343148311268 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 116.1143186105138 / first axis value at the reference pixel CRVAL2 = -37.10379155240744 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / first axis coordinate type CTYPE2 = 'DEC--TAN' / second axis coordinate type CUNIT1 = 'deg ' / first axis units CUNIT2 = 'deg ' / second axis units CDELT1 = 3.06939222222222E-05 / first axis increment per pixel CDELT2 = 3.09266472222222E-05 / second axis increment per pixel PC1_1 = 0.7126851741563699 / linear transformation matrix element PC1_2 = -0.7014840287117766 / linear transformation matrix element PC2_1 = -0.7014840287117766 / linear transformation matrix element PC2_2 = -0.7126851741563699 / linear transformation matrix element S_REGION= 'POLYGON ICRS 116.109015775 -37.077801934 116.137452855 &' CONTINUE '-37.099953144 116.109736734 -37.122161001 116.081603965 &' CONTINUE '-37.100169352&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 116.1143186105138 / [deg] Right ascension of the reference point DEC_REF = -37.10379155240744 / [deg] Declination of the reference point ROLL_REF= 219.7107404549584 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 1818 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '1818B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T DATE = '2024-10-18T15:17:08.055' / UTC date file created FILENAME= 'add_wcs_with_db.fits' / Name of the file DATAMODL= 'Level1bModel' / Type of data model Observation identifiers DATE-OBS= '2017-01-01' / [yyyy-mm-dd] UTC date at start of exposure Visit information ENGQLPTG= 'CALCULATED_COARSE_TR_202111' / Quality of pointing information from E Target information TARG_RA = 345.0 / Target RA at mid time of exposure TARG_DEC= -87.0 / Target Dec at mid time of exposure Exposure parameters EXP_TYPE= 'MIR_IMAGE' / Type of data in the exposure EXPSTART= 59733.72615740741 / [d] exposure start time in MJD EXPEND = 59733.72634259259 / [d] exposure end time in MJD Aperture information APERNAME= 'MIRIM_FULL' / PRD science aperture used Guide star information GS_RA = 345.0001 / guide star right ascension GS_DEC = -86.9999 / guide star declination END XTENSION= 'IMAGE ' / Image extension BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions PCOUNT = 0 / number of parameters GCOUNT = 1 / number of groups EXTNAME = 'SCI ' / extension name PA_APER = 116.78328104798904 / [deg] Position angle of aperture used Information about the coordinates in the file RADESYS = 'ICRS ' / Name of the coordinate reference frame Spacecraft pointing information RA_V1 = 151.9058983478287 / [deg] RA of telescope V1 axis DEC_V1 = -40.51600163033943 / [deg] Dec of telescope V1 axis PA_V3 = 111.90593481429273 / [deg] Position angle of telescope V3 axis WCS parameters WCSAXES = 2 / number of World Coordinate System axes CRPIX1 = 693.5 / axis 1 coordinate of the reference pixel CRPIX2 = 512.5 / axis 2 coordinate of the reference pixel CRVAL1 = 151.8411287953228 / first axis value at the reference pixel CRVAL2 = -40.36039857194743 / second axis value at the reference pixel CTYPE1 = 'RA---TAN' / Axis 1 type CTYPE2 = 'DEC--TAN' / Axis 2 type CUNIT1 = 'deg ' / Axis 1 units CUNIT2 = 'deg ' / Axis 2 units CDELT1 = 3.06939222222222E-05 / Axis 1 coordinate increment at reference point CDELT2 = 3.09266472222222E-05 / Axis 2 coordinate increment at reference point PC1_1 = 0.4506170642772821 / linear transformation matrix element PC1_2 = 0.8927173468585249 / linear transformation matrix element PC2_1 = 0.8927173468585249 / linear transformation matrix element PC2_2 = -0.4506170642772821 / linear transformation matrix element S_REGION= 'POLYGON ICRS 151.810335621 -40.372352342 151.828949847 &' CONTINUE '-40.343996389 151.865548906 -40.358271042 151.847056469 &' CONTINUE '-40.386350812&' CONTINUE '' / spatial extent of the observation V2_REF = -453.37849 / [arcsec] Telescope V2 coord of reference point V3_REF = -373.810549 / [arcsec] Telescope V3 coord of reference point VPARITY = -1 / Relative sense of rotation between Ideal xy andV3I_YANG= 4.83544897 / [deg] Angle from V3 axis to Ideal y axis RA_REF = 151.8411287953228 / [deg] Right ascension of the reference point DEC_REF = -40.36039857194743 / [deg] Declination of the reference point ROLL_REF= 111.94783207798903 / [deg] V3 roll angle at the ref point (N over E)END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 1991 / length of dimension 1 NAXIS2 = 1 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 1 / number of table fields TTYPE1 = 'ASDF_METADATA' TFORM1 = '1991B ' EXTNAME = 'ASDF ' / extension name END #ASDF 1.0.0 #ASDF_STANDARD 1.5.0 %YAML 1.1 %TAG ! tag:stsci.edu:asdf/ --- !core/asdf-1.1.0 asdf_library: !core/software-1.0.0 {author: The ASDF Developers, homepage: 'http://github.com/asdf-format/asdf', - name: asdf, version: 2.12.0} + name: asdf, version: 3.4.0} history: extensions: - !core/extension_metadata-1.0.0 - extension_class: asdf.extension.BuiltinExtension - software: !core/software-1.0.0 {name: asdf, version: 2.12.0} -_fits_hash: e2f5396a9e2cbff0fad387a821acdc5fb66bf1eb54ec39f41d18f12d23cccca7 + extension_class: asdf.extension._manifest.ManifestExtension + extension_uri: asdf://asdf-format.org/core/extensions/core-1.5.0 + manifest_software: !core/software-1.0.0 {name: asdf_standard, version: 1.1.1} + software: !core/software-1.0.0 {name: asdf, version: 3.4.0} +_fits_hash: 56ceab2464e9670930f8da17b77ea934d22ee2f7562a1aacaf2104d3ffb728d4 meta: - aperture: {name: MIRIM_FULL, position_angle: 224.54618942495836} + aperture: {name: MIRIM_FULL, position_angle: 116.78328104798904} coordinates: {reference_frame: ICRS} - date: '2022-07-29T03:35:16.299' + date: '2024-10-18T15:17:08.055' 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"Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2KM2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2KM3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_MINUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_MINUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS2P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS3P3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS46P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_MINUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_MINUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS2P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS3P3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS46P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FSW_ICE_INTTMP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_CMD_TSETPT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_COL_START", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_COL_STOP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_DET_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_FRM_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_HSG_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_I_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_INT_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P2V5P", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P3V3V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P5V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_PN7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_TS_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDDOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDETCOM", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNCOL", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNPWELLR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNPWELLT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNROW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNSTAT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VROWOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSLEW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSSOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSSUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CCC_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CCC_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_CCC_OP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_CCC_SEN", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_FW_OP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_FW_SEN", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_GW23_OP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CPGW14_SEN", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CPGW23_SEN", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_FW_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_FW_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_OP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW23_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW23_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IFU_AMB", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IFU_CRYO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IMG_AMB", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IMG_CRYO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_INTER_TEMP", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMP_AMB", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMP_CRYO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMR_AMB", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMR_CRYO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN1AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN2AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN3AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN4AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN5AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN6AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_SN7AVGTEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T1P_AMB", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T1P_CRYO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T2R_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T2R_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T3LW_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T3LW_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T4SW_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T4SW_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T5IMG_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T5IMG_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T6DECKAMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T6DECKCRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T7IOC_AMB", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T7IOC_CRYO", "Subsystem": "MIRI", "RawType": "FL64", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_TLM_STAT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_CMD_TSETPT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_COL_START", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_COL_STOP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_DET_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_FRM_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_HSG_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_I_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_INT_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": 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"SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VDETCOM", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNCOL", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNPWELLR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNPWELLT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNROW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNSTAT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": 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{"TlmMnemonic": "IGDP_MIR_SW_P3V3V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_P5V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_P7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_PN7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_TS_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VDDOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", 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{"TlmMnemonic": "IGDP_MIR_SW_V_VROWOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VSLEW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VSSOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VSSUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_FW_LW_ELEM", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_FW_LW_POS", "Subsystem": "NIRCAM", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_FW_SW_ELEM", "Subsystem": 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"AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_OBA_TS2", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_PIL", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_SWFPAH1", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_SWFPAH2", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FA_ACE1_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FA_ACE2_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FA_ACE3_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": 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{"TlmMnemonic": "IMIR_HK_GW14_POSSENPOS", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_CUR_POS", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_CUR_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_POS_OUT_V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_POS_RATIO", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_POS_VOLT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_GW23_POSSENPOS", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_IC_ANA_TEMP", 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"SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_CAL_CI_C", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_CAL_CI_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_CAL_CI_V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_DIAG_MODE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_DIAG_READOT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_INT_TEMP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_MTR_COIL1_C", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_MTR_COIL1_V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_MTR_COIL2_C", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_MTR_COIL2_V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_POM_HTR_C", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_POM_HTR_V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_PWR_STATE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_SEC_VOLT1", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE_SEC_VOLT2", 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{"TlmMnemonic": "IMIR_HK_ICE16_C_CF_I", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE16_C_CF_V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE16_C_CI_I", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE16_C_CI_V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE17_C_HTR_CL", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_ICE17_C_SEC_V_4", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_IFU_CAL_LOOP", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_HK_IFU_CAL_METHOD", 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{"TlmMnemonic": "IMIR_LW_CMD_ROW_STOP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_ROWON_FRAME", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_ROWON_MASK", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_ROWON_ROW", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_ROWUNDERLAP", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_RST_WIDTH", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_RSTOFF_DLY", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": 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"Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_TESTENABLE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_TESTSEL", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_CMD_WAKEDLYADC", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_CNT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_START_LSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_START_MSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_STATE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_STOP_LSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXP_STOP_MSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_EXPO_CTRL", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_FLUSH_STATE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_FRAME_START_LSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_FRAME_START_MSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_FRM_CNT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": 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"EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_HSG_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_HTR_MODE", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_HTRDAC", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_I_DET_HTR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_PID_CONTROL3", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_PID_ERROR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_PID_HTR_PWR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_PID_IGAIN", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_PID_PGAIN", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_TEMPSETPNT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_TS_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_V_DET_HTR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TC_VINDAC_DET", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_TP_STATE", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_N7V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_P2V5P", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_P3V3P", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_P5V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_P7V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VDDOUT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VDDUC", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VDETCOM", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VNCOL", 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{"TlmMnemonic": "IMIR_LW_V_VSLEW", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VSSOUT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VSSUC", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_MOTOR_HOLD_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_MOTOR_IFS_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_MOTOR_REL_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_MOTOR_SEL_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_MOTOR_STATE_STAT", "Subsystem": "MIRI", 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"UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_TEMPSETPNT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_TS_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_V_DET_HTR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_VINDAC_DET", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TP_STATE", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_N7V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_P2V5P", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_P3V3P", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_P5V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_P7V", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VDDOUT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VDDUC", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VDETCOM", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNCOL", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNPWELLR", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNPWELLT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNROW", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNSTAT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VROWOFF", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSLEW", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSSOUT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSSUC", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SWITCH_RLY_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_TEMPCHK_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_TIME_UPDATE_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_UNLOCK_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_VALID_CMD", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_WAVEFORM_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_WHEEL_TEST_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICHTRDAC", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICHTRTHRES", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICSUPPVOLT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICTEMP", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICVDDCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_CLKSRC", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_COARSETOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_COLCORNER_A", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "INIS_DWC_STATE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FINETOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_DSTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_DUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_MOV", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_MSTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_MUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_POS", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMC_RGOOD", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FMMTRCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPAHEATER_DAC", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPATEMP", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPATEMP_MAINT", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPATEMP_STABLE", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPATEMPSETPNT", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FPESTATE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_DUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_FILTER", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MDSTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MOV", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_RDBKCMD", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_RDBKPOS", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_STEPMOVE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_VARSCURR", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_POS", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_RGOOD", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_TOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_VR_FLAG", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWMTRCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWVRVOL", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ICE_MTR_RDBKPOS", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_ICESTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMEF1", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMEF2", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMES1", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMES2", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMEF1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMEF2", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMES1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMES2", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_POWF1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_POWF2", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_POWS1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_POWS2", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_NCOLS_A", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "INIS_NROWS_A", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_DUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MDSTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MOV", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MTR_RDBKCMD", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MTR_RDBKPOS", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MTR_STEPMOVE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MTR_VRSCURR", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_MUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_POS", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_PUPIL", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_RGOOD", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_TOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWC_VR_FLAG", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWMTRCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_PWVRVOL", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ROWCORNER_A", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_0", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_10", "Subsystem": "NIRISS", "RawType": "UI16", 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{"TlmMnemonic": "IFGS_ACQ_ERR", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_ESLOT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_FLATF", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_GS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_INUM", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_NCOLS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_NROWS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_OUTOFFOV", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_PSFXWIDTH", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_PSFYWIDTH", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_ROWCORNER", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_SIGNOISERATIO", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_SOFTXCOR", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_SOFTXSIZ", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_SOFTYCOR", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_SOFTYSIZ", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_STAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_WINDOWCHANGE", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_XPOSG", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ACQ_YPOSG", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTD_AMPLITUDE", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTD_CNTD", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTD_GS", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTD_ID", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTDGS_X", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_CTDGS_Y", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ACCTIME1", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ACCTIME2", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ASICHEATDAC", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ASICHTRTHRES", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ASICSUPPVOLT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ASICTEMP", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ASICVDDCURR", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_CLKSRC", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_COARSETOL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_DUNEXOFF", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FINETOL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FMC_DSTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FMC_MSTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FMMTRCURR", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FPASETPOINT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FPATEMP", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_FPESTATE", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_HEATER_DAC", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ICESTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_MOV", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_MUNEXOFF", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ONTIME1", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_ONTIME2", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_POS", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_PWR1", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_PWR2", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_RGOOD", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_TEMP_MAINT", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG1_TEMP_STBL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ACCTIME1", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ACCTIME2", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ASICHEATDAC", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ASICHTRTHRES", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ASICSUPPVOLT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ASICTEMP", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ASICVDDCURR", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_CLKSRC", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_COARSETOL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_DUNEXOFF", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FINETOL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FMC_DSTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FMC_MSTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FMMTRCURR", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FPASETPOINT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FPATEMP", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_FPESTATE", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_HEATER_DAC", "Subsystem": "FGS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ICESTAT", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_MOV", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_MUNEXOFF", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ONTIME1", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_ONTIME2", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_POS", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_PWR1", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_PWR2", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_RGOOD", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_TEMP_MAINT", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ENGG2_TEMP_STBL", "Subsystem": "FGS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G1_COLCORNER_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G1_NCOLS_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G1_NROWS_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G1_ROWCORNER_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G2_COLCORNER_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G2_NCOLS_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G2_NROWS_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_G2_ROWCORNER_A", "Subsystem": "FGS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_GUIDERSTATE1", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_GUIDERSTATE2", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_ALGM", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_CAND", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE10", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE4", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE6", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_COUNTRATE8", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": 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"AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_GS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_IPS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_NCOLS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_NROWS", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_REF", "Subsystem": "FGS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_STARID1", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_STARID10", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IFGS_ID_STARID2", "Subsystem": "FGS", "RawType": "UI32", "EUType": "UI32", 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{"TlmMnemonic": "IGDP_MIR_SW_PN7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_TS_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VDDOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VDETCOM", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VNCOL", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_V_VNPWELLR", "Subsystem": "MIRI", 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"EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_T_OBA_TS1", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_T_OBA_TS2", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_T_PIL", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_T_SWFPAH1", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_A_T_SWFPAH2", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_FW_LW_ELEM", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_FW_LW_POS", "Subsystem": "NIRCAM", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_FW_SW_ELEM", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_FW_SW_POS", "Subsystem": "NIRCAM", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_PIL_POS", "Subsystem": "NIRCAM", "RawType": "SI16", "EUType": "SI16", "SQLType": "SMALLINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_PW_LW_ELEM", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_PW_LW_POS", "Subsystem": "NIRCAM", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_PW_SW_ELEM", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_PW_SW_POS", "Subsystem": "NIRCAM", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_COM", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_FWA_LW_FW", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_FWA_LW_PW", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_FWA_SW_FW", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_FWA_SW_PW", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_ICE_DIO_EL", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_ICE_MOT_EL", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_B_T_ICE_POS_EL", 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"AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_ACE1_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_ACE2_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_ACE3_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_ACE4_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_ACE5_SCTEMP", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC1_LW_SEN", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC1_LWMV_T", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC1_SW_SEN", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC1_SWMV_T", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC2_LW_SEN", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC2_LWMV_T", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC2_SW_SEN", "Subsystem": "NIRCAM", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRC_FB_TMC2_SWMV_T", "Subsystem": "NIRCAM", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_NRSI_RMA_MICRON", "Subsystem": "NIRSPEC", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": 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"AllPoints": 1}, {"TlmMnemonic": "IMIR_TIME_UPDATE_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_UNLOCK_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_VALID_CMD", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_WAVEFORM_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_WHEEL_TEST_STAT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICHTRDAC", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICHTRTHRES", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICSUPPVOLT", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICTEMP", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ASICVDDCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_CLKSRC", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_COARSETOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_COLCORNER_A", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "INIS_DWC_STATE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FINETOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": 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"EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_FILTER", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MDSTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MOV", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_RDBKCMD", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_RDBKPOS", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_STEPMOVE", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MTR_VARSCURR", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_MUNEXOFF", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_POS", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_RGOOD", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_TOL", "Subsystem": "NIRISS", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWC_VR_FLAG", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWMTRCURR", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_FWVRVOL", "Subsystem": "NIRISS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "INIS_ICE_MTR_RDBKPOS", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_ICESTAT", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMEF1", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMEF2", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMES1", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ACCTIMES2", "Subsystem": "NIRISS", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMEF1", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_LAMP_ONTIMEF2", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": 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{"TlmMnemonic": "INIS_SCE_HKTLM_1_20", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_21", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_22", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_23", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_24", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_25", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_26", "Subsystem": "NIRISS", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "INIS_SCE_HKTLM_1_27", "Subsystem": "NIRISS", 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"EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG1_TSI", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG1KM1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG1KM2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG1KM3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_FHC1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_FHC2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_HOUSE", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_PCU1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_PCU2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_SCE1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_SCE2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_SHOE", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_SPW1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_SPW2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_TMA", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2_TSI", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2KM1", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2KM2", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_TMPG2KM3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_MINUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_MINUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS2P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS3P3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS46P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG1_PLUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_MINUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_MINUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS12", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS2P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS3P3", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS46P5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS5", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IFGS_VOLTG2_PLUS7", "Subsystem": "FGS", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM1", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM10", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM2", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM21", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM22", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM23", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM24", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM25", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM26", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM27", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM28", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM29", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM3", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM30", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM4", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM5", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM6", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM7", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM8", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FPE_PARM9", "Subsystem": "MIRI", "RawType": "CS", "EUType": "CS", "SQLType": "VARCHAR(359)", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_FSW_ICE_INTTMP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_CMD_TSETPT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_COL_START", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_COL_STOP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_DET_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_FRM_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_HSG_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_I_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_INT_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P2V5P", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P3V3V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P5V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_P7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_PN7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_TS_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDDOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VDETCOM", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNCOL", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNPWELLR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNPWELLT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNROW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VNSTAT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VROWOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSLEW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSSOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_IC_V_VSSUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CCC_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CCC_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_CCC_OP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_CCC_SEN", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_FW_OP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_FW_SEN", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CP_GW23_OP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CPGW14_SEN", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_CPGW23_SEN", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_FW_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_FW_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW14_OP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW23_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_GW23_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IFU_AMB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IFU_CRYO", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IMG_AMB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_IMG_CRYO", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_INTER_TEMP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_PARM49", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_PARM50", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMP_AMB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMP_CRYO", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMR_AMB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_POMR_CRYO", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": 