Merge pull request #340 from cosanlab/clean_code

Clean code

Author: ljchang

nltools/data/brain_data.py

@@ -399,16 +399,28 @@ def median(self, axis=0):
                 raise ValueError('axis must be 0 or 1')
         return out
 
-    def std(self):
-        """ Get standard deviation of each voxel across images. """
+    def std(self, axis=0):
+        ''' Get standard deviation of each voxel or image.
+            
+            Args:
+                axis: (int) across images=0 (default), within images=1
+            
+            Returns:
+                out: (float/np.array/Brain_Data)
+        '''
 
         out = deepcopy(self)
-        if len(self.shape()) > 1:
-            out.data = np.std(self.data, axis=0)
-            out.X = pd.DataFrame()
-            out.Y = pd.DataFrame()
-        else:
+        if check_brain_data_is_single(self):
             out = np.std(self.data)
+        else:
+            if axis == 0:
+                out.data = np.std(self.data, axis=0)
+                out.X = pd.DataFrame()
+                out.Y = pd.DataFrame()
+            elif axis == 1:
+                out = np.std(self.data, axis=1)
+            else:
+                raise ValueError('axis must be 0 or 1')
         return out
 
     def sum(self):

nltools/datasets.py

@@ -13,21 +13,16 @@
            'get_collection_image_metadata',
            'download_collection',
            'fetch_emotion_ratings',
-           'fetch_pain',
-           'fetch_localizer']
+           'fetch_pain']
 __author__ = ["Luke Chang"]
 __license__ = "MIT"
 
 import os
 import pandas as pd
-import numpy as np
 from nltools.data import Brain_Data
 from nilearn.datasets.utils import (_get_dataset_dir,
                                     _fetch_file,
-                                    _fetch_files,
-                                    _get_dataset_descr)
-from nilearn._utils.compat import _