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"RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T5IMG_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T5IMG_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T6DECKAMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T6DECKCRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T7IOC_AMB", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_T7IOC_CRYO", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_ICE_TLM_STAT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": 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{"TlmMnemonic": "IGDP_MIR_LW_INT_CNT", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_P2V5P", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_P3V3V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_P5V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_P7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_PN7V", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_TS_TEMP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_DETHTR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VDDOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VDDUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VDETCOM", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNCOL", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNPWELLR", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNPWELLT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNROW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VNSTAT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VP", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VROWOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VSLEW", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VSSOUT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_LW_V_VSSUC", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE_SIM_EXP_ID", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE_SIM_INT_ID", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE_SIM_OBS_ID", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE1_PARM26", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE1_PARM39", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE1_PARM40", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE1_PARM41", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE1_PARM42", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": 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"EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM42", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM43", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM44", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM45", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM46", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM47", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM48", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE2_PARM49", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM0", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM1", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM2", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM26", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM3", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM39", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM40", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM41", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM42", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM43", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM44", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM45", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM46", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM47", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM48", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SCE3_PARM49", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SIM_EXP_FLAG", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SIM_FFC_FLAG", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_CMD_TSETPT", "Subsystem": "MIRI", "RawType": "FL32", "EUType": "FL32", "SQLType": "REAL", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_COL_START", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IGDP_MIR_SW_COL_STOP", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": 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"AllPoints": 1}, {"TlmMnemonic": "IMIR_LW_V_VSSUC", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_PDU_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SPW_ACTIVE_CLOCK", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SPW_ASSY", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SPW_CMD_CLOCK", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SPW_TIME_MILLISEC", "Subsystem": "MIRI", "RawType": "UI32", "EUType": "UI32", "SQLType": "BIGINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_ACTIVE_DET_MODE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_ADC_DELAY", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_ADD_RST", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_BURST_MODE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_COL_START", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_DET_MODE", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_FRAME_CNT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_FRAME_RST", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_INT_CNT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": 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{"TlmMnemonic": "IMIR_SW_CMD_SKIPCOLAFTR", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_SKIPCOLBFR", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_SKIPCOLDRNG", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_CMD_TESTSEL", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_EXP_CNT", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "UI8", "SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_EXP_START_LSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_EXP_START_MSB", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_EXP_STATE", 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"SQLType": "TINYINT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_RESET", "Subsystem": "MIRI", "RawType": "UI8", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_SCE_ANA_TEMP1", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_SCE_DIG_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "FL64", "SQLType": "FLOAT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_DET_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_HSG_TEMP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_HTR_MODE", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "DS", "SQLType": "VARCHAR(50)", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_TC_TEMPSETPNT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": 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"SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNROW", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VNSTAT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VP", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VROWOFF", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VRSTOFF", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSLEW", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSSOUT", "Subsystem": "MIRI", "RawType": "UI16", "EUType": "UI16", "SQLType": "INT", "AllPoints": 1}, {"TlmMnemonic": "IMIR_SW_V_VSSUC", "Subsystem": "MIRI", "RawType": 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"EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zaducmdy_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zaducmdy_data.json deleted file mode 100644 index 9c73ab942f..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zaducmdy_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZADUCMDY", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zattest1_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zattest1_data.json deleted file mode 100644 index 76319e508f..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zattest1_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZATTEST1", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": -0.3691528614} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zattest2_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zattest2_data.json deleted file mode 100644 index 9dc5dafd56..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zattest2_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZATTEST2", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.3376328232} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zattest3_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zattest3_data.json deleted file mode 100644 index 80ab143e6c..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zattest3_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZATTEST3", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.05758532642} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zattest4_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zattest4_data.json deleted file mode 100644 index 2f67da4a94..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zattest4_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZATTEST4", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.8639526444} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zfgdetid_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zfgdetid_data.json deleted file mode 100644 index 04f4c6c998..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zfgdetid_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZFGDETID", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 1} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdx_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdx_data.json deleted file mode 100644 index 4ab32e13b7..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdx_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZFGGSCMDX", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdy_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdy_data.json deleted file mode 100644 index 594e60d5e8..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zfggscmdy_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZFGGSCMDY", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zfggsposx_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zfggsposx_data.json deleted file mode 100644 index f7feff1974..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zfggsposx_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZFGGSPOSX", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zfggsposy_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zfggsposy_data.json deleted file mode 100644 index 01f0bf3760..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zfggsposy_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZFGGSPOSY", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.0} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j11_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j11_data.json deleted file mode 100644 index b14ea3d184..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j11_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J11", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": -0.001004440003} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j12_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j12_data.json deleted file mode 100644 index cdbb01c37c..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j12_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J12", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.9999994955} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j13_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j13_data.json deleted file mode 100644 index d2dec2fe04..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j13_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J13", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 3.396491461e-06} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j21_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j21_data.json deleted file mode 100644 index 0ee8e2246d..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j21_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J21", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.003381458357} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j22_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j22_data.json deleted file mode 100644 index 11d792555a..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j22_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J22", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": -3.9e-14} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j23_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j23_data.json deleted file mode 100644 index 05aef3a640..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j23_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J23", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.9999942829} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j31_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j31_data.json deleted file mode 100644 index fbc0ebeb36..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j31_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J31", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.9999937784} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j32_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j32_data.json deleted file mode 100644 index 82617c4ed0..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j32_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": "SA_ZRFGS2J32", - "AllPoints": 1, - "Count": 2, - "Data": [ - {"ObsTime": "/Date(1388577600000)/", "EUValue": 0.0}, - {"ObsTime": "/Date(1388750400000)/", "EUValue": 0.001004445746} - ] -} diff --git a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j33_data.json b/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j33_data.json deleted file mode 100644 index eac1e75847..0000000000 --- a/jwst/lib/tests/data/engdb_ngas/sa_zrfgs2j33_data.json +++ /dev/null @@ -1,9 +0,0 @@ -{ - "TlmMnemonic": 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E09;Fo#Q*>R diff --git a/jwst/lib/tests/engdb_mock.py b/jwst/lib/tests/engdb_mock.py deleted file mode 100644 index 1ca140ffdc..0000000000 --- a/jwst/lib/tests/engdb_mock.py +++ /dev/null @@ -1,329 +0,0 @@ -""" -Tools to mock the JWST Engineering Database -""" -from copy import copy -from functools import lru_cache -import json -import os -import re -import requests_mock - -from astropy.time import Time - -from jwst.lib import engdb_direct -from jwst.lib.engdb_lib import mnemonic_data_fname - -__all__ = [ - 'ENGDB_PATH', - 'cache_engdb' -] - -# Setup for local testing cache -ENGDB_PATH = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - 'data', - 'engdb' -) -MNEMONICS_TO_CACHE = [ - 'IFGS_ACQ_DETXCOR', - 'IFGS_ACQ_DETYCOR', - 'IFGS_ACQ_DETXSIZ', - 'IFGS_ACQ_DETYSIZ', - 'IFGS_ACQ_XPOSG', - 'IFGS_ACQ_YPOSG', - 'IFGS_CTDGS_X', - 'IFGS_CTDGS_Y', - 'IFGS_ID_XPOSG', - 'IFGS_ID_YPOSG', - 'IFGS_ID_DETXCOR', - 'IFGS_ID_DETYCOR', - 'IFGS_ID_DETXSIZ', - 'IFGS_ID_DETYSIZ', - 'IFGS_TFGGS_X', - 'IFGS_TFGGS_Y', - 'IFGS_TFGDET_XCOR', - 'IFGS_TFGDET_YCOR', - 'IFGS_TFGDET_XSIZ', - 'IFGS_TFGDET_YSIZ', - 'INRSI_GWA_Y_TILT_AVGED', - 'SA_ZADUCMDX', - 'SA_ZADUCMDY', - 'SA_ZATTEST1', - 'SA_ZATTEST2', - 'SA_ZATTEST3', - 'SA_ZATTEST4', - 'SA_ZFGDETID', - 'SA_ZFGGSCMDX', - 'SA_ZFGGSCMDY', - 'SA_ZFGGSPOSX', - 'SA_ZFGGSPOSY', - 'SA_ZRFGS2J11', - 'SA_ZRFGS2J21', - 'SA_ZRFGS2J31', - 'SA_ZRFGS2J12', - 'SA_ZRFGS2J22', - 'SA_ZRFGS2J32', - 'SA_ZRFGS2J13', - 'SA_ZRFGS2J23', - 'SA_ZRFGS2J33', -] - -# Path templates -META = 'meta.json' -DATA = '_data.json' - - -class EngDB_Mocker(requests_mock.Mocker): - """ - Setup a mock of the JWST Engineering DB. - """ - - def __init__(self, *args, real_http=True, **kwargs): - db_path = kwargs.pop('db_path', ENGDB_PATH) - super(EngDB_Mocker, self).__init__(*args, real_http=real_http, **kwargs) - - # Setup the local engineering cache - self.cache = EngDB_Local(db_path) - - # Setup from meta query - meta_query = re.compile(''.join([ - engdb_direct.ENGDB_METADATA, - '.*' - ])) - self.get(meta_query, json=self.response_meta) - - # Setup to return a general data query - data_query = re.compile(''.join([ - engdb_direct.ENGDB_DATA, - r'.+\?sTime=.+\&eTime=.+' - ])) - self.get(data_query, json=self.response_data) - - def response_meta(self, request, context): - """ - Respond with the meta data - - Parameters - ---------- - request, context: - request-mock parameters - - Returns - ------- - response: json - The expected JSON response - - Modifies - -------- - context - `Update status_code` with the HTTP expected status - """ - - # Get the mnemonic substring. - mnemonic = os.path.basename(request.path) - results = self.cache.fetch_meta(mnemonic) - context.status_code = 200 - return results - - def response_data(self, request, context): - """ - Respond with the mnemonic data - - Parameters - ---------- - request, context: - request-mock parameters - - Returns - ------- - response: json - The expected JSON response - - Modifies - -------- - context - `Update status_code` with the HTTP expected status - """ - mnemonic = os.path.basename(request.path) - data = self.cache.fetch_data( - mnemonic, - request.qs['stime'][0], - request.qs['etime'][0] - ) - context.status_code = 200 - return data - - -class EngDB_Local(): - """ - Fetch engineering data from the local cache - """ - - def __init__(self, db_path=''): - self.db_path = db_path - self.json_load.cache_clear() - - def fetch_data(self, mnemonic, starttime, endtime): - """ - Get data for a mnemonic. - - Parameters - mnemonic:z str - The engineering mnemonic to retrieve - - starttime: str or astropy.time.Time - The, inclusive, start time to retrieve from. - - endtime: str or astropy.time.Time - The, inclusive, end time to retrieve from. - - Returns - ------- - mnemonic_data: dict - The returned structure - """ - db_data = self.json_load(mnemonic) - - # Find the range of data - stime = Time(starttime, format='iso') - etime = Time(endtime, format='iso') - stime_mil = int(stime.unix * 1000) - etime_mil = int(etime.unix * 1000) - - data = db_data['Data'] - start_idx = 0 - try: - while engdb_direct.extract_db_time(data[start_idx]['ObsTime']) < stime_mil: - start_idx += 1 - except IndexError: - pass - end_idx = start_idx - try: - while engdb_direct.extract_db_time(data[end_idx]['ObsTime']) <= etime_mil: - end_idx += 1 - except IndexError: - end_idx -= 1 - - # Real database always returns a bracketed result. - start_idx = max(0, start_idx - 1) - end_idx = min(len(data), end_idx + 1) - - # Construct the resultant data - data_return = [ - data[idx] - for idx in range(start_idx, end_idx) - ] - - # Construct the return structure - result = copy(db_data) - result['ReqSTime'] = '/Date({:013d}+0000)/'.format(stime_mil) - result['ReqETime'] = '/Date({:013d}+0000)/'.format(etime_mil) - result['Count'] = len(data_return) - result['Data'] = data_return - - return result - - def fetch_meta(self, mnemonic_substr=''): - """ - Get the meta for the match to the mnemonic - - Parameters - ---------- - mnemonic_substr: str - The substring to match in all available mnemonics - - Returns - ------- - The Meta structure which matches the live engineering - RESTful interface - """ - with open( - os.path.join( - self.db_path, - META - ), - 'r' - ) as fp: - meta = json.load(fp) - - tlmmnemonics = meta['TlmMnemonics'] - - # Now look for only the requested mnemonic substring - mnemonic_substr = mnemonic_substr.lower() - mnemonics = [ - tlmmnemonics[idx] - for idx, mnemonic in enumerate(tlmmnemonics) - if mnemonic_substr in mnemonic['TlmMnemonic'].lower() - ] - - # Construct the return structure - result = copy(meta) - result['Count'] = len(mnemonics) - result['TlmMnemonics'] = mnemonics - - return result - - @lru_cache(maxsize=128) - def json_load(self, mnemonic): - with open( - os.path.join( - self.db_path, - mnemonic_data_fname(mnemonic)), - 'r') as fp: - db_data = json.load(fp) - return db_data - - -# ######### -# Utilities -# ######### -def cache_engdb( - mnemonics=MNEMONICS_TO_CACHE, - starttime='2016-01-18T15:40:00', - endtime='2016-01-18T15:40:05', - db_path=ENGDB_PATH -): - """Create a local cache of the database - - Parameters - ---------- - mnemonics: iterable - List of mnemonics to retrieve - - starttime: str or astropy.time.Time - The, inclusive, start time to retrieve from. - - endtime: str or astropy.time.Time - The, inclusive, end time to retrieve from. - - path: str - Path of the cache directory. - - Notes - ----- - Suggested shell command is - `python -c 'from jwst.lib.tests.engdb_mock import cache_engdb; cache_engdb()'` - """ - if not os.path.exists(db_path): - os.mkdir(db_path) - - edb = engdb_direct.EngdbDirect() - - # Get the meta info for all mnemonics regardless. - meta = edb.get_meta() - with open(os.path.join(db_path, META), 'w') as fp: - json.dump(meta, fp) - - for mnemonic in mnemonics: - records = edb._get_records(mnemonic, starttime, endtime) - - # Remove the request times. These are filled back in - # during retrieval. - del records['ReqSTime'] - del records['ReqETime'] - - with open( - os.path.join(db_path, mnemonic_data_fname(mnemonic)), - 'w' - ) as fp: - json.dump(records, fp) diff --git a/jwst/lib/tests/test_engdb_mast.py b/jwst/lib/tests/test_engdb_mast.py index a7b88a4d8f..faa6d242b8 100644 --- a/jwst/lib/tests/test_engdb_mast.py +++ b/jwst/lib/tests/test_engdb_mast.py @@ -1,5 +1,4 @@ """Test the MAST Engineering interface""" -import os import pytest import requests @@ -90,14 +89,3 @@ def test_negative_aliveness(): """Ensure failure occurs with a bad url""" with pytest.raises(RuntimeError): engdb_mast.EngdbMast(base_url='https://127.0.0.1/_engdb_mast_test', token='dummytoken') - - -def test_notoken(jail_environ): - """Check that failure occurs without a token""" - try: - del os.environ['MAST_API_TOKEN'] - except KeyError: - pass - - with pytest.raises(RuntimeError): - engdb_mast.EngdbMast() diff --git a/jwst/lib/tests/test_engdb_mock.py b/jwst/lib/tests/test_engdb_mock.py deleted file mode 100644 index fe0e20bacb..0000000000 --- a/jwst/lib/tests/test_engdb_mock.py +++ /dev/null @@ -1,243 +0,0 @@ -""" -Tests of engdb_mock -""" -import os -import pytest -import requests -from tempfile import TemporaryDirectory -import warnings - -from astropy.time import Time - -from jwst.lib.tests import engdb_mock -from jwst.lib import engdb_direct, engdb_tools - -# For mocking, just use localhost -ENGDB_MOCK_URL = 'http://localhost/' - -# Midpoint is about 2021-01-26T02:32:26.205 -GOOD_STARTTIME = '2021-01-26 02:29:02.188' -GOOD_ENDTIME = '2021-01-26 02:35:50.185' -GOOD_MNEMONIC = 'inrsi_gwa_y_tilt_avged' -EARLY_STARTTIME = '2014-01-01' -EARLY_ENDTIME = '2014-01-02' -LATE_STARTTIME = '2034-01-01' -LATE_ENDTIME = '2034-01-02' - - -def test_file_names(): - """ - Test the file names are constructed - """ - fname = engdb_mock.mnemonic_data_fname(GOOD_MNEMONIC) - assert fname == GOOD_MNEMONIC.lower() + engdb_mock.DATA - - -def test_cache_engdb(engdb): - """ - Test cache creation - - Notes - ----- - The engdb fixture is called since the cache routine requires - the live engineering db RESTful service. - """ - with TemporaryDirectory() as db_path: - engdb_mock.cache_engdb( - mnemonics=[GOOD_MNEMONIC], - starttime=GOOD_STARTTIME, - endtime=GOOD_ENDTIME, - db_path=db_path - ) - - assert os.path.isfile(os.path.join( - db_path, engdb_mock.META - )) - assert os.path.isfile(os.path.join( - db_path, - engdb_mock.mnemonic_data_fname(GOOD_MNEMONIC) - )) - - -@pytest.mark.parametrize( - "mnemonic", - [ - GOOD_MNEMONIC, - 'CAL', - '' - ] -) -def test_cache_meta(db_cache, engdb, mnemonic): - """ - Test read of the meta information - """ - meta = db_cache.fetch_meta(mnemonic) - live_meta = engdb.get_meta(mnemonic) - assert meta == live_meta - - -def test_cache_data(db_cache, engdb): - """ - Test read of the data information - """ - data = db_cache.fetch_data(GOOD_MNEMONIC, GOOD_STARTTIME, GOOD_ENDTIME) - live_data = engdb._get_records(GOOD_MNEMONIC, GOOD_STARTTIME, GOOD_ENDTIME) - assert data == live_data - - -@pytest.mark.xfail(reason="may have been a change in EngDB coverage") -def test_cache_partial_data(db_cache, engdb): - """ - Test read of some data. - """ - data = db_cache.fetch_data(GOOD_MNEMONIC, GOOD_STARTTIME, GOOD_ENDTIME) - assert data['Count'] > 4 # Just to make sure we have some data - endtime = Time( - engdb_direct.extract_db_time(data['Data'][1]['ObsTime']) / 1000., - format='unix' - ) - - data_short = db_cache.fetch_data( - GOOD_MNEMONIC, - GOOD_STARTTIME, - endtime.iso - ) - live_data_short = engdb._get_records( - GOOD_MNEMONIC, - GOOD_STARTTIME, - endtime.iso - ) - assert data_short == live_data_short - - -@pytest.mark.xfail(reason="may have been a change in EngDB coverage") -def test_cache_end_data(db_cache, engdb): - """ - Test read of some data. - """ - data = db_cache.fetch_data(GOOD_MNEMONIC, GOOD_STARTTIME, GOOD_ENDTIME) - assert data['Count'] > 4 # Just to make sure we have some data - - # First test pre-data. - data_short = db_cache.fetch_data( - GOOD_MNEMONIC, - EARLY_STARTTIME, - EARLY_ENDTIME - ) - live_data_short = engdb._get_records( - GOOD_MNEMONIC, - EARLY_STARTTIME, - EARLY_ENDTIME - ) - # We only check counts because the actual data may be different. - assert data_short['Count'] == live_data_short['Count'] - - # Filter ERFA warnings for times far into the future, LATE_STARTTIME and - # LATE_ENDTIME. We don't know leap seconds between now and 2034. - warnings.filterwarnings('ignore', message='ERFA function ') - - # Now for post data - data_short = db_cache.fetch_data( - GOOD_MNEMONIC, - LATE_STARTTIME, - LATE_ENDTIME - ) - live_data_short = engdb._get_records( - GOOD_MNEMONIC, - LATE_STARTTIME, - LATE_ENDTIME - ) - # We only check counts because the actual data may be different. - assert data_short['Count'] == live_data_short['Count'] - - -def test_mocker_alive(db_cache): - with engdb_mock.EngDB_Mocker(db_path=db_cache.db_path): - query = ''.join([ - ENGDB_MOCK_URL, - engdb_direct.ENGDB_METADATA - ]) - response = requests.get(query) - assert response.status_code == 200 - - -@pytest.mark.parametrize( - 'mnemonic, count', - [ - (GOOD_MNEMONIC, 1), - ('CAL', 14), - ('', 2100), - ] -) -def test_mocker_meta(db_cache, mnemonic, count): - with engdb_mock.EngDB_Mocker(db_path=db_cache.db_path): - query = ''.join([ - ENGDB_MOCK_URL, - engdb_direct.ENGDB_METADATA, - mnemonic - ]) - response = requests.get(query) - assert response.status_code == 200 - meta = response.json() - assert meta['Count'] >= count - - -def test_mocker_data(db_cache, engdb): - with engdb_mock.EngDB_Mocker(db_path=db_cache.db_path): - query = ''.join([ - ENGDB_MOCK_URL, - engdb_direct.ENGDB_DATA, - GOOD_MNEMONIC, - '?sTime=', - GOOD_STARTTIME, - '&eTime=', - GOOD_ENDTIME - ]) - response = requests.get(query) - - live_data = engdb._get_records(GOOD_MNEMONIC, GOOD_STARTTIME, GOOD_ENDTIME) - assert response.json() == live_data - - -# ######### -# Utilities -# ######### -@pytest.fixture -def engdb(jail_environ): - """ - Ensure the live engineering RESTful service is available - """ - try: - del os.environ['MAST_API_TOKEN'] - except KeyError: - pass - try: - engdb = engdb_tools.ENGDB_Service() - except Exception as exception: - pytest.skip(f'ENGDB service is not accessible: {exception}') - else: - return engdb - - -@pytest.fixture -def db_path(): - """ - Provide a local cache directory - """ - with TemporaryDirectory() as db_path: - yield db_path - - -@pytest.fixture -def db_cache(engdb, db_path): - """ - Provide a local cache - """ - engdb_mock.cache_engdb( - mnemonics=[GOOD_MNEMONIC], - starttime=GOOD_STARTTIME, - endtime=GOOD_ENDTIME, - db_path=db_path - ) - - return engdb_mock.EngDB_Local(db_path=db_path) diff --git a/jwst/lib/tests/test_engdb_tools.py b/jwst/lib/tests/test_engdb_tools.py index a28b3fed47..cecca4efc5 100644 --- a/jwst/lib/tests/test_engdb_tools.py +++ b/jwst/lib/tests/test_engdb_tools.py @@ -15,22 +15,18 @@ from astropy.time import Time from jwst.lib import engdb_direct, engdb_tools -from jwst.lib.tests.engdb_mock import EngDB_Mocker GOOD_MNEMONIC = 'INRSI_GWA_Y_TILT_AVGED' -GOOD_STARTTIME = '2021-01-25' -GOOD_ENDTIME = '2021-01-26' +GOOD_STARTTIME = '2022-01-25 23:29:02.188' +GOOD_ENDTIME = '2022-01-26' -SHORT_STARTTIME = '2021-01-26 02:29:02.188' +SHORT_STARTTIME = '2022-01-26 02:29:02.188' BAD_SERVER = 'https://www.stsci.edu' BAD_MNEMONIC = 'No_Such_MNEMONIC' NODATA_STARTIME = '2014-01-01' NODATA_ENDTIME = '2014-01-02' -ALTERNATE_HOST = 'https://twjwdmsemwebag.stsci.edu' -ALTERNATE_URL = ALTERNATE_HOST + '/JWDMSEngFqAccSide2/TlmMnemonicDataSrv.svc/' - def is_alive(url): """Check if a url is alive @@ -57,22 +53,7 @@ def is_alive(url): @pytest.fixture def engdb(): """Setup the service to operate through the mock service""" - with EngDB_Mocker(): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb - - -@pytest.mark.skipif( - not is_alive(ALTERNATE_HOST), - reason='Alternate test host not available.' -) -def test_environmental(jail_environ): - os.environ['ENG_BASE_URL'] = ALTERNATE_URL - try: - engdb = engdb_tools.ENGDB_Service() - except Exception: - pytest.skip('Alternate engineering db not available for test.') - assert engdb.base_url == ALTERNATE_URL + yield engdb_tools.ENGDB_Service() def test_environmental_bad(jail_environ): @@ -111,11 +92,11 @@ def test_values(engdb): records = engdb._get_records( GOOD_MNEMONIC, SHORT_STARTTIME, SHORT_STARTTIME ) - assert records['Count'] == 2 + assert len(records) == 2 values = engdb.get_values( GOOD_MNEMONIC, GOOD_STARTTIME, SHORT_STARTTIME ) - assert len(values) == 1 + assert len(values) == 10547 assert values[0] == 0 @@ -123,7 +104,7 @@ def test_values_with_bracket(engdb): records = engdb._get_records( GOOD_MNEMONIC, SHORT_STARTTIME, SHORT_STARTTIME ) - assert records['Count'] == 2 + assert len(records) == 2 values = engdb.get_values( GOOD_MNEMONIC, SHORT_STARTTIME, SHORT_STARTTIME, include_bracket_values=True @@ -149,7 +130,10 @@ def test_novalues(engdb): def test_meta(engdb): - response = engdb.get_meta(GOOD_MNEMONIC) + try: + response = engdb.get_meta(GOOD_MNEMONIC) + except NotImplementedError: + pytest.skip('Test only valid with Direct EngDB connection.') assert response['Count'] == 1 assert response['TlmMnemonics'][0]['TlmMnemonic'] == GOOD_MNEMONIC diff --git a/jwst/lib/tests/test_pointing_summary.py b/jwst/lib/tests/test_pointing_summary.py index b7665e1292..0561cf86ae 100644 --- a/jwst/lib/tests/test_pointing_summary.py +++ b/jwst/lib/tests/test_pointing_summary.py @@ -12,7 +12,6 @@ from jwst.lib import engdb_tools import jwst.lib.pointing_summary as ps -from jwst.lib.tests.engdb_mock import EngDB_Mocker DATA_PATH = Path(__file__).parent / 'data' @@ -24,8 +23,7 @@ @pytest.fixture def engdb(): """Setup the service to operate through the mock service""" - with EngDB_Mocker(): - yield engdb_tools.ENGDB_Service(base_url='http://localhost') + yield engdb_tools.ENGDB_Service() @pytest.fixture(scope='module') diff --git a/jwst/lib/tests/test_set_telescope_pointing.py b/jwst/lib/tests/test_set_telescope_pointing.py index cdc8228b58..da562a8a41 100644 --- a/jwst/lib/tests/test_set_telescope_pointing.py +++ b/jwst/lib/tests/test_set_telescope_pointing.py @@ -18,10 +18,8 @@ from stdatamodels.jwst import datamodels # noqa: E402 from jwst.lib import engdb_mast # noqa: E402 -from jwst.lib import engdb_tools # noqa: E402 from jwst.lib import set_telescope_pointing as stp # noqa: E402 from jwst.lib import siafdb # noqa: E402 -from jwst.lib.tests.engdb_mock import EngDB_Mocker # noqa: E402 from jwst.tests.helpers import word_precision_check # noqa: E402 # Ensure that `set_telescope_pointing` logs. @@ -29,8 +27,8 @@ stp.logger.addHandler(logging.StreamHandler()) # Setup mock engineering service -STARTTIME = Time('2014-01-03') -ENDTIME = Time('2014-01-04') +STARTTIME = Time('2022-06-03T17:25:40', format='isot') +ENDTIME = Time('2022-06-03T17:25:56', format='isot') ZEROTIME_START = Time('2014-01-01') ZEROTIME_END = Time('2014-01-02') @@ -40,21 +38,21 @@ # Get the mock databases DATA_PATH = Path(__file__).parent / 'data' -db_ngas_path = DATA_PATH / 'engdb_ngas' -db_jw703_path = DATA_PATH / 'engdb_jw00703' -siaf_path = DATA_PATH / 'xml_data_siafxml' / 'SIAFXML' -# Some expected values -Q_EXPECTED = np.asarray( - [-0.36915286, 0.33763282, 0.05758533, 0.86395264] -) -J2FGS_MATRIX_EXPECTED = np.asarray( - [-1.00444000e-03, 9.99999496e-01, 3.39649146e-06, - 3.38145836e-03, -3.90000000e-14, 9.99994283e-01, - 9.99993778e-01, 1.00444575e-03, -3.38145665e-03] -) -FSMCORR_EXPECTED = np.zeros((2,)) -OBSTIME_EXPECTED = STARTTIME +Q_EXPECTED = np.array([0.37671179, 0.70705936, -0.57895271, 0.15155541]) +J2FGS_EXPECTED = np.array([ + -4.93963971e-05, 4.25990115e-03, 9.99990925e-01, 9.99745568e-01, + -2.25561320e-02, 1.45472042e-04, 2.25565470e-02, 9.99736502e-01, + -4.25770310e-03]) + +J2FGS_MATRIX_EXPECTED = np.array([ + -4.89526137e-05, 4.25977338e-03, 9.99990926e-01, 9.99745568e-01, + -2.25561320e-02, 1.45025485e-04, 2.25565451e-02, 9.99736503e-01, + -4.25758537e-03]) + +FSMCORR_0_EXPECTED = np.array([-0.00188433, -0.21604178]) +FSMCORR_EXPECTED = np.array([0.03592864, -0.18001011]) +OBSTIME_EXPECTED = Time(1654277147.967113, format='unix') # Meta attributes for test comparisons METAS_EQUALITY = ['meta.visit.engdb_pointing_quality', @@ -132,12 +130,12 @@ def test_allow_any_file(file_case, allow_any_file): warnings.filterwarnings("ignore", "model_type not found") if not allow and not allow_any_file: with pytest.raises(TypeError): - stp.add_wcs(path, siaf_path=siaf_path, allow_any_file=allow_any_file, dry_run=True) + stp.add_wcs(path, allow_any_file=allow_any_file, dry_run=True) else: # Expected error when trying to actually add the wcs. # The provided files do not have sufficient info to do the calculations. with pytest.raises(AttributeError): - stp.add_wcs(path, siaf_path=siaf_path, allow_any_file=allow_any_file, dry_run=True) + stp.add_wcs(path, allow_any_file=allow_any_file, dry_run=True) @pytest.mark.parametrize( @@ -241,8 +239,8 @@ def test_change_engdb_url(): """ with pytest.raises(ValueError): stp.get_pointing( - STARTTIME.mjd, - ENDTIME.mjd, + Time('2015-06-15').mjd, + Time('2015-06-17').mjd, engdb_url=engdb_mast.MAST_BASE_URL ) @@ -257,27 +255,14 @@ def test_change_engdb_url_fail(): ) -def test_strict_pointing(data_file, eng_db_jw703): +def test_strict_pointing(data_file_strict): """Test failure on strict pointing""" with pytest.raises(ValueError): - stp.add_wcs(data_file, siaf_path=siaf_path, tolerance=0) + stp.add_wcs(data_file_strict, tolerance=0) -def test_pointing_averaging(eng_db_jw703): +def test_get_pointing(): """Ensure that the averaging works.""" - q_exp = np.array([0.62383733, 0.53552715, -0.49252283, 0.28541008]) - j2fgs_exp = np.array([ - -1.00962794e-03, 9.99999464e-01, 3.41404261e-06, - 3.38429719e-03, 2.85793453e-09, 9.99994300e-01, - 9.99993742e-01, 1.00963370e-03, -3.38429548e-03 - ]) - j2fgs_exp = np.array([ - -1.00962794e-03, 3.38429719e-03, 9.99993742e-01, - 9.99999464e-01, 2.85793453e-09, 1.00963370e-03, - 3.41404261e-06, 9.99994300e-01, -3.38429548e-03 - ]) - fsmcorr_exp = np.array([-0.02558673, -0.00200601]) - obstime_exp = Time(1559582740.4880004, format='unix') (q, j2fgs_matrix, @@ -286,15 +271,14 @@ def test_pointing_averaging(eng_db_jw703): gs_commanded, fgsid, gs_position) = stp.get_pointing( - Time('2019-06-03T17:25:40', format='isot').mjd, - Time('2019-06-03T17:25:56', format='isot').mjd, - engdb_url='http://localhost' + STARTTIME.mjd, + ENDTIME.mjd ) - assert np.allclose(q, q_exp) - assert np.allclose(j2fgs_matrix, j2fgs_exp) - assert np.allclose(fsmcorr, fsmcorr_exp) - assert np.isclose(obstime.value, obstime_exp.value) + assert np.allclose(q, Q_EXPECTED) + assert np.allclose(j2fgs_matrix, J2FGS_EXPECTED) + assert np.allclose(fsmcorr, FSMCORR_EXPECTED) + assert np.isclose(obstime.value, OBSTIME_EXPECTED.value) def test_get_pointing_fail(): @@ -306,74 +290,37 @@ def test_get_pointing_fail(): gs_commanded) = stp.get_pointing(47892.0, 48256.0) -def test_get_pointing(eng_db_ngas): - (q, - j2fgs_matrix, - fsmcorr, - obstime, - gs_commanded, - fgsid, - gs_position) = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd, engdb_url='http://localhost') - assert np.isclose(q, Q_EXPECTED).all() - assert np.isclose(j2fgs_matrix, J2FGS_MATRIX_EXPECTED).all() - assert np.isclose(fsmcorr, FSMCORR_EXPECTED).all() - assert STARTTIME <= obstime <= ENDTIME - - -def test_logging(eng_db_ngas, caplog): +def test_logging(caplog): (q, j2fgs_matrix, fsmcorr, obstime, gs_commanded, fgsid, - gs_position) = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd, engdb_url='http://localhost') + gs_position) = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd) assert 'Determining pointing between observations times' in caplog.text assert 'Telemetry search tolerance' in caplog.text assert 'Reduction function' in caplog.text assert 'Querying engineering DB' in caplog.text -def test_get_pointing_list(eng_db_ngas): - results = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd, reduce_func=stp.all_pointings, engdb_url='http://localhost') +def test_get_pointing_list(): + results = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd, reduce_func=stp.all_pointings) assert isinstance(results, list) assert len(results) > 0 assert np.isclose(results[0].q, Q_EXPECTED).all() assert np.isclose(results[0].j2fgs_matrix, J2FGS_MATRIX_EXPECTED).all() - assert np.isclose(results[0].fsmcorr, FSMCORR_EXPECTED).all() + assert np.isclose(results[0].fsmcorr, FSMCORR_0_EXPECTED).all() assert STARTTIME <= results[0].obstime <= ENDTIME -def test_get_pointing_with_zeros(eng_db_ngas): - (q, - j2fgs_matrix, - fsmcorr, - obstime, - gs_commanded, - fgsid, - gs_position) = stp.get_pointing(ZEROTIME_START.mjd, ENDTIME.mjd, - reduce_func=stp.first_pointing, - engdb_url='http://localhost') - assert j2fgs_matrix.any() - (q_desired, - j2fgs_matrix_desired, - fsmcorr_desired, - obstime, - gs_commanded, - fgsid, - gs_position) = stp.get_pointing(STARTTIME.mjd, ENDTIME.mjd, engdb_url='http://localhost') - assert np.array_equal(q, q_desired) - assert np.array_equal(j2fgs_matrix, j2fgs_matrix_desired) - assert np.array_equal(fsmcorr, fsmcorr_desired) - - def test_add_wcs_default(data_file, tmp_path): """Handle when no pointing exists and the default is used.""" expected_name = 'add_wcs_default.fits' try: stp.add_wcs( - data_file, siaf_path=siaf_path, tolerance=0, allow_default=True + data_file, tolerance=0, allow_default=True ) except ValueError: pass # This is what we want for the test. @@ -405,7 +352,7 @@ def test_add_wcs_default_fgsacq(tmp_path): expected_name = 'add_wcs_default_acq1.fits' try: stp.update_wcs( - model, siaf_path=siaf_path, tolerance=0, allow_default=True + model, tolerance=0, allow_default=True ) except ValueError: pass # This is what we want for the test. @@ -427,15 +374,15 @@ def test_add_wcs_default_nosiaf(data_file_nosiaf, caplog): """Handle when no pointing exists and the default is used and no SIAF specified.""" with pytest.raises(ValueError): stp.add_wcs( - data_file_nosiaf, siaf_path=siaf_path, tolerance=0, allow_default=True + data_file_nosiaf, tolerance=0, allow_default=True ) -def test_add_wcs_with_db(eng_db_ngas, data_file, tmp_path): +def test_add_wcs_with_db(data_file, tmp_path): """Test using the database""" expected_name = 'add_wcs_with_db.fits' - stp.add_wcs(data_file, siaf_path=siaf_path, engdb_url='http://localhost') + stp.add_wcs(data_file) # Tests with datamodels.Level1bModel(data_file) as model: @@ -466,7 +413,7 @@ def test_add_wcs_with_mast(data_file_fromsim, fgsid, tmp_path): # Execute the operation. try: - stp.add_wcs(data_file_fromsim, siaf_path=siaf_path, engdb_url=engdb_mast.MAST_BASE_URL, + stp.add_wcs(data_file_fromsim, engdb_url=engdb_mast.MAST_BASE_URL, fgsid=fgsid) except ValueError as exception: pytest.xfail(f'No telemetry exists. Update test to use existing telemetry. Exception: {exception}') @@ -487,12 +434,12 @@ def test_add_wcs_with_mast(data_file_fromsim, fgsid, tmp_path): assert word_precision_check(model.meta.wcsinfo.s_region, expected.meta.wcsinfo.s_region) -def test_add_wcs_method_full_nosiafdb(eng_db_ngas, data_file, tmp_path): +def test_add_wcs_method_full_nosiafdb(data_file, tmp_path): """Test using the database""" expected_name = 'add_wcs_method_full_nosiafdb.fits' # Calculate - stp.add_wcs(data_file, method=stp.Methods.OPS_TR_202111, engdb_url='http://localhost') + stp.add_wcs(data_file, method=stp.Methods.OPS_TR_202111) # Tests with datamodels.Level1bModel(data_file) as model: @@ -510,34 +457,7 @@ def test_add_wcs_method_full_nosiafdb(eng_db_ngas, data_file, tmp_path): assert word_precision_check(model.meta.wcsinfo.s_region, expected.meta.wcsinfo.s_region) -@pytest.mark.parametrize('source, prd', [(siaf_path, None), (None, 'prdopssoc-053')]) -def test_add_wcs_method_full_siafdb(eng_db_ngas, data_file, tmp_path, source, prd): - """Test using the database and a specified siaf db""" - expected_name = 'add_wcs_method_full_siafdb.fits' - - # Calculate - stp.add_wcs(data_file, siaf_path=siaf_path, prd=prd, method=stp.Methods.OPS_TR_202111, engdb_url='http://localhost') - - # Test - with datamodels.Level1bModel(data_file) as model: - - # Save for post-test comparison and update - model.save(tmp_path / expected_name) - - with datamodels.open(DATA_PATH / expected_name) as expected: - for meta in METAS_EQUALITY: - if isinstance(model[meta], str): - assert model[meta] == expected[meta] - else: - assert np.isclose(model[meta], expected[meta], atol=1e-13) - - for meta in METAS_ISCLOSE: - assert np.isclose(model[meta], expected[meta]) - - assert word_precision_check(model.meta.wcsinfo.s_region, expected.meta.wcsinfo.s_region) - - -def test_default_siaf_values(eng_db_ngas, data_file_nosiaf): +def test_default_siaf_values(data_file_nosiaf): """ Test that FITS WCS default values were set. """ @@ -549,14 +469,14 @@ def test_default_siaf_values(eng_db_ngas, data_file_nosiaf): model.meta.aperture.name = "MIRIM_TAFULL" model.meta.observation.date = '2017-01-01' model.meta.exposure.type = "MIR_IMAGE" - stp.update_wcs(model, siaf_path=siaf_path, allow_default=False, engdb_url='http://localhost') + stp.update_wcs(model, allow_default=False) assert model.meta.wcsinfo.crpix1 == 24.5 assert model.meta.wcsinfo.crpix2 == 24.5 assert model.meta.wcsinfo.cdelt1 == 3.0693922222222226e-05 assert model.meta.wcsinfo.cdelt2 == 3.092664722222222e-05 -def test_tsgrism_siaf_values(eng_db_ngas, data_file_nosiaf): +def test_tsgrism_siaf_values(data_file_nosiaf): """ Test that FITS WCS default values were set. """ @@ -567,12 +487,12 @@ def test_tsgrism_siaf_values(eng_db_ngas, data_file_nosiaf): model.meta.observation.date = '2017-01-01' model.meta.exposure.type = "NRC_TSGRISM" model.meta.visit.tsovisit = True - stp.update_wcs(model, siaf_path=siaf_path, engdb_url='http://localhost') + stp.update_wcs(model) assert model.meta.wcsinfo.siaf_xref_sci == 858.0 assert model.meta.wcsinfo.siaf_yref_sci == 35 -def test_mirim_tamrs_siaf_values(eng_db_ngas, data_file_nosiaf): +def test_mirim_tamrs_siaf_values(data_file_nosiaf): """ Test that FITS WCS default values were set. """ @@ -582,12 +502,12 @@ def test_mirim_tamrs_siaf_values(eng_db_ngas, data_file_nosiaf): model.meta.aperture.name = 'MIRIM_TAMRS' model.meta.observation.date = '2017-01-01' model.meta.exposure.type = "MIR_TACQ" - stp.update_wcs(model, siaf_path=siaf_path, engdb_url='http://localhost') + stp.update_wcs(model) assert model.meta.wcsinfo.crpix1 == 997.5 assert model.meta.wcsinfo.crpix2 == 993.5 -def test_moving_target_update(caplog, eng_db_ngas, data_file_moving_target): +def test_moving_target_update(caplog, data_file_moving_target): """Test moving target table updates.""" with datamodels.open(data_file_moving_target) as model: stp.update_mt_kwds(model) @@ -602,7 +522,7 @@ def test_moving_target_update(caplog, eng_db_ngas, data_file_moving_target): assert "Moving target RA and Dec updated" in caplog.text -def test_moving_target_no_mtt(caplog, eng_db_ngas, data_file): +def test_moving_target_no_mtt(caplog, data_file): """Test moving target table updates, no table present.""" with datamodels.open(data_file) as model: stp.update_mt_kwds(model) @@ -614,7 +534,7 @@ def test_moving_target_no_mtt(caplog, eng_db_ngas, data_file): assert "Moving target position table not found" in caplog.text -def test_moving_target_tnotinrange(caplog, eng_db_ngas, data_file_moving_target): +def test_moving_target_tnotinrange(caplog, data_file_moving_target): """Test moving target table updates, time not in range.""" with datamodels.open(data_file_moving_target) as model: model.meta.exposure.mid_time -= 0.2 @@ -664,7 +584,7 @@ def make_t_pars(detector='any', fgsid_telem=1, fgsid_user=None): t_pars.siaf = siafdb.SIAF(v2_ref=120.525464, v3_ref=-527.543132, v3yangle=-0.5627898, vparity=-1, crpix1=1024.5, crpix2=1024.5, cdelt1=0.03113928, cdelt2=0.03132232, vertices_idl=(-32.1682, 32.0906, 31.6586, -31.7234, -32.1683, -32.1904, 32.0823, 31.9456)) - t_pars.siaf_db = siafdb.SiafDb(siaf_path) + t_pars.siaf_db = siafdb.SiafDb() return t_pars @@ -767,6 +687,28 @@ def data_file(tmp_path): yield file_path +@pytest.fixture +def data_file_strict(tmp_path): + model = datamodels.Level1bModel() + model.meta.exposure.start_time = STARTTIME.mjd + model.meta.exposure.end_time = STARTTIME.mjd + model.meta.target.ra = TARG_RA + model.meta.target.dec = TARG_DEC + model.meta.guidestar.gs_ra = TARG_RA + 0.0001 + model.meta.guidestar.gs_dec = TARG_DEC + 0.0001 + model.meta.aperture.name = "MIRIM_FULL" + model.meta.observation.date = '2017-01-01' + model.meta.exposure.type = "MIR_IMAGE" + model.meta.ephemeris.velocity_x = -25.021 + model.meta.ephemeris.velocity_y = -16.507 + model.meta.ephemeris.velocity_z = -7.187 + + file_path = tmp_path / 'file.fits' + model.save(file_path) + model.close() + yield file_path + + @pytest.fixture def data_file_nosiaf(): model = datamodels.Level1bModel() @@ -868,19 +810,3 @@ def data_file_moving_target(tmp_path): model.save(file_path) model.close() yield file_path - - -@pytest.fixture() -def eng_db_jw703(): - """Setup the test engineering database""" - with EngDB_Mocker(db_path=db_jw703_path): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb - - -@pytest.fixture() -def eng_db_ngas(): - """Setup the test engineering database""" - with EngDB_Mocker(db_path=db_ngas_path): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb diff --git a/jwst/lib/tests/test_siafdb.py b/jwst/lib/tests/test_siafdb.py index f070140105..6bbf22da4a 100644 --- a/jwst/lib/tests/test_siafdb.py +++ b/jwst/lib/tests/test_siafdb.py @@ -6,7 +6,6 @@ from jwst.lib import siafdb -pytest.importorskip('pysiaf') import pysiaf # type: ignore[import-not-found] # noqa: E402 # Database paths @@ -56,7 +55,7 @@ def test_create(source, prd, xml_path, exception, jail_environ): crpix1=1024.5, crpix2=1024.5, cdelt1=0.06839158, cdelt2=0.06993081, vertices_idl=(-68.8543, 70.1233, 71.5697, -70.2482, 72.1764, 68.8086, -75.5918, -70.7457))), ('FGS2_FULL_OSS', False, - siafdb.SIAF(v2_ref=22.835, v3_ref=-699.423, v3yangle=0.2914828, vparity=1, + siafdb.SIAF(v2_ref=22.865, v3_ref=-699.28, v3yangle=0.2914828, vparity=1, crpix1=1024.5, crpix2=1024.5, cdelt1=0.06787747, cdelt2=0.06976441, vertices_idl=(-70.7843, 69.9807, 68.6042, -69.4153, -74.3558, -71.5516, 71.3065, 69.3639))), ('MIRIM_TAMRS', False, @@ -71,7 +70,7 @@ def test_create(source, prd, xml_path, exception, jail_environ): ) def test_get_wcs(aperture, to_detector, expected): """Test retrieval of wcs information.""" - siaf_db = siafdb.SiafDb(SIAFXML_PATH) + siaf_db = siafdb.SiafDb() siaf = siaf_db.get_wcs(aperture, to_detector=to_detector) assert siaf == expected @@ -98,17 +97,3 @@ def test_nearest_prd(prd, expected, exception): prd_to_use, _ = siafdb.nearest_prd(pysiaf, prd) if expected != 'ANYPRD': assert prd_to_use == expected - - -@pytest.mark.parametrize('source, prd, expected', [ - (None, None, XML_DATA_SIAFXML_PATH / 'SIAFXML'), - (None, 'PRDOPSSOC-055', XML_DATA_SIAFXML_PATH / 'SIAFXML'), - (SIAFXML_PATH, None, SIAFXML_PATH) -]) -def test_xml_data_overrides(source, prd, expected, jail_environ): - """Test cases where XML_DATA should be used and not used.""" - os.environ['XML_DATA'] = str(XML_DATA_SIAFXML_PATH) - - siaf_db = siafdb.SiafDb(source=source, prd=prd) - - assert siaf_db.xml_path == expected diff --git a/jwst/lib/tests/test_v1_calculate.py b/jwst/lib/tests/test_v1_calculate.py index cf32e06863..62a88b0781 100644 --- a/jwst/lib/tests/test_v1_calculate.py +++ b/jwst/lib/tests/test_v1_calculate.py @@ -10,15 +10,9 @@ from stdatamodels.jwst.datamodels import ImageModel from jwst.lib import engdb_mast -from jwst.lib import engdb_tools import jwst.lib.set_telescope_pointing as stp import jwst.lib.v1_calculate as v1c -from jwst.lib.tests.engdb_mock import EngDB_Mocker - -# Requires pysiaf -pytest.importorskip('pysiaf') - DATA_PATH = Path(__file__).parent / 'data' # Engineering parameters @@ -27,44 +21,6 @@ MAST_GOOD_STARTTIME = Time('59612.93401553055', format='mjd') MAST_GOOD_ENDTIME = Time('59612.93500967592', format='mjd') -# Time range for the mock database -MOCK_GOOD_STARTTIME = Time('59240.10349754328', format='mjd') -MOCK_GOOD_ENDTIME = Time('59240.1082197338', format='mjd') - - -def test_from_models(engdb_service, tmp_path): - """Test v1_calculate_from_models for basic running""" - model = ImageModel() - model.meta.exposure.start_time = MOCK_GOOD_STARTTIME.mjd - model.meta.exposure.end_time = MOCK_GOOD_ENDTIME.mjd - - v1_table = v1c.v1_calculate_from_models([model], method=stp.Methods.COARSE_TR_202111, engdb_url=engdb_service.base_url) - v1_formatted = v1c.simplify_table(v1_table) - - # Save for post-test examination - v1_formatted.write(tmp_path / 'test_from_models_service.ecsv', format='ascii.ecsv') - - truth = Table.read(DATA_PATH / 'test_from_models_service.ecsv') - errors = v1_compare_simplified_tables(v1_formatted, truth) - errors_str = '\n'.join(errors) - assert len(errors) == 0, f'V1 tables are different: {errors_str}' - - -def test_over_time(engdb_service, tmp_path): - """Test v1_calculate_over_time for basic running""" - v1_table = v1c.v1_calculate_over_time(MOCK_GOOD_STARTTIME.mjd, MOCK_GOOD_ENDTIME.mjd, - method=stp.Methods.COARSE_TR_202111, - engdb_url=engdb_service.base_url) - v1_formatted = v1c.simplify_table(v1_table) - - # Save for post-test examination - v1_formatted.write(tmp_path / 'test_over_time_service.ecsv', format='ascii.ecsv') - - truth = Table.read(DATA_PATH / 'test_over_time_service.ecsv') - errors = v1_compare_simplified_tables(v1_formatted, truth) - errors_str = '\n'.join(errors) - assert len(errors) == 0, f'V1 tables are different: {errors_str}' - def test_from_models_mast(tmp_path): """Test v1_calculate_from_models for basic running""" @@ -110,9 +66,7 @@ def test_over_time_mast(tmp_path): # ###################### @pytest.fixture def engdb_service(): - """Setup the service to operate through the mock service""" - with EngDB_Mocker(): - yield engdb_tools.ENGDB_Service(base_url='http://localhost') + yield engdb_mast.EngdbMast() def v1_compare_simplified_tables(a, b, rtol=1e-05): diff --git a/jwst/regtest/test_fgs_pointing.py b/jwst/regtest/test_fgs_pointing.py index ba90dce043..6501914ad6 100644 --- a/jwst/regtest/test_fgs_pointing.py +++ b/jwst/regtest/test_fgs_pointing.py @@ -10,12 +10,8 @@ from numpy import allclose, isclose # noqa: E402 from jwst.datamodels import Level1bModel # type: ignore[attr-defined] # noqa: E402 -from jwst.lib import engdb_mast # noqa: E402 -from jwst.lib import engdb_tools # noqa: E402 +from jwst.lib import siafdb # noqa: E402 from jwst.lib import set_telescope_pointing as stp # noqa: E402 -from jwst.lib.file_utils import pushdir # noqa: E402 -from jwst.lib.siafdb import SiafDb # noqa: E402 -from jwst.lib.tests.engdb_mock import EngDB_Mocker # noqa: E402 # Set logging for the module to be tested. logger = logging.getLogger(stp.__name__) @@ -187,13 +183,11 @@ def get_guider_wcs_id(path): @pytest.fixture(params=FGS_PATHS, ids=get_guider_wcs_id, scope='module') -def get_guider_wcs(request, multi_engdb): - siaf_db = SiafDb(SIAF_PATH) - engdb = multi_engdb +def get_guider_wcs(request): with Level1bModel(request.param) as model: exp_type = model.meta.exposure.type.lower() detector = model.meta.instrument.detector.lower() - t_pars = stp.t_pars_from_model(model, siaf_db=siaf_db, engdb_url=engdb.base_url) + t_pars = stp.t_pars_from_model(model, siaf_db=siafdb.SiafDb()) wcs = stp.calc_wcs_guiding(model, t_pars) wcs = {key: value for key, value in zip(WCS_ATTRIBUTES, wcs)} @@ -213,49 +207,15 @@ def test_update_wcs(update_wcs, attr): # Utilities # --------- @pytest.fixture(scope='module') -def update_wcs(multi_engdb, make_level1b): +def update_wcs(make_level1b): """Update the model wcs info""" - engdb = multi_engdb model, expected = make_level1b - stp.update_wcs(model, engdb_url=engdb.base_url) + stp.update_wcs(model) return model, expected -@pytest.fixture(scope='module') -def engdb_jw01029(request, rtdata_module): - """Setup the test engineering database""" - rtdata = rtdata_module - if not request.config.getoption('--bigdata'): - pytest.skip('"--bigdata" not specified') - - db_path = Path('engdb_jw0109') - db_path.mkdir() - with pushdir(db_path): - for path in rtdata.data_glob('fgs/pointing/engdb_jw01029'): - rtdata.get_data(path) - with EngDB_Mocker(db_path=db_path): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb - - -@pytest.fixture(scope='module') -def mast(): - """Use the Mast database.""" - try: - engdb = engdb_mast.EngdbMast(base_url=engdb_mast.MAST_BASE_URL) - except RuntimeError as exception: - pytest.skip(f'Live MAST Engineering Service not available: {exception}') - yield engdb - - -@pytest.fixture(params=['engdb_jw01029', 'mast'], scope='module') -def multi_engdb(request): - """Allow a test to use multiple database fixtures""" - return request.getfixturevalue(request.param) - - @pytest.fixture(scope='module', params=[META_FGS1, META_FGS2]) def make_level1b(request): model_meta = request.param diff --git a/jwst/regtest/test_miri_setpointing.py b/jwst/regtest/test_miri_setpointing.py index 9582cb01d7..f27c36c60a 100644 --- a/jwst/regtest/test_miri_setpointing.py +++ b/jwst/regtest/test_miri_setpointing.py @@ -1,44 +1,25 @@ import pytest from astropy.io.fits.diff import FITSDiff -from pathlib import Path - -from jwst.lib import engdb_tools from jwst.lib.set_telescope_pointing import add_wcs -from jwst.lib.tests.engdb_mock import EngDB_Mocker - -# Get the mock databases -DATA_PATH = Path(__file__).parents[1] / 'lib' / 'tests' / 'data' -ENGDB_PATH = DATA_PATH / 'engdb' @pytest.mark.bigdata -def test_miri_setpointing(rtdata, engdb, fitsdiff_default_kwargs): +def test_miri_setpointing(rtdata, fitsdiff_default_kwargs): """ Regression test of the set_telescope_pointing script on a level-1b MIRI image. """ # Get the input level-1b file - rtdata.get_data("miri/image/jw80600010001_02101_00001_mirimage_uncal.fits") + rtdata.get_data("miri/mrs/jw01282004001_02101_00001_mirifulong_uncal.fits") # The add_wcs function overwrites its input, so output = input rtdata.output = rtdata.input # Call the WCS routine, using the ENGDB_Service - add_wcs(rtdata.input, allow_default=True, engdb_url='http://localhost', prd='PRDOPSSOC-055') + add_wcs(rtdata.input) # Compare the results - rtdata.get_truth("truth/test_miri_setpointing/jw80600010001_02101_00001_mirimage_uncal.fits") + rtdata.get_truth("truth/test_miri_setpointing/jw01282004001_02101_00001_mirifulong_uncal.fits") fitsdiff_default_kwargs['rtol'] = 1e-6 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - - -# ######## -# Fixtures -# ######## -@pytest.fixture -def engdb(): - """Setup the mock engineering database""" - with EngDB_Mocker(db_path=ENGDB_PATH): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb diff --git a/jwst/regtest/test_nircam_tsgrism.py b/jwst/regtest/test_nircam_tsgrism.py index 4af5c81748..3657a233cd 100644 --- a/jwst/regtest/test_nircam_tsgrism.py +++ b/jwst/regtest/test_nircam_tsgrism.py @@ -104,19 +104,14 @@ def test_nircam_setpointing_tsgrism(rtdata, fitsdiff_default_kwargs): """ Regression test of the set_telescope_pointing script on a level-1b NIRCam file. """ - # Get SIAF PRD database file - siaf_path = rtdata.get_data("common/prd.db") - rtdata.get_data("nircam/tsgrism/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") + rtdata.get_data("nircam/tsgrism/jw02459001001_03103_00001-seg001_nrcalong_uncal.fits") # The add_wcs function overwrites its input rtdata.output = rtdata.input - # Call the WCS routine, using the ENGDB_Service - try: - add_wcs(rtdata.input, siaf_path=siaf_path) - except ValueError: - pytest.skip('Engineering Database not available.') + # Call the WCS routine + add_wcs(rtdata.input) - rtdata.get_truth("truth/test_nircam_setpointing/jw00721012001_03103_00001-seg001_nrcalong_uncal.fits") + rtdata.get_truth("truth/test_nircam_setpointing/jw02459001001_03103_00001-seg001_nrcalong_uncal.fits") fitsdiff_default_kwargs['rtol'] = 1e-6 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) diff --git a/jwst/regtest/test_nircam_tsimg.py b/jwst/regtest/test_nircam_tsimg.py index cf9f38ce23..190de76ddf 100644 --- a/jwst/regtest/test_nircam_tsimg.py +++ b/jwst/regtest/test_nircam_tsimg.py @@ -1,10 +1,7 @@ -from pathlib import Path import pytest from astropy.io.fits.diff import FITSDiff -from jwst.lib import engdb_tools from jwst.lib.set_telescope_pointing import add_wcs -from jwst.lib.tests.engdb_mock import EngDB_Mocker from jwst.stpipe import Step @@ -58,37 +55,22 @@ def test_nircam_tsimage_stage3_phot(run_pipelines, diff_astropy_tables): @pytest.mark.bigdata -def test_nircam_setpointing_tsimg(rtdata, engdb, fitsdiff_default_kwargs): +def test_nircam_setpointing_tsimg(rtdata, fitsdiff_default_kwargs): """ Regression test of the set_telescope_pointing script on a level-1b NIRCam TSO imaging file. """ - # Get SIAF PRD database file - siaf_path = rtdata.get_data("common/prd.db") - rtdata.get_data("nircam/tsimg/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") + + rtdata.get_data("nircam/tsimg/jw06780001001_02103_00001-seg002_nrcblong_uncal.fits") # The add_wcs function overwrites its input, so output = input rtdata.output = rtdata.input - # Call the WCS routine, using the ENGDB_Service - try: - add_wcs(rtdata.input, engdb_url='http://localhost', siaf_path=siaf_path) - except ValueError: - pytest.skip('Engineering Database not available.') + # Call the WCS routine + add_wcs(rtdata.input) - rtdata.get_truth("truth/test_nircam_setpointing/jw00312006001_02102_00001-seg001_nrcb1_uncal.fits") + rtdata.get_truth("truth/test_nircam_setpointing/jw06780001001_02103_00001-seg002_nrcblong_uncal.fits") fitsdiff_default_kwargs['rtol'] = 1e-6 diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - -# ######## -# Fixtures -# ######## -@pytest.fixture -def engdb(): - """Setup the mock engineering database""" - db_path = Path(__file__).parents[1] / 'lib' / 'tests' / 'data' / 'engdb' - with EngDB_Mocker(db_path=db_path): - engdb = engdb_tools.ENGDB_Service(base_url='http://localhost') - yield engdb diff --git a/jwst/regtest/test_stp_jw00697013.py b/jwst/regtest/test_stp_jw00697013.py deleted file mode 100644 index ef39a9049e..0000000000 --- a/jwst/regtest/test_stp_jw00697013.py +++ /dev/null @@ -1,144 +0,0 @@ -"""set_telescope_pointing unit test using data from Mini-OTE-1 data - -Mini-OTE-1 executed proposal jw00697, observation 13. As of 2021-11, this test -is considered the gold standard in pointing, where all known issues with the -pointing chain, from APT, through the OTB, through DMS processing, have been -resolved. - -The test below uses the meta information from those exposures, and an offline -version of the engineering database telemetry, to compare current code -to the expected values from the Mini-OTE_1 test. - -The full set of data covers 420 exposures. However, only a subset of exposures -are actually tested. -""" -import pytest - -from pathlib import Path - -import asdf -import numpy as np - -import stdatamodels.jwst.datamodels as dm - -from jwst.lib import siafdb -from jwst.lib.file_utils import pushdir -import jwst.lib.set_telescope_pointing as stp -from jwst.lib.tests import engdb_mock - -# Requires pysiaf -pytest.importorskip('pysiaf') - -EXPS = ['jw00697013001_03101_00001_nrcblong_uncal', - 'jw00697013004_03103_00001_nrcb1_uncal', - 'jw00697013007_03101_00001_nrca4_uncal', - 'jw00697013009_03105_00001_nrcb1_uncal', - 'jw00697013002_03105_00001_nrcb3_uncal', - 'jw00697013008_03101_00001_nrcb2_uncal', - 'jw00697013008_03103_00001_nrcb3_uncal', - 'jw00697013002_03107_00001_nrcblong_uncal', - 'jw00697013007_03105_00001_nrcalong_uncal', - 'jw00697013003_03101_00001_nrcb1_uncal', - 'jw00697013006_03103_00001_nrca3_uncal'] - - -@pytest.mark.bigdata -def test_aperture_ra(calc_wcs): - """Compare aperture WCS information""" - model, _,wcsinfo, _, _ = calc_wcs - assert np.isclose(model.meta.wcsinfo.ra_ref, wcsinfo.ra) - - -@pytest.mark.bigdata -def test_aperture_dec(calc_wcs): - """Compare aperture WCS information""" - model, _,wcsinfo, _, _ = calc_wcs - assert np.isclose(model.meta.wcsinfo.dec_ref, wcsinfo.dec) - - -@pytest.mark.bigdata -def test_aperture_pa(calc_wcs): - """Compare aperture WCS information""" - model, _,wcsinfo, _, _ = calc_wcs - assert np.isclose(model.meta.aperture.position_angle, wcsinfo.pa) - - -@pytest.mark.bigdata -def test_v1_ra(calc_wcs): - """Compare v1 WCS information""" - model, _,_, vinfo, _ = calc_wcs - assert np.isclose(model.meta.pointing.ra_v1, vinfo.ra) - - -@pytest.mark.bigdata -def test_v1_dec(calc_wcs): - """Compare v1 WCS information""" - model, _,_, vinfo, _ = calc_wcs - assert np.isclose(model.meta.pointing.dec_v1, vinfo.dec) - - -@pytest.mark.bigdata -def test_v1_pa(calc_wcs): - """Compare v1 WCS information""" - model, _,_, vinfo, _ = calc_wcs - assert np.isclose(model.meta.pointing.pa_v3, vinfo.pa) - - -@pytest.mark.bigdata -def test_roll_ref(calc_wcs): - """Compare roll ref""" - model, t_pars, wcsinfo, _, _ = calc_wcs - roll_ref = stp.pa_to_roll_ref(wcsinfo.pa, t_pars.siaf) - assert np.isclose(model.meta.wcsinfo.roll_ref, roll_ref) - - -@pytest.fixture(scope='module', params=EXPS) -def calc_wcs(databases, request): - """Calculate the V1 and aperture """ - exposure = request.param - siaf_db, metas = databases - - with dm.ImageModel((10, 10)) as model: - model.update({'meta': metas[exposure]}) - - # Calculate the pointing information - t_pars = stp.t_pars_from_model(model, siaf_db=siaf_db, engdb_url='http://localhost') - t_pars.update_pointing() - wcsinfo, vinfo, transforms = stp.calc_wcs(t_pars) - - return model, t_pars, wcsinfo, vinfo, transforms - - -@pytest.fixture(scope='module') -def databases(rtdata_module): - """Create the necessary databases needed to run pointing code - - The SIAF database needs to be open explicitly and requires `pysiaf`. - - The engineering database is handled as a context. - - Returns - ------- - siaf_db, metas : `set_telescope_pointing.SiafDb`, dict - Returns the tuple of the siaf database and all exposures meta information. - """ - - # Pin the PRD. Not testing changes in PRD - siaf_db = siafdb.SiafDb(prd='PRDOPSSOC-055') - - # Get the exposures' meta information - metas_path = rtdata_module.get_data('pointing/jw00697013_metas.asdf') - with asdf.open(metas_path) as metas_asdf: - metas = metas_asdf['metas'] - - # Setup the engineering database - engdb_path = Path('engdb') - engdb_path.mkdir() - with pushdir(engdb_path): - paths = rtdata_module.data_glob('pointing/jw00697013_engdb') - for path in paths: - rtdata_module.get_data(path) - - # Pass on the database info. - with engdb_mock.EngDB_Mocker(db_path=engdb_path): - yield siaf_db, metas diff --git a/pyproject.toml b/pyproject.toml index c1343788a4..36bd029be8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -111,6 +111,7 @@ test = [ "colorama>=0.4.1", "readchar>=3.0", "ruff", + "pysiaf>=0.13.0", "pytest>=6.0.0", "pytest-cov>=2.9.0", "pytest-doctestplus>=0.10.0", From f9842372475d8fc4eb1634f2c0995a2489b0f327 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 15:06:10 -0400 Subject: [PATCH 588/687] Update test data for master background with dedicated background observation --- jwst/regtest/test_miri_lrs_dedicated_mbkg.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_dedicated_mbkg.py b/jwst/regtest/test_miri_lrs_dedicated_mbkg.py index 50ef78cb5c..1949f5646c 100644 --- a/jwst/regtest/test_miri_lrs_dedicated_mbkg.py +++ b/jwst/regtest/test_miri_lrs_dedicated_mbkg.py @@ -10,9 +10,9 @@ def run_pipeline(rtdata_module): rtdata = rtdata_module - rtdata.get_asn("miri/lrs/miri_lrs_mbkg_dedicated_spec3_asn.json") + rtdata.get_asn("miri/lrs/jw01529-o003_spec3_with_bg_00001_asn.json") - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='master_background') + MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='mbsub') return rtdata @@ -22,12 +22,12 @@ def test_miri_lrs_dedicated_mbkg(run_pipeline, fitsdiff_default_kwargs): """Run a test for MIRI LRS data with dedicated background exposures.""" rtdata = run_pipeline - rtdata.output = "miri_lrs_seq2_exp2_master_background.fits" + rtdata.output = "jw01529003001_03103_00011_mirimage_mbsub.fits" # Get the truth file rtdata.get_truth(os.path.join( "truth/test_miri_lrs_dedicated_mbkg", - "miri_lrs_seq2_exp2_master_background.fits")) + "jw01529003001_03103_00011_mirimage_mbsub.fits")) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From c1898e4533e301badca971527ac55327e0c66083 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 17:05:28 -0400 Subject: [PATCH 589/687] Copy wavelengths for first spectrum so they are not sorted in place --- jwst/combine_1d/combine1d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index c4a6021e9d..9d69109ceb 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -355,7 +355,7 @@ def count_input(input_spectra): # only include spectra that have more than 1 data point if len(input_wl) > 1: if wl is None: - wl = input_wl + wl = input_wl.copy() else: wl = np.hstack((input_wl, wl)) wl.sort() From 359a7b90c6f2195c29b27bee6a2cdbc199f01384 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 17:06:38 -0400 Subject: [PATCH 590/687] Small fixes for new ModelContainer --- jwst/master_background/master_background_step.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 727b351c47..ce9c21b560 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -90,7 +90,6 @@ def process(self, input): del _ result = ModelContainer() background_2d_collection = ModelContainer() - background_2d_collection.update(input_data) for model in input_data: background_2d = expand_to_2d(model, self.user_background) result.append(subtract_2d_background(model, background_2d)) @@ -102,7 +101,7 @@ def process(self, input): else: asn_id = None background_2d = expand_to_2d(input_data, self.user_background) - background_2d_collection = background_2d + background_2d_collection = ModelContainer([background_2d]) result = subtract_2d_background(input_data, background_2d) # Record name of user-supplied master background spectrum result.meta.background.master_background_file = basename(self.user_background) From 86b767ce78a392abc5cfeaef7d1985b5f11794f3 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 17:19:53 -0400 Subject: [PATCH 591/687] Update data and test background products --- jwst/regtest/test_miri_lrs_dedicated_mbkg.py | 14 ++++++++------ jwst/regtest/test_miri_lrs_masterbg_user.py | 16 +++++++++------- 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_dedicated_mbkg.py b/jwst/regtest/test_miri_lrs_dedicated_mbkg.py index 1949f5646c..a2bcf42eb3 100644 --- a/jwst/regtest/test_miri_lrs_dedicated_mbkg.py +++ b/jwst/regtest/test_miri_lrs_dedicated_mbkg.py @@ -12,22 +12,24 @@ def run_pipeline(rtdata_module): rtdata.get_asn("miri/lrs/jw01529-o003_spec3_with_bg_00001_asn.json") - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='mbsub') + MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='mbsub', + save_background=True) return rtdata @pytest.mark.bigdata -def test_miri_lrs_dedicated_mbkg(run_pipeline, fitsdiff_default_kwargs): +@pytest.mark.parametrize('output', ['jw01529-o004_t002_miri_p750l_o003_masterbg1d.fits', + 'jw01529003001_03103_00011_mirimage_o003_masterbg2d.fits', + 'jw01529003001_03103_00011_mirimage_mbsub.fits']) +def test_miri_lrs_dedicated_mbkg(run_pipeline, fitsdiff_default_kwargs, output): """Run a test for MIRI LRS data with dedicated background exposures.""" rtdata = run_pipeline - rtdata.output = "jw01529003001_03103_00011_mirimage_mbsub.fits" + rtdata.output = output # Get the truth file - rtdata.get_truth(os.path.join( - "truth/test_miri_lrs_dedicated_mbkg", - "jw01529003001_03103_00011_mirimage_mbsub.fits")) + rtdata.get_truth(os.path.join("truth/test_miri_lrs_dedicated_mbkg", output)) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) diff --git a/jwst/regtest/test_miri_lrs_masterbg_user.py b/jwst/regtest/test_miri_lrs_masterbg_user.py index e57570bdbc..6e49984924 100644 --- a/jwst/regtest/test_miri_lrs_masterbg_user.py +++ b/jwst/regtest/test_miri_lrs_masterbg_user.py @@ -11,28 +11,30 @@ def run_pipeline(rtdata_module): rtdata = rtdata_module # This is the user-supplied background file. - rtdata.get_data("miri/lrs/miri_lrs_bkg_x1d.fits") + rtdata.get_data("miri/lrs/jw01529-o004_t002_miri_p750l_x1d.fits") user_bkg = rtdata.input # This is the input file for the master_background step. - rtdata.get_data("miri/lrs/miri_lrs_sci+bkg_cal.fits") + rtdata.get_data("miri/lrs/jw01529003001_03103_00011_mirimage_cal.fits") MasterBackgroundStep.call(rtdata.input, user_background=user_bkg, - save_results=True, suffix='master_background') + save_results=True, suffix='user_mbsub', + save_background=True) return rtdata @pytest.mark.bigdata -def test_miri_lrs_masterbg_user(run_pipeline, fitsdiff_default_kwargs): +@pytest.mark.parametrize('output', ['jw01529003001_03103_00011_mirimage_masterbg2d.fits', + 'jw01529003001_03103_00011_mirimage_user_mbsub.fits']) +def test_miri_lrs_masterbg_user(run_pipeline, fitsdiff_default_kwargs, output): """Run a test for MIRI LRS data with a user-supplied background file.""" rtdata = run_pipeline - rtdata.output = "miri_lrs_sci+bkg_master_background.fits" + rtdata.output = output # Get the truth file - rtdata.get_truth(os.path.join("truth/test_miri_lrs_masterbg_user", - "miri_lrs_sci+bkg_master_background.fits")) + rtdata.get_truth(os.path.join("truth/test_miri_lrs_masterbg_user", output)) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From c889fa393997b1a1202e54ecde127512b7616bed Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 28 Oct 2024 15:30:01 -0400 Subject: [PATCH 592/687] Add mbsub to the list of known suffixes --- jwst/lib/suffix.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/lib/suffix.py b/jwst/lib/suffix.py index a7af86beb7..b72306dce4 100644 --- a/jwst/lib/suffix.py +++ b/jwst/lib/suffix.py @@ -41,7 +41,7 @@ 'c1d', 'cal', 'calints', 'cat', 'crf', 'crfints', 'dark', 'i2d', - 'median', + 'mbsub', 'median', 'phot', 'psf-amiavg', 'psfalign', 'psfstack', 'psfsub', 'ramp', 'rate', 'rateints', 'residual_fringe', 's2d', 's3d', 'snr', From c2cd58f690deb92e187c6359b99dd1a85094b943 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 28 Oct 2024 15:34:26 -0400 Subject: [PATCH 593/687] Add a test/demo for background extraction for nodded LRS data --- jwst/regtest/test_miri_lrs_nod_masterbg.py | 83 ++++++++++++++++++---- 1 file changed, 71 insertions(+), 12 deletions(-) diff --git a/jwst/regtest/test_miri_lrs_nod_masterbg.py b/jwst/regtest/test_miri_lrs_nod_masterbg.py index 0a3b2ba203..d17b65fad2 100644 --- a/jwst/regtest/test_miri_lrs_nod_masterbg.py +++ b/jwst/regtest/test_miri_lrs_nod_masterbg.py @@ -2,33 +2,92 @@ import pytest from astropy.io.fits.diff import FITSDiff -from jwst.master_background import MasterBackgroundStep +from jwst.stpipe import Step @pytest.fixture(scope="module") -def run_pipeline(rtdata_module): +def run_pipeline_with_master_bg(rtdata_module): rtdata = rtdata_module - rtdata.get_asn("miri/lrs/miri_lrs_mbkg_nodded_spec3_asn.json") + # Get rate files and custom extraction parameters + rate_files = ['jw01530005001_03103_00001_mirimage_rate.fits', + 'jw01530005001_03103_00002_mirimage_rate.fits'] + ref_files = ['jwst_miri_extract1d_nod1_bg.json', + 'jwst_miri_extract1d_nod2_bg.json'] + for rate, ref in zip(rate_files, ref_files): + rtdata.get_data(f"miri/lrs/{ref}") + rtdata.get_data(f"miri/lrs/{rate}") - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='master_background') + Step.from_cmdline([ + "calwebb_spec2", + rate, + f"--steps.extract_1d.override_extract1d={ref}", + "--steps.extract_1d.use_source_pos=False", + ]) + + # Get the ASN, but not the data - use the spec2 input products + rtdata.get_data("miri/lrs/jw01530-o005_20221202t204827_spec3_00001_asn.json") + Step.from_cmdline([ + "calwebb_spec3", + rtdata.input, + "--steps.master_background.skip=False", + "--steps.master_background.save_background=True", + "--steps.master_background.save_results=True", + ]) return rtdata @pytest.mark.bigdata -@pytest.mark.parametrize("nod_seq", ["seq1", "seq2"]) -def test_miri_lrs_nod_masterbg(run_pipeline, fitsdiff_default_kwargs, nod_seq): - """Run a regression test for nodded MIRI LRS data.""" +@pytest.mark.parametrize("nod", [1, 2]) +@pytest.mark.parametrize("suffix", ["cal", "x1d", "s2d", "mbsub", "o005_crf", + "o005_masterbg2d"]) +def test_miri_lrs_nod_bg(run_pipeline_with_master_bg, fitsdiff_default_kwargs, + nod, suffix): + """Run a regression test for nodded MIRI LRS data with background extraction.""" + rtdata = run_pipeline_with_master_bg + + # Check output products for nod 1 or 2 + output_file = f"jw01530005001_03103_0000{nod}_mirimage_{suffix}.fits" + rtdata.output = output_file + + # Get the truth file + rtdata.get_truth(os.path.join("truth/test_miri_lrs_nod_masterbg", output_file)) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + +def test_miri_lrs_nod_masterbg1d(run_pipeline_with_master_bg, fitsdiff_default_kwargs): + rtdata = run_pipeline_with_master_bg + + # Check 1D masterbg output product: created with root name from nod 1 + output_file = "jw01530005001_03103_00001_mirimage_o005_masterbg1d.fits" + rtdata.output = output_file + + # Get the truth file + rtdata.get_truth(os.path.join("truth/test_miri_lrs_nod_masterbg", output_file)) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["s2d", "x1d"]) +def test_miri_lrs_nod_bg_spec3(run_pipeline_with_master_bg, fitsdiff_default_kwargs, + suffix): + """Run a regression test for nodded MIRI LRS with master background subtraction.""" + rtdata = run_pipeline_with_master_bg - rtdata = run_pipeline - rtdata.output = "miri_lrs_nod_" + nod_seq + "_exp1_master_background.fits" + # Check combined output products + output_file = f"jw01530-o005_t004_miri_p750l_{suffix}.fits" + rtdata.output = output_file # Get the truth file - rtdata.get_truth(os.path.join( - "truth/test_miri_lrs_nod_masterbg", - "miri_lrs_nod_" + nod_seq + "_exp1_master_background.fits")) + rtdata.get_truth(os.path.join("truth/test_miri_lrs_nod_masterbg", output_file)) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From ce815ebd3c9d7bbcbd17fa5a5ea0de005ba051f8 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 28 Oct 2024 16:37:05 -0400 Subject: [PATCH 594/687] Add change notes --- changes/8927.combine_1d.rst | 1 + changes/8927.master_background.rst | 1 + changes/8927.pipeline.rst | 1 + 3 files changed, 3 insertions(+) create mode 100644 changes/8927.combine_1d.rst create mode 100644 changes/8927.master_background.rst create mode 100644 changes/8927.pipeline.rst diff --git a/changes/8927.combine_1d.rst b/changes/8927.combine_1d.rst new file mode 100644 index 0000000000..b05b3e95bc --- /dev/null +++ b/changes/8927.combine_1d.rst @@ -0,0 +1 @@ +Fix wavelength sort order for single input spectrum. diff --git a/changes/8927.master_background.rst b/changes/8927.master_background.rst new file mode 100644 index 0000000000..cf71da1e52 --- /dev/null +++ b/changes/8927.master_background.rst @@ -0,0 +1 @@ +Fix ModelContainer handling for user background input. diff --git a/changes/8927.pipeline.rst b/changes/8927.pipeline.rst new file mode 100644 index 0000000000..e03ac78254 --- /dev/null +++ b/changes/8927.pipeline.rst @@ -0,0 +1 @@ +Add 'mbsub' to the list of known suffixes, for ``master_background`` correction in ``calwebb_spec3``. From 952846af742cc1045ba4d50fc7907e909d80a66c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 28 Oct 2024 16:56:26 -0400 Subject: [PATCH 595/687] Add missing 'bigdata' mark --- jwst/regtest/test_miri_lrs_nod_masterbg.py | 1 + 1 file changed, 1 insertion(+) diff --git a/jwst/regtest/test_miri_lrs_nod_masterbg.py b/jwst/regtest/test_miri_lrs_nod_masterbg.py index d17b65fad2..69554b5a5a 100644 --- a/jwst/regtest/test_miri_lrs_nod_masterbg.py +++ b/jwst/regtest/test_miri_lrs_nod_masterbg.py @@ -60,6 +60,7 @@ def test_miri_lrs_nod_bg(run_pipeline_with_master_bg, fitsdiff_default_kwargs, assert diff.identical, diff.report() +@pytest.mark.bigdata def test_miri_lrs_nod_masterbg1d(run_pipeline_with_master_bg, fitsdiff_default_kwargs): rtdata = run_pipeline_with_master_bg From cdc98f9a8e11c904caf2e8f012fd37bf8babc30c Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 25 Oct 2024 13:18:45 -0400 Subject: [PATCH 596/687] Update dark data for MIRI dark pipeline tests --- jwst/regtest/test_miri_dark.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/jwst/regtest/test_miri_dark.py b/jwst/regtest/test_miri_dark.py index 61aeb1f9d0..78c3f5617b 100644 --- a/jwst/regtest/test_miri_dark.py +++ b/jwst/regtest/test_miri_dark.py @@ -7,7 +7,9 @@ @pytest.mark.bigdata @pytest.mark.parametrize( 'exposure', - ['jw00001001001_01101_00001_mirimage', 'jw02201001001_01101_00001_MIRIMAGE'] + ['jw04482014001_02102_00001_mirifulong', + 'jw04482014001_02102_00001_mirifushort', + 'jw04482014001_02102_00001_mirimage'] ) def test_miri_dark_pipeline(exposure, rtdata, fitsdiff_default_kwargs): """Test the DarkPipeline on MIRI dark exposures""" From c1890897ca0e09694b68c4bc54fabef4299e8f63 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 24 Oct 2024 11:16:53 -0400 Subject: [PATCH 597/687] Update NIRSpec BOTS regtest data --- jwst/regtest/test_nirspec_brightobj.py | 37 +++++++++++--------------- 1 file changed, 16 insertions(+), 21 deletions(-) diff --git a/jwst/regtest/test_nirspec_brightobj.py b/jwst/regtest/test_nirspec_brightobj.py index cc5fedb94b..b70d074127 100644 --- a/jwst/regtest/test_nirspec_brightobj.py +++ b/jwst/regtest/test_nirspec_brightobj.py @@ -20,7 +20,7 @@ def run_tso_spec2_pipeline(rtdata_module, request): rtdata = rtdata_module # Get the input exposure - rtdata.get_data('nirspec/tso/jw02420001001_04101_00001-seg001_nrs1_rateints.fits') + rtdata.get_data('nirspec/tso/jw02420001001_04101_00001-first100_nrs1_rateints.fits') # Run the calwebb_spec2 pipeline; args = ["calwebb_spec2", rtdata.input, @@ -28,6 +28,7 @@ def run_tso_spec2_pipeline(rtdata_module, request): "--steps.extract_2d.save_results=True", "--steps.wavecorr.save_results=True", "--steps.flat_field.save_results=True", + "--steps.flat_field.save_interpolated_flat=True", "--steps.photom.save_results=True"] Step.from_cmdline(args) @@ -35,7 +36,9 @@ def run_tso_spec2_pipeline(rtdata_module, request): @pytest.mark.bigdata -@pytest.mark.parametrize("suffix", ['assign_wcs', 'extract_2d', 'wavecorr', 'flat_field', 'photom', 'calints', 'x1dints']) +@pytest.mark.parametrize("suffix", ['assign_wcs', 'extract_2d', 'wavecorr', + 'flat_field', 'photom', 'calints', 'x1dints', + 'interpolatedflat']) def test_nirspec_brightobj_spec2(run_tso_spec2_pipeline, fitsdiff_default_kwargs, suffix): """ Regression test of calwebb_spec2 pipeline performed on NIRSpec @@ -57,27 +60,18 @@ def test_nirspec_brightobj_spec2(run_tso_spec2_pipeline, fitsdiff_default_kwargs @pytest.mark.bigdata def test_flat_field_step_user_supplied_flat(rtdata, fitsdiff_default_kwargs): """Test providing a user-supplied flat field to the FlatFieldStep""" - data = rtdata.get_data('nirspec/tso/nrs2_wavecorr.fits') - user_supplied_flat = rtdata.get_data('nirspec/tso/nrs2_interpolatedflat.fits') + basename = 'jw02420001001_04101_00001-first100_nrs1' + output_file = f'{basename}_flat_from_user_file.fits' - data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat) - rtdata.output = 'flat_fielded_step_user_supplied.fits' - data_flat_fielded.write(rtdata.output) - - rtdata.get_truth('truth/test_nirspec_brightobj_spec2/flat_fielded_step_user_supplied.fits') - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() + data = rtdata.get_data(f'nirspec/tso/{basename}_wavecorr.fits') + user_supplied_flat = rtdata.get_data(f'nirspec/tso/{basename}_user_flat.fits') + data_flat_fielded = FlatFieldStep.call(data, user_supplied_flat=user_supplied_flat, + save_results=False) + rtdata.output = output_file + data_flat_fielded.write(rtdata.output) -@pytest.mark.bigdata -def test_flat_field_bots_interp_flat(rtdata, fitsdiff_default_kwargs): - """Test the interpolated flat for a NRS BOTS exposure""" - data = rtdata.get_data('nirspec/tso/jw93056001001_short_nrs1_wavecorr.fits') - - FlatFieldStep.call(data, save_interpolated_flat=True) - rtdata.output = 'jw93056001001_short_nrs1_wavecorr_interpolatedflat.fits' - - rtdata.get_truth('truth/test_nirspec_brightobj_spec2/jw93056001001_short_nrs1_wavecorr_interpolatedflat.fits') + rtdata.get_truth(f'truth/test_nirspec_brightobj_spec2/{output_file}') diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() @@ -85,7 +79,8 @@ def test_flat_field_bots_interp_flat(rtdata, fitsdiff_default_kwargs): @pytest.mark.bigdata def test_ff_inv(rtdata, fitsdiff_default_kwargs): """Test flat field inversion""" - with dm.open(rtdata.get_data('nirspec/tso/nrs2_wavecorr.fits')) as data: + basename = 'jw02420001001_04101_00001-first100_nrs1' + with dm.open(rtdata.get_data(f'nirspec/tso/{basename}_wavecorr.fits')) as data: flatted = FlatFieldStep.call(data) unflatted = FlatFieldStep.call(flatted, inverse=True) From 2b277b91466e24a1fea011b2e9493eb1da4e1b31 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Thu, 24 Oct 2024 13:43:55 -0400 Subject: [PATCH 598/687] Update inputs for NIRSpec FS steps --- jwst/regtest/test_nirspec_brightobj.py | 3 +++ jwst/regtest/test_nirspec_fs_spec2.py | 11 +++++++---- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_nirspec_brightobj.py b/jwst/regtest/test_nirspec_brightobj.py index b70d074127..0a976050bc 100644 --- a/jwst/regtest/test_nirspec_brightobj.py +++ b/jwst/regtest/test_nirspec_brightobj.py @@ -20,6 +20,9 @@ def run_tso_spec2_pipeline(rtdata_module, request): rtdata = rtdata_module # Get the input exposure + # Input data is from jw02420001001_04101_00001-seg001_nrs1_rateints.fits, + # modified to truncate the data to the first 100 integrations, for + # faster processing. rtdata.get_data('nirspec/tso/jw02420001001_04101_00001-first100_nrs1_rateints.fits') # Run the calwebb_spec2 pipeline; diff --git a/jwst/regtest/test_nirspec_fs_spec2.py b/jwst/regtest/test_nirspec_fs_spec2.py index c2094dbcb8..e0bb412a1c 100644 --- a/jwst/regtest/test_nirspec_fs_spec2.py +++ b/jwst/regtest/test_nirspec_fs_spec2.py @@ -130,7 +130,8 @@ def test_nirspec_fs_spec2_pixel_replace(run_pipeline_pixel_replace, fitsdiff_def @pytest.mark.bigdata def test_pathloss_corrpars(rtdata): """Test PathLossStep using correction_pars""" - with dm.open(rtdata.get_data('nirspec/fs/nrs1_flat_field.fits')) as data: + basename = 'jw02072002001_05101_00001_nrs1_flatfieldstep' + with dm.open(rtdata.get_data(f'nirspec/fs/{basename}.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -148,7 +149,8 @@ def test_pathloss_corrpars(rtdata): @pytest.mark.bigdata def test_pathloss_inverse(rtdata): """Test PathLossStep using inversion""" - with dm.open(rtdata.get_data('nirspec/fs/nrs1_flat_field.fits')) as data: + basename = 'jw02072002001_05101_00001_nrs1_flatfieldstep' + with dm.open(rtdata.get_data(f'nirspec/fs/{basename}.fits')) as data: pls = PathLossStep() corrected = pls.run(data) @@ -168,7 +170,8 @@ def test_pathloss_inverse(rtdata): @pytest.mark.bigdata def test_pathloss_source_type(rtdata): """Test PathLossStep forcing source type""" - with dm.open(rtdata.get_data('nirspec/fs/nrs1_flat_field.fits')) as data: + basename = 'jw02072002001_05101_00001_nrs1_flatfieldstep' + with dm.open(rtdata.get_data(f'nirspec/fs/{basename}.fits')) as data: pls = PathLossStep() pls.source_type = 'extended' pls.run(data) @@ -189,7 +192,7 @@ def test_nirspec_fs_rateints_spec2(rtdata_module): """ rtdata = rtdata_module - rtdata.get_data("nirspec/fs/jw01128004001_03102_00001_nrs1_new_rateints.fits") + rtdata.get_data("nirspec/fs/jw01128004001_03102_00001_nrs1_rateints.fits") # Run the spec2 pipeline on a (3D) _rateints file args = ["calwebb_spec2", rtdata.input] From 5f26060868888d26635bd98d953ff147fe06d1e2 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Fri, 18 Oct 2024 13:15:35 -0400 Subject: [PATCH 599/687] Update MOS data for masterbackground tests --- jwst/regtest/test_nirspec_masterbackground.py | 35 ++++++++++++++----- 1 file changed, 26 insertions(+), 9 deletions(-) diff --git a/jwst/regtest/test_nirspec_masterbackground.py b/jwst/regtest/test_nirspec_masterbackground.py index 5c45d41265..39172222bc 100644 --- a/jwst/regtest/test_nirspec_masterbackground.py +++ b/jwst/regtest/test_nirspec_masterbackground.py @@ -18,8 +18,8 @@ def run_spec2_mbkg(rtdata_module): rtdata = rtdata_module # Get data - rtdata.get_data('nirspec/mos/jw01180025001_01_msa.fits') - rtdata.get_data('nirspec/mos/jw01180025001_05101_00001_nrs2_rate.fits') + rtdata.get_data('nirspec/mos/jw01448011001_01_msa.fits') + rtdata.get_data('nirspec/mos/jw01448011001_02101_00001_nrs2_rate.fits') # Run the pipeline step_params = { @@ -39,16 +39,18 @@ def run_spec2_mbkg_user(rtdata_module): rtdata = rtdata_module # Get data - rtdata.get_data('nirspec/mos/nrs_mos_3pointnod_1_msa.fits') - rtdata.get_data('nirspec/mos/jw00626030001_02103_00001_nrs1_masterbg1d.fits') - rtdata.get_data('nirspec/mos/jw00626030001_02103_00001_nrs1_rate.fits') + user_bg = 'jw01448011001_02101_00001_nrs2_user_bg.fits' + rtdata.get_data('nirspec/mos/jw01448011001_01_msa.fits') + rtdata.get_data(f'nirspec/mos/{user_bg}') + rtdata.get_data('nirspec/mos/jw01448011001_02101_00001_nrs2_rate.fits') # Run the pipeline step_params = { 'step': 'calwebb_spec2', 'args': [ '--steps.master_background_mos.skip=false', - '--steps.master_background_mos.user_background=jw00626030001_02103_00001_nrs1_masterbg1d.fits' + f'--steps.master_background_mos.user_background={user_bg}', + f'--output_file={user_bg}' ] } rtdata = rt.run_step_from_dict(rtdata, **step_params) @@ -57,7 +59,7 @@ def run_spec2_mbkg_user(rtdata_module): def test_masterbkg_rerun(rtdata): """Test to ensure sequential runs of the step are consistent""" - with dm.open(rtdata.get_data('nirspec/mos/nrs_mos_with_bkgslits_srctype.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01448011001_02101_00001_nrs2_srctype.fits')) as data: mbs = MasterBackgroundMosStep() corrected = mbs.run(data) corrected_again = mbs.run(data) @@ -72,7 +74,7 @@ def test_masterbkg_rerun(rtdata): def test_masterbkg_corrpars(rtdata): """Test for correction parameters""" - with dm.open(rtdata.get_data('nirspec/mos/nrs_mos_with_bkgslits_srctype.fits')) as data: + with dm.open(rtdata.get_data('nirspec/mos/jw01448011001_02101_00001_nrs2_srctype.fits')) as data: mbs = MasterBackgroundMosStep() corrected = mbs.run(data) @@ -94,6 +96,11 @@ def test_masterbkg_corrpars(rtdata): def test_nirspec_mos_mbkg(suffix, run_spec2_mbkg, fitsdiff_default_kwargs): """Run spec2 with master background""" rtdata = run_spec2_mbkg + + # Adjust tolerance for machine precision with float32 drizzle code + if suffix == "s2d": + fitsdiff_default_kwargs["rtol"] = 1e-2 + fitsdiff_default_kwargs["atol"] = 2e-4 rt.is_like_truth(rtdata, fitsdiff_default_kwargs, suffix, truth_path='truth/test_nirspec_mos_mbkg') @@ -104,7 +111,17 @@ def test_nirspec_mos_mbkg(suffix, run_spec2_mbkg, fitsdiff_default_kwargs): def test_nirspec_mos_mbkg_user(suffix, run_spec2_mbkg_user, fitsdiff_default_kwargs): """Run spec2 with master background and user-supplied mbkg""" rtdata = run_spec2_mbkg_user - rt.is_like_truth(rtdata, fitsdiff_default_kwargs, suffix, truth_path='truth/test_nirspec_mos_mbkg_user') + + output_filename = f"jw01448011001_02101_00001_nrs2_user_bg_{suffix}.fits" + rtdata.output = output_filename + rtdata.get_truth(f"truth/test_nirspec_mos_mbkg_user/{output_filename}") + + # Adjust tolerance for machine precision with float32 drizzle code + if suffix == "s2d": + fitsdiff_default_kwargs["rtol"] = 1e-2 + fitsdiff_default_kwargs["atol"] = 2e-4 + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() def test_nirspec_fs_mbkg_user(rtdata, fitsdiff_default_kwargs): From a72868d7844c549ce683b21af03c944acdd5b8c4 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Mon, 21 Oct 2024 17:35:14 -0400 Subject: [PATCH 600/687] Update FS master background check to use existing regtest data + new user bg --- jwst/regtest/test_nirspec_masterbackground.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_nirspec_masterbackground.py b/jwst/regtest/test_nirspec_masterbackground.py index 39172222bc..157f53f833 100644 --- a/jwst/regtest/test_nirspec_masterbackground.py +++ b/jwst/regtest/test_nirspec_masterbackground.py @@ -128,16 +128,16 @@ def test_nirspec_fs_mbkg_user(rtdata, fitsdiff_default_kwargs): """Run a test for NIRSpec FS data with a user-supplied background file.""" # Get user-supplied background - user_background = "v2_nrs_bkg_user_clean_x1d.fits" + user_background = "jw01309-o022_b000000021_nirspec_f290lp-g395h-s200a1-allslits_x1d.fits" rtdata.get_data(f"nirspec/fs/{user_background}") # Get input data - rtdata.get_data("nirspec/fs/nrs_sci+bkg_cal.fits") + rtdata.get_data("nirspec/fs/jw01309022001_04102_00001_nrs1_cal.fits") - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='master_background', + MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='mbsub', user_background=user_background) - output = "nrs_sci+bkg_master_background.fits" + output = "jw01309022001_04102_00001_nrs1_mbsub.fits" rtdata.output = output # Get the truth file From 73c8ea0243bceb2883d2a9927fe1262ccace43d1 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 22 Oct 2024 18:39:47 -0400 Subject: [PATCH 601/687] Update IFU data for master background test --- jwst/regtest/test_nirspec_masterbackground.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/jwst/regtest/test_nirspec_masterbackground.py b/jwst/regtest/test_nirspec_masterbackground.py index 157f53f833..f55d6ae783 100644 --- a/jwst/regtest/test_nirspec_masterbackground.py +++ b/jwst/regtest/test_nirspec_masterbackground.py @@ -151,16 +151,16 @@ def test_nirspec_fs_mbkg_user(rtdata, fitsdiff_default_kwargs): def test_nirspec_ifu_mbkg_user(rtdata, fitsdiff_default_kwargs): """Test NIRSpec IFU data with a user-supplied background file.""" # Get user-supplied background - user_background = "prism_bkg_x1d.fits" + user_background = "jw01252-o002_t005_nirspec_prism-clear_x1d.fits" rtdata.get_data(f"nirspec/ifu/{user_background}") # Get input data - rtdata.get_data("nirspec/ifu/prism_sci_bkg_cal.fits") + rtdata.get_data("nirspec/ifu/jw01252001001_03101_00001_nrs1_cal.fits") MasterBackgroundStep.call(rtdata.input, user_background=user_background, - save_results=True, suffix='master_background') + save_results=True, suffix='mbsub') - output = "prism_sci_bkg_master_background.fits" + output = "jw01252001001_03101_00001_nrs1_mbsub.fits" rtdata.output = output # Get the truth file @@ -173,18 +173,18 @@ def test_nirspec_ifu_mbkg_user(rtdata, fitsdiff_default_kwargs): @pytest.mark.parametrize( 'output_file', - ['ifu_prism_source_on_NRS1_master_background.fits', - 'ifu_prism_source_off_NRS1_o001_masterbg1d.fits', - 'ifu_prism_source_on_NRS1_o001_masterbg2d.fits'], + ['jw01252001001_03101_00001_nrs1_mbsub.fits', + 'jw01252-o002_t005_nirspec_prism-clear_o001_masterbg1d.fits', + 'jw01252001001_03101_00001_nrs1_o001_masterbg2d.fits'], ids=["on-source", "off-source", "on-source2d"] ) def test_nirspec_ifu_mbkg_nod(rtdata, fitsdiff_default_kwargs, output_file): """Test NIRSpec IFU prism nodded data.""" # Get input data - rtdata.get_asn("nirspec/ifu/nirspec_spec3_asn.json") + rtdata.get_asn("nirspec/ifu/jw01252-o001_spec3_00003_asn_with_bg.json") MasterBackgroundStep.call(rtdata.input, save_background=True, save_results=True, - suffix='master_background') + suffix='mbsub') rtdata.output = output_file From 6acd75570a63eeeef77385604f06f84ec7a83343 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Wed, 30 Oct 2024 12:00:54 -0400 Subject: [PATCH 602/687] add changelog fragment --- changes/8932.master_background.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8932.master_background.rst diff --git a/changes/8932.master_background.rst b/changes/8932.master_background.rst new file mode 100644 index 0000000000..4ad13be5d5 --- /dev/null +++ b/changes/8932.master_background.rst @@ -0,0 +1 @@ +Added pixel-to-pixel sigma clipping on input backgrounds in the NIRSpec MOS master_background_mos step. Added option to median filter master background spectrum in both the master_background and master_background_mos steps. From 147115c8897593c54aaa2a2bb28fad6b32f63c33 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Wed, 30 Oct 2024 12:05:25 -0400 Subject: [PATCH 603/687] add combine_1d tests --- jwst/combine_1d/combine_1d_step.py | 3 +- jwst/combine_1d/tests/test_dq_err.py | 41 ++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+), 1 deletion(-) diff --git a/jwst/combine_1d/combine_1d_step.py b/jwst/combine_1d/combine_1d_step.py index a5d23d6389..d328f9dc49 100644 --- a/jwst/combine_1d/combine_1d_step.py +++ b/jwst/combine_1d/combine_1d_step.py @@ -27,6 +27,7 @@ def process(self, input_file): with datamodels.open(input_file) as input_model: result = combine1d.combine_1d_spectra(input_model, - self.exptime_key) + self.exptime_key, + sigma_clip=self.sigma_clip) return result diff --git a/jwst/combine_1d/tests/test_dq_err.py b/jwst/combine_1d/tests/test_dq_err.py index 310f3dad48..a69f16dddd 100644 --- a/jwst/combine_1d/tests/test_dq_err.py +++ b/jwst/combine_1d/tests/test_dq_err.py @@ -49,6 +49,47 @@ def test_err(): assert np.allclose(result.spec[0].spec_table['SB_ERROR'], expected_error) +def test_nan(): + """Test that nan exclusion works""" + spec1 = create_spec_model(flux=1e-9) + spec2 = create_spec_model(flux=1e-9) + ms = datamodels.MultiSpecModel() + ms.meta.exposure.exposure_time = 1 + ms.spec.append(spec1) + ms.spec.append(spec2) + + # Make one pixel bad by being nan, without being marked as DO_NOT_USE. + # Result should just contain the second spectrum value + BAD_PIX = 5 + spec1.spec_table['FLUX'][BAD_PIX] = np.nan + result = Combine1dStep.call(ms) + assert np.isclose(result.spec[0].spec_table['FLUX'][BAD_PIX], spec2.spec_table['FLUX'][BAD_PIX]) + + +def test_sigmaclip(): + """Test that sigma clipping works""" + spec1 = create_spec_model(flux=1e-9) + spec2 = create_spec_model(flux=1e-9) + spec3 = create_spec_model(flux=1e-9) + ms = datamodels.MultiSpecModel() + ms.meta.exposure.exposure_time = 1 + ms.spec.append(spec1) + ms.spec.append(spec2) + ms.spec.append(spec3) + + # Make one pixel bad by being large. + # Result should be large + BAD_PIX = 5 + spec1.spec_table['FLUX'][BAD_PIX] = 1.0 + result = Combine1dStep.call(ms) + assert np.isclose(result.spec[0].spec_table['FLUX'][BAD_PIX], 1/3) + + # Now process with sigma_clipping turned on + # Result should not just contain the second and third spectra values. + result_dq = Combine1dStep.call(ms, sigma_clip=3) + assert np.isclose(result_dq.spec[0].spec_table['FLUX'][BAD_PIX], spec2.spec_table['FLUX'][BAD_PIX]) + + def create_spec_model(npoints=10, flux=1e-9, error=1e-10, wave_range=(11, 13)): """Create a SpecModel""" From 4af3b4344f66b334a4d3870643f2e634e78e6763 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Wed, 30 Oct 2024 12:11:57 -0400 Subject: [PATCH 604/687] cleaning formating --- jwst/combine_1d/combine1d.py | 5 ++--- .../master_background_mos_step.py | 2 +- .../master_background_step.py | 6 ++--- .../tests/test_master_background.py | 22 +++++++++---------- 4 files changed, 17 insertions(+), 18 deletions(-) diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index ec6dbfc80e..c978a1daa8 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -285,7 +285,7 @@ def combine_spectra(self, flux, flux_error, surf_bright, sb_error, Parameters ---------- flux : ndarray, 2-D - Tabulated fluxes for the input spectra in the format + Tabulated fluxes for the input spectra in the format [N spectra, M wavelengths]. flux_error : ndarray, 2-D Flux errors of input spectra. @@ -298,7 +298,7 @@ def combine_spectra(self, flux, flux_error, surf_bright, sb_error, count : ndarray, 2-D Count of how many values at each index in the input arrays. sigma_clip : float, optional - Factor for clipping outliers. Compares input spectra to the + Factor for clipping outliers. Compares input spectra to the median and medaian absolute devaition, by default None. Returns @@ -315,7 +315,6 @@ def combine_spectra(self, flux, flux_error, surf_bright, sb_error, Total, per wavelength weights. count : ndarray, 1-D Total count of spectra contributing to each wavelength. - """ # Catch warnings for all NaN slices in an array. diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 0466df3f67..f23aa8ad1d 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -86,7 +86,7 @@ def process(self, data): - "masterbkg_2d": `~jwst.datamodels.MultiSlitModel` The 2D slit-based version of the master background. - + sigma_clip : None or float Optional factor for sigma clipping outliers when combining background spectra. diff --git a/jwst/master_background/master_background_step.py b/jwst/master_background/master_background_step.py index 357404ad22..dd13fc1ce3 100755 --- a/jwst/master_background/master_background_step.py +++ b/jwst/master_background/master_background_step.py @@ -38,7 +38,7 @@ def process(self, input): input : `~jwst.datamodels.ImageModel`, `~jwst.datamodels.IFUImageModel`, `~jwst.datamodels.ModelContainer`, association Input target datamodel(s) to which master background subtraction is to be applied - + median_kernel : integer, optional Optional user-supplied kernel with which to moving-median boxcar filter the master background spectrum. Must be an odd integer, even integers will be rounded down to the nearest @@ -118,7 +118,7 @@ def process(self, input): # the master background so just save the expanded 2d background if self.save_background: self.save_container(background_2d_collection, suffix='masterbg2d', force=True, asn_id=asn_id) - + # Compute master background and subtract it else: if isinstance(input_data, ModelContainer): @@ -254,7 +254,7 @@ def _do_sub(self): "run again and set force_subtract = True.") return do_sub - + def save_container(self, container, suffix="", asn_id="", force=True): """Save all models in container for intermediate background subtraction""" for i, model in enumerate(container): diff --git a/jwst/master_background/tests/test_master_background.py b/jwst/master_background/tests/test_master_background.py index c11fa704ce..6daa6de59a 100644 --- a/jwst/master_background/tests/test_master_background.py +++ b/jwst/master_background/tests/test_master_background.py @@ -40,8 +40,8 @@ def nirspec_rate(): im = datamodels.ImageModel(shape) im.var_rnoise += 1 im.meta.target = { - 'ra': 100.1237, - 'dec': 39.86, + 'ra': 100.1237, + 'dec': 39.86, 'source_type_apt': 'EXTENDED'} im.meta.wcsinfo = { 'dec_ref': 40, @@ -124,15 +124,15 @@ def nirspec_cal_pair(nirspec_rate): def nirspec_asn(nirspec_cal_pair, tmp_cwd): """Create an association with the mock data""" sci, im2 = nirspec_cal_pair - + sci_filename = sci.meta.filename sci.save(sci_filename) - + x1d = Extract1dStep.call(im2) - + # Add a CR to the x1d x1d.spec[0].spec_table['SURF_BRIGHT'][10] += 10 - + x1d_filename = im2.meta.filename.replace('cal', 'x1d') x1d.meta.filename = x1d_filename x1d.save(x1d_filename) @@ -152,7 +152,7 @@ def nirspec_asn(nirspec_cal_pair, tmp_cwd): ] }, ]} - + # Save the association new_data = json.dumps(asn) asn_file = 'asn.json' @@ -208,7 +208,7 @@ def test_master_background_medfilt(tmp_cwd, nirspec_asn): result = MasterBackgroundStep.call( container, ) - + # Run with a median filter using kernel size 4. This should be rounded # down to 3 otherwise medfilt will throw an error. result_medfilt = MasterBackgroundStep.call( @@ -218,13 +218,13 @@ def test_master_background_medfilt(tmp_cwd, nirspec_asn): # Check that the background correction was run in both cases assert query_step_status(result, "master_background") == 'COMPLETE' assert query_step_status(result_medfilt, "master_background") == 'COMPLETE' - + # Check that there is a difference between the median filter on and off data_nofilt = result[0].slits[0].data data_medfilt = result_medfilt[0].slits[0].data assert not np.all(data_nofilt == data_medfilt) - - # Check that once filtered any pixels where the background correction + + # Check that once filtered any pixels where the background correction # was applied have zero flux bkg_sub_pixels = data_medfilt[data_medfilt != 1] assert np.allclose(bkg_sub_pixels, 0) From d0d57b31423e74f17b6e17176d25e720a7cdafa8 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Wed, 30 Oct 2024 16:34:34 -0400 Subject: [PATCH 605/687] add master_background_mos tests --- .../tests/test_master_background_mos.py | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 jwst/master_background/tests/test_master_background_mos.py diff --git a/jwst/master_background/tests/test_master_background_mos.py b/jwst/master_background/tests/test_master_background_mos.py new file mode 100644 index 0000000000..490ffc4e1a --- /dev/null +++ b/jwst/master_background/tests/test_master_background_mos.py @@ -0,0 +1,251 @@ +import numpy as np +import pytest +from astropy.io import fits +from astropy.table import Table +from stdatamodels.jwst.datamodels import ImageModel + +from jwst.stpipe import query_step_status +from jwst.assign_wcs import AssignWcsStep +from jwst.extract_2d.extract_2d_step import Extract2dStep +from jwst.master_background import MasterBackgroundMosStep +from jwst.master_background import nirspec_utils + + +# WCS keywords, borrowed from NIRCam grism tests +WCS_KEYS = {'wcsaxes': 2, 'ra_ref': 53.1490299775, 'dec_ref': -27.8168745624, + 'v2_ref': 86.103458, 'v3_ref': -493.227512, 'roll_ref': 45.04234459270135, + 'v3i_yang': 0.0, 'vparity': -1, + 'crpix1': 1024.5, 'crpix2': 1024.5, + 'crval1': 53.1490299775, 'crval2': -27.8168745624, + 'cdelt1': 1.81661111111111e-05, 'cdelt2': 1.8303611111111e-05, + 'ctype1': 'RA---TAN', 'ctype2': 'DEC--TAN', + 'pc1_1': -0.707688557183348, 'pc1_2': 0.7065245261360363, + 'pc2_1': 0.7065245261360363, 'pc2_2': 1.75306861111111e-05, + 'cunit1': 'deg', 'cunit2': 'deg'} + + +def create_nirspec_hdul(detector='NRS1', grating='G395M', filter_name='F290LP', + exptype='NRS_MSASPEC', subarray='FULL', slit=None, nint=1, + wcskeys=None): + if wcskeys is None: + wcskeys = WCS_KEYS.copy() + + hdul = fits.HDUList() + phdu = fits.PrimaryHDU() + phdu.header['TELESCOP'] = 'JWST' + phdu.header['INSTRUME'] = 'NIRSPEC' + phdu.header['DETECTOR'] = detector + phdu.header['FILTER'] = filter_name + phdu.header['GRATING'] = grating + phdu.header['PROGRAM'] = '01234' + phdu.header['TIME-OBS'] = '8:59:37' + phdu.header['DATE-OBS'] = '2023-01-05' + phdu.header['EXP_TYPE'] = exptype + phdu.header['EFFEXPTM'] = 1.0 + phdu.header['DURATION'] = 1.0 + phdu.header['PATT_NUM'] = 1 + phdu.header['SUBARRAY'] = subarray + phdu.header['XOFFSET'] = 0.0 + phdu.header['YOFFSET'] = 0.0 + if subarray == 'SUBS200A1': + phdu.header['SUBSIZE1'] = 2048 + phdu.header['SUBSIZE2'] = 64 + phdu.header['SUBSTRT1'] = 1 + phdu.header['SUBSTRT2'] = 1041 + elif subarray == 'SUB2048': + phdu.header['SUBSIZE1'] = 2048 + phdu.header['SUBSIZE2'] = 32 + phdu.header['SUBSTRT1'] = 1 + phdu.header['SUBSTRT2'] = 946 + else: + phdu.header['SUBSIZE1'] = 2048 + phdu.header['SUBSIZE2'] = 2048 + phdu.header['SUBSTRT1'] = 1 + phdu.header['SUBSTRT2'] = 1 + + if exptype == 'NRS_MSASPEC': + phdu.header['MSAMETID'] = 1 + phdu.header['MSAMETFL'] = 'test_msa_01.fits' + + if slit is not None: + phdu.header['FXD_SLIT'] = slit + phdu.header['APERNAME'] = f'NRS_{slit}_SLIT' + + scihdu = fits.ImageHDU() + scihdu.header['EXTNAME'] = "SCI" + scihdu.header.update(wcskeys) + if nint > 1: + scihdu.data = np.ones((nint, phdu.header['SUBSIZE2'], phdu.header['SUBSIZE1'])) + else: + scihdu.data = np.ones((phdu.header['SUBSIZE2'], phdu.header['SUBSIZE1'])) + + hdul.append(phdu) + hdul.append(scihdu) + return hdul + + +def create_msa_hdul(): + # Four shutters open on MSA, the first three are background slits only + # with the fourth having a source + shutter_data = { + 'slitlet_id': [12, 13, 14, 100], + 'msa_metadata_id': [1, 1, 1, 1], + 'shutter_quadrant': [4, 4, 4, 4], + 'shutter_row': [10, 10, 10, 10], + 'shutter_column': [22, 23, 24, 25], + 'source_id': [0, 0, 0, 2], + 'background': ['Y', 'Y', 'Y', 'N'], + 'shutter_state': ['OPEN', 'OPEN', 'OPEN', 'OPEN'], + 'estimated_source_in_shutter_x': [np.nan, np.nan, np.nan, 0.18283921], + 'estimated_source_in_shutter_y': [np.nan, np.nan, np.nan, 0.31907734], + 'dither_point_index': [1, 1, 1, 1], + 'primary_source': ['N', 'N', 'N', 'Y'], + 'fixed_slit': ['NONE', 'NONE', 'NONE', 'NONE']} + + source_data = { + 'program': [95065, 95065], + 'source_id': [1, 2], + 'source_name': ['95065_1', '95065_2'], + 'alias': ['2122', '2123'], + 'ra': [53.139904, 53.15], + 'dec': [-27.805002, -27.81], + 'preimage_id': ['95065001_000', '95065001_000'], + 'stellarity': [1.0, 1.0]} + + shutter_table = Table(shutter_data) + source_table = Table(source_data) + + hdul = fits.HDUList() + hdul.append(fits.PrimaryHDU()) + hdul.append(fits.ImageHDU()) + hdul.append(fits.table_to_hdu(shutter_table)) + hdul.append(fits.table_to_hdu(source_table)) + hdul[2].name = 'SHUTTER_INFO' + hdul[3].name = 'SOURCE_INFO' + + return hdul + + +@pytest.fixture +def nirspec_msa_rate(tmp_path): + hdul = create_nirspec_hdul() + hdul[0].header['MSAMETFL'] = str(tmp_path / 'test_msa_01.fits') + filename = str(tmp_path / 'test_nrs_msa_rate.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + + +@pytest.fixture +def nirspec_msa_metfl(tmp_path): + hdul = create_msa_hdul() + filename = str(tmp_path / 'test_msa_01.fits') + hdul.writeto(filename, overwrite=True) + hdul.close() + return filename + +@pytest.fixture +def nirspec_msa_extracted2d(nirspec_msa_rate, nirspec_msa_metfl): + model = ImageModel(nirspec_msa_rate) + model = AssignWcsStep.call(model) + model = Extract2dStep.call(model) + return model + + +def test_master_background_mos(nirspec_msa_extracted2d): + model = nirspec_msa_extracted2d + + result = MasterBackgroundMosStep.call(model) + + # Check that the master_background_mos step was run + assert query_step_status(result, "master_background") == 'COMPLETE' + + finite = np.isfinite(result.slits[-1].data) & np.isfinite(model.slits[-1].data) + sci_orig = model.slits[-1].data[finite] + sci_bkgsub = result.slits[-1].data[finite] + + # Check that a background was subtracted from the science data + assert not np.allclose(sci_orig, sci_bkgsub) + + model.close() + result.close() + + +def test_create_background_from_multislit(nirspec_msa_extracted2d): + model = nirspec_msa_extracted2d + + # Insert a outliers into one of the background spectra + nypix = len(model.slits[0].data) + nxpix = len(model.slits[0].data) + model.slits[0].data[nypix//2,nxpix//2-1:nxpix//2+1] = 10 + + # First check that we can make a master background from the inputs + + # Check that with sigma_clip=None, the outlier is retained + master_background, _ = nirspec_utils.create_background_from_multislit( + model, sigma_clip=None) + assert np.any(master_background.spec[0].spec_table['surf_bright'] > 1) + + # Confirm that using a median_filter will filter out the outlier + master_background, _ = nirspec_utils.create_background_from_multislit( + model, median_kernel=4) + assert np.allclose(master_background.spec[0].spec_table['surf_bright'], 1) + + # Confirm that using a sigma clipping when combining background spectra + # removes the outlier + master_background, _ = nirspec_utils.create_background_from_multislit( + model, sigma_clip=3) + assert np.allclose(master_background.spec[0].spec_table['surf_bright'], 1) + + model.close() + +def test_map_to_science_slits(nirspec_msa_extracted2d): + model = nirspec_msa_extracted2d + + master_background, _ = nirspec_utils.create_background_from_multislit( + model) + + # Check that the master background is expanded to the shape of the input slits + mb_multislit = nirspec_utils.map_to_science_slits(model, master_background) + assert mb_multislit.slits[0].data.shape == model.slits[0].data.shape + + # background should be all ones, but won't be expanded to populate the whole + # 2D array, so any non-zero background pixels should have a value of 1 + slit_data = mb_multislit.slits[0].data + nonzero = slit_data != 0 + assert np.allclose(slit_data[nonzero], 1) + + model.close() + +def test_apply_master_background(nirspec_msa_extracted2d): + model = nirspec_msa_extracted2d + + master_background, _ = nirspec_utils.create_background_from_multislit( + model) + mb_multislit = nirspec_utils.map_to_science_slits(model, master_background) + + result = nirspec_utils.apply_master_background(model, mb_multislit, inverse=False) + + # where the background is applied to the science it should be 0 and elsewhere 1 + sci_data_orig = model.slits[-1].data + sci_data_bkgsub = result.slits[-1].data + diff = sci_data_orig - sci_data_bkgsub + assert np.any(diff != 0) + assert np.allclose(diff[diff != 0], 1) + + # Check inverse application + result = nirspec_utils.apply_master_background(model, mb_multislit, inverse=True) + + # where the background is applied to the science it should be 0 and elsewhere 1 + sci_data_orig = model.slits[-1].data + sci_data_bkgsub = result.slits[-1].data + diff = sci_data_orig - sci_data_bkgsub + + # Background subtraction was inverted so the differences will be -1 + assert np.any(diff != 0) + assert np.allclose(diff[diff != 0], -1) + + model.close() + result.close() + + From fa9d957ab1541d87be90bc6ceebb7005c3e781d8 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Wed, 30 Oct 2024 17:39:45 -0400 Subject: [PATCH 606/687] updating docs --- docs/jwst/combine_1d/arguments.rst | 9 ++++++++- docs/jwst/master_background/arguments.rst | 20 ++++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/docs/jwst/combine_1d/arguments.rst b/docs/jwst/combine_1d/arguments.rst index 4fb4c5060b..b17c691be4 100644 --- a/docs/jwst/combine_1d/arguments.rst +++ b/docs/jwst/combine_1d/arguments.rst @@ -1,7 +1,7 @@ Step Arguments ============== -The ``combine_1d`` step has one step-specific argument: +The ``combine_1d`` step has twp step-specific arguments: ``--exptime_key`` This is a case-insensitive string that identifies the metadata element @@ -13,3 +13,10 @@ The ``combine_1d`` step has one step-specific argument: the string is "unit_weight" or "unit weight", the same weight (1) will be used for all input spectra. If the string is anything else, a warning will be logged and unit weight will be used. + +``--sigma_clip`` + Optional factor for sigma clipping outliers when combining spectra. If + a floating point value is provided for ``sigma_clip``, this value will be + used to set an outlier threshold for any pixels in the input spectra that + deviate from the median and median absolute deviation of the inputs. + Defaults to None (such that no clipping is performed). diff --git a/docs/jwst/master_background/arguments.rst b/docs/jwst/master_background/arguments.rst index 758bb10cd7..a8482b249c 100644 --- a/docs/jwst/master_background/arguments.rst +++ b/docs/jwst/master_background/arguments.rst @@ -4,6 +4,12 @@ Step Arguments ============== The master background subtraction step uses the following optional arguments. +``--median_kernel`` + Optional user-supplied kernel size for a moving-median boxcar filter to filter + outliers in the master background spectrum. The kernel size must be an odd integer. + Even integers will be rounded down to the nearest odd integer. + Defaults to 1 (which applies no median filtering). + ``--user_background`` The file name of a user-supplied 1-D master background spectrum. Must be in the form of a standard :ref:`x1d ` product containing a single 'EXTRACT1D' extension. @@ -15,6 +21,10 @@ The master background subtraction step uses the following optional arguments. ``--save_background`` A boolean indicating whether the computed 1-D master background spectrum should be saved to a file. The file name uses a product type suffix of "masterbg". + For the `master_background_mos` step, multiple files will be produced including the 1-D + master background spectrum (saved with the suffix "masterbg1d"), the expanded 2-D background spectra + for each MOS slitlet (with the suffix "masterbg2d"), and the 1-D background spectra + that were combined into the master background spectrum (with the suffix "bkgx1d"). If a user-supplied background is specified, this argument is ignored. Defaults to ``False``. @@ -28,3 +38,13 @@ The master background subtraction step uses the following optional arguments. ``--output_use_model`` A boolean indicating whether to use the "filename" meta attribute in the data model to determine the name of the output file created by the step. Defaults to ``True``. + +``--sigma_clip`` + Factor for sigma clipping outliers and contaminated spectra when combining MOS + background spectra in the `master_background_mos` step. The value of ``sigma_clip`` + will be used to set an outlier threshold for clipping any pixels in the background + spectra that deviate from the median and median absolute deviation of the inputs before + combining the background spectra. Setting ``sigma_clip`` to None will + skip any outlier clipping. This parameter is only available in `master_background_mos` + step and is not available in the generic `master_background` step. + Defaults to 3. \ No newline at end of file From ae0fa9e61e6993028cfd522f7687028f786c54b3 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Thu, 31 Oct 2024 11:19:32 -0400 Subject: [PATCH 607/687] include lib tests xml and db files in package data (#8935) --- changes/8935.general.rst | 1 + pyproject.toml | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 changes/8935.general.rst diff --git a/changes/8935.general.rst b/changes/8935.general.rst new file mode 100644 index 0000000000..fe0b6a58e2 --- /dev/null +++ b/changes/8935.general.rst @@ -0,0 +1 @@ +Include xml and db test data as package data for lib tests. diff --git a/pyproject.toml b/pyproject.toml index 36bd029be8..489fea3c68 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -157,7 +157,8 @@ namespaces = false ] "jwst.lib" = [ "tests/data/*.asdf", - "tests/data/*.db", + "tests/data/**/*.db", + "tests/data/**/*.xml", "tests/data/*.ecsv", "tests/data/*.fits", ] From 2a55d5bd523f428f2f0c72fe375c3e766862f05e Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Thu, 31 Oct 2024 13:32:57 -0400 Subject: [PATCH 608/687] JP-3426: Ensure proper order of bitwise operations (#8916) --- changes/8916.background.rst | 1 + jwst/background/background_sub.py | 22 +++++++++++++++------- jwst/background/tests/test_background.py | 24 +++++++++++++++++++++++- 3 files changed, 39 insertions(+), 8 deletions(-) create mode 100644 changes/8916.background.rst diff --git a/changes/8916.background.rst b/changes/8916.background.rst new file mode 100644 index 0000000000..e2e4414b14 --- /dev/null +++ b/changes/8916.background.rst @@ -0,0 +1 @@ +Apply bitwise operations in correct order when counting good pixels in the background mask during WFSS background subtraction. \ No newline at end of file diff --git a/jwst/background/background_sub.py b/jwst/background/background_sub.py index b5fa267bfb..9edd641c23 100755 --- a/jwst/background/background_sub.py +++ b/jwst/background/background_sub.py @@ -272,6 +272,20 @@ def average_background(input_model, bkg_list, sigma, maxiters): return avg_bkg +def sufficient_background_pixels(dq_array, bkg_mask, min_pixels=100): + """Count number of good pixels for background use. + + Check DQ flags of pixels selected for bkg use - XOR the DQ values with + the DO_NOT_USE flag to flip the DO_NOT_USE bit. Then count the number + of pixels that AND with the DO_NOT_USE flag, i.e. initially did not have + the DO_NOT_USE bit set. + """ + return np.count_nonzero((dq_array[bkg_mask] + ^ pixel['DO_NOT_USE']) + & pixel['DO_NOT_USE'] + ) > min_pixels + + def subtract_wfss_bkg(input_model, bkg_filename, wl_range_name, mmag_extract=None): """Scale and subtract a background reference image from WFSS/GRISM data. @@ -310,18 +324,12 @@ def subtract_wfss_bkg(input_model, bkg_filename, wl_range_name, mmag_extract=Non # i.e. in regions we can use as background. if got_catalog: bkg_mask = mask_from_source_cat(input_model, wl_range_name, mmag_extract) - # Ensure mask has 100 pixels and that those pixels correspond to valid - # pixels using model DQ array - if np.count_nonzero(input_model.dq[bkg_mask] - ^ pixel['DO_NOT_USE'] - & pixel['DO_NOT_USE'] - ) < 100: + if not sufficient_background_pixels(input_model.dq, bkg_mask): log.warning("Not enough background pixels to work with.") log.warning("Step will be SKIPPED.") return None else: bkg_mask = np.ones(input_model.data.shape, dtype=bool) - # Compute the mean values of science image and background reference # image, including only regions where there are no identified sources. # Exclude pixel values in the lower and upper 25% of the histogram. diff --git a/jwst/background/tests/test_background.py b/jwst/background/tests/test_background.py index 7e895b7ddd..00b04c93a8 100644 --- a/jwst/background/tests/test_background.py +++ b/jwst/background/tests/test_background.py @@ -9,11 +9,13 @@ from numpy.testing import assert_allclose from stdatamodels.jwst import datamodels +from stdatamodels.jwst.datamodels.dqflags import pixel from jwst.assign_wcs import AssignWcsStep from jwst.background import BackgroundStep from jwst.stpipe import Step -from jwst.background.background_sub import robust_mean, mask_from_source_cat, no_NaN +from jwst.background.background_sub import (robust_mean, mask_from_source_cat, + no_NaN, sufficient_background_pixels) @pytest.fixture(scope="module") @@ -403,3 +405,23 @@ def test_no_nan(): # Make sure arrays are equal. assert np.array_equal(model.data, result.data) + + +def test_sufficient_background_pixels(): + model = datamodels.ImageModel(data=np.zeros((2048, 2048)), + dq=np.zeros((2048, 2048))) + refpix_flags = pixel['DO_NOT_USE'] | pixel['REFERENCE_PIXEL'] + model.dq[:4, :] = refpix_flags + model.dq[-4:, :] = refpix_flags + model.dq[:, :4] = refpix_flags + model.dq[:, -4:] = refpix_flags + + bkg_mask = np.ones((2048, 2048), dtype=bool) + # With full array minux refpix available for bkg, should be sufficient + assert sufficient_background_pixels(model.dq, bkg_mask) + + bkg_mask[4: -4, :] = 0 + bkg_mask[:, 4: -4] = 0 + # Now mask out entire array, mocking full source coverage of detector - + # no pixels should be available for bkg + assert not sufficient_background_pixels(model.dq, bkg_mask) From 83da2a668deb4b79e0c8fe583b34af2e369fd7d9 Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 22 Oct 2024 15:40:00 -0400 Subject: [PATCH 609/687] remove schema_editor --- jwst/datamodels/__init__.py | 4 +- jwst/regtest/test_schema_editor.py | 118 ----------------------------- jwst/scripts/schema_editor.py | 42 ---------- jwst/scripts/tests/test_scripts.py | 1 - pyproject.toml | 1 - 5 files changed, 2 insertions(+), 164 deletions(-) delete mode 100644 jwst/regtest/test_schema_editor.py delete mode 100755 jwst/scripts/schema_editor.py diff --git a/jwst/datamodels/__init__.py b/jwst/datamodels/__init__.py index a3d64a70e3..508ac27400 100644 --- a/jwst/datamodels/__init__.py +++ b/jwst/datamodels/__init__.py @@ -31,7 +31,7 @@ _jwst_models = ["ModelContainer", "SourceModelContainer", "ModelLibrary"] # Deprecated modules in stdatamodels -_deprecated_modules = ['schema'] +_deprecated_modules = ['schema', 'schema_editor'] # Deprecated models in stdatamodels _deprecated_models: list[str] = [] @@ -52,6 +52,6 @@ sys.modules[f"jwst.datamodels.{attr}"] = obj # Add a few submodules to sys.modules without exposing them locally -for _submodule_name in ['schema_editor', 'validate']: +for _submodule_name in ['validate']: _submodule = importlib.import_module(f"stdatamodels.jwst.datamodels.{_submodule_name}") sys.modules[f"jwst.datamodels.{_submodule_name}"] = _submodule diff --git a/jwst/regtest/test_schema_editor.py b/jwst/regtest/test_schema_editor.py deleted file mode 100644 index bc111133a6..0000000000 --- a/jwst/regtest/test_schema_editor.py +++ /dev/null @@ -1,118 +0,0 @@ -"""Tests for the schema editor - -Notes ------ -The tests do not have coverage. The original code was written without tests. -The tests here are for later modifications. -""" -import os -from pathlib import Path -import yaml - -import pytest - -import stdatamodels.jwst.datamodels.schema_editor as se - -from jwst.regtest.regtestdata import text_diff -from jwst.lib.file_utils import pushdir - -# Define data locations in artifactory -KEYWORD_DB = 'datamodels/keyword_db' -FIXED_SCHEMA = 'fixed' -SCHEMA_TRUTH = 'truth/test_schema_editor' - - -@pytest.fixture(scope='module') -def keyword_db(rtdata_module): - """Define the keyword database""" - rt = rtdata_module - - keyword_db = Path('keyword_db') - keyword_db.mkdir() - with pushdir(str(keyword_db)): - schemas = rt.data_glob(KEYWORD_DB, glob='*.json') - for schema in schemas: - rt.get_data(schema) - - return keyword_db - - -@pytest.fixture(scope='module') -def model_db(): - """Create a full Model_db""" - models = se.Model_db() - return models - - -@pytest.fixture(scope='module') -def run_editor_full(tmp_cwd_module, keyword_db): - """Just run the editor""" - - editor = se.Schema_editor( - input=str(keyword_db), output=FIXED_SCHEMA, - add=True, delete=True, edit=True, rename=True - ) - editor.change() - - # The fixed schema live in a date-stamped folder under FIXED_SCHEMA. - # It will be the only folder, so we can just return the first from the list. - return os.path.join(FIXED_SCHEMA, os.listdir(FIXED_SCHEMA)[0]) - - -@pytest.mark.bigdata -def test_limit_datamodels_from_file(tmp_cwd_module, model_db, keyword_db, rtdata_module): - """Test limiting datamodels from Schema_editor""" - - # Create the exclusion file. - exclude = list(model_db)[1:] - exclude_path = 'exclude.yaml' - with open(exclude_path, 'w') as fh: - yaml.dump(exclude, fh) - - # Run the editor - editor = se.Schema_editor(input=str(keyword_db), exclude_file=exclude_path, - list=True, rename=True) - editor.change() - assert len(editor.model_db) == 1 - - -def test_limit_datamodels(model_db): - """Limit the datamodel list""" - - # Reload the models but exclude everything except the first model. - exclude_list = list(model_db)[1:] - models_limited = se.Model_db(exclude=exclude_list) - assert len(models_limited) == 1 - - -@pytest.mark.bigdata -@pytest.mark.parametrize( - 'schema', - ['core.schema.yaml', - 'extract1dimage.schema.yaml', - 'guider_meta.schema.yaml', - 'ifucube.schema.yaml', - 'keyword_exptype.schema.yaml', 'keyword_pband.schema.yaml', 'keyword_readpatt.schema.yaml', - 'multiextract1d.schema.yaml', 'multispec.schema.yaml', - 'pathloss.schema.yaml', - 'referencefile.schema.yaml', - 'slitmeta.schema.yaml', - 'wcsinfo.schema.yaml', ] -) -def test_full_run(tmp_cwd_module, schema, run_editor_full, rtdata_module): - """Check fixed schema files""" - rt = rtdata_module - rt.output = os.path.join(run_editor_full, schema) - rt.get_truth(SCHEMA_TRUTH + '/' + schema) - - assert text_diff(rt.output, rt.truth) - - -@pytest.mark.bigdata -def test_no_option_warning(tmp_cwd_module, keyword_db): - """If no operations is given, raise an error""" - editor = se.Schema_editor( - input=keyword_db - ) - with pytest.raises(RuntimeError): - editor.change() diff --git a/jwst/scripts/schema_editor.py b/jwst/scripts/schema_editor.py deleted file mode 100755 index 941b04d751..0000000000 --- a/jwst/scripts/schema_editor.py +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/env python - -# Copyright (C) 2018 Association of Universities for Research in Astronomy (AURA) - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above -# copyright notice, this list of conditions and the following -# disclaimer in the documentation and/or other materials provided -# with the distribution. - -# 3. The name of AURA and its representatives may not be used to -# endorse or promote products derived from this software without -# specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY AURA ``AS IS'' AND ANY EXPRESS OR IMPLIED -# WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL AURA BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS -# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR -# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE -# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -# DAMAGE. - -from stdatamodels.jwst.datamodels import schema_editor - - -def main(): - options = schema_editor.Options() - editor = schema_editor.Schema_editor() - editor.change(options) - - -if __name__ == '__main__': - main() diff --git a/jwst/scripts/tests/test_scripts.py b/jwst/scripts/tests/test_scripts.py index 18e702b597..b9ba4e8d08 100644 --- a/jwst/scripts/tests/test_scripts.py +++ b/jwst/scripts/tests/test_scripts.py @@ -11,7 +11,6 @@ 'create_data', 'okify_regtests', 'pointing_summary', - 'schema_editor', 'schemadoc', 'set_telescope_pointing', 'set_telescope_pointing.py', diff --git a/pyproject.toml b/pyproject.toml index 489fea3c68..b615cd49e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,7 +77,6 @@ csvconvert = "jwst.csv_tools.csvconvert:CSVConvertScript" exp_to_source = "jwst.exp_to_source.main:Main" okify_regtests = "jwst.scripts.okify_regtests:main" pointing_summary = "jwst.scripts.pointing_summary:main" -schema_editor = "jwst.scripts.schema_editor:main" schemadoc = "jwst.scripts.schemadoc:main" set_telescope_pointing = "jwst.scripts.set_telescope_pointing:main" "set_telescope_pointing.py" = "jwst.scripts.set_telescope_pointing:deprecated_name" From 1f87cb2f9ea1e357057041fd36a176a7ab81c370 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 30 Oct 2024 16:18:27 -0400 Subject: [PATCH 610/687] add changelog fragment --- changes/8909.scripts.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8909.scripts.rst diff --git a/changes/8909.scripts.rst b/changes/8909.scripts.rst new file mode 100644 index 0000000000..0addc426d1 --- /dev/null +++ b/changes/8909.scripts.rst @@ -0,0 +1 @@ +Remove the outdated schema_editor script. From 974c5b017219c4556022a06fae2f850e4c01b1c6 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Thu, 31 Oct 2024 18:14:49 -0400 Subject: [PATCH 611/687] catch error when using user_background --- jwst/master_background/master_background_mos_step.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index f23aa8ad1d..698a5f40d4 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -125,7 +125,8 @@ def process(self, data): if self.user_background: self.log.info(f'Calculating master background from user-supplied background {self.user_background}') user_background = datamodels.open(self.user_background) - master_background, mb_multislit = self._calc_master_background(data_model, user_background) + master_background, mb_multislit, bkg_x1d_spectra = self._calc_master_background( + data_model, user_background) elif self.use_correction_pars: self.log.info('Using pre-calculated correction parameters.') master_background = self.correction_pars['masterbkg_1d'] @@ -163,7 +164,8 @@ def process(self, data): if self.save_background: self.save_model(master_background, suffix='masterbg1d', force=True) self.save_model(mb_multislit, suffix='masterbg2d', force=True) - self.save_model(bkg_x1d_spectra, suffix='bkgx1d', force=True) + if bkg_x1d_spectra is not None: + self.save_model(bkg_x1d_spectra, suffix='bkgx1d', force=True) return result From c508146a274ed6e4414b9f96b69e8c06f32ee6eb Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 4 Nov 2024 11:14:29 -0800 Subject: [PATCH 612/687] AL-837: Compute resample output WCS from known s_region instead of recalculating footprints (#8893) --- changes/8893.resample.rst | 1 + jwst/assign_mtwcs/moving_target_wcs.py | 4 +++ .../tests/test_outlier_detection.py | 2 ++ jwst/resample/resample_utils.py | 29 +++++++++++-------- 4 files changed, 24 insertions(+), 12 deletions(-) create mode 100644 changes/8893.resample.rst diff --git a/changes/8893.resample.rst b/changes/8893.resample.rst new file mode 100644 index 0000000000..9e48ecea89 --- /dev/null +++ b/changes/8893.resample.rst @@ -0,0 +1 @@ +Use s_region list to calculate output footprint instead of re-computing via WCS transforms diff --git a/jwst/assign_mtwcs/moving_target_wcs.py b/jwst/assign_mtwcs/moving_target_wcs.py index be4a5a1b8b..0e3eb679ab 100644 --- a/jwst/assign_mtwcs/moving_target_wcs.py +++ b/jwst/assign_mtwcs/moving_target_wcs.py @@ -18,6 +18,8 @@ from jwst.datamodels import ModelLibrary from jwst.stpipe.utilities import record_step_status +from jwst.assign_wcs.util import update_s_region_imaging +from jwst.lib.exposure_types import IMAGING_TYPES log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) @@ -69,6 +71,8 @@ def assign_moving_target_wcs(input_models): model.meta.wcsinfo.mt_ra, model.meta.wcsinfo.mt_dec) del model.meta.wcs model.meta.wcs = new_wcs + if model.meta.exposure.type.lower() in IMAGING_TYPES: + update_s_region_imaging(model) record_step_status(model, "assign_mtwcs", True) input_models.shelve(model, i, modify=True) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index b6dfe1c3eb..7d9849fc46 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -6,6 +6,7 @@ from gwcs.wcs import WCS from stdatamodels.jwst import datamodels +from stcal.alignment.util import compute_s_region_imaging from jwst.datamodels import ModelContainer, ModelLibrary from jwst.assign_wcs import AssignWcsStep @@ -151,6 +152,7 @@ def we_many_sci( # Replace the FITS-type WCS with an Identity WCS sci1.meta.wcs = create_fitswcs(sci1) + sci1.meta.wcsinfo.s_region = compute_s_region_imaging(sci1.meta.wcs, shape=shape, center=False) rng = np.random.default_rng(720) sci1.data = rng.normal(loc=background, size=shape, scale=sigma) sci1.err = np.zeros(shape) + sigma diff --git a/jwst/resample/resample_utils.py b/jwst/resample/resample_utils.py index c527b450f1..4d126ed28c 100644 --- a/jwst/resample/resample_utils.py +++ b/jwst/resample/resample_utils.py @@ -28,9 +28,15 @@ def make_output_wcs(input_models, ref_wcs=None, Parameters ---------- input_models : `~jwst.datamodel.ModelLibrary` - Each datamodel must have a ~gwcs.WCS object. + The datamodels to combine into a single output WCS. Each datamodel must + have a ``meta.wcs.s_region`` attribute. - pscale_ratio : float, optional + ref_wcs : gwcs.WCS, None, optional + Custom WCS to use as the output WCS. If not provided, + the reference WCS will be taken as the WCS of the first input model, with + its bounding box adjusted to encompass all input frames. + + pscale_ratio : float, None, optional Ratio of input to output pixel scale. Ignored when ``pscale`` is provided. @@ -67,27 +73,26 @@ def make_output_wcs(input_models, ref_wcs=None, WCS object, with defined domain, covering entire set of input frames """ if ref_wcs is None: + sregion_list = [] with input_models: - wcslist = [] for i, model in enumerate(input_models): - w = model.meta.wcs - if w.bounding_box is None: - w.bounding_box = wcs_bbox_from_shape(model.data.shape) - wcslist.append(w) + sregion_list.append(model.meta.wcsinfo.s_region) if i == 0: example_model = model + ref_wcs = example_model.meta.wcs + ref_wcsinfo = example_model.meta.wcsinfo.instance input_models.shelve(model) - naxes = wcslist[0].output_frame.naxes + naxes = ref_wcs.output_frame.naxes if naxes != 2: msg = ("Output WCS needs 2 spatial axes " f"but the supplied WCS has {naxes} axes.") raise RuntimeError(msg) - output_wcs = util.wcs_from_footprints( - wcslist, - ref_wcs=wcslist[0], - ref_wcsinfo=example_model.meta.wcsinfo.instance, + output_wcs = util.wcs_from_sregions( + sregion_list, + ref_wcs, + ref_wcsinfo, pscale_ratio=pscale_ratio, pscale=pscale, rotation=rotation, From 61032a14c59cf2f762bcb0035dcb1b52b9e605b4 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 29 Oct 2024 15:24:05 -0400 Subject: [PATCH 613/687] Update test data for MIRI MRS TSO --- jwst/regtest/test_miri_mrs_tso.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_tso.py b/jwst/regtest/test_miri_mrs_tso.py index 434a3cbaf9..e602c64bb4 100644 --- a/jwst/regtest/test_miri_mrs_tso.py +++ b/jwst/regtest/test_miri_mrs_tso.py @@ -15,7 +15,7 @@ def run_spec2(rtdata_module): rtdata = rtdata_module # Setup the inputs - file_name = 'jw80600018001_02101_00003_mirifushort_rateints.fits' + file_name = 'jw01556001001_04102_00001-seg001_mirifushort_rateints.fits' rtdata.get_data(INPUT_PATH + '/' + file_name) # Run the pipeline @@ -37,7 +37,7 @@ def run_spec2(rtdata_module): def test_spec2(rtdata_module, run_spec2, fitsdiff_default_kwargs, suffix): """Test ensuring the calwebb_tso-spec2 is operating appropriately for MIRI MRS TSO data""" rtdata = rtdata_module - output = f"jw80600018001_02101_00003_mirifushort_{suffix}.fits" + output = f"jw01556001001_04102_00001-seg001_mirifushort_{suffix}.fits" rtdata.output = output rtdata.get_truth(f"{TRUTH_PATH}/{output}") From ea46c6023615fc420d923d05655baa73f8dede60 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 29 Oct 2024 17:41:37 -0400 Subject: [PATCH 614/687] Update MIRI MRS dedicated background data --- jwst/regtest/test_miri_mrs_dedicated_mbkg.py | 34 ++++++++++++++------ 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_dedicated_mbkg.py b/jwst/regtest/test_miri_mrs_dedicated_mbkg.py index 93e5bcdba3..70edbee567 100644 --- a/jwst/regtest/test_miri_mrs_dedicated_mbkg.py +++ b/jwst/regtest/test_miri_mrs_dedicated_mbkg.py @@ -10,27 +10,41 @@ def run_pipeline(rtdata_module): rtdata = rtdata_module - rtdata.get_asn("miri/mrs/miri_mrs_mbkg_0304_spec3_asn.json") + rtdata.get_asn("miri/mrs/jw01031-c1004_20241028t205539_spec3_subset_asn.json") - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='master_background') + MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='mbsub', + save_background=True) return rtdata @pytest.mark.bigdata -@pytest.mark.parametrize("exp_seq", ["exp1", "exp2", "exp3"]) -def test_miri_mrs_dedicated_mbkg(run_pipeline, fitsdiff_default_kwargs, - exp_seq): +@pytest.mark.parametrize("exposure", [1, 2, 3, 4]) +@pytest.mark.parametrize("suffix", ["mbsub", "c1004_masterbg2d"]) +def test_miri_mrs_dedicated_mbkg(run_pipeline, fitsdiff_default_kwargs, exposure, suffix): """Run a test for MIRI MRS data with dedicated background exposures.""" + rtdata = run_pipeline + output_file = f"jw01031001001_02101_0000{exposure}_mirifulong_{suffix}.fits" + rtdata.output = output_file + + # Get the truth file + rtdata.get_truth(os.path.join("truth/test_miri_mrs_dedicated_mbkg", output_file)) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + +@pytest.mark.bigdata +def test_miri_mrs_dedicated_masterbg1d(run_pipeline, fitsdiff_default_kwargs): rtdata = run_pipeline - rtdata.output = "miri_mrs_seq1_long_34_" + exp_seq + \ - "_master_background.fits" + + # Check 1D masterbg output product: created with root name from nod 1 + output_file = "jw01031006001_02101_00001_mirifulong_c1004_masterbg1d.fits" + rtdata.output = output_file # Get the truth file - rtdata.get_truth(os.path.join( - "truth/test_miri_mrs_dedicated_mbkg", - "miri_mrs_seq1_long_34_" + exp_seq + "_master_background.fits")) + rtdata.get_truth(os.path.join("truth/test_miri_mrs_dedicated_mbkg", output_file)) # Compare the results diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) From 3cb6359e0df4975cf9a33b0273a1df7a2e065325 Mon Sep 17 00:00:00 2001 From: Melanie Clarke Date: Tue, 5 Nov 2024 09:36:51 -0500 Subject: [PATCH 615/687] Remove unneeded test for MIRI MRS nodded background --- jwst/regtest/test_miri_mrs_nod_masterbg.py | 36 ---------------------- 1 file changed, 36 deletions(-) delete mode 100644 jwst/regtest/test_miri_mrs_nod_masterbg.py diff --git a/jwst/regtest/test_miri_mrs_nod_masterbg.py b/jwst/regtest/test_miri_mrs_nod_masterbg.py deleted file mode 100644 index d12a3bae20..0000000000 --- a/jwst/regtest/test_miri_mrs_nod_masterbg.py +++ /dev/null @@ -1,36 +0,0 @@ -import os -import pytest -from astropy.io.fits.diff import FITSDiff - -from jwst.master_background import MasterBackgroundStep - - -@pytest.fixture(scope="module") -def run_pipeline(rtdata_module): - - rtdata = rtdata_module - - rtdata.get_asn("miri/mrs/miri_mrs_mbkg_nodded_spec3_asn.json") - - MasterBackgroundStep.call(rtdata.input, save_results=True, suffix='master_background') - - return rtdata - - -@pytest.mark.bigdata -@pytest.mark.parametrize("nod_seq", ["seq1", "seq2"]) -def test_miri_mrs_nod_masterbg(run_pipeline, fitsdiff_default_kwargs, nod_seq): - """Run a test for MIRI MRS data with nodded background exposures.""" - - rtdata = run_pipeline - rtdata.output = "miri_mrs_nod_" + nod_seq + \ - "_short12_exp1_master_background.fits" - - # Get the truth file - rtdata.get_truth(os.path.join( - "truth/test_miri_mrs_nod_masterbg", - "miri_mrs_nod_" + nod_seq + "_short12_exp1_master_background.fits")) - - # Compare the results - diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From 6fd952c7983dfac63d46a8b9b163dd58c2342fdd Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 6 Nov 2024 17:14:51 -0500 Subject: [PATCH 616/687] replace pipeline Step.__call__ uses with run --- jwst/pipeline/calwebb_ami3.py | 6 +- jwst/pipeline/calwebb_coron3.py | 12 +-- jwst/pipeline/calwebb_dark.py | 34 ++++---- jwst/pipeline/calwebb_detector1.py | 58 +++++++------- jwst/pipeline/calwebb_guider.py | 6 +- jwst/pipeline/calwebb_image2.py | 10 +-- jwst/pipeline/calwebb_image3.py | 16 ++-- jwst/pipeline/calwebb_spec2.py | 122 ++++++++++++++--------------- jwst/pipeline/calwebb_spec3.py | 28 +++---- jwst/pipeline/calwebb_tso3.py | 12 +-- 10 files changed, 152 insertions(+), 152 deletions(-) diff --git a/jwst/pipeline/calwebb_ami3.py b/jwst/pipeline/calwebb_ami3.py index ee58f4ac79..cbc6cd2caf 100644 --- a/jwst/pipeline/calwebb_ami3.py +++ b/jwst/pipeline/calwebb_ami3.py @@ -74,7 +74,7 @@ def process(self, input): # Do the LG analysis for this image log.debug('Do LG processing for member %s', input_file) - result1, result2, result3 = self.ami_analyze(input_file) + result1, result2, result3 = self.ami_analyze.run(input_file) # Save the averaged LG analysis results to a file result1.meta.asn.pool_name = asn['asn_pool'] @@ -90,7 +90,7 @@ def process(self, input): # Do the LG analysis for this image log.debug('Do LG processing for member %s', input_file) - result1, result2, result3 = self.ami_analyze(input_file) + result1, result2, result3 = self.ami_analyze.run(input_file) # Save the LG analysis results to a file result1.meta.asn.pool_name = asn['asn_pool'] @@ -104,7 +104,7 @@ def process(self, input): # assuming one ref star exposure per targ exposure if (len(psf_files) > 0) & (len(targ_files) > 0): for (targ, psf) in zip(targ_lg,psf_lg): - result = self.ami_normalize(targ, psf) + result = self.ami_normalize.run(targ, psf) # Save the result result.meta.asn.pool_name = asn['asn_pool'] result.meta.asn.table_name = op.basename(asn.filename) diff --git a/jwst/pipeline/calwebb_coron3.py b/jwst/pipeline/calwebb_coron3.py index 26310a5e7a..54b2b093ec 100644 --- a/jwst/pipeline/calwebb_coron3.py +++ b/jwst/pipeline/calwebb_coron3.py @@ -151,7 +151,7 @@ def process(self, user_input): # Perform outlier detection on the PSFs. if not skip_outlier_detection: for model in psf_models: - self.outlier_detection(model) + self.outlier_detection.run(model) # step may have been skipped for this model; # turn back on for next model self.outlier_detection.skip = False @@ -159,7 +159,7 @@ def process(self, user_input): self.log.info('Outlier detection skipped for PSF\'s') # Stack all the PSF images into a single CubeModel - psf_stack = self.stack_refs(psf_models) + psf_stack = self.stack_refs.run(psf_models) psf_models.close() # Save the resulting PSF stack @@ -175,13 +175,13 @@ def process(self, user_input): # Remove outliers from the target if not skip_outlier_detection: - target = self.outlier_detection(target) + target = self.outlier_detection.run(target) # step may have been skipped for this model; # turn back on for next model self.outlier_detection.skip = False # Call align_refs - psf_aligned = self.align_refs(target, psf_stack) + psf_aligned = self.align_refs.run(target, psf_stack) # Save the alignment results self.save_model( @@ -190,7 +190,7 @@ def process(self, user_input): ) # Call KLIP - psf_sub = self.klip(target, psf_aligned) + psf_sub = self.klip.run(target, psf_aligned) psf_aligned.close() # Save the psf subtraction results @@ -210,7 +210,7 @@ def process(self, user_input): resample_library = ModelLibrary(resample_input, on_disk=False) # Output is a single datamodel - result = self.resample(resample_library) + result = self.resample.run(resample_library) # Blend the science headers try: diff --git a/jwst/pipeline/calwebb_dark.py b/jwst/pipeline/calwebb_dark.py index 9673ffd882..68bc7d0c90 100644 --- a/jwst/pipeline/calwebb_dark.py +++ b/jwst/pipeline/calwebb_dark.py @@ -67,29 +67,29 @@ def process(self, input): # the steps are in a different order than NIR log.debug('Processing a MIRI exposure') - input = self.group_scale(input) - input = self.dq_init(input) - input = self.emicorr(input) - input = self.saturation(input) - input = self.ipc(input) - input = self.firstframe(input) - input = self.lastframe(input) - input = self.reset(input) - input = self.linearity(input) - input = self.rscd(input) + input = self.group_scale.run(input) + input = self.dq_init.run(input) + input = self.emicorr.run(input) + input = self.saturation.run(input) + input = self.ipc.run(input) + input = self.firstframe.run(input) + input = self.lastframe.run(input) + input = self.reset.run(input) + input = self.linearity.run(input) + input = self.rscd.run(input) else: # process Near-IR exposures log.debug('Processing a Near-IR exposure') - input = self.group_scale(input) - input = self.dq_init(input) - input = self.saturation(input) - input = self.ipc(input) - input = self.superbias(input) - input = self.refpix(input) - input = self.linearity(input) + input = self.group_scale.run(input) + input = self.dq_init.run(input) + input = self.saturation.run(input) + input = self.ipc.run(input) + input = self.superbias.run(input) + input = self.refpix.run(input) + input = self.linearity.run(input) log.info('... ending calwebb_dark') diff --git a/jwst/pipeline/calwebb_detector1.py b/jwst/pipeline/calwebb_detector1.py index afc3bc5ab3..44d80450d5 100644 --- a/jwst/pipeline/calwebb_detector1.py +++ b/jwst/pipeline/calwebb_detector1.py @@ -89,18 +89,18 @@ def process(self, input): # the steps are in a different order than NIR log.debug('Processing a MIRI exposure') - input = self.group_scale(input) - input = self.dq_init(input) - input = self.emicorr(input) - input = self.saturation(input) - input = self.ipc(input) - input = self.firstframe(input) - input = self.lastframe(input) - input = self.reset(input) - input = self.linearity(input) - input = self.rscd(input) - input = self.dark_current(input) - input = self.refpix(input) + input = self.group_scale.run(input) + input = self.dq_init.run(input) + input = self.emicorr.run(input) + input = self.saturation.run(input) + input = self.ipc.run(input) + input = self.firstframe.run(input) + input = self.lastframe.run(input) + input = self.reset.run(input) + input = self.linearity.run(input) + input = self.rscd.run(input) + input = self.dark_current.run(input) + input = self.refpix.run(input) # skip until MIRI team has figured out an algorithm # input = self.persistence(input) @@ -110,28 +110,28 @@ def process(self, input): # process Near-IR exposures log.debug('Processing a Near-IR exposure') - input = self.group_scale(input) - input = self.dq_init(input) - input = self.saturation(input) - input = self.ipc(input) - input = self.superbias(input) - input = self.refpix(input) - input = self.linearity(input) + input = self.group_scale.run(input) + input = self.dq_init.run(input) + input = self.saturation.run(input) + input = self.ipc.run(input) + input = self.superbias.run(input) + input = self.refpix.run(input) + input = self.linearity.run(input) # skip persistence for NIRSpec if instrument != 'NIRSPEC': - input = self.persistence(input) + input = self.persistence.run(input) - input = self.dark_current(input) + input = self.dark_current.run(input) # apply the charge_migration step - input = self.charge_migration(input) + input = self.charge_migration.run(input) # apply the jump step - input = self.jump(input) + input = self.jump.run(input) # apply the clean_flicker_noise step - input = self.clean_flicker_noise(input) + input = self.clean_flicker_noise.run(input) # save the corrected ramp data, if requested if self.save_calibrated_ramp: @@ -143,15 +143,15 @@ def process(self, input): # objects, but when the step is skipped due to `skip = True`, # only the input is returned when the step is invoked. if self.ramp_fit.skip: - input = self.ramp_fit(input) + input = self.ramp_fit.run(input) ints_model = None else: - input, ints_model = self.ramp_fit(input) + input, ints_model = self.ramp_fit.run(input) # apply the gain_scale step to the exposure-level product if input is not None: self.gain_scale.suffix = 'gain_scale' - input = self.gain_scale(input) + input = self.gain_scale.run(input) else: log.info("NoneType returned from ramp_fit. Gain Scale step skipped.") @@ -159,7 +159,7 @@ def process(self, input): # if it exists, and then save it if ints_model is not None: self.gain_scale.suffix = 'gain_scaleints' - ints_model = self.gain_scale(ints_model) + ints_model = self.gain_scale.run(ints_model) self.save_model(ints_model, 'rateints') # setup output_file for saving @@ -176,4 +176,4 @@ def setup_output(self, input): if input.meta.cal_step.ramp_fit == 'COMPLETE': self.suffix = 'rate' else: - self.suffix = 'ramp' \ No newline at end of file + self.suffix = 'ramp' diff --git a/jwst/pipeline/calwebb_guider.py b/jwst/pipeline/calwebb_guider.py index 87bfc864b0..0f519b5954 100644 --- a/jwst/pipeline/calwebb_guider.py +++ b/jwst/pipeline/calwebb_guider.py @@ -52,9 +52,9 @@ def process(self, input): input = datamodels.GuiderRawModel(input) # Apply the steps - input = self.dq_init(input) - input = self.guider_cds(input) - input = self.flat_field(input) + input = self.dq_init.run(input) + input = self.guider_cds.run(input) + input = self.flat_field.run(input) log.info('... ending calwebb_guider') diff --git a/jwst/pipeline/calwebb_image2.py b/jwst/pipeline/calwebb_image2.py index 64aab36d80..82c18ea149 100644 --- a/jwst/pipeline/calwebb_image2.py +++ b/jwst/pipeline/calwebb_image2.py @@ -150,12 +150,12 @@ def process_exposure_product( self.bkg_subtract.save_results = True # Call the background subtraction step - input = self.bkg_subtract(input, members_by_type['background']) + input = self.bkg_subtract.run(input, members_by_type['background']) # work on slope images - input = self.assign_wcs(input) - input = self.flat_field(input) - input = self.photom(input) + input = self.assign_wcs.run(input) + input = self.flat_field.run(input) + input = self.photom.run(input) # Resample individual exposures, but only if it's one of the # regular 2D science image types @@ -163,7 +163,7 @@ def process_exposure_product( len(input.data.shape) == 2: self.resample.save_results = self.save_results self.resample.suffix = 'i2d' - self.resample(input) + self.resample.run(input) # That's all folks self.log.info( diff --git a/jwst/pipeline/calwebb_image3.py b/jwst/pipeline/calwebb_image3.py index 757622a627..36d1a85680 100644 --- a/jwst/pipeline/calwebb_image3.py +++ b/jwst/pipeline/calwebb_image3.py @@ -84,24 +84,24 @@ def process(self, input_data): is_moving = is_moving_target(model) input_models.shelve(model, 0, modify=False) if is_moving: - input_models = self.assign_mtwcs(input_models) + input_models = self.assign_mtwcs.run(input_models) else: - input_models = self.tweakreg(input_models) + input_models = self.tweakreg.run(input_models) - input_models = self.skymatch(input_models) - input_models = self.outlier_detection(input_models) + input_models = self.skymatch.run(input_models) + input_models = self.outlier_detection.run(input_models) elif self.skymatch.skymethod == 'match': self.log.warning("Turning 'skymatch' step off for a single " "input image when 'skymethod' is 'match'") else: - input_models = self.skymatch(input_models) + input_models = self.skymatch.run(input_models) - result = self.resample(input_models) + result = self.resample.run(input_models) del input_models if isinstance(result, datamodels.ImageModel) and result.meta.cal_step.resample == 'COMPLETE': - self.source_catalog(result) + self.source_catalog.run(result) def _load_input_as_library(self, input): @@ -128,4 +128,4 @@ def _load_input_as_library(self, input): elif isinstance(input, datamodels.JwstDataModel): return ModelLibrary([input], asn_exptypes=['science'], on_disk=not self.in_memory) else: - raise TypeError(f"Input type {type(input)} not supported.") \ No newline at end of file + raise TypeError(f"Input type {type(input)} not supported.") diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 36e2993113..1502338cbd 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -225,7 +225,7 @@ def process_exposure_product( # `assign_wcs` is the critical step. Without it, processing cannot proceed. assign_wcs_exception = None try: - calibrated = self.assign_wcs(science) + calibrated = self.assign_wcs.run(science) except Exception as exception: assign_wcs_exception = exception if assign_wcs_exception is not None or \ @@ -252,7 +252,7 @@ def process_exposure_product( # Self-calibrate to flag bad/warm pixels, and apply flags # to both background and science exposures. # skipped by default for all modes - result = self.badpix_selfcal( + result = self.badpix_selfcal.run( calibrated, members_by_type['selfcal'], members_by_type['background'], ) if isinstance(result, datamodels.JwstDataModel): @@ -264,10 +264,10 @@ def process_exposure_product( members_by_type['background'] = bkg_outlier_flagged # apply msa_flagging (flag stuck open shutters for NIRSpec IFU and MOS) - calibrated = self.msa_flagging(calibrated) + calibrated = self.msa_flagging.run(calibrated) # apply the "nsclean" 1/f correction to NIRSpec images - calibrated = self.nsclean(calibrated) + calibrated = self.nsclean.run(calibrated) # Apply nsclean to NIRSpec imprint and background members if not self.nsclean.skip: @@ -279,7 +279,7 @@ def process_exposure_product( self.nsclean.output_file = imprint_file.meta.filename else: self.nsclean.output_file = os.path.basename(imprint_file) - imprint_nsclean = self.nsclean(imprint_file) + imprint_nsclean = self.nsclean.run(imprint_file) members_by_type['imprint'][i] = imprint_nsclean for i, bkg_file in enumerate(members_by_type['background']): @@ -288,21 +288,21 @@ def process_exposure_product( self.nsclean.output_file = bkg_file.meta.filename else: self.nsclean.output_file = os.path.basename(bkg_file) - bkg_nsclean = self.nsclean(bkg_file) + bkg_nsclean = self.nsclean.run(bkg_file) members_by_type['background'][i] = bkg_nsclean # Leakcal subtraction (imprint) occurs before background subtraction on a per-exposure basis. # If there is only one `imprint` member, this imprint exposure is subtracted from all the # science and background exposures. Otherwise, there will be as many `imprint` members as # there are science plus background members. - calibrated = self.imprint_subtract(calibrated, members_by_type['imprint']) + calibrated = self.imprint_subtract.run(calibrated, members_by_type['imprint']) # for each background image subtract an associated leak cal for i, bkg_file in enumerate(members_by_type['background']): - bkg_imprint_sub = self.imprint_subtract(bkg_file, members_by_type['imprint']) + bkg_imprint_sub = self.imprint_subtract.run(bkg_file, members_by_type['imprint']) members_by_type['background'][i] = bkg_imprint_sub - calibrated = self.bkg_subtract(calibrated, members_by_type['background']) + calibrated = self.bkg_subtract.run(calibrated, members_by_type['background']) # The order of the next few steps is tricky, depending on mode: # WFSS/Grism data need flat_field before extract_2d, but other modes @@ -334,8 +334,8 @@ def process_exposure_product( resampled = calibrated.copy() # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - resampled = self.pixel_replace(resampled) - resampled = self.resample_spec(resampled) + resampled = self.pixel_replace.run(resampled) + resampled = self.resample_spec.run(resampled) elif is_nrs_slit_linelamp(calibrated): @@ -343,8 +343,8 @@ def process_exposure_product( resampled = calibrated.copy() # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - resampled = self.pixel_replace(resampled) - resampled = self.resample_spec(resampled) + resampled = self.pixel_replace.run(resampled) + resampled = self.resample_spec.run(resampled) elif (exp_type in ['MIR_MRS', 'NRS_IFU']) or is_nrs_ifu_linelamp(calibrated): @@ -355,21 +355,21 @@ def process_exposure_product( resampled = calibrated.copy() # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - resampled = self.pixel_replace(resampled) - resampled = self.cube_build(resampled) + resampled = self.pixel_replace.run(resampled) + resampled = self.cube_build.run(resampled) if query_step_status(resampled, "cube_build") == 'COMPLETE': self.save_model(resampled[0], suffix='s3d') elif exp_type in ['MIR_LRS-SLITLESS']: resampled = calibrated.copy() # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - resampled = self.pixel_replace(resampled) + resampled = self.pixel_replace.run(resampled) else: # will be run if set in parameter ref file or by user resampled = calibrated.copy() # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - resampled = self.pixel_replace(resampled) + resampled = self.pixel_replace.run(resampled) # Extract a 1D spectrum from the 2D/3D data if exp_type in ['MIR_MRS', 'NRS_IFU'] and query_step_status(resampled, "cube_build") == 'SKIPPED': # Skip extract_1d for IFU modes where no cube was built @@ -384,7 +384,7 @@ def process_exposure_product( self.photom.suffix = 'x1d' self.extract_1d.save_results = False x1d = resampled.copy() - x1d = self.extract_1d(x1d) + x1d = self.extract_1d.run(x1d) # Possible that no fit was possible - if so, skip photom if (x1d is None) or (x1d.meta.cal_step.extract_1d == "SKIPPED"): @@ -401,7 +401,7 @@ def process_exposure_product( x1d = resampled.copy() else: x1d = resampled.copy() - x1d = self.extract_1d(x1d) + x1d = self.extract_1d.run(x1d) resampled.close() if x1d is not None: @@ -510,7 +510,7 @@ def _process_grism(self, data): """ # Apply flat-field correction - calibrated = self.flat_field(data) + calibrated = self.flat_field.run(data) # Create and save a WFSS e-/sec image, if requested if self.save_wfss_esec: @@ -547,14 +547,14 @@ def _process_grism(self, data): # Continue with remaining calibration steps, using the original # DN/sec image - calibrated = self.extract_2d(calibrated) - calibrated = self.srctype(calibrated) - calibrated = self.straylight(calibrated) - calibrated = self.fringe(calibrated) - calibrated = self.pathloss(calibrated) - calibrated = self.barshadow(calibrated) - calibrated = self.wfss_contam(calibrated) - calibrated = self.photom(calibrated) + calibrated = self.extract_2d.run(calibrated) + calibrated = self.srctype.run(calibrated) + calibrated = self.straylight.run(calibrated) + calibrated = self.fringe.run(calibrated) + calibrated = self.pathloss.run(calibrated) + calibrated = self.barshadow.run(calibrated) + calibrated = self.wfss_contam.run(calibrated) + calibrated = self.photom.run(calibrated) return calibrated def _process_nirspec_slits(self, data): @@ -568,14 +568,14 @@ def _process_nirspec_slits(self, data): Note that NIRSpec MOS and FS need srctype and wavecorr before flat_field. """ - calibrated = self.extract_2d(data) - calibrated = self.srctype(calibrated) - calibrated = self.master_background_mos(calibrated) - calibrated = self.wavecorr(calibrated) - calibrated = self.flat_field(calibrated) - calibrated = self.pathloss(calibrated) - calibrated = self.barshadow(calibrated) - calibrated = self.photom(calibrated) + calibrated = self.extract_2d.run(data) + calibrated = self.srctype.run(calibrated) + calibrated = self.master_background_mos.run(calibrated) + calibrated = self.wavecorr.run(calibrated) + calibrated = self.flat_field.run(calibrated) + calibrated = self.pathloss.run(calibrated) + calibrated = self.barshadow.run(calibrated) + calibrated = self.photom.run(calibrated) return calibrated @@ -592,8 +592,8 @@ def _process_nirspec_msa_slits(self, data): Note that NIRSpec MOS and FS need srctype and wavecorr before flat_field. Also have to deal with master background operations. """ - calibrated = self.extract_2d(data) - calibrated = self.srctype(calibrated) + calibrated = self.extract_2d.run(data) + calibrated = self.srctype.run(calibrated) # Split the datamodel into 2 pieces: one with MOS slits and # the other with FS slits @@ -609,12 +609,12 @@ def _process_nirspec_msa_slits(self, data): # First process MOS slits through all remaining steps calib_mos.update(calibrated) if len(calib_mos.slits) > 0: - calib_mos = self.master_background_mos(calib_mos) - calib_mos = self.wavecorr(calib_mos) - calib_mos = self.flat_field(calib_mos) - calib_mos = self.pathloss(calib_mos) - calib_mos = self.barshadow(calib_mos) - calib_mos = self.photom(calib_mos) + calib_mos = self.master_background_mos.run(calib_mos) + calib_mos = self.wavecorr.run(calib_mos) + calib_mos = self.flat_field.run(calib_mos) + calib_mos = self.pathloss.run(calib_mos) + calib_mos = self.barshadow.run(calib_mos) + calib_mos = self.photom.run(calib_mos) # Now repeat for FS slits if len(calib_fss.slits) > 0: @@ -631,7 +631,7 @@ def _process_nirspec_msa_slits(self, data): step.suffix = f'{current_suffix}_fs' # Run step - calib_fss = step(calib_fss) + calib_fss = step.run(calib_fss) # Reset suffix step.suffix = current_suffix @@ -655,25 +655,25 @@ def _process_niriss_soss(self, data): New SOSS extraction requires input to extract_1d step in units of DN/s, with photom step to be run afterwards. """ - calibrated = self.srctype(data) - calibrated = self.flat_field(calibrated) - calibrated = self.straylight(calibrated) - calibrated = self.fringe(calibrated) - calibrated = self.pathloss(calibrated) - calibrated = self.barshadow(calibrated) + calibrated = self.srctype.run(data) + calibrated = self.flat_field.run(calibrated) + calibrated = self.straylight.run(calibrated) + calibrated = self.fringe.run(calibrated) + calibrated = self.pathloss.run(calibrated) + calibrated = self.barshadow.run(calibrated) return calibrated def _process_common(self, data): """Common spectral processing""" - calibrated = self.srctype(data) - calibrated = self.straylight(calibrated) - calibrated = self.flat_field(calibrated) - calibrated = self.fringe(calibrated) - calibrated = self.pathloss(calibrated) - calibrated = self.barshadow(calibrated) - calibrated = self.photom(calibrated) - calibrated = self.residual_fringe(calibrated) # only run on MIRI_MRS data + calibrated = self.srctype.run(data) + calibrated = self.straylight.run(calibrated) + calibrated = self.flat_field.run(calibrated) + calibrated = self.fringe.run(calibrated) + calibrated = self.pathloss.run(calibrated) + calibrated = self.barshadow.run(calibrated) + calibrated = self.photom.run(calibrated) + calibrated = self.residual_fringe.run(calibrated) # only run on MIRI_MRS data return calibrated @@ -707,13 +707,13 @@ def _extract_nirspec_msa_slits(self, resampled): self.extract_1d.save_results = False if len(resamp_mos.slits) > 0: self.log.info(f'Extracting {len(resamp_mos.slits)} MSA slitlets') - x1d = self.extract_1d(resamp_mos) + x1d = self.extract_1d.run(resamp_mos) # Extract the FS slits if len(resamp_fss.slits) > 0: self.log.info(f'Extracting {len(resamp_fss.slits)} fixed slits') resamp_fss.meta.exposure.type = "NRS_FIXEDSLIT" - x1d_fss = self.extract_1d(resamp_fss) + x1d_fss = self.extract_1d.run(resamp_fss) if x1d is None: x1d = x1d_fss x1d.meta.exposure.type = "NRS_MSASPEC" diff --git a/jwst/pipeline/calwebb_spec3.py b/jwst/pipeline/calwebb_spec3.py index d62bcc926f..707a67f5c0 100644 --- a/jwst/pipeline/calwebb_spec3.py +++ b/jwst/pipeline/calwebb_spec3.py @@ -140,11 +140,11 @@ def process(self, input): if is_moving_target(input_models[0]): self.log.info("Assigning WCS to a Moving Target exposure.") # assign_mtwcs modifies input_models in-place - self.assign_mtwcs(input_models) + self.assign_mtwcs.run(input_models) # If background data are present, call the master background step if members_by_type['background']: - source_models = self.master_background(input_models) + source_models = self.master_background.run(input_models) source_models.asn_table = input_models.asn_table # If the step is skipped, do the container splitting that @@ -218,7 +218,7 @@ def process(self, input): # Call the skymatch step for MIRI MRS data if exptype in ['MIR_MRS']: - result = self.mrs_imatch(result) + result = self.mrs_imatch.run(result) # Call outlier detection and pixel replacement if exptype not in SLITLESS_TYPES: @@ -231,21 +231,21 @@ def process(self, input): self.outlier_detection.mode = 'ifu' else: self.outlier_detection.mode = 'spec' - result = self.outlier_detection(result) + result = self.outlier_detection.run(result) # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - result = self.pixel_replace(result) + result = self.pixel_replace.run(result) # Resample time. Dependent on whether the data is IFU or not. resample_complete = None if exptype in IFU_EXPTYPES: - result = self.cube_build(result) + result = self.cube_build.run(result) try: resample_complete = result[0].meta.cal_step.cube_build except AttributeError: pass else: - result = self.resample_spec(result) + result = self.resample_spec.run(result) try: resample_complete = result.meta.cal_step.resample except AttributeError: @@ -256,7 +256,7 @@ def process(self, input): # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE - result = self.pixel_replace(result) + result = self.pixel_replace.run(result) # For slitless data, extract 1D spectra and then combine them if exptype in ['NIS_SOSS']: @@ -265,17 +265,17 @@ def process(self, input): # those instead. self.extract_1d.save_results = False - result = self.extract_1d(result) + result = self.extract_1d.run(result) # SOSS F277W may return None - don't bother with that. if result is not None: self.photom.save_results = self.save_results self.photom.suffix = 'x1d' - result = self.photom(result) + result = self.photom.run(result) else: - result = self.extract_1d(result) + result = self.extract_1d.run(result) - result = self.combine_1d(result) + result = self.combine_1d.run(result) elif resample_complete is not None and resample_complete.upper() == 'COMPLETE': @@ -290,10 +290,10 @@ def process(self, input): self.spectral_leak.search_output_file = False self.spectral_leak.save_results = self.save_results - result = self.extract_1d(result) + result = self.extract_1d.run(result) if exptype in ['MIR_MRS']: - result = self.spectral_leak(result) + result = self.spectral_leak.run(result) else: self.log.warning( 'Resampling was not completed. Skipping extract_1d.' diff --git a/jwst/pipeline/calwebb_tso3.py b/jwst/pipeline/calwebb_tso3.py index f5d974f48b..58c87d753b 100644 --- a/jwst/pipeline/calwebb_tso3.py +++ b/jwst/pipeline/calwebb_tso3.py @@ -91,7 +91,7 @@ def process(self, input): break self.log.info("Performing outlier detection on input images ...") - cube = self.outlier_detection(cube) + cube = self.outlier_detection.run(cube) # Save crfints products if cube.meta.cal_step.outlier_detection == 'COMPLETE': @@ -124,7 +124,7 @@ def process(self, input): for cube in input_models: # Extract Photometry from imaging data - phot_result_list.append(self.tso_photometry(cube)) + phot_result_list.append(self.tso_photometry.run(cube)) # Spectroscopy else: @@ -147,12 +147,12 @@ def process(self, input): for cube in input_models: # interpolate pixels that have a NaN value or are flagged # as DO_NOT_USE or NON_SCIENCE. - cube = self.pixel_replace(cube) + cube = self.pixel_replace.run(cube) state = cube.meta.cal_step.pixel_replace # Process spectroscopic TSO data # extract 1D self.log.info("Extracting 1-D spectra ...") - result = self.extract_1d(cube) + result = self.extract_1d.run(cube) for row in cube.int_times: # Subtract one to assign 1-indexed int_nums to int_times array locations x1d_result.int_times[row[0] - 1] = row @@ -164,13 +164,13 @@ def process(self, input): if input_exptype == 'NIS_SOSS': # SOSS data have yet to be photometrically calibrated # Calibrate 1D spectra here. - result = self.photom(result) + result = self.photom.run(result) x1d_result.spec.extend(result.spec) # perform white-light photometry on 1d extracted data self.log.info("Performing white-light photometry ...") - phot_result_list.append(self.white_light(result)) + phot_result_list.append(self.white_light.run(result)) # Update some metadata from the association x1d_result.meta.asn.pool_name = input_models.asn_table["asn_pool"] From 4029bf57a89fe22192d29136942347341491c065 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 6 Nov 2024 17:42:01 -0500 Subject: [PATCH 617/687] remove Step.__call__ use in tests --- jwst/stpipe/tests/steps/__init__.py | 16 ++++++++-------- jwst/stpipe/tests/test_pipeline.py | 6 +++--- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/jwst/stpipe/tests/steps/__init__.py b/jwst/stpipe/tests/steps/__init__.py index eab156a0e5..dd1f2f6295 100644 --- a/jwst/stpipe/tests/steps/__init__.py +++ b/jwst/stpipe/tests/steps/__init__.py @@ -23,7 +23,7 @@ class PipeWithReference(Pipeline): step_defs = {'step_with_reference': StepWithReference} def process(self, data): - result = self.step_with_reference(data) + result = self.step_with_reference.run(data) return result @@ -236,13 +236,13 @@ def process(self, *args): model = ImageModel(args[0]) self.stepwithmodel.suffix = 'swm' - r = self.stepwithmodel(model) + r = self.stepwithmodel.run(model) self.another_stepwithmodel.suffix = 'aswm' - r = self.another_stepwithmodel(r) + r = self.another_stepwithmodel.run(r) self.stepwithcontainer.suffix = 'swc' - r = self.stepwithcontainer(r) + r = self.stepwithcontainer.run(r) self.withdefaultsstep.suffix = 'wds' - r = self.withdefaultsstep(r) + r = self.withdefaultsstep.run(r) return r @@ -261,8 +261,8 @@ class SavePipeline(Pipeline): def process(self, *args): model = ImageModel(args[0]) - r = self.stepwithmodel(model) - r = self.savestep(r) + r = self.stepwithmodel.run(model) + r = self.savestep.run(r) return r @@ -280,4 +280,4 @@ class MakeListPipeline(Pipeline): def process(self, *args): - return self.make_list(*args) + return self.make_list.run(*args) diff --git a/jwst/stpipe/tests/test_pipeline.py b/jwst/stpipe/tests/test_pipeline.py index 96b17a20f7..3075d74429 100644 --- a/jwst/stpipe/tests/test_pipeline.py +++ b/jwst/stpipe/tests/test_pipeline.py @@ -94,9 +94,9 @@ def process(self, *args): self.flat_filename = join(dirname(__file__), "data", "flat.fits") flat = datamodels.open(self.flat_filename) calibrated = [] - calibrated.append(self.flat_field(science, flat)) - combined = self.combine(calibrated) - self.display(combined) + calibrated.append(self.flat_field.run(science, flat)) + combined = self.combine.run(calibrated) + self.display.run(combined) dm = datamodels.ImageModel(combined) self.save_model(dm) science.close() From 13dbcc313d989d4d2a13d0d3299e6897cdddc5cf Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 7 Nov 2024 08:29:45 -0500 Subject: [PATCH 618/687] remove Step.__call__ use in master background --- .../master_background_mos_step.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 45c641ff5c..9b8baa8791 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -235,10 +235,10 @@ def _calc_master_background(self, data, user_background=None): self.barshadow.source_type = 'EXTENDED' self.photom.source_type = 'EXTENDED' - pre_calibrated = self.flat_field(data) - pre_calibrated = self.pathloss(pre_calibrated) - pre_calibrated = self.barshadow(pre_calibrated) - pre_calibrated = self.photom(pre_calibrated) + pre_calibrated = self.flat_field.run(data) + pre_calibrated = self.pathloss.run(pre_calibrated) + pre_calibrated = self.barshadow.run(pre_calibrated) + pre_calibrated = self.photom.run(pre_calibrated) # Create the 1D, fully calibrated master background. if user_background: @@ -269,9 +269,9 @@ def _calc_master_background(self, data, user_background=None): self.flat_field.use_correction_pars = True self.flat_field.inverse = True - mb_multislit = self.photom(mb_multislit) - mb_multislit = self.barshadow(mb_multislit) - mb_multislit = self.pathloss(mb_multislit) - mb_multislit = self.flat_field(mb_multislit) + mb_multislit = self.photom.run(mb_multislit) + mb_multislit = self.barshadow.run(mb_multislit) + mb_multislit = self.pathloss.run(mb_multislit) + mb_multislit = self.flat_field.run(mb_multislit) return master_background, mb_multislit From 50df638e78cfb20f4bd794c91eef2c14b764d751 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 7 Nov 2024 08:30:40 -0500 Subject: [PATCH 619/687] remove Step.__call__ use in calwebb_spec2 --- jwst/pipeline/calwebb_spec2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 1502338cbd..98b26c586b 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -391,7 +391,7 @@ def process_exposure_product( self.log.warning("Extract_1d did not return a DataModel - skipping photom.") else: self.photom.save_results = self.save_results - x1d = self.photom(x1d) + x1d = self.photom.run(x1d) elif exp_type == 'NRS_MSASPEC': # Special handling for MSA spectra, to handle mixed-in # fixed slits separately From ba195b9a8346fea576ee4b628ba0b783c2d353a1 Mon Sep 17 00:00:00 2001 From: Brett Date: Thu, 7 Nov 2024 12:30:14 -0500 Subject: [PATCH 620/687] add changelog --- changes/8945.stpipe.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8945.stpipe.rst diff --git a/changes/8945.stpipe.rst b/changes/8945.stpipe.rst new file mode 100644 index 0000000000..73937c9f0f --- /dev/null +++ b/changes/8945.stpipe.rst @@ -0,0 +1 @@ +Remove all uses of Step.__call__ to allow it's deprecation. From 4cf6ed537018dc19abd64282f629c74a4e59cbdc Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 12 Nov 2024 13:49:13 -0500 Subject: [PATCH 621/687] including combine_1d into mos bkgd pipeline --- .../master_background_mos_step.py | 11 +++++---- jwst/master_background/nirspec_utils.py | 24 ------------------- 2 files changed, 6 insertions(+), 29 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index eed75e1aaf..2bd1c8ddef 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -12,6 +12,7 @@ from ..pixel_replace import pixel_replace_step from ..resample import resample_spec_step from ..extract_1d import extract_1d_step +from ..combine_1d.combine1d import combine_1d_spectra from ..stpipe import Pipeline __all__ = ['MasterBackgroundMosStep'] @@ -66,6 +67,7 @@ class MasterBackgroundMosStep(Pipeline): 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'resample_spec': resample_spec_step.ResampleSpecStep, 'extract_1d': extract_1d_step.Extract1dStep, + 'combine_1d': combine_1d_spectra } # No need to prefetch. This will have been done by the parent step. @@ -180,9 +182,9 @@ def set_pars_from_parent(self): del pars[par] getattr(self, step).update_pars(pars) - def _set_steps_params(self): - """Get substep parameters to pass on""" - steps = ['pixel_replace', 'resample_spec', 'extract_1d'] + def _set_mospipe_steps_params(self): + """Get substep parameters for MOS pipeline to pass on""" + steps = ['pixel_replace', 'resample_spec', 'extract_1d', 'combine_1d'] for step in steps: pars = getattr(self, step).get_pars() @@ -281,8 +283,7 @@ def _calc_master_background(self, data, user_background=None): bkg_model = self.pixel_replace(bkg_model) bkg_model = self.resample_spec(bkg_model) bkg_model = self.extract_1d(bkg_model) - # Call combine_1d to combine the 1D background spectra - master_background = nirspec_utils.create_background_from_multislit(bkg_model) + master_background = self.combine_1d_spectra(bkg_model, exptime_key='exposure_time') del bkg_model else: master_background = None diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index c2df331fe6..9b89527dc3 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -74,30 +74,6 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(input_model): - """Create a 1D master background spectrum from a set of - calibrated background MOS slitlets in the input - MultiSlitModel. - - Parameters - ---------- - input_model : `~jwst.datamodels.MultiSlitModel` - The input data model containing all slit instances. - - Returns - ------- - master_bkg: `~jwst.datamodels.CombinedSpecModel` - The 1D master background spectrum created from the inputs. - """ - from ..combine_1d.combine1d import combine_1d_spectra - - # Call combine_1d to combine the 1D background spectra - log.info('Combining 1D background spectra into master background') - master_bkg = combine_1d_spectra(input_model, exptime_key='exposure_time') - - return master_bkg - - def correct_nrs_ifu_bkg(input_model): """Apply point source vs. uniform source pathloss adjustments to a NIRSpec IFU 2D master background array. From 6bf4cbad8a405cf5355638ac8e1fb9e10a55be8e Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 12 Nov 2024 15:21:12 -0500 Subject: [PATCH 622/687] fixed bug in combine_1d --- jwst/master_background/master_background_mos_step.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 2bd1c8ddef..e887f385dc 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -12,7 +12,7 @@ from ..pixel_replace import pixel_replace_step from ..resample import resample_spec_step from ..extract_1d import extract_1d_step -from ..combine_1d.combine1d import combine_1d_spectra +from ..combine_1d import combine_1d_step from ..stpipe import Pipeline __all__ = ['MasterBackgroundMosStep'] @@ -67,7 +67,7 @@ class MasterBackgroundMosStep(Pipeline): 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'resample_spec': resample_spec_step.ResampleSpecStep, 'extract_1d': extract_1d_step.Extract1dStep, - 'combine_1d': combine_1d_spectra + 'combine_1d': combine_1d_step.Combine1dStep } # No need to prefetch. This will have been done by the parent step. @@ -277,13 +277,13 @@ def _calc_master_background(self, data, user_background=None): master_background = user_background else: self.log.info('Creating MOS master background from background slitlets') - self._set_steps_params() + self._set_mospipe_steps_params() bkg_model = self._extend_bg_slits(pre_calibrated) if bkg_model is not None: bkg_model = self.pixel_replace(bkg_model) bkg_model = self.resample_spec(bkg_model) bkg_model = self.extract_1d(bkg_model) - master_background = self.combine_1d_spectra(bkg_model, exptime_key='exposure_time') + master_background = self.combine_1d(bkg_model) del bkg_model else: master_background = None From 977695319e5392fbc01dafbaf9f73fddc3ef59fa Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 12 Nov 2024 15:26:58 -0500 Subject: [PATCH 623/687] added combine_1d to changes --- changes/8847.refpix.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/changes/8847.refpix.rst b/changes/8847.refpix.rst index f394cd8e5d..3fe299a962 100644 --- a/changes/8847.refpix.rst +++ b/changes/8847.refpix.rst @@ -1 +1 @@ -Include resample, pixel_replace, and extract_1d into MOS background pipeline +Include resample, pixel_replace, extract_1d, and combine_1d into MOS background pipeline From 33b3fb3639d9cb88a4edaac49a40386dd56f4685 Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Mon, 28 Oct 2024 03:15:14 -0400 Subject: [PATCH 624/687] Adding ramp fit multiprocessing regression test. --- jwst/regtest/test_niriss_image.py | 37 +++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index cf07ae16df..7f5a0cf79a 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -9,6 +9,7 @@ from jwst import datamodels from jwst.stpipe import Step from jwst.tweakreg import TweakRegStep +from jwst.ramp_fitting.ramp_fit_step import RampFitStep @pytest.fixture(scope="module") @@ -35,6 +36,30 @@ def run_detector1(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_multi(rtdata_module): + """Run calwebb_detector1 pipeline on NIRISS imaging data.""" + rtdata = rtdata_module + + rtdata.get_data("niriss/imaging/jw01094001002_02107_00001_nis_uncal.fits") + + # Run detector1 pipeline on an _uncal files + args = ["calwebb_detector1", rtdata.input, + "--steps.persistence.save_trapsfilled=False", + "--steps.dq_init.save_results=True", + "--steps.saturation.save_results=True", + "--steps.superbias.save_results=True", + "--steps.refpix.save_results=True", + "--steps.linearity.save_results=True", + "--steps.dark_current.save_results=True", + "--steps.charge_migration.skip=False", + "--steps.charge_migration.save_results=True", + "--steps.jump.save_results=True", + "--steps.ramp_fit.maximum_cores=2", # Multiprocessing + ] + + Step.from_cmdline(args) + @pytest.fixture(scope="module") def run_detector1_with_clean_flicker_noise(rtdata_module): @@ -67,6 +92,7 @@ def test_niriss_image_detector1(run_detector1, rtdata_module, fitsdiff_default_k _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir) + @pytest.mark.bigdata @pytest.mark.parametrize("suffix", ["cfn_clean_flicker_noise", "mask", "flicker_bkg", "flicker_noise", @@ -110,6 +136,17 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): result.shelve(model, modify=False) +def test_niriss_image_detector1_multi( + run_detector1_multi, rtdata_module, fitsdiff_default_kwargs): + """ + Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing + on two processors. + """ + truth_dir = 'test_niriss_image' + # fitsdiff_default_kwargs["numdiffs"] = 10 + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate", truth_dir) + + def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir): """Assertion helper for the above tests""" rtdata = rtdata_module From 7ed7bfb6cf28a93854422c3cda4d60891b093843 Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Mon, 28 Oct 2024 11:37:26 -0400 Subject: [PATCH 625/687] Adding multiprocessing regression testing for ramp fit and jump. --- jwst/regtest/test_miri_image.py | 56 +++++++++++++++++++++++++++++++ jwst/regtest/test_niriss_image.py | 44 ++++++++++++++++++++++-- 2 files changed, 97 insertions(+), 3 deletions(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 2d6f46061f..4b79c4b6ab 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -32,6 +32,52 @@ def run_detector1(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_multi_rate(rtdata_module): + """Run detector1 pipeline on MIRI imaging data.""" + rtdata = rtdata_module + rtdata.get_data("miri/image/jw01024001001_04101_00001_mirimage_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files + args = ["jwst.pipeline.Detector1Pipeline", rtdata.input, + "--save_calibrated_ramp=True", + "--steps.dq_init.save_results=True", + "--steps.saturation.save_results=True", + "--steps.firstframe.save_results=True", + "--steps.lastframe.save_results=True", + "--steps.reset.save_results=True", + "--steps.linearity.save_results=True", + "--steps.rscd.save_results=True", + "--steps.dark_current.save_results=True", + "--steps.refpix.save_results=True", + "--steps.ramp_fit.maximum_cores=2", # Multiprocessing + ] + Step.from_cmdline(args) + + +@pytest.fixture(scope="module") +def run_detector1_multi_jump(rtdata_module): + """Run detector1 pipeline on MIRI imaging data.""" + rtdata = rtdata_module + rtdata.get_data("miri/image/jw01024001001_04101_00001_mirimage_uncal.fits") + + # Run detector1 pipeline only on one of the _uncal files + args = ["jwst.pipeline.Detector1Pipeline", rtdata.input, + "--save_calibrated_ramp=True", + "--steps.dq_init.save_results=True", + "--steps.saturation.save_results=True", + "--steps.firstframe.save_results=True", + "--steps.lastframe.save_results=True", + "--steps.reset.save_results=True", + "--steps.linearity.save_results=True", + "--steps.rscd.save_results=True", + "--steps.dark_current.save_results=True", + "--steps.refpix.save_results=True", + "--steps.jump.maximum_cores=2", # Multiprocessing + ] + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_detector1_with_average_dark_current(rtdata_module): """Run detector1 pipeline on MIRI imaging data, providing an @@ -115,6 +161,16 @@ def test_miri_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwa _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) +def test_miri_image_detector1_multi_rate(run_detector1_multi_rate, rtdata_module, fitsdiff_default_kwargs): + """Regression test of detector1 pipeline performed on MIRI imaging data.""" + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") + + +def test_miri_image_detector1_multi_jump(run_detector1_multi_jump, rtdata_module, fitsdiff_default_kwargs): + """Regression test of detector1 pipeline performed on MIRI imaging data.""" + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") + + @pytest.mark.bigdata def test_detector1_mem_usage(rtdata_module): """Determine the memory usage for Detector 1""" diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 7f5a0cf79a..7ccca052bd 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -36,8 +36,9 @@ def run_detector1(rtdata_module): Step.from_cmdline(args) + @pytest.fixture(scope="module") -def run_detector1_multi(rtdata_module): +def run_detector1_multi_rate(rtdata_module): """Run calwebb_detector1 pipeline on NIRISS imaging data.""" rtdata = rtdata_module @@ -61,6 +62,32 @@ def run_detector1_multi(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_multi_jump(rtdata_module): + """Run calwebb_detector1 pipeline on NIRISS imaging data.""" + rtdata = rtdata_module + + rtdata.get_data("niriss/imaging/jw01094001002_02107_00001_nis_uncal.fits") + + # Run detector1 pipeline on an _uncal files + args = ["calwebb_detector1", rtdata.input, + "--steps.persistence.save_trapsfilled=False", + "--steps.dq_init.save_results=True", + "--steps.saturation.save_results=True", + "--steps.superbias.save_results=True", + "--steps.refpix.save_results=True", + "--steps.linearity.save_results=True", + "--steps.dark_current.save_results=True", + "--steps.charge_migration.skip=False", + "--steps.charge_migration.save_results=True", + "--steps.jump.save_results=True", + "--steps.jump.maximum_cores=2", # Multiprocessing + "--steps.ramp_fit.skip=True", + ] + + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_detector1_with_clean_flicker_noise(rtdata_module): """Run detector1 pipeline on NIRISS imaging data with noise cleaning.""" @@ -136,8 +163,8 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): result.shelve(model, modify=False) -def test_niriss_image_detector1_multi( - run_detector1_multi, rtdata_module, fitsdiff_default_kwargs): +def test_niriss_image_detector1_multi_rate( + run_detector1_multi_rate, rtdata_module, fitsdiff_default_kwargs): """ Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing on two processors. @@ -147,6 +174,17 @@ def test_niriss_image_detector1_multi( _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate", truth_dir) +def test_niriss_image_detector1_multi_jump( + run_detector1_multi_jump, rtdata_module, fitsdiff_default_kwargs): + """ + Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing + on two processors. + """ + truth_dir = 'test_niriss_image' + # fitsdiff_default_kwargs["numdiffs"] = 10 + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "jump", truth_dir) + + def _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir): """Assertion helper for the above tests""" rtdata = rtdata_module From 08e5449140daf93e85211b196879f8017b442fa4 Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Thu, 7 Nov 2024 14:00:13 -0500 Subject: [PATCH 626/687] Changing names to make it more clear the tests are for multiprocessing. --- jwst/regtest/test_miri_image.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 4b79c4b6ab..12d8c0b326 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -33,7 +33,7 @@ def run_detector1(rtdata_module): @pytest.fixture(scope="module") -def run_detector1_multi_rate(rtdata_module): +def run_detector1_multiprocess_rate(rtdata_module): """Run detector1 pipeline on MIRI imaging data.""" rtdata = rtdata_module rtdata.get_data("miri/image/jw01024001001_04101_00001_mirimage_uncal.fits") @@ -56,7 +56,7 @@ def run_detector1_multi_rate(rtdata_module): @pytest.fixture(scope="module") -def run_detector1_multi_jump(rtdata_module): +def run_detector1_multiprocess_jump(rtdata_module): """Run detector1 pipeline on MIRI imaging data.""" rtdata = rtdata_module rtdata.get_data("miri/image/jw01024001001_04101_00001_mirimage_uncal.fits") @@ -161,12 +161,12 @@ def test_miri_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwa _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) -def test_miri_image_detector1_multi_rate(run_detector1_multi_rate, rtdata_module, fitsdiff_default_kwargs): +def test_miri_image_detector1_multiprocess_rate(run_detector1_multiprocess_rate, rtdata_module, fitsdiff_default_kwargs): """Regression test of detector1 pipeline performed on MIRI imaging data.""" _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") -def test_miri_image_detector1_multi_jump(run_detector1_multi_jump, rtdata_module, fitsdiff_default_kwargs): +def test_miri_image_detector1_multiprocess_jump(run_detector1_multiprocess_jump, rtdata_module, fitsdiff_default_kwargs): """Regression test of detector1 pipeline performed on MIRI imaging data.""" _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") From 1b1fde674c9a61b659c96b551828d6868864ee2e Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Thu, 7 Nov 2024 14:01:07 -0500 Subject: [PATCH 627/687] Changing names to clarify the tests are for multiprocessing. --- jwst/regtest/test_niriss_image.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 7ccca052bd..f2d2c1e629 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -38,7 +38,7 @@ def run_detector1(rtdata_module): @pytest.fixture(scope="module") -def run_detector1_multi_rate(rtdata_module): +def run_detector1_multiprocess_rate(rtdata_module): """Run calwebb_detector1 pipeline on NIRISS imaging data.""" rtdata = rtdata_module @@ -63,7 +63,7 @@ def run_detector1_multi_rate(rtdata_module): @pytest.fixture(scope="module") -def run_detector1_multi_jump(rtdata_module): +def run_detector1_multiprocess_jump(rtdata_module): """Run calwebb_detector1 pipeline on NIRISS imaging data.""" rtdata = rtdata_module @@ -163,8 +163,8 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): result.shelve(model, modify=False) -def test_niriss_image_detector1_multi_rate( - run_detector1_multi_rate, rtdata_module, fitsdiff_default_kwargs): +def test_niriss_image_detector1_multiprocess_rate( + run_detector1_multiprocess_rate, rtdata_module, fitsdiff_default_kwargs): """ Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing on two processors. @@ -174,8 +174,8 @@ def test_niriss_image_detector1_multi_rate( _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate", truth_dir) -def test_niriss_image_detector1_multi_jump( - run_detector1_multi_jump, rtdata_module, fitsdiff_default_kwargs): +def test_niriss_image_detector1_multiprocess_jump( + run_detector1_multiprocess_jump, rtdata_module, fitsdiff_default_kwargs): """ Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing on two processors. From 48c88f14da9804b5b4c0f10a9a74366bade71647 Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Tue, 12 Nov 2024 23:04:41 -0500 Subject: [PATCH 628/687] Adding ramp fit multiprocessing test with the optional results product. --- jwst/regtest/test_niriss_image.py | 42 +++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index f2d2c1e629..23ccf8ff9d 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -62,6 +62,33 @@ def run_detector1_multiprocess_rate(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_multiprocess_rate_save_opt(rtdata_module): + """Run calwebb_detector1 pipeline on NIRISS imaging data.""" + rtdata = rtdata_module + + rtdata.get_data("niriss/imaging/jw01094001002_02107_00001_nis_uncal.fits") + + # Run detector1 pipeline on an _uncal files + args = ["calwebb_detector1", rtdata.input, + "--steps.persistence.save_trapsfilled=False", + "--steps.dq_init.save_results=True", + "--steps.saturation.save_results=True", + "--steps.superbias.save_results=True", + "--steps.refpix.save_results=True", + "--steps.linearity.save_results=True", + "--steps.dark_current.save_results=True", + "--steps.charge_migration.skip=False", + "--steps.charge_migration.save_results=True", + "--steps.jump.save_results=True", + "--steps.ramp_fit.maximum_cores=2", # Multiprocessing + "--steps.ramp_fit.save_opt=True", + "--steps.ramp_fit.opt_name=jw01094001002_02107_00001_nis.fits", + ] + + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_detector1_multiprocess_jump(rtdata_module): """Run calwebb_detector1 pipeline on NIRISS imaging data.""" @@ -163,6 +190,7 @@ def test_niriss_tweakreg_no_sources(rtdata, fitsdiff_default_kwargs): result.shelve(model, modify=False) +@pytest.mark.bigdata def test_niriss_image_detector1_multiprocess_rate( run_detector1_multiprocess_rate, rtdata_module, fitsdiff_default_kwargs): """ @@ -174,6 +202,20 @@ def test_niriss_image_detector1_multiprocess_rate( _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate", truth_dir) +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["rate", "fitopt"]) +def test_niriss_image_detector1_multiprocess_rate_save_opt( + run_detector1_multiprocess_rate_save_opt, rtdata_module, fitsdiff_default_kwargs, suffix): + """ + Runs test_niriss_image_detector1[rate], but with ramp fitting run with multiprocessing + on two processors and the optional results product. + """ + truth_dir = 'test_niriss_image' + # fitsdiff_default_kwargs["numdiffs"] = 10 + _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix, truth_dir) + + +@pytest.mark.bigdata def test_niriss_image_detector1_multiprocess_jump( run_detector1_multiprocess_jump, rtdata_module, fitsdiff_default_kwargs): """ From f4bc6d018293066da169692f9b9fccfd314ea978 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 13 Nov 2024 14:51:13 -0500 Subject: [PATCH 629/687] don't store empty columns in HDRTAB --- jwst/model_blender/_tablebuilder.py | 12 ++++++++++-- jwst/model_blender/tests/test_blend.py | 6 ++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/jwst/model_blender/_tablebuilder.py b/jwst/model_blender/_tablebuilder.py index e677d0699e..7bfd2b4efb 100644 --- a/jwst/model_blender/_tablebuilder.py +++ b/jwst/model_blender/_tablebuilder.py @@ -1,6 +1,12 @@ import numpy as np +class _MissingValueType: + pass + +_MISSING_VALUE = _MissingValueType() + + def _convert_dtype(value): """Convert numarray column dtype into YAML-compatible format description""" if 'U' in value: @@ -99,7 +105,7 @@ def header_to_row(self, header): def _add_row(self, row): for attr, col in self.attr_to_column.items(): - self.columns[col].append(row[attr] if attr in row else np.nan) + self.columns[col].append(row[attr] if attr in row else _MISSING_VALUE) def build_table(self): """ @@ -113,6 +119,8 @@ def build_table(self): arrays = [] table_dtype = [] for col, items in self.columns.items(): - arrays.append(np.array(items)) + if all((i is _MISSING_VALUE for i in items)): + continue + arrays.append(np.array([np.nan if i is _MISSING_VALUE else i for i in items])) table_dtype.append((col, arrays[-1].dtype)) return np.rec.fromarrays(arrays, dtype=table_dtype) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 37c9d705dc..5702084cbf 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -37,6 +37,11 @@ # even if the metadata is defined in the schema KNOWN_MISSING_FIRSTS = [None, '1', '2'] +# None of the below test data defines this attribute. +# It is expected to be missing from the resulting +# table (as is checked below). +MISSING_COLUMN = "TIME-OBS" + def _make_data(): """Create a set of input models to blendmeta @@ -195,6 +200,7 @@ def test_blendtab(blend): # Ensure all the expected FITS keywords are in the table. colnames = set(newtab.dtype.fields) assert not fits_expected.difference(colnames) + assert not MISSING_COLUMN in colnames for col in colnames: if col in input_values: assert newtab[col] == input_values[col] From 609e8fc55b78177dcd611678f49d89e33bdfa7e5 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 13 Nov 2024 14:54:28 -0500 Subject: [PATCH 630/687] add changelog --- changes/8958.general.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8958.general.rst diff --git a/changes/8958.general.rst b/changes/8958.general.rst new file mode 100644 index 0000000000..a84befe1ff --- /dev/null +++ b/changes/8958.general.rst @@ -0,0 +1 @@ +When blending metadata don't store columns containing all missing value (nans). From acd3c7aa846f6aea45c1dff1204dc438a6189ee0 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 13 Nov 2024 14:59:58 -0500 Subject: [PATCH 631/687] appease ruff --- jwst/model_blender/tests/test_blend.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/model_blender/tests/test_blend.py b/jwst/model_blender/tests/test_blend.py index 5702084cbf..48f0fbd891 100644 --- a/jwst/model_blender/tests/test_blend.py +++ b/jwst/model_blender/tests/test_blend.py @@ -200,7 +200,7 @@ def test_blendtab(blend): # Ensure all the expected FITS keywords are in the table. colnames = set(newtab.dtype.fields) assert not fits_expected.difference(colnames) - assert not MISSING_COLUMN in colnames + assert MISSING_COLUMN not in colnames for col in colnames: if col in input_values: assert newtab[col] == input_values[col] From e210b1b84cae33ab20e40b605cffb9ddde98cf4a Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Wed, 13 Nov 2024 16:14:38 -0500 Subject: [PATCH 632/687] combining set_param functions --- .../master_background_mos_step.py | 23 ++++++++----------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index e887f385dc..3b06928524 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -164,11 +164,12 @@ def process(self, data): return result def set_pars_from_parent(self): - """Set substep parameters from the parents substeps""" + """Set substep parameters from the parents substeps when needed""" if not self.parent: return - steps = ['barshadow', 'flat_field', 'pathloss', 'photom'] + steps = ['barshadow', 'flat_field', 'pathloss', 'photom', + 'pixel_replace', 'resample_spec', 'extract_1d', 'combine_1d'] pars_to_ignore = { 'barshadow': ['source_type'], 'flat_field': ['save_interpolated_flat'], @@ -177,17 +178,12 @@ def set_pars_from_parent(self): } for step in steps: - pars = getattr(self.parent, step).get_pars() - for par in pars_to_ignore[step] + GLOBAL_PARS_TO_IGNORE: - del pars[par] - getattr(self, step).update_pars(pars) - - def _set_mospipe_steps_params(self): - """Get substep parameters for MOS pipeline to pass on""" - steps = ['pixel_replace', 'resample_spec', 'extract_1d', 'combine_1d'] - - for step in steps: - pars = getattr(self, step).get_pars() + if step in pars_to_ignore: + pars = getattr(self.parent, step).get_pars() + for par in pars_to_ignore[step] + GLOBAL_PARS_TO_IGNORE: + del pars[par] + else: + pars = getattr(self, step).get_pars() getattr(self, step).update_pars(pars) def _extend_bg_slits(self, pre_calibrated): @@ -277,7 +273,6 @@ def _calc_master_background(self, data, user_background=None): master_background = user_background else: self.log.info('Creating MOS master background from background slitlets') - self._set_mospipe_steps_params() bkg_model = self._extend_bg_slits(pre_calibrated) if bkg_model is not None: bkg_model = self.pixel_replace(bkg_model) From e9a482dd0d08d8a56de42ee40a2c26a71cf542cd Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Thu, 14 Nov 2024 10:43:45 -0500 Subject: [PATCH 633/687] Update changelog and README to reflect patch release (#8960) --- CHANGES.rst | 9 +++++++++ README.md | 13 +++++++------ jwst/resample/resample_spec.py | 5 ++--- 3 files changed, 18 insertions(+), 9 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index 7500ad652f..1041652782 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -1,3 +1,12 @@ +1.16.1 (2024-10-30) +=================== + +resample_spec +------------- + +- Update NIRSpec spectral resampling to add a missing correction factor in resampled + WCS tangent plane transformation. [#8908] + 1.16.0 (2024-09-20) =================== diff --git a/README.md b/README.md index b1bd23db8b..84d50d76c8 100644 --- a/README.md +++ b/README.md @@ -216,12 +216,13 @@ the specified context and less than the context for the next release. | jwst tag | DMS build | SDP_VER | CRDS_CONTEXT | Released | Ops Install | Notes | |---------------------|-----------|----------|--------------|------------|-------------|-----------------------------------------------| -| 1.16.0 | B11.1rc1 | TBD | 1281 | 2024-09-20 | TBD | First release candidate for B11.1 | -| 1.15.1 | B11.0 | 2024.2.2 | 1242 | 2024-07-08 | 2024-09-12 | Final release candidate for B11.0 | -| 1.15.0 | B11.0rc1 | | 1241 | 2024-06-26 | | First release candidate for B11.0 | -| 1.14.1 | | | 1238 | 2024-06-27 | | PyPI-only release for external users | -| 1.14.0 | B10.2.1 | 2024.1.1 | 1238 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | -| 1.13.4 | | | 1185 | 2024-01-25 | | PyPI-only release for external users | +| 1.16.1 | B11.1.1 | 2024.3.1 | 1298 | 2024-11-13 | TBD | Final release candidate for B11.1 | +| 1.16.0 | B11.1 | 2024.3.0 | 1298 | 2024-09-20 | TBD | First release candidate for B11.1 | +| 1.15.1 | B11.0 | 2024.2.2 | 1293 | 2024-07-08 | 2024-09-12 | Final release candidate for B11.0 | +| 1.15.0 | B11.0rc1 | | 1274 | 2024-06-26 | | First release candidate for B11.0 | +| 1.14.1 | | | 1240 | 2024-06-27 | | PyPI-only release for external users | +| 1.14.0 | B10.2.1 | 2024.1.1 | 1240 | 2024-03-29 | 2024-06-12 | Final release candidate for B10.2.1 | +| 1.13.4 | | | 1210 | 2024-01-25 | | PyPI-only release for external users | | 1.13.3 | B10.1 | 2023.4.0 | 1181 | 2024-01-05 | | Final release candidate for B10.1 | | 1.13.2 | B10.1rc3 | 2023.4.0 | 1181 | 2023-12-21 | | Third release candidate for B10.1 | | 1.13.1 | B10.1rc2 | 2023.4.0 | 1181 | 2023-12-19 | | Second release candidate for B10.1 | diff --git a/jwst/resample/resample_spec.py b/jwst/resample/resample_spec.py index 5160034ddb..7872513c1c 100644 --- a/jwst/resample/resample_spec.py +++ b/jwst/resample/resample_spec.py @@ -362,8 +362,8 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): # Correct the intercept for the new minimum value. # Also account for integer pixel size to make sure the # data is centered in the array. - offset_y = ny/2 * pix_to_tan_slope_y - diff_y/2 - offset_x = ny/2 * pix_to_tan_slope_x - diff_x/2 + offset_y = ny / 2 * pix_to_tan_slope_y - diff_y / 2 + offset_x = ny / 2 * pix_to_tan_slope_x - diff_x / 2 if slope_sign_y > 0: zero_value_y = min_tan_y @@ -375,7 +375,6 @@ def build_nirspec_output_wcs(self, input_models, refmodel=None): else: zero_value_x = max_tan_x - pix_to_ytan.intercept = zero_value_y - slope_sign_y * offset_y pix_to_xtan.intercept = zero_value_x - slope_sign_x * offset_x From 0bfcd45751b15cfa51411bba4d922c73555f2c54 Mon Sep 17 00:00:00 2001 From: Jane Morrison Date: Fri, 15 Nov 2024 12:22:49 -0700 Subject: [PATCH 634/687] JP-3800 update dark current documentation to remove confusing statement about bias subtraction (#8955) --- docs/jwst/dark_current/description.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/jwst/dark_current/description.rst b/docs/jwst/dark_current/description.rst index cc433f4c84..3f8824854f 100644 --- a/docs/jwst/dark_current/description.rst +++ b/docs/jwst/dark_current/description.rst @@ -6,8 +6,7 @@ Description Assumptions ----------- -It is assumed that the input science data have *NOT* had the zero group (or -bias) subtracted. We also do not want the dark subtraction process to remove +We do not want the dark subtraction process to remove the bias signal from the science exposure, therefore the dark reference data should have their own group zero subtracted from all groups. This means that group zero of the dark reference data will effectively be zero-valued. From 315a2e55e9b00bcfd8c9943f0d8086860bfb265d Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Fri, 15 Nov 2024 14:25:01 -0500 Subject: [PATCH 635/687] bump min crds for build context (#8950) --- changes/8950.general.rst | 1 + pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 changes/8950.general.rst diff --git a/changes/8950.general.rst b/changes/8950.general.rst new file mode 100644 index 0000000000..466db73740 --- /dev/null +++ b/changes/8950.general.rst @@ -0,0 +1 @@ +Update minimum required version of crds to allow for "data release style" contexts. diff --git a/pyproject.toml b/pyproject.toml index b615cd49e6..155e2ea531 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ dependencies = [ "asdf>=3.1.0,<4", "astropy>=5.3", "BayesicFitting>=3.0.1", - "crds>=11.17.14", + "crds>=12.0.3", "drizzle>=2.0.0", "gwcs>=0.21.0,<0.23.0", "numpy>=1.22,<2.0", From 4d11f0923598128cd814f8892e5e2b3a7652e2c5 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Fri, 15 Nov 2024 14:35:22 -0500 Subject: [PATCH 636/687] fix docs typo --- docs/jwst/combine_1d/arguments.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/combine_1d/arguments.rst b/docs/jwst/combine_1d/arguments.rst index b17c691be4..4476335354 100644 --- a/docs/jwst/combine_1d/arguments.rst +++ b/docs/jwst/combine_1d/arguments.rst @@ -1,7 +1,7 @@ Step Arguments ============== -The ``combine_1d`` step has twp step-specific arguments: +The ``combine_1d`` step has two step-specific arguments: ``--exptime_key`` This is a case-insensitive string that identifies the metadata element From 7578fd67f0620dd82f41c81cc95d6574a273f2de Mon Sep 17 00:00:00 2001 From: Ken MacDonald Date: Fri, 15 Nov 2024 20:09:55 -0500 Subject: [PATCH 637/687] Adding bigdata decorator to new regression test. --- jwst/regtest/test_miri_image.py | 2 ++ jwst/regtest/test_niriss_image.py | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/jwst/regtest/test_miri_image.py b/jwst/regtest/test_miri_image.py index 12d8c0b326..b6863eccec 100644 --- a/jwst/regtest/test_miri_image.py +++ b/jwst/regtest/test_miri_image.py @@ -161,11 +161,13 @@ def test_miri_image_detector1(run_detector1, rtdata_module, fitsdiff_default_kwa _assert_is_same(rtdata_module, fitsdiff_default_kwargs, suffix) +@pytest.mark.bigdata def test_miri_image_detector1_multiprocess_rate(run_detector1_multiprocess_rate, rtdata_module, fitsdiff_default_kwargs): """Regression test of detector1 pipeline performed on MIRI imaging data.""" _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") +@pytest.mark.bigdata def test_miri_image_detector1_multiprocess_jump(run_detector1_multiprocess_jump, rtdata_module, fitsdiff_default_kwargs): """Regression test of detector1 pipeline performed on MIRI imaging data.""" _assert_is_same(rtdata_module, fitsdiff_default_kwargs, "rate") diff --git a/jwst/regtest/test_niriss_image.py b/jwst/regtest/test_niriss_image.py index 23ccf8ff9d..be31fbda67 100644 --- a/jwst/regtest/test_niriss_image.py +++ b/jwst/regtest/test_niriss_image.py @@ -9,7 +9,6 @@ from jwst import datamodels from jwst.stpipe import Step from jwst.tweakreg import TweakRegStep -from jwst.ramp_fitting.ramp_fit_step import RampFitStep @pytest.fixture(scope="module") From 04a7de7ef65e6de3285a4f86228e5570ba061662 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Mon, 18 Nov 2024 14:12:36 -0500 Subject: [PATCH 638/687] minor formatting changes Co-authored-by: Melanie Clarke --- jwst/combine_1d/combine1d.py | 2 +- jwst/master_background/master_background_mos_step.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/combine_1d/combine1d.py b/jwst/combine_1d/combine1d.py index 884c55a633..5cf83affc2 100644 --- a/jwst/combine_1d/combine1d.py +++ b/jwst/combine_1d/combine1d.py @@ -481,7 +481,7 @@ def count_input(input_spectra): return wl, n_input_spectra -def compute_output_wl(wl, n_input_spectra, ): +def compute_output_wl(wl, n_input_spectra): """Compute output wavelengths. In summary, the output wavelengths are computed by binning the diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 5c39709b24..78a6bcfb96 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -87,7 +87,7 @@ def process(self, data): - "masterbkg_2d": `~jwst.datamodels.MultiSlitModel` The 2D slit-based version of the master background. - sigma_clip : None or float + sigma_clip : None or float Optional factor for sigma clipping outliers when combining background spectra. median_kernel : integer, optional From a627c2c97ac99f865492dc2ee330dfa0f273fabd Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Mon, 18 Nov 2024 16:40:05 -0500 Subject: [PATCH 639/687] remove duplicated test function --- .../tests/test_master_background_mos.py | 94 +++---------------- 1 file changed, 12 insertions(+), 82 deletions(-) diff --git a/jwst/master_background/tests/test_master_background_mos.py b/jwst/master_background/tests/test_master_background_mos.py index 490ffc4e1a..b683f49616 100644 --- a/jwst/master_background/tests/test_master_background_mos.py +++ b/jwst/master_background/tests/test_master_background_mos.py @@ -7,83 +7,11 @@ from jwst.stpipe import query_step_status from jwst.assign_wcs import AssignWcsStep from jwst.extract_2d.extract_2d_step import Extract2dStep +from jwst.extract_2d.tests.test_nirspec import create_nirspec_hdul from jwst.master_background import MasterBackgroundMosStep from jwst.master_background import nirspec_utils -# WCS keywords, borrowed from NIRCam grism tests -WCS_KEYS = {'wcsaxes': 2, 'ra_ref': 53.1490299775, 'dec_ref': -27.8168745624, - 'v2_ref': 86.103458, 'v3_ref': -493.227512, 'roll_ref': 45.04234459270135, - 'v3i_yang': 0.0, 'vparity': -1, - 'crpix1': 1024.5, 'crpix2': 1024.5, - 'crval1': 53.1490299775, 'crval2': -27.8168745624, - 'cdelt1': 1.81661111111111e-05, 'cdelt2': 1.8303611111111e-05, - 'ctype1': 'RA---TAN', 'ctype2': 'DEC--TAN', - 'pc1_1': -0.707688557183348, 'pc1_2': 0.7065245261360363, - 'pc2_1': 0.7065245261360363, 'pc2_2': 1.75306861111111e-05, - 'cunit1': 'deg', 'cunit2': 'deg'} - - -def create_nirspec_hdul(detector='NRS1', grating='G395M', filter_name='F290LP', - exptype='NRS_MSASPEC', subarray='FULL', slit=None, nint=1, - wcskeys=None): - if wcskeys is None: - wcskeys = WCS_KEYS.copy() - - hdul = fits.HDUList() - phdu = fits.PrimaryHDU() - phdu.header['TELESCOP'] = 'JWST' - phdu.header['INSTRUME'] = 'NIRSPEC' - phdu.header['DETECTOR'] = detector - phdu.header['FILTER'] = filter_name - phdu.header['GRATING'] = grating - phdu.header['PROGRAM'] = '01234' - phdu.header['TIME-OBS'] = '8:59:37' - phdu.header['DATE-OBS'] = '2023-01-05' - phdu.header['EXP_TYPE'] = exptype - phdu.header['EFFEXPTM'] = 1.0 - phdu.header['DURATION'] = 1.0 - phdu.header['PATT_NUM'] = 1 - phdu.header['SUBARRAY'] = subarray - phdu.header['XOFFSET'] = 0.0 - phdu.header['YOFFSET'] = 0.0 - if subarray == 'SUBS200A1': - phdu.header['SUBSIZE1'] = 2048 - phdu.header['SUBSIZE2'] = 64 - phdu.header['SUBSTRT1'] = 1 - phdu.header['SUBSTRT2'] = 1041 - elif subarray == 'SUB2048': - phdu.header['SUBSIZE1'] = 2048 - phdu.header['SUBSIZE2'] = 32 - phdu.header['SUBSTRT1'] = 1 - phdu.header['SUBSTRT2'] = 946 - else: - phdu.header['SUBSIZE1'] = 2048 - phdu.header['SUBSIZE2'] = 2048 - phdu.header['SUBSTRT1'] = 1 - phdu.header['SUBSTRT2'] = 1 - - if exptype == 'NRS_MSASPEC': - phdu.header['MSAMETID'] = 1 - phdu.header['MSAMETFL'] = 'test_msa_01.fits' - - if slit is not None: - phdu.header['FXD_SLIT'] = slit - phdu.header['APERNAME'] = f'NRS_{slit}_SLIT' - - scihdu = fits.ImageHDU() - scihdu.header['EXTNAME'] = "SCI" - scihdu.header.update(wcskeys) - if nint > 1: - scihdu.data = np.ones((nint, phdu.header['SUBSIZE2'], phdu.header['SUBSIZE1'])) - else: - scihdu.data = np.ones((phdu.header['SUBSIZE2'], phdu.header['SUBSIZE1'])) - - hdul.append(phdu) - hdul.append(scihdu) - return hdul - - def create_msa_hdul(): # Four shutters open on MSA, the first three are background slits only # with the fourth having a source @@ -93,7 +21,7 @@ def create_msa_hdul(): 'shutter_quadrant': [4, 4, 4, 4], 'shutter_row': [10, 10, 10, 10], 'shutter_column': [22, 23, 24, 25], - 'source_id': [0, 0, 0, 2], + 'source_id': [0, 0, 0, 1], 'background': ['Y', 'Y', 'Y', 'N'], 'shutter_state': ['OPEN', 'OPEN', 'OPEN', 'OPEN'], 'estimated_source_in_shutter_x': [np.nan, np.nan, np.nan, 0.18283921], @@ -103,14 +31,14 @@ def create_msa_hdul(): 'fixed_slit': ['NONE', 'NONE', 'NONE', 'NONE']} source_data = { - 'program': [95065, 95065], - 'source_id': [1, 2], - 'source_name': ['95065_1', '95065_2'], - 'alias': ['2122', '2123'], - 'ra': [53.139904, 53.15], - 'dec': [-27.805002, -27.81], - 'preimage_id': ['95065001_000', '95065001_000'], - 'stellarity': [1.0, 1.0]} + 'program': [95065], + 'source_id': [2], + 'source_name': ['95065_2'], + 'alias': ['2123'], + 'ra': [53.15], + 'dec': [-27.81], + 'preimage_id': ['95065001_000'], + 'stellarity': [1.0]} shutter_table = Table(shutter_data) source_table = Table(source_data) @@ -130,6 +58,8 @@ def create_msa_hdul(): def nirspec_msa_rate(tmp_path): hdul = create_nirspec_hdul() hdul[0].header['MSAMETFL'] = str(tmp_path / 'test_msa_01.fits') + hdul[0].header['EFFEXPTM'] = 1.0 + hdul[0].header['DURATION'] = 1.0 filename = str(tmp_path / 'test_nrs_msa_rate.fits') hdul.writeto(filename, overwrite=True) hdul.close() From ce9e9c393679116708bab9fc2b55f470b12500e3 Mon Sep 17 00:00:00 2001 From: Christian Hayes Date: Mon, 18 Nov 2024 16:41:43 -0500 Subject: [PATCH 640/687] add bkgx1d to regtest --- jwst/regtest/test_nirspec_masterbackground.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/regtest/test_nirspec_masterbackground.py b/jwst/regtest/test_nirspec_masterbackground.py index f55d6ae783..8937e176b9 100644 --- a/jwst/regtest/test_nirspec_masterbackground.py +++ b/jwst/regtest/test_nirspec_masterbackground.py @@ -91,7 +91,7 @@ def test_masterbkg_corrpars(rtdata): @pytest.mark.parametrize( 'suffix', - ['masterbg1d', 'masterbg2d', 'cal', 's2d', 'x1d'] + ['masterbg1d', 'masterbg2d', 'bkgx1d', 'cal', 's2d', 'x1d'] ) def test_nirspec_mos_mbkg(suffix, run_spec2_mbkg, fitsdiff_default_kwargs): """Run spec2 with master background""" From 99a7dcf2c797d9c133d55ddfb5f09e8af2515c82 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Tue, 19 Nov 2024 12:08:47 -0500 Subject: [PATCH 641/687] JP-3798: Transfer datamodel wcsinfo during Spec2Pipeline MOS+FS processing (#8947) --- changes/8947.pipeline.rst | 1 + jwst/pipeline/calwebb_spec2.py | 1 + jwst/regtest/test_nirspec_mos_movingtarget.py | 86 +++++++++++++++++++ 3 files changed, 88 insertions(+) create mode 100644 changes/8947.pipeline.rst create mode 100644 jwst/regtest/test_nirspec_mos_movingtarget.py diff --git a/changes/8947.pipeline.rst b/changes/8947.pipeline.rst new file mode 100644 index 0000000000..2c24cc6f0b --- /dev/null +++ b/changes/8947.pipeline.rst @@ -0,0 +1 @@ +Transfer wcsinfo metadata to new MultiSlitModel created during ``calwebb_spec2`` processing of NIRSpec MSA data. \ No newline at end of file diff --git a/jwst/pipeline/calwebb_spec2.py b/jwst/pipeline/calwebb_spec2.py index 98b26c586b..a0ebe60664 100644 --- a/jwst/pipeline/calwebb_spec2.py +++ b/jwst/pipeline/calwebb_spec2.py @@ -608,6 +608,7 @@ def _process_nirspec_msa_slits(self, data): # First process MOS slits through all remaining steps calib_mos.update(calibrated) + calib_mos.meta.wcsinfo = calibrated.meta.wcsinfo.instance if len(calib_mos.slits) > 0: calib_mos = self.master_background_mos.run(calib_mos) calib_mos = self.wavecorr.run(calib_mos) diff --git a/jwst/regtest/test_nirspec_mos_movingtarget.py b/jwst/regtest/test_nirspec_mos_movingtarget.py new file mode 100644 index 0000000000..6cda35f9a8 --- /dev/null +++ b/jwst/regtest/test_nirspec_mos_movingtarget.py @@ -0,0 +1,86 @@ +import pytest + +from astropy.io.fits.diff import FITSDiff + +from jwst.stpipe import Step + + +@pytest.fixture(scope="module") +def run_spec2_pipeline(rtdata_module): + """Run the calwebb_spec2 pipeline on a single NIRSpec MOS Moving Target exposure.""" + + rtdata = rtdata_module + + # Get the MSA metadata file referenced in the input exposure + rtdata.get_data("nirspec/mos/jw01444003001_01_msa.fits") + + # Get the input ASN file and exposures + rtdata.get_data("nirspec/mos/jw01444003001_04102_00001_nrs1_rate.fits") + + # Run the calwebb_spec2 pipeline; save results from intermediate steps + args = ["calwebb_spec2", rtdata.input, + "--steps.assign_wcs.save_results=true", + "--steps.msa_flagging.save_results=true", + "--steps.master_background_mos.save_results=true", + "--steps.extract_2d.save_results=true", + "--steps.srctype.save_results=true", + "--steps.wavecorr.save_results=true", + "--steps.flat_field.save_results=true", + "--steps.pathloss.save_results=true", + "--steps.barshadow.save_results=true"] + Step.from_cmdline(args) + + return rtdata + + +@pytest.fixture(scope="module") +def run_spec3_pipeline(run_spec2_pipeline, rtdata_module): + """Run calwebb_spec3 on NIRSpec MOS data.""" + rtdata = rtdata_module + rtdata.get_data("nirspec/mos/jw01444-o003_20240815t170722_spec3_00001_asn.json") + + # Run the calwebb_spec3 pipeline on the association + args = ["calwebb_spec3", rtdata.input] + Step.from_cmdline(args) + + return rtdata + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", [ + "assign_wcs", "msa_flagging", "extract_2d", "srctype", + "master_background_mos", "wavecorr", "flat_field", "pathloss", "barshadow", + "cal", "s2d", "x1d"]) +def test_nirspec_mos_mt_spec2(run_spec2_pipeline, fitsdiff_default_kwargs, suffix): + """Regression test for calwebb_spec2 on a NIRSpec MOS Moving Target exposure.""" + + # Run the pipeline and retrieve outputs + rtdata = run_spec2_pipeline + output = f"jw01444003001_04102_00001_nrs1_{suffix}.fits" + rtdata.output = output + + # Get the truth files + rtdata.get_truth("truth/test_nirspec_mos_movingtarget/" + output) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + +@pytest.mark.bigdata +@pytest.mark.parametrize("suffix", ["cal", "crf", "s2d", "x1d"]) +@pytest.mark.parametrize("source_id", ["v000000001"]) +def test_nirspec_mos_mt_spec3(run_spec3_pipeline, fitsdiff_default_kwargs, suffix, source_id): + """Regression test for calwebb_spec3 on a NIRSpec MOS Moving Target exposure.""" + + # Run the pipeline and retrieve outputs + rtdata = run_spec3_pipeline + output = f"jw01444-o003_{source_id}_nirspec_clear-prism_{suffix}.fits" + rtdata.output = output + + # Get the truth files + rtdata.get_truth("truth/test_nirspec_mos_movingtarget/" + output) + + # Compare the results + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() From 15f5e802cb8ff33dbd9c1abf578b99ac57d46741 Mon Sep 17 00:00:00 2001 From: "Karl D. Gordon" Date: Wed, 7 Feb 2024 17:21:20 -0500 Subject: [PATCH 642/687] code and parameter for using group1 when saturating after group2 and before group3 --- jwst/firstframe/firstframe_step.py | 11 +++++++++-- jwst/firstframe/firstframe_sub.py | 22 +++++++++++++++++++--- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 47e9fcb72b..0b1c24bcfc 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -10,9 +10,13 @@ class FirstFrameStep(Step): """ FirstFrameStep: This is a MIRI specific task. If the number of groups is greater than 3, the DO_NOT_USE group data quality flag is added to - first group. + first group. """ + spec = """ + bright_use_group1 = boolean(default=False) # do not flag group1 if group2 is not saturated and group3 is saturated + """ + class_alias = "firstframe" spec = """ @@ -25,7 +29,10 @@ def process(self, step_input): # check the data is MIRI data detector = input_model.meta.instrument.detector.upper() - if detector[:3] != 'MIR': + if detector[:3] == 'MIR': + # Do the firstframe correction subtraction + result = firstframe_sub.do_correction(input_model, bright_use_group1=self.bright_use_group1) + else: self.log.warning('First Frame Correction is only for MIRI data') self.log.warning('First frame step will be skipped') input_model.meta.cal_step.firstframe = 'SKIPPED' diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index a535fb203e..a39f4b50b0 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -10,7 +10,8 @@ log.setLevel(logging.DEBUG) -def do_correction(output): +def do_correction(input_model, + bright_use_group1=False): """ Short Summary ------------- @@ -22,6 +23,8 @@ def do_correction(output): ---------- output: data model object science data to be corrected + bright_use_group1: boolean + do not flag group1 for bright pixels = saturating before group3 and after group2 Returns ------- @@ -36,8 +39,21 @@ def do_correction(output): # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' if sci_ngroups > 3: - output.groupdq[:, 0, :, :] = \ - np.bitwise_or(output.groupdq[:, 0, :, :], dqflags.group['DO_NOT_USE']) + if bright_use_group1: + # do not set DO_NOT_USE in the case where saturation happens + # after group2 and before group3 + # in this case, the first frame effect is small compared to the + # signal in frame2-frame1 + # input_model. + svals = (((output.groupdq[:, 1, :, :] & dqflags.group['SATURATED']) == 0) + & ((output.groupdq[:, 2, :, :] & dqflags.group['SATURATED']) > 0)) + tvals = output.groupdq[:, 0, :, :] + tvals[~svals] = np.bitwise_or((output.groupdq[:, 0, :, :])[~svals], dqflags.group['DO_NOT_USE']) + output.groupdq[:, 0, :, :] = tvals + log.info(f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame") + else: + output.groupdq[:, 0, :, :] = \ + np.bitwise_or(output.groupdq[:, 0, :, :], dqflags.group['DO_NOT_USE']) log.debug("FirstFrame Sub: resetting GROUPDQ in first frame to DO_NOT_USE") output.meta.cal_step.firstframe = 'COMPLETE' else: # too few groups From 3abf614126a1fcfef7c0195e8e6b02a6cc341d03 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 29 Oct 2024 10:43:25 -0400 Subject: [PATCH 643/687] fix variable name --- jwst/firstframe/firstframe_sub.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index a39f4b50b0..7bc72ff8c3 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -10,7 +10,7 @@ log.setLevel(logging.DEBUG) -def do_correction(input_model, +def do_correction(output, bright_use_group1=False): """ Short Summary @@ -43,7 +43,7 @@ def do_correction(input_model, # do not set DO_NOT_USE in the case where saturation happens # after group2 and before group3 # in this case, the first frame effect is small compared to the - # signal in frame2-frame1 + # signal in group2-group1 # input_model. svals = (((output.groupdq[:, 1, :, :] & dqflags.group['SATURATED']) == 0) & ((output.groupdq[:, 2, :, :] & dqflags.group['SATURATED']) > 0)) From b2c562d5a9820d3323026cc83e87e98f57013a64 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 29 Oct 2024 10:44:36 -0400 Subject: [PATCH 644/687] cleanup format --- jwst/firstframe/firstframe_step.py | 16 +++++++++------- jwst/firstframe/firstframe_sub.py | 29 +++++++++++++++++------------ 2 files changed, 26 insertions(+), 19 deletions(-) diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 0b1c24bcfc..c60571e754 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -10,12 +10,12 @@ class FirstFrameStep(Step): """ FirstFrameStep: This is a MIRI specific task. If the number of groups is greater than 3, the DO_NOT_USE group data quality flag is added to - first group. + first group. """ spec = """ bright_use_group1 = boolean(default=False) # do not flag group1 if group2 is not saturated and group3 is saturated - """ + """ class_alias = "firstframe" @@ -29,13 +29,15 @@ def process(self, step_input): # check the data is MIRI data detector = input_model.meta.instrument.detector.upper() - if detector[:3] == 'MIR': + if detector[:3] == "MIR": # Do the firstframe correction subtraction - result = firstframe_sub.do_correction(input_model, bright_use_group1=self.bright_use_group1) + result = firstframe_sub.do_correction( + input_model, bright_use_group1=self.bright_use_group1 + ) else: - self.log.warning('First Frame Correction is only for MIRI data') - self.log.warning('First frame step will be skipped') - input_model.meta.cal_step.firstframe = 'SKIPPED' + self.log.warning("First Frame Correction is only for MIRI data") + self.log.warning("First frame step will be skipped") + input_model.meta.cal_step.firstframe = "SKIPPED" return input_model # Cork on a copy diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 7bc72ff8c3..67aa8b7899 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -10,8 +10,7 @@ log.setLevel(logging.DEBUG) -def do_correction(output, - bright_use_group1=False): +def do_correction(output, bright_use_group1=False): """ Short Summary ------------- @@ -42,23 +41,29 @@ def do_correction(output, if bright_use_group1: # do not set DO_NOT_USE in the case where saturation happens # after group2 and before group3 - # in this case, the first frame effect is small compared to the + # in this case, the first frame effect is small compared to the # signal in group2-group1 # input_model. - svals = (((output.groupdq[:, 1, :, :] & dqflags.group['SATURATED']) == 0) - & ((output.groupdq[:, 2, :, :] & dqflags.group['SATURATED']) > 0)) + svals = ((output.groupdq[:, 1, :, :] & dqflags.group["SATURATED"]) == 0) & ( + (output.groupdq[:, 2, :, :] & dqflags.group["SATURATED"]) > 0 + ) tvals = output.groupdq[:, 0, :, :] - tvals[~svals] = np.bitwise_or((output.groupdq[:, 0, :, :])[~svals], dqflags.group['DO_NOT_USE']) + tvals[~svals] = np.bitwise_or( + (output.groupdq[:, 0, :, :])[~svals], dqflags.group["DO_NOT_USE"] + ) output.groupdq[:, 0, :, :] = tvals - log.info(f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame") + log.info( + f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame" + ) else: - output.groupdq[:, 0, :, :] = \ - np.bitwise_or(output.groupdq[:, 0, :, :], dqflags.group['DO_NOT_USE']) + output.groupdq[:, 0, :, :] = np.bitwise_or( + output.groupdq[:, 0, :, :], dqflags.group["DO_NOT_USE"] + ) log.debug("FirstFrame Sub: resetting GROUPDQ in first frame to DO_NOT_USE") - output.meta.cal_step.firstframe = 'COMPLETE' - else: # too few groups + output.meta.cal_step.firstframe = "COMPLETE" + else: # too few groups log.warning("Too few groups to apply correction") log.warning("Step will be skipped") - output.meta.cal_step.firstframe = 'SKIPPED' + output.meta.cal_step.firstframe = "SKIPPED" return output From a8f96b49e02831419302b993c97df8e84fefe135 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 29 Oct 2024 10:49:22 -0400 Subject: [PATCH 645/687] updating to new step organization --- jwst/firstframe/firstframe_step.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index c60571e754..7201128958 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -29,21 +29,18 @@ def process(self, step_input): # check the data is MIRI data detector = input_model.meta.instrument.detector.upper() - if detector[:3] == "MIR": - # Do the firstframe correction subtraction - result = firstframe_sub.do_correction( - input_model, bright_use_group1=self.bright_use_group1 - ) - else: - self.log.warning("First Frame Correction is only for MIRI data") - self.log.warning("First frame step will be skipped") - input_model.meta.cal_step.firstframe = "SKIPPED" + if detector[:3] != 'MIR': + self.log.warning('First Frame Correction is only for MIRI data') + self.log.warning('First frame step will be skipped') + input_model.meta.cal_step.firstframe = 'SKIPPED' return input_model # Cork on a copy result = input_model.copy() # Do the firstframe correction subtraction - result = firstframe_sub.do_correction(result) + result = firstframe_sub.do_correction( + input_model, bright_use_group1=self.bright_use_group1 + ) return result From 45c58937d800907b312b0ed7469a3b2c99d4a8e2 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 29 Oct 2024 15:40:20 -0400 Subject: [PATCH 646/687] working again! --- jwst/firstframe/firstframe_step.py | 13 +++++-------- jwst/firstframe/firstframe_sub.py | 7 ++++--- 2 files changed, 9 insertions(+), 11 deletions(-) diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 7201128958..49f5f0268b 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -19,9 +19,6 @@ class FirstFrameStep(Step): class_alias = "firstframe" - spec = """ - """ - def process(self, step_input): # Open the input data model @@ -29,10 +26,10 @@ def process(self, step_input): # check the data is MIRI data detector = input_model.meta.instrument.detector.upper() - if detector[:3] != 'MIR': - self.log.warning('First Frame Correction is only for MIRI data') - self.log.warning('First frame step will be skipped') - input_model.meta.cal_step.firstframe = 'SKIPPED' + if detector[:3] != "MIR": + self.log.warning("First Frame Correction is only for MIRI data") + self.log.warning("First frame step will be skipped") + input_model.meta.cal_step.firstframe = "SKIPPED" return input_model # Cork on a copy @@ -40,7 +37,7 @@ def process(self, step_input): # Do the firstframe correction subtraction result = firstframe_sub.do_correction( - input_model, bright_use_group1=self.bright_use_group1 + result, bright_use_group1=self.bright_use_group1 ) return result diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 67aa8b7899..9f6a31d82b 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -37,6 +37,7 @@ def do_correction(output, bright_use_group1=False): # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' + # bright_use_group1 = False if sci_ngroups > 3: if bright_use_group1: # do not set DO_NOT_USE in the case where saturation happens @@ -56,9 +57,9 @@ def do_correction(output, bright_use_group1=False): f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame" ) else: - output.groupdq[:, 0, :, :] = np.bitwise_or( - output.groupdq[:, 0, :, :], dqflags.group["DO_NOT_USE"] - ) + output.groupdq[:, 0, :, :] = \ + np.bitwise_or(output.groupdq[:, 0, :, :], dqflags.group['DO_NOT_USE']) + log.debug("FirstFrame Sub: resetting GROUPDQ in first frame to DO_NOT_USE") output.meta.cal_step.firstframe = "COMPLETE" else: # too few groups From 101f72879ec97ad4f097137f60bc9c76365bfa35 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 29 Oct 2024 16:23:33 -0400 Subject: [PATCH 647/687] minor fixes --- jwst/firstframe/firstframe_step.py | 4 ++-- jwst/firstframe/firstframe_sub.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 49f5f0268b..5035236b89 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -13,12 +13,12 @@ class FirstFrameStep(Step): first group. """ + class_alias = "firstframe" + spec = """ bright_use_group1 = boolean(default=False) # do not flag group1 if group2 is not saturated and group3 is saturated """ - class_alias = "firstframe" - def process(self, step_input): # Open the input data model diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 9f6a31d82b..ae00b09224 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -57,8 +57,9 @@ def do_correction(output, bright_use_group1=False): f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame" ) else: - output.groupdq[:, 0, :, :] = \ - np.bitwise_or(output.groupdq[:, 0, :, :], dqflags.group['DO_NOT_USE']) + output.groupdq[:, 0, :, :] = np.bitwise_or( + output.groupdq[:, 0, :, :], dqflags.group["DO_NOT_USE"] + ) log.debug("FirstFrame Sub: resetting GROUPDQ in first frame to DO_NOT_USE") output.meta.cal_step.firstframe = "COMPLETE" From 593d39a90dd33f35c26e02142abd307f1c42824c Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 5 Nov 2024 10:00:18 -0500 Subject: [PATCH 648/687] updating firstframe docs --- docs/jwst/firstframe/arguments.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/jwst/firstframe/arguments.rst b/docs/jwst/firstframe/arguments.rst index e0d91688e0..37b1b7bb0f 100644 --- a/docs/jwst/firstframe/arguments.rst +++ b/docs/jwst/firstframe/arguments.rst @@ -1,4 +1,8 @@ Step Arguments ============== -The first frame correction has no step-specific arguments. +The ``firstframe`` step has the following step-specific arguments. + +``--bright_use_group1`` (boolean, default=True) + If True, for pixels that have the saturation flag set for group 3, + but not group 2 will not have their group 1 groupdq set to DO_NOT_USE. From a074ca14f931fdd728a302a92190162c6de3a647 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 5 Nov 2024 10:12:34 -0500 Subject: [PATCH 649/687] adding bright_use_group1=True test --- jwst/firstframe/tests/test_firstframe.py | 54 +++++++++++++++++++++++- 1 file changed, 53 insertions(+), 1 deletion(-) diff --git a/jwst/firstframe/tests/test_firstframe.py b/jwst/firstframe/tests/test_firstframe.py index 7d48d5bfce..9f89a72606 100644 --- a/jwst/firstframe/tests/test_firstframe.py +++ b/jwst/firstframe/tests/test_firstframe.py @@ -9,7 +9,7 @@ def test_firstframe_set_groupdq(): """ Test if the firstframe code set the groupdq flag on the first - group to 'do_not_use' for 5 integrations + group to 'do_not_use' for 5 groups """ # size of integration @@ -216,3 +216,55 @@ def test_miri(): dq_diff, err_msg='Diff in groupdq flags is not ' + 'equal to DO_NOT_USE') + + +def test_firstframe_bright_use_group1(): + """ + Test if the firstframe code when bright_use_group1 is set to True. + + The groupdq flag for group 1 should not be set to DO_NOT_USE for the pixels that saturate + between the 2nd and 3rd groups. Otherwise, all other pixels should have their group1 groupdq + flags set to DO_NOT_USE. + """ + + # size of integration + ngroups = 5 + xsize = 1032 + ysize = 1024 + + # create the data and groupdq arrays + csize = (1, ngroups, ysize, xsize) + data = np.full(csize, 1.0) + groupdq = np.zeros(csize, dtype=int) + + # set a fraction of the pixels to saturate in between the 2nd and 3rd groups + groupdq[0, 2, 0:100, :] = dqflags.group['SATURATED'] + + # create a JWST datamodel for MIRI data + dm_ramp = RampModel(data=data, groupdq=groupdq) + + # run the first frame correction step on a copy (the detection to make the copy or + # not would have happened at _step.py) + dm_ramp_firstframe = do_correction(dm_ramp.copy(), bright_use_group1=True) + + # check that the difference in the groupdq flags is equal to + # the 'do_not_use' flag + dq_diff = dm_ramp_firstframe.groupdq[0, 0, :, :] - dm_ramp.groupdq[0, 0, :, :] + + expected_diff = np.full((ysize, xsize), dqflags.group['DO_NOT_USE'], dtype=int) + expected_diff[0:100, :] = 0 + + np.testing.assert_array_equal(expected_diff, + dq_diff, + err_msg='Diff in groupdq flags is not ' + + 'equal to the DO_NOT_USE flag') + + # test that the groupdq flags are not changed for the rest of the groups + dq_diff = (dm_ramp_firstframe.groupdq[0, 1:ngroups, :, :] + - dm_ramp.groupdq[0, 1:ngroups, :, :]) + np.testing.assert_array_equal(np.full((ngroups - 1, ysize, xsize), + 0, + dtype=int), + dq_diff, + err_msg='n >= 2 groupdq flags changes ' + + 'and they should not be') \ No newline at end of file From 46eed546f454772d008200c01bcd05adeedeb21b Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 5 Nov 2024 10:15:22 -0500 Subject: [PATCH 650/687] comment cleanup --- jwst/firstframe/firstframe_sub.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index ae00b09224..6403d452d4 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -37,14 +37,11 @@ def do_correction(output, bright_use_group1=False): # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' - # bright_use_group1 = False if sci_ngroups > 3: if bright_use_group1: - # do not set DO_NOT_USE in the case where saturation happens - # after group2 and before group3 - # in this case, the first frame effect is small compared to the + # do not set DO_NOT_USE in the case where saturation happens after group2 and + # before group3 in this case, the first frame effect is small compared to the # signal in group2-group1 - # input_model. svals = ((output.groupdq[:, 1, :, :] & dqflags.group["SATURATED"]) == 0) & ( (output.groupdq[:, 2, :, :] & dqflags.group["SATURATED"]) > 0 ) From 74851b16cd0ef694c3bda39cd0cdfe361c524314 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 5 Nov 2024 10:16:24 -0500 Subject: [PATCH 651/687] updating log output --- jwst/firstframe/firstframe_sub.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 6403d452d4..98311498e6 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -51,7 +51,7 @@ def do_correction(output, bright_use_group1=False): ) output.groupdq[:, 0, :, :] = tvals log.info( - f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels using first frame" + f"FirstFrame Sub: bright_first_frame set, #{np.sum(svals)} bright pixels group1 not set to DO_NOT_USE" ) else: output.groupdq[:, 0, :, :] = np.bitwise_or( From 93cb8cbdc763e9fa4292527fe26d7c1128653a78 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Wed, 13 Nov 2024 07:27:43 -0500 Subject: [PATCH 652/687] adding changes info --- changes/8952.firstframe.rst | 1 + jwst/firstframe/firstframe_sub.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 changes/8952.firstframe.rst diff --git a/changes/8952.firstframe.rst b/changes/8952.firstframe.rst new file mode 100644 index 0000000000..513679e1d9 --- /dev/null +++ b/changes/8952.firstframe.rst @@ -0,0 +1 @@ +Update firstframe step to optionally not flag when ramp saturations between groups 2 and 3 \ No newline at end of file diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 98311498e6..7c92ef3bba 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -33,7 +33,7 @@ def do_correction(output, bright_use_group1=False): """ # Save some data params for easy use later - sci_ngroups = output.data.shape[1] + sci_ngroups = output.meta.exposure.ngroups # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' From 7d63874ade794c11dde1860f4e919efad34ddda4 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Wed, 13 Nov 2024 07:45:25 -0500 Subject: [PATCH 653/687] fixing grammer --- changes/8952.firstframe.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/changes/8952.firstframe.rst b/changes/8952.firstframe.rst index 513679e1d9..55aa79e08c 100644 --- a/changes/8952.firstframe.rst +++ b/changes/8952.firstframe.rst @@ -1 +1 @@ -Update firstframe step to optionally not flag when ramp saturations between groups 2 and 3 \ No newline at end of file +Update the firstframe step to optionally not flag when a ramp saturates between groups 2 and 3 \ No newline at end of file From 7280cb663ebe02b5d05c4084ef2c8832dc4994a7 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Wed, 13 Nov 2024 11:07:06 -0500 Subject: [PATCH 654/687] rewording the argument text and reverting one line of code --- docs/jwst/firstframe/arguments.rst | 7 ++++--- jwst/firstframe/firstframe_sub.py | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/jwst/firstframe/arguments.rst b/docs/jwst/firstframe/arguments.rst index 37b1b7bb0f..7001871772 100644 --- a/docs/jwst/firstframe/arguments.rst +++ b/docs/jwst/firstframe/arguments.rst @@ -3,6 +3,7 @@ Step Arguments The ``firstframe`` step has the following step-specific arguments. -``--bright_use_group1`` (boolean, default=True) - If True, for pixels that have the saturation flag set for group 3, - but not group 2 will not have their group 1 groupdq set to DO_NOT_USE. +``--bright_use_group1`` (boolean, default=False) + If True, setting the group 1 groupdq to DO_NOT_USE will not be done + for pixels that have the saturation flag set for group 3, but not group 2. + This will allow a slope to be determined for this pixel. diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 7c92ef3bba..98311498e6 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -33,7 +33,7 @@ def do_correction(output, bright_use_group1=False): """ # Save some data params for easy use later - sci_ngroups = output.meta.exposure.ngroups + sci_ngroups = output.data.shape[1] # Update the step status, and if ngroups > 3, set all GROUPDQ in # the first group to 'DO_NOT_USE' From cf6ed2884743a201ea5e90c738c68fd120ed21a2 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 19 Nov 2024 14:44:36 -0500 Subject: [PATCH 655/687] changing flagging to allow for group 2 to be saturated as well --- docs/jwst/firstframe/arguments.rst | 9 +++++++-- jwst/firstframe/firstframe_sub.py | 8 +++----- jwst/firstframe/tests/test_firstframe.py | 5 +++++ 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/docs/jwst/firstframe/arguments.rst b/docs/jwst/firstframe/arguments.rst index 7001871772..ec5633d5fc 100644 --- a/docs/jwst/firstframe/arguments.rst +++ b/docs/jwst/firstframe/arguments.rst @@ -5,5 +5,10 @@ The ``firstframe`` step has the following step-specific arguments. ``--bright_use_group1`` (boolean, default=False) If True, setting the group 1 groupdq to DO_NOT_USE will not be done - for pixels that have the saturation flag set for group 3, but not group 2. - This will allow a slope to be determined for this pixel. + for pixels that have the saturation flag set for group 3. + This will allow a slope to be determined for pixels that saturate + between groups 2 and 3. + This change in flagging will only impact pixels that saturate in group 3, the behavior + for all other pixels will be unchanged. + The `bright_use_group1` flag can be set for all data, only data/pixels that saturate + in group 3 will see a difference in behavior. diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index 98311498e6..bb58e1a556 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -39,12 +39,10 @@ def do_correction(output, bright_use_group1=False): # the first group to 'DO_NOT_USE' if sci_ngroups > 3: if bright_use_group1: - # do not set DO_NOT_USE in the case where saturation happens after group2 and - # before group3 in this case, the first frame effect is small compared to the + # do not set DO_NOT_USE in the case where saturation happens in + # group3 as in this case the first frame effect is small compared to the # signal in group2-group1 - svals = ((output.groupdq[:, 1, :, :] & dqflags.group["SATURATED"]) == 0) & ( - (output.groupdq[:, 2, :, :] & dqflags.group["SATURATED"]) > 0 - ) + svals = (output.groupdq[:, 2, :, :] & dqflags.group["SATURATED"]) > 0 tvals = output.groupdq[:, 0, :, :] tvals[~svals] = np.bitwise_or( (output.groupdq[:, 0, :, :])[~svals], dqflags.group["DO_NOT_USE"] diff --git a/jwst/firstframe/tests/test_firstframe.py b/jwst/firstframe/tests/test_firstframe.py index 9f89a72606..be6485d0e3 100644 --- a/jwst/firstframe/tests/test_firstframe.py +++ b/jwst/firstframe/tests/test_firstframe.py @@ -240,6 +240,10 @@ def test_firstframe_bright_use_group1(): # set a fraction of the pixels to saturate in between the 2nd and 3rd groups groupdq[0, 2, 0:100, :] = dqflags.group['SATURATED'] + # set a fraction of the pixels to saturate in between the 1st and 2nd groups + groupdq[0, 2, 200:300, :] = dqflags.group['SATURATED'] + groupdq[0, 1, 200:300, :] = dqflags.group['SATURATED'] + # create a JWST datamodel for MIRI data dm_ramp = RampModel(data=data, groupdq=groupdq) @@ -253,6 +257,7 @@ def test_firstframe_bright_use_group1(): expected_diff = np.full((ysize, xsize), dqflags.group['DO_NOT_USE'], dtype=int) expected_diff[0:100, :] = 0 + expected_diff[200:300, :] = 0 np.testing.assert_array_equal(expected_diff, dq_diff, From 0f2fe15410dfcfa42df6b7ff43eec8ae2acd964e Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Tue, 19 Nov 2024 17:00:29 -0500 Subject: [PATCH 656/687] updating docs in various places --- changes/8952.firstframe.rst | 2 +- jwst/firstframe/firstframe_step.py | 2 +- jwst/firstframe/firstframe_sub.py | 2 +- jwst/firstframe/tests/test_firstframe.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/changes/8952.firstframe.rst b/changes/8952.firstframe.rst index 55aa79e08c..ff41581d2f 100644 --- a/changes/8952.firstframe.rst +++ b/changes/8952.firstframe.rst @@ -1 +1 @@ -Update the firstframe step to optionally not flag when a ramp saturates between groups 2 and 3 \ No newline at end of file +Update the firstframe step to optionally not flag when a ramp saturates in group 3 \ No newline at end of file diff --git a/jwst/firstframe/firstframe_step.py b/jwst/firstframe/firstframe_step.py index 5035236b89..0e4d593b01 100755 --- a/jwst/firstframe/firstframe_step.py +++ b/jwst/firstframe/firstframe_step.py @@ -16,7 +16,7 @@ class FirstFrameStep(Step): class_alias = "firstframe" spec = """ - bright_use_group1 = boolean(default=False) # do not flag group1 if group2 is not saturated and group3 is saturated + bright_use_group1 = boolean(default=False) # do not flag group1 if group3 is saturated """ def process(self, step_input): diff --git a/jwst/firstframe/firstframe_sub.py b/jwst/firstframe/firstframe_sub.py index bb58e1a556..401904fa10 100644 --- a/jwst/firstframe/firstframe_sub.py +++ b/jwst/firstframe/firstframe_sub.py @@ -23,7 +23,7 @@ def do_correction(output, bright_use_group1=False): output: data model object science data to be corrected bright_use_group1: boolean - do not flag group1 for bright pixels = saturating before group3 and after group2 + do not flag group1 for bright pixels = group 3 saturated Returns ------- diff --git a/jwst/firstframe/tests/test_firstframe.py b/jwst/firstframe/tests/test_firstframe.py index be6485d0e3..9cf4437fad 100644 --- a/jwst/firstframe/tests/test_firstframe.py +++ b/jwst/firstframe/tests/test_firstframe.py @@ -223,7 +223,7 @@ def test_firstframe_bright_use_group1(): Test if the firstframe code when bright_use_group1 is set to True. The groupdq flag for group 1 should not be set to DO_NOT_USE for the pixels that saturate - between the 2nd and 3rd groups. Otherwise, all other pixels should have their group1 groupdq + the 3rd group. Otherwise, all other pixels should have their group1 groupdq flags set to DO_NOT_USE. """ From aa32e1678a51b5f0bded8eb7c6a915fe2c1f4015 Mon Sep 17 00:00:00 2001 From: Karl Gordon Date: Wed, 20 Nov 2024 09:54:04 -0500 Subject: [PATCH 657/687] fixing docs --- docs/jwst/firstframe/arguments.rst | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/jwst/firstframe/arguments.rst b/docs/jwst/firstframe/arguments.rst index ec5633d5fc..0bbf2623f6 100644 --- a/docs/jwst/firstframe/arguments.rst +++ b/docs/jwst/firstframe/arguments.rst @@ -6,8 +6,7 @@ The ``firstframe`` step has the following step-specific arguments. ``--bright_use_group1`` (boolean, default=False) If True, setting the group 1 groupdq to DO_NOT_USE will not be done for pixels that have the saturation flag set for group 3. - This will allow a slope to be determined for pixels that saturate - between groups 2 and 3. + This will allow a slope to be determined for pixels that saturate in group 3. This change in flagging will only impact pixels that saturate in group 3, the behavior for all other pixels will be unchanged. The `bright_use_group1` flag can be set for all data, only data/pixels that saturate From 0e4719a0597ec9bf3bc6e914d10e575744951e80 Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 20 Nov 2024 11:41:53 -0500 Subject: [PATCH 658/687] fix test_miri_mrs_badpix_selfcal_bkg --- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index a65d0cbea4..39e5d8608f 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -59,22 +59,19 @@ def test_miri_mrs_badpix_selfcal(run_pipeline_selfcal, fitsdiff_default_kwargs): assert not os.path.isfile(fname) +@pytest.mark.parametrize("basename", ( + [f"{OUTSTEM_BKG}_badpix_selfcal.fits",] + + [f"{OUTSTEM_BKG}_badpix_selfcal_bkg_{idx}.fits" for idx in range(4)])) @pytest.mark.bigdata -def test_miri_mrs_badpix_selfcal_bkg(run_pipeline_background, fitsdiff_default_kwargs): +def test_miri_mrs_badpix_selfcal_bkg(basename, run_pipeline_background, fitsdiff_default_kwargs): """Run a test for MIRI MRS data with dedicated background exposures.""" rtdata = run_pipeline_background # Get the truth file - rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{OUTSTEM_BKG}_badpix_selfcal.fits") + rtdata.output = basename + rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{basename}") - # Compare the results + # Compare the results and heck the bkg files in the background case, but not in the selfcal case diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() - - # check the bkg files in the background case, but not in the selfcal case - for idx in range(4): - fname = f"{OUTSTEM_BKG}_badpix_selfcal_bkg_{idx}.fits" - truth = rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{fname}") - diff = FITSDiff(fname, truth, **fitsdiff_default_kwargs) - assert diff.identical, diff.report() From 5a2d0cd8ee5865f9e394c07b68d0fb1c84b6e6ec Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Wed, 20 Nov 2024 12:13:15 -0500 Subject: [PATCH 659/687] Update jwst/regtest/test_miri_mrs_badpix_selfcal.py --- jwst/regtest/test_miri_mrs_badpix_selfcal.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/regtest/test_miri_mrs_badpix_selfcal.py b/jwst/regtest/test_miri_mrs_badpix_selfcal.py index 39e5d8608f..89fbbc864c 100644 --- a/jwst/regtest/test_miri_mrs_badpix_selfcal.py +++ b/jwst/regtest/test_miri_mrs_badpix_selfcal.py @@ -72,6 +72,6 @@ def test_miri_mrs_badpix_selfcal_bkg(basename, run_pipeline_background, fitsdiff rtdata.output = basename rtdata.get_truth(f"truth/test_miri_mrs_badpix_selfcal/{basename}") - # Compare the results and heck the bkg files in the background case, but not in the selfcal case + # Compare the results and check the bkg files in the background case, but not in the selfcal case diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) assert diff.identical, diff.report() From e220e303c3eede21529a50a37fb40d38011c4588 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Wed, 20 Nov 2024 15:22:08 -0500 Subject: [PATCH 660/687] removing unused import --- jwst/master_background/nirspec_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 3bb9896447..2b27367a34 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -3,7 +3,6 @@ from scipy.signal import medfilt -from stdatamodels.jwst import datamodels log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) From ae9ce3f14c275ae745715082e8db6e06a569178a Mon Sep 17 00:00:00 2001 From: Zach Burnett Date: Thu, 21 Nov 2024 11:20:58 -0500 Subject: [PATCH 661/687] unskip a regression test (#8953) --- jwst/ramp_fitting/tests/test_ramp_fit.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/ramp_fitting/tests/test_ramp_fit.py b/jwst/ramp_fitting/tests/test_ramp_fit.py index 9cbe029b2d..84e580f558 100644 --- a/jwst/ramp_fitting/tests/test_ramp_fit.py +++ b/jwst/ramp_fitting/tests/test_ramp_fit.py @@ -413,7 +413,6 @@ def test_photon_noise_only_bad_last_frame_two_groups(self, method): data = slopes.data np.testing.assert_allclose(data[50, 50], cds_slope, 1e-6) - @pytest.mark.skip(reason="Unweighted fit not implemented.") def test_photon_noise_with_unweighted_fit(self, method): model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=5, gain=1000, readnoise=1) model1.data[0, 0, 50, 50] = 10.0 From eba67c0a42e28afaa81521e60fef6d1051e81b0e Mon Sep 17 00:00:00 2001 From: Brett Date: Wed, 20 Nov 2024 11:18:25 -0500 Subject: [PATCH 662/687] avoid file clobbering in okify --- jwst/scripts/okify_regtests.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/jwst/scripts/okify_regtests.py b/jwst/scripts/okify_regtests.py index 4a2494bb1d..4784ad1662 100755 --- a/jwst/scripts/okify_regtests.py +++ b/jwst/scripts/okify_regtests.py @@ -13,7 +13,6 @@ import tempfile from argparse import ArgumentParser from contextlib import contextmanager -from glob import glob from pathlib import Path import asdf @@ -110,11 +109,10 @@ def artifactory_get_breadcrumbs(build_number, suffix): args = list( ['jfrog', 'rt', 'dl'] + [f"{ARTIFACTORY_REPO}/*_GITHUB_CI_*-{build_number}/*{suffix}"] - + ['--flat'] ) subprocess.run(args, check=True, capture_output=True) - return sorted(glob(f'*{suffix}')) + return sorted([str(p) for p in Path().rglob(f'*{suffix}')]) def artifactory_get_build_artifacts(build_number): From fd095591dd961a868e21fc6c566d583acdb8457a Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 22 Nov 2024 16:01:21 -0500 Subject: [PATCH 663/687] addressing PR comments --- docs/jwst/master_background/description.rst | 3 +- .../master_background_mos_step.py | 7 +- jwst/master_background/nirspec_utils.py | 6 +- .../tests/test_master_background_mos.py | 65 +++++++++++++------ 4 files changed, 53 insertions(+), 28 deletions(-) diff --git a/docs/jwst/master_background/description.rst b/docs/jwst/master_background/description.rst index 96f90e82aa..75b27b8680 100644 --- a/docs/jwst/master_background/description.rst +++ b/docs/jwst/master_background/description.rst @@ -375,7 +375,8 @@ as follows: steps up through :ref:`photom ` to all slits, treating them all as extended sources (appropriate for background signal), and saving the extended source correction arrays for each slit in an internal copy of the data model -#. If a user-supplied master background spectrum is **not** given, the +#. If a user-supplied master background spectrum **not** given, the + :ref:`pixel_replace `, :ref:`resample_spec ` and :ref:`extract_1d ` steps are applied to the calibrated background slits, resulting in extracted 1D background spectra diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 426e32ef0e..3aaafe7722 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -12,7 +12,6 @@ from ..pixel_replace import pixel_replace_step from ..resample import resample_spec_step from ..extract_1d import extract_1d_step -from ..combine_1d import combine_1d_step from ..stpipe import Pipeline __all__ = ['MasterBackgroundMosStep'] @@ -69,7 +68,6 @@ class MasterBackgroundMosStep(Pipeline): 'pixel_replace': pixel_replace_step.PixelReplaceStep, 'resample_spec': resample_spec_step.ResampleSpecStep, 'extract_1d': extract_1d_step.Extract1dStep, - 'combine_1d': combine_1d_step.Combine1dStep } # No need to prefetch. This will have been done by the parent step. @@ -135,6 +133,7 @@ def process(self, data): user_background = datamodels.open(self.user_background) master_background, mb_multislit, bkg_x1d_spectra = self._calc_master_background( data_model, user_background) + # data, user_background=None, sigma_clip=3, median_kernel=1) elif self.use_correction_pars: self.log.info('Using pre-calculated correction parameters.') master_background = self.correction_pars['masterbkg_1d'] @@ -183,7 +182,7 @@ def set_pars_from_parent(self): return steps = ['barshadow', 'flat_field', 'pathloss', 'photom', - 'pixel_replace', 'resample_spec', 'extract_1d', 'combine_1d'] + 'pixel_replace', 'resample_spec', 'extract_1d'] pars_to_ignore = { 'barshadow': ['source_type'], 'flat_field': ['save_interpolated_flat'], @@ -302,7 +301,7 @@ def _calc_master_background( bkg_model = self.pixel_replace(bkg_model) bkg_model = self.resample_spec(bkg_model) bkg_x1d_spectra = self.extract_1d(bkg_model) - master_background = nirspec_utils.create_background_from_multislit( + master_background = nirspec_utils.create_background_from_multispec( bkg_x1d_spectra, sigma_clip=sigma_clip, median_kernel=median_kernel) else: master_background = None diff --git a/jwst/master_background/nirspec_utils.py b/jwst/master_background/nirspec_utils.py index 2b27367a34..3420deca6a 100644 --- a/jwst/master_background/nirspec_utils.py +++ b/jwst/master_background/nirspec_utils.py @@ -77,14 +77,14 @@ def map_to_science_slits(input_model, master_bkg): return output_model -def create_background_from_multislit(bkg_model, sigma_clip=3, median_kernel=1): +def create_background_from_multispec(bkg_model, sigma_clip=3, median_kernel=1): """Create a 1D master background spectrum from a set of calibrated background MOS slitlets in the input - MultiSlitModel. + MultiSpecModel. Parameters ---------- - bkg_model : `~jwst.datamodels.MultiSlitModel` + bkg_model : `~jwst.datamodels.MultiSpecModel` The input data model containing all slit instances. sigma_clip : None or float, optional Optional factor for sigma clipping outliers when combining background spectra. diff --git a/jwst/master_background/tests/test_master_background_mos.py b/jwst/master_background/tests/test_master_background_mos.py index b683f49616..f0fc67042d 100644 --- a/jwst/master_background/tests/test_master_background_mos.py +++ b/jwst/master_background/tests/test_master_background_mos.py @@ -2,7 +2,7 @@ import pytest from astropy.io import fits from astropy.table import Table -from stdatamodels.jwst.datamodels import ImageModel +from stdatamodels.jwst.datamodels import ImageModel, MultiSlitModel from jwst.stpipe import query_step_status from jwst.assign_wcs import AssignWcsStep @@ -10,6 +10,9 @@ from jwst.extract_2d.tests.test_nirspec import create_nirspec_hdul from jwst.master_background import MasterBackgroundMosStep from jwst.master_background import nirspec_utils +from jwst.pixel_replace import PixelReplaceStep +from jwst.resample import ResampleSpecStep +from jwst.extract_1d import Extract1dStep def create_msa_hdul(): @@ -74,6 +77,7 @@ def nirspec_msa_metfl(tmp_path): hdul.close() return filename + @pytest.fixture def nirspec_msa_extracted2d(nirspec_msa_rate, nirspec_msa_metfl): model = ImageModel(nirspec_msa_rate) @@ -82,6 +86,20 @@ def nirspec_msa_extracted2d(nirspec_msa_rate, nirspec_msa_metfl): return model +def mk_multispec(model): + specs_model = MultiSlitModel() + specs_model.update(model) + slits = [] + for slit in model.slits: + if nirspec_utils.is_background_msa_slit(slit): + slits.append(slit) + specs_model.slits.extend(slits) + specs_model = PixelReplaceStep.call(specs_model) + specs_model = ResampleSpecStep.call(specs_model) + specs_model = Extract1dStep.call(specs_model) + return specs_model + + def test_master_background_mos(nirspec_msa_extracted2d): model = nirspec_msa_extracted2d @@ -97,43 +115,47 @@ def test_master_background_mos(nirspec_msa_extracted2d): # Check that a background was subtracted from the science data assert not np.allclose(sci_orig, sci_bkgsub) - model.close() - result.close() + del model + del result -def test_create_background_from_multislit(nirspec_msa_extracted2d): +def test_create_background_from_multispec(nirspec_msa_extracted2d): model = nirspec_msa_extracted2d - # Insert a outliers into one of the background spectra + # Insert outliers into one of the background spectra nypix = len(model.slits[0].data) nxpix = len(model.slits[0].data) - model.slits[0].data[nypix//2,nxpix//2-1:nxpix//2+1] = 10 + model.slits[0].data[nypix//2, nxpix//2-1:nxpix//2+1] = 10 + + specs_model = mk_multispec(model) # First check that we can make a master background from the inputs # Check that with sigma_clip=None, the outlier is retained - master_background, _ = nirspec_utils.create_background_from_multislit( - model, sigma_clip=None) + master_background = nirspec_utils.create_background_from_multispec( + specs_model, sigma_clip=None) assert np.any(master_background.spec[0].spec_table['surf_bright'] > 1) # Confirm that using a median_filter will filter out the outlier - master_background, _ = nirspec_utils.create_background_from_multislit( - model, median_kernel=4) + master_background = nirspec_utils.create_background_from_multispec( + specs_model, median_kernel=4) assert np.allclose(master_background.spec[0].spec_table['surf_bright'], 1) # Confirm that using a sigma clipping when combining background spectra # removes the outlier - master_background, _ = nirspec_utils.create_background_from_multislit( - model, sigma_clip=3) + master_background = nirspec_utils.create_background_from_multispec( + specs_model, sigma_clip=3) assert np.allclose(master_background.spec[0].spec_table['surf_bright'], 1) - model.close() + del model + del specs_model + def test_map_to_science_slits(nirspec_msa_extracted2d): model = nirspec_msa_extracted2d + specs_model = mk_multispec(model) - master_background, _ = nirspec_utils.create_background_from_multislit( - model) + master_background = nirspec_utils.create_background_from_multispec(specs_model) # Check that the master background is expanded to the shape of the input slits mb_multislit = nirspec_utils.map_to_science_slits(model, master_background) @@ -145,13 +167,15 @@ def test_map_to_science_slits(nirspec_msa_extracted2d): nonzero = slit_data != 0 assert np.allclose(slit_data[nonzero], 1) - model.close() + del model + del specs_model + def test_apply_master_background(nirspec_msa_extracted2d): model = nirspec_msa_extracted2d + specs_model = mk_multispec(model) - master_background, _ = nirspec_utils.create_background_from_multislit( - model) + master_background = nirspec_utils.create_background_from_multispec(specs_model) mb_multislit = nirspec_utils.map_to_science_slits(model, master_background) result = nirspec_utils.apply_master_background(model, mb_multislit, inverse=False) @@ -175,7 +199,8 @@ def test_apply_master_background(nirspec_msa_extracted2d): assert np.any(diff != 0) assert np.allclose(diff[diff != 0], -1) - model.close() - result.close() + del model + del result + del specs_model From 9ec2783231bf2f42b52a639314cd1a5f8f4b928d Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Fri, 22 Nov 2024 16:05:57 -0500 Subject: [PATCH 664/687] fixed typo --- docs/jwst/master_background/description.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/jwst/master_background/description.rst b/docs/jwst/master_background/description.rst index 75b27b8680..d54b1eb0e6 100644 --- a/docs/jwst/master_background/description.rst +++ b/docs/jwst/master_background/description.rst @@ -375,7 +375,7 @@ as follows: steps up through :ref:`photom ` to all slits, treating them all as extended sources (appropriate for background signal), and saving the extended source correction arrays for each slit in an internal copy of the data model -#. If a user-supplied master background spectrum **not** given, the +#. If a user-supplied master background spectrum is **not** given, the :ref:`pixel_replace `, :ref:`resample_spec ` and :ref:`extract_1d ` steps are applied to the calibrated background slits, resulting From 6fc485bfab70171b2f554aebed08f89cfa415d06 Mon Sep 17 00:00:00 2001 From: Ned Molter Date: Mon, 25 Nov 2024 07:36:35 -0800 Subject: [PATCH 665/687] JP-3742: Remove allowed_memory and associated environment variable (#8975) --- changes/8975.resample.rst | 1 + docs/jwst/outlier_detection/arguments.rst | 9 ----- docs/jwst/resample/arguments.rst | 9 ----- jwst/outlier_detection/imaging.py | 2 -- .../outlier_detection_step.py | 2 -- .../tests/test_outlier_detection.py | 1 - jwst/regtest/test_fgs_image3.py | 21 ------------ jwst/resample/resample.py | 34 +------------------ jwst/resample/resample_step.py | 2 -- pyproject.toml | 1 - 10 files changed, 2 insertions(+), 80 deletions(-) create mode 100644 changes/8975.resample.rst diff --git a/changes/8975.resample.rst b/changes/8975.resample.rst new file mode 100644 index 0000000000..95d48e855e --- /dev/null +++ b/changes/8975.resample.rst @@ -0,0 +1 @@ +Removed allowed_memory parameter and DMODEL_ALLOWED_MEMORY environment variable diff --git a/docs/jwst/outlier_detection/arguments.rst b/docs/jwst/outlier_detection/arguments.rst index 4deae0e47b..be41db97d4 100644 --- a/docs/jwst/outlier_detection/arguments.rst +++ b/docs/jwst/outlier_detection/arguments.rst @@ -79,15 +79,6 @@ Step Arguments for Imaging and Slit-like Spectroscopic data Specifies whether or not to resample the input images when performing outlier detection. -``--allowed_memory`` - Specifies the fractional amount of - free memory to allow when creating the resampled image. If ``None``, the - environment variable ``DMODEL_ALLOWED_MEMORY`` is used. If not defined, no - check is made. If the resampled image would be larger than specified, an - ``OutputTooLargeError`` exception will be generated. - For example, if set to ``0.5``, only resampled images that use less than half - the available memory can be created. - ``--in_memory`` Specifies whether or not to load and create all images that are used during processing into memory. If ``False``, input files are loaded from disk when diff --git a/docs/jwst/resample/arguments.rst b/docs/jwst/resample/arguments.rst index 9332bc6dcd..2b2a47a6a0 100644 --- a/docs/jwst/resample/arguments.rst +++ b/docs/jwst/resample/arguments.rst @@ -108,15 +108,6 @@ image. ``--blendheaders`` (bool, default=True) Blend metadata from all input images into the resampled output image. -``--allowed_memory`` (float, default=None) - Specifies the fractional amount of free memory to allow when creating the - resampled image. If ``None``, the environment variable - ``DMODEL_ALLOWED_MEMORY`` is used. If not defined, no check is made. If the - resampled image would be larger than specified, an ``OutputTooLargeError`` - exception will be generated. - - For example, if set to ``0.5``, only resampled images that use less than - half the available memory can be created. ``--in_memory`` (boolean, default=True) Specifies whether or not to load and create all images that are used during diff --git a/jwst/outlier_detection/imaging.py b/jwst/outlier_detection/imaging.py index c964f3e9ad..0c5d7d163a 100644 --- a/jwst/outlier_detection/imaging.py +++ b/jwst/outlier_detection/imaging.py @@ -35,7 +35,6 @@ def detect_outliers( pixfrac, kernel, fillval, - allowed_memory, in_memory, make_output_path, ): @@ -65,7 +64,6 @@ def detect_outliers( kernel=kernel, fillval=fillval, good_bits=good_bits, - allowed_memory=allowed_memory, ) median_data, median_wcs = median_with_resampling(input_models, resamp, diff --git a/jwst/outlier_detection/outlier_detection_step.py b/jwst/outlier_detection/outlier_detection_step.py index 1b6b42f733..3f5e634522 100644 --- a/jwst/outlier_detection/outlier_detection_step.py +++ b/jwst/outlier_detection/outlier_detection_step.py @@ -59,7 +59,6 @@ class OutlierDetectionStep(Step): resample_data = boolean(default=True) good_bits = string(default="~DO_NOT_USE") # DQ flags to allow search_output_file = boolean(default=False) - allowed_memory = float(default=None) # Fraction of memory to use for the combined image in_memory = boolean(default=False) # ignored if run within the pipeline; set at pipeline level instead """ @@ -113,7 +112,6 @@ def process(self, input_data): self.pixfrac, self.kernel, self.fillval, - self.allowed_memory, self.in_memory, self.make_output_path, ) diff --git a/jwst/outlier_detection/tests/test_outlier_detection.py b/jwst/outlier_detection/tests/test_outlier_detection.py index 7d9849fc46..008ae19c64 100644 --- a/jwst/outlier_detection/tests/test_outlier_detection.py +++ b/jwst/outlier_detection/tests/test_outlier_detection.py @@ -677,6 +677,5 @@ def make_resamp(input_models): good_bits="~DO_NOT_USE", in_memory=in_memory, asn_id="test", - allowed_memory=None, ) return resamp diff --git a/jwst/regtest/test_fgs_image3.py b/jwst/regtest/test_fgs_image3.py index b9f574e5dc..d6679d6a56 100644 --- a/jwst/regtest/test_fgs_image3.py +++ b/jwst/regtest/test_fgs_image3.py @@ -1,7 +1,6 @@ import pytest from astropy.io.fits.diff import FITSDiff -from jwst.resample.resample import OutputTooLargeError from jwst.stpipe import Step @@ -34,23 +33,3 @@ def test_fgs_image3_catalog(run_fgs_image3, rtdata_module, diff_astropy_tables): rtdata.get_truth("truth/test_fgs_image3/jw01029-o001_t009_fgs_clear_cat.ecsv") assert diff_astropy_tables(rtdata.output, rtdata.truth, rtol=1e-3, atol=1e-4) - - -@pytest.mark.bigdata -def test_fgs_toobig(rtdata, fitsdiff_default_kwargs, caplog, monkeypatch): - """Test for the situation where the combined mosaic is too large""" - - # Set the environment to not allow the resultant too-large image. - # Note: this test was originally run on two pre-flight images - # with WCSs from very different parts of the sky. - # This condition should hopefully never be encountered in reductions - # of in-flight data. To test the software failsafe, we now use real data - # that makes a reasonable sized mosaic, but set the allowed memory to a - # small value. - monkeypatch.setenv('DMODEL_ALLOWED_MEMORY', "0.0001") - - rtdata.get_asn('fgs/image3/jw01029-o001_20240716t172128_image3_00001_asn.json') - - args = ['jwst.resample.ResampleStep', rtdata.input] - with pytest.raises(OutputTooLargeError): - Step.from_cmdline(args) diff --git a/jwst/resample/resample.py b/jwst/resample/resample.py index 72b325cbd3..673c4c7f07 100644 --- a/jwst/resample/resample.py +++ b/jwst/resample/resample.py @@ -4,12 +4,10 @@ import json import numpy as np -import psutil from drizzle.resample import Drizzle from spherical_geometry.polygon import SphericalPolygon from stdatamodels.jwst import datamodels -from stdatamodels.jwst.library.basic_utils import bytes2human from jwst.datamodels import ModelLibrary from jwst.associations.asn_from_list import asn_from_list @@ -20,11 +18,7 @@ log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) -__all__ = ["OutputTooLargeError", "ResampleData"] - - -class OutputTooLargeError(RuntimeError): - """Raised when the output is too large for in-memory instantiation""" +__all__ = ["ResampleData"] class ResampleData: @@ -154,32 +148,6 @@ def __init__(self, input_models, output=None, single=False, blendheaders=True, log.debug(f"Output mosaic size: {tuple(self.output_wcs.pixel_shape)}") - allowed_memory = kwargs['allowed_memory'] - if allowed_memory is None: - allowed_memory = os.environ.get('DMODEL_ALLOWED_MEMORY', allowed_memory) - if allowed_memory: - allowed_memory = float(allowed_memory) - # make a small image model to get the dtype - dtype = datamodels.ImageModel((1, 1)).data.dtype - - # get the available memory - available_memory = psutil.virtual_memory().available + psutil.swap_memory().total - - # compute the output array size - required_memory = np.prod(self.output_array_shape) * dtype.itemsize - - # compare used to available - used_fraction = required_memory / available_memory - can_allocate = used_fraction <= allowed_memory - else: - can_allocate = True - - if not can_allocate: - raise OutputTooLargeError( - f'Combined ImageModel size {self.output_array_shape} ' - f'requires {bytes2human(required_memory)}. ' - f'Model cannot be instantiated.' - ) def _create_output_model(self, ref_input_model=None): """ Create a new blank model and update it's meta with info from ``ref_input_model``. """ diff --git a/jwst/resample/resample_step.py b/jwst/resample/resample_step.py index 4132850918..0909227bbb 100755 --- a/jwst/resample/resample_step.py +++ b/jwst/resample/resample_step.py @@ -52,7 +52,6 @@ class ResampleStep(Step): output_wcs = string(default='') # Custom output WCS single = boolean(default=False) # Resample each input to its own output grid blendheaders = boolean(default=True) # Blend metadata from inputs into output - allowed_memory = float(default=None) # Fraction of memory to use for the combined image in_memory = boolean(default=True) # Keep images in memory """ @@ -234,7 +233,6 @@ def get_drizpars(self): good_bits=GOOD_BITS, single=self.single, blendheaders=self.blendheaders, - allowed_memory=self.allowed_memory, in_memory=self.in_memory ) diff --git a/pyproject.toml b/pyproject.toml index 155e2ea531..5c487d65b1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,6 @@ dependencies = [ "numpy>=1.22,<2.0", "opencv-python-headless>=4.6.0.66", "photutils>=1.5.0", - "psutil>=5.7.2", "poppy>=1.0.2", "pyparsing>=2.2.1", "requests>=2.22", From 364e64e223c37b9f49114db686d0a3e8c056645b Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 22 Nov 2024 16:01:23 -0500 Subject: [PATCH 666/687] remove invalid cal_step --- jwst/refpix/irs2_subtract_reference.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/refpix/irs2_subtract_reference.py b/jwst/refpix/irs2_subtract_reference.py index b05261538e..8b7730bb0b 100644 --- a/jwst/refpix/irs2_subtract_reference.py +++ b/jwst/refpix/irs2_subtract_reference.py @@ -78,7 +78,6 @@ def correct_model(output_model, irs2_model, scipix_n_default=16, refpix_r_defaul # of the input model will be copied to output at the end of the step. data = output_model.data.copy() pixeldq = output_model.pixeldq.copy() - output_model.meta.cal_step.refpix = 'not specified yet' # Load the reference file data. # The reference file data are complex, but they're stored as float, with From b5d12cedc80064fbf044107214f2ed1a19b18fc0 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 22 Nov 2024 16:04:14 -0500 Subject: [PATCH 667/687] change cal_step SKIP to SKIPPED --- jwst/residual_fringe/tests/test_configuration.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/residual_fringe/tests/test_configuration.py b/jwst/residual_fringe/tests/test_configuration.py index d0253333bb..33ab11b589 100644 --- a/jwst/residual_fringe/tests/test_configuration.py +++ b/jwst/residual_fringe/tests/test_configuration.py @@ -44,7 +44,7 @@ def test_call_residual_fringe(tmp_cwd, miri_image): def test_fringe_flat_applied(tmp_cwd, miri_image): - miri_image.meta.cal_step.fringe = 'SKIP' + miri_image.meta.cal_step.fringe = 'SKIPPED' residual_fringe_reference_file = None regions_reference_file = None save_intermediate_results = False From 208e7ce61dcc8a64133cf3d534681b6f08ae53d3 Mon Sep 17 00:00:00 2001 From: Brett Date: Fri, 22 Nov 2024 16:04:59 -0500 Subject: [PATCH 668/687] turn stdatamodels validationwarnings into errors in ci --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 5c487d65b1..26d333cffd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -214,6 +214,7 @@ addopts = [ ] filterwarnings = [ "error::ResourceWarning", + "error::stdatamodels.validate.ValidationWarning", "ignore:Models in math_functions:astropy.utils.exceptions.AstropyUserWarning", ] From 31164eabe9e0b4d84f9bf34fafb12e01e87207c4 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Wed, 20 Nov 2024 12:45:17 -0700 Subject: [PATCH 669/687] add log message on number of pixels flagged --- jwst/badpix_selfcal/badpix_selfcal_step.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index b83aaa0f63..12261441ee 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -123,10 +123,12 @@ def process(self, input, selfcal_list=None, bkg_list=None): warnings.filterwarnings("ignore", category=RuntimeWarning, message="All-NaN") minimg = np.nanmin(np.asarray(selfcal_3d), axis=0) bad_indices = badpix_selfcal.badpix_selfcal(minimg, self.flagfrac_lower, self.flagfrac_upper, self.kernel_size, dispaxis) - + + # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) + self.log.info("Number of new bad pixels flagged %d " %len(bad_indices[0])) # apply the flags to the background data to be passed to background sub step if len(bkg_list) > 0: for i, background_model in enumerate(bkg_list): From 01c55125dab97cc15875ceac6ac016f57855614e Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 21 Nov 2024 07:35:17 -0700 Subject: [PATCH 670/687] changed snytax --- jwst/badpix_selfcal/badpix_selfcal_step.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 12261441ee..0cb8c19069 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -128,7 +128,7 @@ def process(self, input, selfcal_list=None, bkg_list=None): # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) - self.log.info("Number of new bad pixels flagged %d " %len(bad_indices[0])) + self.log.info(f"Number of new bad pixels flagged: " {len(bad_indices[0])}") # apply the flags to the background data to be passed to background sub step if len(bkg_list) > 0: for i, background_model in enumerate(bkg_list): From 2ab8f8e199dde5b9eb9ae375fdf6adabbe57f6f9 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 22 Nov 2024 09:01:32 -0700 Subject: [PATCH 671/687] added changes --- changes/8973.badpix_selfcal.rst | 1 + 1 file changed, 1 insertion(+) create mode 100644 changes/8973.badpix_selfcal.rst diff --git a/changes/8973.badpix_selfcal.rst b/changes/8973.badpix_selfcal.rst new file mode 100644 index 0000000000..0d40d2b80d --- /dev/null +++ b/changes/8973.badpix_selfcal.rst @@ -0,0 +1 @@ +added number of pixels flagged as DO_NOT_USE From 7eb9432e8b9fc45c50998e347aaaad18a4307723 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Fri, 22 Nov 2024 09:04:17 -0700 Subject: [PATCH 672/687] fix typo --- jwst/badpix_selfcal/badpix_selfcal_step.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/badpix_selfcal/badpix_selfcal_step.py b/jwst/badpix_selfcal/badpix_selfcal_step.py index 0cb8c19069..7cead42102 100644 --- a/jwst/badpix_selfcal/badpix_selfcal_step.py +++ b/jwst/badpix_selfcal/badpix_selfcal_step.py @@ -128,7 +128,7 @@ def process(self, input, selfcal_list=None, bkg_list=None): # apply the flags to the science data input_sci = badpix_selfcal.apply_flags(input_sci, bad_indices) - self.log.info(f"Number of new bad pixels flagged: " {len(bad_indices[0])}") + self.log.info(f"Number of new bad pixels flagged: {len(bad_indices[0])}") # apply the flags to the background data to be passed to background sub step if len(bkg_list) > 0: for i, background_model in enumerate(bkg_list): From 5f2b0bc8068df1d6e9034cef598886d2b86e998a Mon Sep 17 00:00:00 2001 From: jemorrison Date: Mon, 25 Nov 2024 13:22:30 -0700 Subject: [PATCH 673/687] removed change log --- changes/8973.badpix_selfcal.rst | 1 - 1 file changed, 1 deletion(-) delete mode 100644 changes/8973.badpix_selfcal.rst diff --git a/changes/8973.badpix_selfcal.rst b/changes/8973.badpix_selfcal.rst deleted file mode 100644 index 0d40d2b80d..0000000000 --- a/changes/8973.badpix_selfcal.rst +++ /dev/null @@ -1 +0,0 @@ -added number of pixels flagged as DO_NOT_USE From 6ea327fefb5c0e35a46a1fbd199d1f7ce9485853 Mon Sep 17 00:00:00 2001 From: Tyler Pauly Date: Tue, 26 Nov 2024 16:28:00 -0500 Subject: [PATCH 674/687] Remove testing statement to ignore import failure (#8977) Co-authored-by: Ned Molter --- jwst/lib/tests/test_siafdb.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/jwst/lib/tests/test_siafdb.py b/jwst/lib/tests/test_siafdb.py index 6bbf22da4a..f518f775e8 100644 --- a/jwst/lib/tests/test_siafdb.py +++ b/jwst/lib/tests/test_siafdb.py @@ -3,10 +3,10 @@ import os from pathlib import Path import pytest +import pysiaf from jwst.lib import siafdb -import pysiaf # type: ignore[import-not-found] # noqa: E402 # Database paths DATA_PATH = Path(__file__).parent / 'data' @@ -53,19 +53,23 @@ def test_create(source, prd, xml_path, exception, jail_environ): ('FGS1_FULL_OSS', False, siafdb.SIAF(v2_ref=206.407, v3_ref=-697.765, v3yangle=-1.24120427, vparity=1, crpix1=1024.5, crpix2=1024.5, cdelt1=0.06839158, cdelt2=0.06993081, - vertices_idl=(-68.8543, 70.1233, 71.5697, -70.2482, 72.1764, 68.8086, -75.5918, -70.7457))), + vertices_idl=(-68.8543, 70.1233, 71.5697, -70.2482, 72.1764, 68.8086, + -75.5918, -70.7457))), ('FGS2_FULL_OSS', False, siafdb.SIAF(v2_ref=22.865, v3_ref=-699.28, v3yangle=0.2914828, vparity=1, crpix1=1024.5, crpix2=1024.5, cdelt1=0.06787747, cdelt2=0.06976441, - vertices_idl=(-70.7843, 69.9807, 68.6042, -69.4153, -74.3558, -71.5516, 71.3065, 69.3639))), + vertices_idl=(-70.7843, 69.9807, 68.6042, -69.4153, -74.3558, -71.5516, + 71.3065, 69.3639))), ('MIRIM_TAMRS', False, siafdb.SIAF(v2_ref=-481.987342, v3_ref=-318.206242, v3yangle=4.83544897, vparity=-1, crpix1=24.5, crpix2=24.5, cdelt1=0.10986808, cdelt2=0.10996394, - vertices_idl=(-2.6187, 2.6558, 2.6186, -2.6535, -2.6097, -2.6715, 2.6068, 2.6683))), + vertices_idl=(-2.6187, 2.6558, 2.6186, -2.6535, -2.6097, -2.6715, + 2.6068, 2.6683))), ('MIRIM_TAMRS', True, siafdb.SIAF(v2_ref=-481.987342, v3_ref=-318.206242, v3yangle=4.83544897, vparity=-1, crpix1=997.5, crpix2=993.5, cdelt1=0.10986808, cdelt2=0.10996394, - vertices_idl=(-2.6187, 2.6558, 2.6186, -2.6535, -2.6097, -2.6715, 2.6068, 2.6683))), + vertices_idl=(-2.6187, 2.6558, 2.6186, -2.6535, -2.6097, -2.6715, + 2.6068, 2.6683))), ] ) def test_get_wcs(aperture, to_detector, expected): From f7b5a18c3b60244d8308919ada1dce114ed2a7fb Mon Sep 17 00:00:00 2001 From: Brett Date: Tue, 26 Nov 2024 16:20:26 -0500 Subject: [PATCH 675/687] update test for numpy 2 compatibility --- jwst/lib/tests/test_engdb_mast.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/jwst/lib/tests/test_engdb_mast.py b/jwst/lib/tests/test_engdb_mast.py index faa6d242b8..66a17df001 100644 --- a/jwst/lib/tests/test_engdb_mast.py +++ b/jwst/lib/tests/test_engdb_mast.py @@ -67,22 +67,23 @@ def test_get_records(engdb): [ ({}, [-0.7914494276, -0.7914494276, -0.7914494276, -0.791449368]), ({'include_obstime': True}, - [EngDB_Value(obstime=Time('2022-02-02T22:24:58.053', scale='utc', format='isot'), value=-0.7914494276), - EngDB_Value(obstime=Time('2022-02-02T22:24:58.309', scale='utc', format='isot'), value=-0.7914494276), - EngDB_Value(obstime=Time('2022-02-02T22:24:58.565', scale='utc', format='isot'), value=-0.7914494276), - EngDB_Value(obstime=Time('2022-02-02T22:24:58.821', scale='utc', format='isot'), value=-0.791449368)]), + [EngDB_Value(obstime=Time(59612.9340052431, format='mjd'), value=-0.7914494276), + EngDB_Value(obstime=Time(59612.9340082060, format='mjd'), value=-0.7914494276), + EngDB_Value(obstime=Time(59612.9340111690, format='mjd'), value=-0.7914494276), + EngDB_Value(obstime=Time(59612.9340141319, format='mjd'), value=-0.791449368)]), ({'include_obstime': True, 'zip_results': False}, EngDB_Value( - obstime=[Time('2022-02-02T22:24:58.053', scale='utc', format='isot'), - Time('2022-02-02T22:24:58.309', scale='utc', format='isot'), - Time('2022-02-02T22:24:58.565', scale='utc', format='isot'), - Time('2022-02-02T22:24:58.821', scale='utc', format='isot')], + obstime=[ + Time(59612.9340052431, format='mjd'), + Time(59612.9340082060, format='mjd'), + Time(59612.9340111690, format='mjd'), + Time(59612.9340141319, format='mjd')], value=[-0.7914494276, -0.7914494276, -0.7914494276, -0.791449368])), ({'include_bracket_values': True}, [-0.791449368, -0.7914494276, -0.7914494276, -0.7914494276, -0.791449368, -0.791449368]) ]) def test_get_values(engdb, pars, expected): values = engdb.get_values(*QUERY, **pars) - assert str(values) == str(expected) + assert values == expected def test_negative_aliveness(): From 9f57dbf8d0fa71a9f5f9188dedd588cfc06bdae0 Mon Sep 17 00:00:00 2001 From: Maria Date: Tue, 26 Nov 2024 18:59:53 -0500 Subject: [PATCH 676/687] Delete changes/8847.refpix.rst removing this because it is for the wrong step --- changes/8847.refpix.rst | 1 - 1 file changed, 1 deletion(-) delete mode 100644 changes/8847.refpix.rst diff --git a/changes/8847.refpix.rst b/changes/8847.refpix.rst deleted file mode 100644 index 3fe299a962..0000000000 --- a/changes/8847.refpix.rst +++ /dev/null @@ -1 +0,0 @@ -Include resample, pixel_replace, extract_1d, and combine_1d into MOS background pipeline From 53cdadd73e479377e43f1fc29ce27600b781ab10 Mon Sep 17 00:00:00 2001 From: Maria Pena-Guerrero Date: Tue, 26 Nov 2024 19:10:38 -0500 Subject: [PATCH 677/687] addressing rebase bugs and comments --- changes/8847.master_background.rst | 1 + .../master_background_mos_step.py | 19 +++++++------------ 2 files changed, 8 insertions(+), 12 deletions(-) create mode 100644 changes/8847.master_background.rst diff --git a/changes/8847.master_background.rst b/changes/8847.master_background.rst new file mode 100644 index 0000000000..f1b516a1f2 --- /dev/null +++ b/changes/8847.master_background.rst @@ -0,0 +1 @@ +Include resample, pixel_replace and extract_1d into MOS background pipeline diff --git a/jwst/master_background/master_background_mos_step.py b/jwst/master_background/master_background_mos_step.py index 3aaafe7722..d5ad86b711 100644 --- a/jwst/master_background/master_background_mos_step.py +++ b/jwst/master_background/master_background_mos_step.py @@ -133,7 +133,6 @@ def process(self, data): user_background = datamodels.open(self.user_background) master_background, mb_multislit, bkg_x1d_spectra = self._calc_master_background( data_model, user_background) - # data, user_background=None, sigma_clip=3, median_kernel=1) elif self.use_correction_pars: self.log.info('Using pre-calculated correction parameters.') master_background = self.correction_pars['masterbkg_1d'] @@ -181,8 +180,7 @@ def set_pars_from_parent(self): if not self.parent: return - steps = ['barshadow', 'flat_field', 'pathloss', 'photom', - 'pixel_replace', 'resample_spec', 'extract_1d'] + steps = ['barshadow', 'flat_field', 'pathloss', 'photom'] pars_to_ignore = { 'barshadow': ['source_type'], 'flat_field': ['save_interpolated_flat'], @@ -191,12 +189,9 @@ def set_pars_from_parent(self): } for step in steps: - if step in pars_to_ignore: - pars = getattr(self.parent, step).get_pars() - for par in pars_to_ignore[step] + GLOBAL_PARS_TO_IGNORE: - del pars[par] - else: - pars = getattr(self, step).get_pars() + pars = getattr(self.parent, step).get_pars() + for par in pars_to_ignore[step] + GLOBAL_PARS_TO_IGNORE: + del pars[par] getattr(self, step).update_pars(pars) def _extend_bg_slits(self, pre_calibrated): @@ -298,9 +293,9 @@ def _calc_master_background( self.log.info('Creating MOS master background from background slitlets') bkg_model = self._extend_bg_slits(pre_calibrated) if bkg_model is not None: - bkg_model = self.pixel_replace(bkg_model) - bkg_model = self.resample_spec(bkg_model) - bkg_x1d_spectra = self.extract_1d(bkg_model) + bkg_model = self.pixel_replace.run(bkg_model) + bkg_model = self.resample_spec.run(bkg_model) + bkg_x1d_spectra = self.extract_1d.run(bkg_model) master_background = nirspec_utils.create_background_from_multispec( bkg_x1d_spectra, sigma_clip=sigma_clip, median_kernel=median_kernel) else: From eac728cfd94f8bb8fd6154894074e05ddec16723 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 7 Nov 2024 10:15:19 -0700 Subject: [PATCH 678/687] fix segmented data --- jwst/rscd/rscd_sub.py | 31 +++++++++--- jwst/rscd/tests/test_rscd.py | 91 ++++++++++++++++++++++++++++++++++-- 2 files changed, 110 insertions(+), 12 deletions(-) diff --git a/jwst/rscd/rscd_sub.py b/jwst/rscd/rscd_sub.py index 305c55f320..160986b6f5 100644 --- a/jwst/rscd/rscd_sub.py +++ b/jwst/rscd/rscd_sub.py @@ -80,12 +80,22 @@ def correction_skip_groups(output, group_skip): RSCD-corrected science data """ - # Save some data params for easy use later - sci_nints = output.data.shape[0] # number of integrations - sci_ngroups = output.data.shape[1] # number of groups + # General exposure parameters + sci_ngroups = output.meta.exposure.ngroups + sci_nints = output.meta.exposure.nints + + # values defined for segmented data + sci_int_start = output.meta.exposure.integration_start + + if sci_int_start is None: + sci_int_start = 1 + log.debug("RSCD correction using: nints=%d, ngroups=%d" % (sci_nints, sci_ngroups)) + log.debug("The first integration in the data is integration: %d" % + (sci_int_start)) + log.info("Number of groups to skip for integrations 2 and higher %d ", group_skip) # If ngroups <= group_skip+3, skip the flagging # the +3 is to ensure there is a slope to be fit including the flagging for @@ -96,10 +106,17 @@ def correction_skip_groups(output, group_skip): output.meta.cal_step.rscd = 'SKIPPED' return output - # If ngroups > group_skip+3, set all of the GROUPDQ in the first group to 'DO_NOT_USE' - output.groupdq[1:, 0:group_skip:, :] = \ - np.bitwise_or(output.groupdq[1:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) - log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") + # If ngroups > group_skip+3, set all of the GROUPDQ in 0: group_skip to 'DO_NOT_USE' + # for integrations 1 and higher. Currently no correction is applied to first integration + if sci_int_start == 1: + output.groupdq[1:, 0:group_skip, :, :] = \ + np.bitwise_or(output.groupdq[1:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) + log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") + + else: # we have segmented data + output.groupdq[:, 0:group_skip, :, :] = \ + np.bitwise_or(output.groupdq[:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) + log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") output.meta.cal_step.rscd = 'COMPLETE' return output diff --git a/jwst/rscd/tests/test_rscd.py b/jwst/rscd/tests/test_rscd.py index eddec0c1a8..e18cd98447 100644 --- a/jwst/rscd/tests/test_rscd.py +++ b/jwst/rscd/tests/test_rscd.py @@ -12,18 +12,27 @@ def test_rscd_baseline_set_groupdq(): groupdq flags in the 1st integration """ + exposure = { + 'integration_start' : None, + 'integration_end': None, + 'ngroups' : 10, + 'nints' : 2 + } # size of integration - ngroups = 10 + ngroups = exposure['ngroups'] + nints = exposure['nints'] + xsize = 10 ysize = 10 # create the data and groupdq arrays - csize = (2, ngroups, ysize, xsize) + csize = (nints, ngroups, ysize, xsize) data = np.full(csize, 1.0, dtype=np.float32) groupdq = np.zeros(csize, dtype=np.uint8) # create a JWST datamodel for MIRI data dm_ramp = RampModel(data=data, groupdq=groupdq) + dm_ramp.meta.exposure._instance.update(exposure) # get the number of groups to flag nflag = 3 @@ -71,11 +80,22 @@ def test_rscd_baseline_too_few_groups(): """ # size of exposure - nints = 2 - ngroups = 3 xsize = 10 ysize = 10 + exposure = { + 'integration_start' : None, + 'integration_end': None, + 'ngroups' : 3, + 'nints' : 2 + } + # size of integration + ngroups = exposure['ngroups'] + nints = exposure['nints'] + + xsize = 10 + ysize = 10 + # create the data and groupdq arrays csize = (nints, ngroups, ysize, xsize) data = np.full(csize, 1.0, dtype=np.float32) @@ -83,7 +103,8 @@ def test_rscd_baseline_too_few_groups(): # create a JWST datamodel for MIRI data on a copy (the copy is created at the step script) dm_ramp = RampModel(data=data, groupdq=groupdq) - + dm_ramp.meta.exposure._instance.update(exposure) + # get the number of groups to flag nflag = 3 @@ -99,3 +120,63 @@ def test_rscd_baseline_too_few_groups(): dq_diff, err_msg='groupdq flags changed when ' + 'not enough groups are present') + + +def test_rscd_tso(): + """ + The RSCD correction is generally skipped for TSO data, but some users + have been running it on TSO data. So this test was added. + Test for TSO segmented data that the correct groups are flagged as 'DO_NOT_USE' + for integration 2 and higher. Set up the segmented data so the segment + is for integration 25 to 50. A rscd correction should be applied to all + the data. + + """ + exposure = { + 'integration_start' : 25, + 'integration_end': 50, + 'ngroups' : 8, + 'nints' : 50 + } + + xsize = 10 + ysize = 10 + ngroups = exposure['ngroups'] + seg_nints = exposure['integration_end'] - exposure['integration_start'] + 1 + input_model = RampModel((seg_nints, exposure['ngroups'], + ysize, xsize)) + + input_model.groupdq[:,:,:,:] = 0 # initize to 0 - all good + input_model.meta.exposure._instance.update(exposure) + + # get the number of groups to flag + nflag = 4 + + # run the RSCD baseline correction step on a copy (the copy is created at the step script) + ramp_rscd = correction_skip_groups(input_model.copy(), nflag) + + + # check that the difference in the groupdq flags is equal to + # the 'DO_NOT_USE' flag for the 1st integration in the segment + # which is actually the 25th integration + dq_diff = (ramp_rscd.groupdq[0, 0:nflag, :, :] + - input_model.groupdq[0, 0:nflag, :, :]) + + np.testing.assert_array_equal(np.full((nflag, ysize, xsize), + dqflags.group['DO_NOT_USE'], + dtype=int), + dq_diff, + err_msg='Diff in groupdq flags is not ' + + 'equal to the DO_NOT_USE flag') + + # test that the groupdq flags are not changed for the rest of the groups + # in the 1st integration in the segment + dq_diff = (ramp_rscd.groupdq[0, nflag:ngroups, :, :] + - input_model.groupdq[0, nflag:ngroups, :, :]) + np.testing.assert_array_equal(np.full((ngroups - nflag, ysize, xsize), + 0, + dtype=int), + dq_diff, + err_msg='groupdq flags changed after ' + + 'maximum requested') + From cbeea987685e3277a84375eff558486554e3b870 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 7 Nov 2024 10:37:08 -0700 Subject: [PATCH 679/687] update change log --- CHANGES.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGES.rst b/CHANGES.rst index 1041652782..824f4125f4 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -362,6 +362,7 @@ rscd ---- - Removed unnecessary copies, and created a single copy at step.py level. [#8676] +- Updated step to work on segmented data. [#8878] saturation ---------- From 9d5604fc417790739b8419ef2ef68cf9bb0f7ea7 Mon Sep 17 00:00:00 2001 From: jemorrison Date: Thu, 7 Nov 2024 11:28:57 -0700 Subject: [PATCH 680/687] fixed change log to new method --- CHANGES.rst | 1 - changes/8946.rscd.rst | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 changes/8946.rscd.rst diff --git a/CHANGES.rst b/CHANGES.rst index 824f4125f4..1041652782 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -362,7 +362,6 @@ rscd ---- - Removed unnecessary copies, and created a single copy at step.py level. [#8676] -- Updated step to work on segmented data. [#8878] saturation ---------- diff --git a/changes/8946.rscd.rst b/changes/8946.rscd.rst new file mode 100644 index 0000000000..5ef30ed606 --- /dev/null +++ b/changes/8946.rscd.rst @@ -0,0 +1 @@ +Updated RSCD step to work on segmented data From 8e41cc429f200f17cf83e92606069510ff37a30a Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 12 Nov 2024 13:37:53 -0700 Subject: [PATCH 681/687] add another miri lrsslitless test --- jwst/regtest/test_miri_lrs_slitless.py | 41 +++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/jwst/regtest/test_miri_lrs_slitless.py b/jwst/regtest/test_miri_lrs_slitless.py index db92f58796..e667963950 100644 --- a/jwst/regtest/test_miri_lrs_slitless.py +++ b/jwst/regtest/test_miri_lrs_slitless.py @@ -10,6 +10,7 @@ DATASET1_ID = "jw01536028001_03103_00001-seg001_mirimage" DATASET2_ID = "jw01536028001_03103_00001-seg002_mirimage" +DATASET3_ID = "jw01281001001_04103_00001-seg002_trim_mirimage" ASN3_FILENAME = "jw01536-o028_20221202t215749_tso3_00001_asn.json" PRODUCT_NAME = "jw01536-o028_t008_miri_p750l-slitlessprism" ASN_ID = "o028" @@ -17,7 +18,7 @@ @pytest.fixture(scope="module") def run_tso1_pipeline(rtdata_module): - """Run the calwebb_tso1 pipeline on a MIRI LRS slitless exposure.""" + """Run the calwebb_detector1 pipeline on a MIRI LRS slitless exposure.""" rtdata = rtdata_module rtdata.get_data(f"miri/lrs/{DATASET1_ID}_uncal.fits") @@ -34,6 +35,25 @@ def run_tso1_pipeline(rtdata_module): Step.from_cmdline(args) +@pytest.fixture(scope="module") +def run_detector1_pipeline(rtdata_module): + """Run calwebb_detector pipeline on a MIRI LRS slitless exposure for Segment 2 data. + Focusing on the steps that depend on integration # and not covered by run_tso1_pipeline. + Also test running RSC step""" + rtdata = rtdata_module + rtdata.get_data(f"miri/lrs/{DATASET3_ID}_uncal.fits") + + args = [ + "calwebb_detector1", + rtdata.input, + "--steps.emicorr.save_results=True", + "--steps.rscd.skip=False", + "--steps.rscd.save_results=True", + "--steps.dark_current.save_results=True", + ] + Step.from_cmdline(args) + + @pytest.fixture(scope="module") def run_tso_spec2_pipeline(run_tso1_pipeline, rtdata_module): """Run the calwebb_tso-spec2 pipeline on a MIRI LRS slitless exposure.""" @@ -86,6 +106,25 @@ def test_miri_lrs_slitless_tso1(run_tso1_pipeline, rtdata_module, fitsdiff_defau assert diff.identical, diff.report() +@pytest.mark.bigdata +@pytest.mark.parametrize("step_suffix", ['rscd', 'emicorr', + 'dark_current', 'ramp', 'rate', 'rateints']) +def test_miri_lrs_slitless_detector1(run_detector1_pipeline, rtdata_module, + fitsdiff_default_kwargs, step_suffix): + """ + Regression test of detector1 pipeline performed on MIRI LRS slitless TSO data. + Testing segment 2 data for RSCD, emicorr and dark_current. + """ + rtdata = rtdata_module + output_filename = f"{DATASET3_ID}_{step_suffix}.fits" + rtdata.output = output_filename + + rtdata.get_truth(f"truth/test_miri_lrs_slitless_detector1/{output_filename}") + + diff = FITSDiff(rtdata.output, rtdata.truth, **fitsdiff_default_kwargs) + assert diff.identical, diff.report() + + @pytest.mark.bigdata @pytest.mark.parametrize("step_suffix", ["assign_wcs", "srctype", "flat_field", "pixel_replace", "calints", "x1dints"]) From f058eb566035054016520b453f796e671ef48d4f Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 12 Nov 2024 14:04:01 -0700 Subject: [PATCH 682/687] ../rscd/rscd_sub.py --- jwst/rscd/rscd_sub.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/jwst/rscd/rscd_sub.py b/jwst/rscd/rscd_sub.py index 160986b6f5..9b61049ee4 100644 --- a/jwst/rscd/rscd_sub.py +++ b/jwst/rscd/rscd_sub.py @@ -106,17 +106,20 @@ def correction_skip_groups(output, group_skip): output.meta.cal_step.rscd = 'SKIPPED' return output - # If ngroups > group_skip+3, set all of the GROUPDQ in 0: group_skip to 'DO_NOT_USE' - # for integrations 1 and higher. Currently no correction is applied to first integration - if sci_int_start == 1: - output.groupdq[1:, 0:group_skip, :, :] = \ - np.bitwise_or(output.groupdq[1:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) - log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") - - else: # we have segmented data - output.groupdq[:, 0:group_skip, :, :] = \ - np.bitwise_or(output.groupdq[:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) - log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") + # The RSCD correction is applied to integrations 2 and higher. + # For segmented data the first integration in the file may not be the first integration in the + # exposure. The value in meta.exposure.integration_start holds the value of the first integration + # in the file. + # If a correction is to be done and if ngroups > group_skip+3, then set all of the GROUPDQ + # in 0: group_skip to 'DO_NOT_USE' + + int_start = 1 + if sci_int_start !=1: # we have segmented data + int_start = 0 + + output.groupdq[int_start:, 0:group_skip, :, :] = \ + np.bitwise_or(output.groupdq[int_start:, 0:group_skip, :, :], dqflags.group['DO_NOT_USE']) + log.debug(f"RSCD Sub: adding DO_NOT_USE to GROUPDQ for the first {group_skip} groups") output.meta.cal_step.rscd = 'COMPLETE' return output From a0d069fbf47ed2ffd5d88adcca3c245f554ffa5c Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 19 Nov 2024 08:09:20 -0700 Subject: [PATCH 683/687] added test_miri_lrs_slitless.py --- jwst/regtest/test_miri_lrs_slitless.py | 1 - 1 file changed, 1 deletion(-) diff --git a/jwst/regtest/test_miri_lrs_slitless.py b/jwst/regtest/test_miri_lrs_slitless.py index e667963950..4645636118 100644 --- a/jwst/regtest/test_miri_lrs_slitless.py +++ b/jwst/regtest/test_miri_lrs_slitless.py @@ -5,7 +5,6 @@ from jwst.stpipe import Step from gwcs.wcstools import grid_from_bounding_box - from stdatamodels.jwst import datamodels DATASET1_ID = "jw01536028001_03103_00001-seg001_mirimage" From a9ab10a3506fa38b0df57537bcf00e6a300227fd Mon Sep 17 00:00:00 2001 From: jemorrison Date: Tue, 19 Nov 2024 08:19:14 -0700 Subject: [PATCH 684/687] fix from review --- jwst/rscd/rscd_sub.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jwst/rscd/rscd_sub.py b/jwst/rscd/rscd_sub.py index 9b61049ee4..c4c1cb5d9c 100644 --- a/jwst/rscd/rscd_sub.py +++ b/jwst/rscd/rscd_sub.py @@ -95,7 +95,7 @@ def correction_skip_groups(output, group_skip): (sci_nints, sci_ngroups)) log.debug("The first integration in the data is integration: %d" % (sci_int_start)) - log.info("Number of groups to skip for integrations 2 and higher %d ", group_skip) + log.info("Number of groups to skip for integrations 2 and higher: %d " %group_skip) # If ngroups <= group_skip+3, skip the flagging # the +3 is to ensure there is a slope to be fit including the flagging for From 056c01d72dc19604907766dd667842e3829e7045 Mon Sep 17 00:00:00 2001 From: Brett Graham Date: Mon, 2 Dec 2024 14:50:00 -0500 Subject: [PATCH 685/687] bump asdf upper pin (#8966) --- changes/8966.general.rst | 1 + pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 changes/8966.general.rst diff --git a/changes/8966.general.rst b/changes/8966.general.rst new file mode 100644 index 0000000000..5b21ca6897 --- /dev/null +++ b/changes/8966.general.rst @@ -0,0 +1 @@ +Increase asdf upper pin to 5 diff --git a/pyproject.toml b/pyproject.toml index 26d333cffd..6d61c69b5c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,7 @@ classifiers = [ "Programming Language :: Python :: 3.12", ] dependencies = [ - "asdf>=3.1.0,<4", + "asdf>=3.1.0,<5", "astropy>=5.3", "BayesicFitting>=3.0.1", "crds>=12.0.3", From 1c797285d7d8f5595c921ee09ee6c3718f788d6d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Dec 2024 23:15:48 +0000 Subject: [PATCH 686/687] Bump OpenAstronomy/github-actions-workflows from 1.13.0 to 1.14.0 Bumps [OpenAstronomy/github-actions-workflows](https://github.com/openastronomy/github-actions-workflows) from 1.13.0 to 1.14.0. - [Release notes](https://github.com/openastronomy/github-actions-workflows/releases) - [Commits](https://github.com/openastronomy/github-actions-workflows/compare/924441154cf3053034c6513d5e06c69d262fb9a6...d68193b68216da64eafaa618f53c59f5d271c56e) --- updated-dependencies: - dependency-name: OpenAstronomy/github-actions-workflows dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/build.yml | 2 +- .github/workflows/ci.yml | 4 ++-- .github/workflows/ci_cron.yml | 2 +- .github/workflows/tests_devdeps.yml | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f9f78fea0d..f5591067eb 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -21,7 +21,7 @@ jobs: build: needs: [ check ] if: always() && (needs.check.result == 'success' || needs.check.result == 'skipped') - uses: OpenAstronomy/github-actions-workflows/.github/workflows/publish.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/publish.yml@d68193b68216da64eafaa618f53c59f5d271c56e # v1.14.0 with: upload_to_pypi: ${{ (github.event_name == 'release') && (github.event.action == 'released') }} targets: | diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3185d02aea..083d21625a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -40,7 +40,7 @@ jobs: python-version: '3.12' - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 check: - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@d68193b68216da64eafaa618f53c59f5d271c56e # v1.14.0 with: default_python: "3.12" envs: | @@ -57,7 +57,7 @@ jobs: outputs: context: ${{ steps.context.outputs.context }} test: - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@d68193b68216da64eafaa618f53c59f5d271c56e # v1.14.0 needs: [ crds_context ] with: setenv: | diff --git a/.github/workflows/ci_cron.yml b/.github/workflows/ci_cron.yml index 05146f9fb2..3e7ab20618 100644 --- a/.github/workflows/ci_cron.yml +++ b/.github/workflows/ci_cron.yml @@ -34,7 +34,7 @@ jobs: context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run scheduled tests'))) - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@d68193b68216da64eafaa618f53c59f5d271c56e # v1.14.0 needs: [ crds_context ] with: setenv: | diff --git a/.github/workflows/tests_devdeps.yml b/.github/workflows/tests_devdeps.yml index e5ad86b187..57715057a6 100644 --- a/.github/workflows/tests_devdeps.yml +++ b/.github/workflows/tests_devdeps.yml @@ -43,7 +43,7 @@ jobs: context: ${{ steps.context.outputs.context }} test: if: (github.repository == 'spacetelescope/jwst' && (github.event_name == 'schedule' || github.event_name == 'push' || github.event_name == 'workflow_dispatch' || contains(github.event.pull_request.labels.*.name, 'run devdeps tests'))) - uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@924441154cf3053034c6513d5e06c69d262fb9a6 # v1.13.0 + uses: OpenAstronomy/github-actions-workflows/.github/workflows/tox.yml@d68193b68216da64eafaa618f53c59f5d271c56e # v1.14.0 needs: [ crds_context ] with: setenv: | From e7ae046be531e77650543969c9905c5fc361b259 Mon Sep 17 00:00:00 2001 From: David Law Date: Tue, 3 Dec 2024 12:36:14 -0500 Subject: [PATCH 687/687] JP-3677: Add maximum_shower_amplitude parameter to jump step (#8890) Co-authored-by: Tyler Pauly --- changes/8890.jump.rst | 2 ++ docs/jwst/jump/arguments.rst | 2 ++ docs/jwst/jump/description.rst | 3 ++- jwst/jump/jump.py | 8 ++++++-- jwst/jump/jump_step.py | 2 ++ 5 files changed, 14 insertions(+), 3 deletions(-) create mode 100644 changes/8890.jump.rst diff --git a/changes/8890.jump.rst b/changes/8890.jump.rst new file mode 100644 index 0000000000..d04413a547 --- /dev/null +++ b/changes/8890.jump.rst @@ -0,0 +1,2 @@ +Add maximum_shower_amplitude parameter to MIRI cosmic rays showers routine +to fix accidental flagging of bright science pixels. diff --git a/docs/jwst/jump/arguments.rst b/docs/jwst/jump/arguments.rst index 9eada8a546..778d42363b 100644 --- a/docs/jwst/jump/arguments.rst +++ b/docs/jwst/jump/arguments.rst @@ -111,6 +111,8 @@ is defined as: * ``--min_diffs_single_pass``: The minimum number of differences to switch to flagging all outliers at once +* ``--max_shower_amplitude``: The maximum allowable amplitude for MIRI showers in DN/s + **Parameter that affects both Snowball and Shower flagging** * ``--max_extended_radius``: The maxiumum extension of the jump and saturation that will be flagged for showers or snowballs diff --git a/docs/jwst/jump/description.rst b/docs/jwst/jump/description.rst index c79f46c141..e7ac9aac9e 100644 --- a/docs/jwst/jump/description.rst +++ b/docs/jwst/jump/description.rst @@ -49,7 +49,8 @@ detectors are almost always circles with a central region that is saturated. The saturated core allows the search for smaller events without false positives. The mid-IR (MIRI) detectors do not, in general, have a saturated center and are only rarely circular. Thus, we fit the minimum enclosing ellipse and do not require that there are saturated pixels -within the ellipse. +within the ellipse. Likewise, MIRI showers are only flagged when detected features are consistent +with the maximum known amplitude (in DN/s) of shower artifacts. Multiprocessing --------------- diff --git a/jwst/jump/jump.py b/jwst/jump/jump.py index 92f70de2fb..febae44348 100644 --- a/jwst/jump/jump.py +++ b/jwst/jump/jump.py @@ -28,7 +28,8 @@ def run_detect_jumps(output_model, gain_model, readnoise_model, minimum_sigclip_groups=100, only_use_ints=True, mask_snowball_persist_next_int=True, - snowball_time_masked_next_int=250 + snowball_time_masked_next_int=250, + max_shower_amplitude=4 ): # Runs `detect_jumps` in stcal @@ -43,6 +44,8 @@ def run_detect_jumps(output_model, gain_model, readnoise_model, after_jump_flag_n2 = int(after_jump_flag_time2 // gtime) grps_masked_after_shower = int(time_masked_after_shower // gtime) snowball_grps_masked_next_int = int(snowball_time_masked_next_int // gtime) + # Likewise, convert a max MIRI shower amplitude in DN/s to DN/group + max_shower_amplitude = max_shower_amplitude * gtime # Get 2D gain and read noise values from their respective models if reffile_utils.ref_matches_sci(output_model, gain_model): gain_2d = gain_model.data @@ -85,7 +88,8 @@ def run_detect_jumps(output_model, gain_model, readnoise_model, minimum_sigclip_groups=minimum_sigclip_groups, only_use_ints=only_use_ints, mask_persist_grps_next_int = mask_snowball_persist_next_int, - persist_grps_flagged = snowball_grps_masked_next_int + persist_grps_flagged = snowball_grps_masked_next_int, + max_shower_amplitude = max_shower_amplitude ) diff --git a/jwst/jump/jump_step.py b/jwst/jump/jump_step.py index 86f2b33f23..7eec5c1780 100755 --- a/jwst/jump/jump_step.py +++ b/jwst/jump/jump_step.py @@ -38,6 +38,7 @@ class JumpStep(Step): mask_snowball_core_next_int = boolean(default=True) # Flag saturated cores of snowballs in the next integration? snowball_time_masked_next_int = integer(default=4000) # Time in seconds over which saturated cores are flagged in next integration find_showers = boolean(default=False) # Apply MIRI shower flagging? + max_shower_amplitude = float(default=4) # Maximum MIRI shower amplitude in DN/s extend_snr_threshold = float(default=1.2) # The SNR minimum for detection of extended showers in MIRI extend_min_area = integer(default=90) # Min area of emission after convolution for the detection of showers extend_inner_radius = float(default=1) # Inner radius of the ring_2D_kernel used for convolution @@ -119,6 +120,7 @@ def process(self, step_input): min_sat_radius_extend=self.min_sat_radius_extend, sat_required_snowball=sat_required_snowball, sat_expand=self.sat_expand * 2, expand_large_events=expand_large_events, find_showers=self.find_showers, + max_shower_amplitude=self.max_shower_amplitude, edge_size=self.edge_size, extend_snr_threshold=self.extend_snr_threshold, extend_min_area=self.extend_min_area, extend_inner_radius=self.extend_inner_radius,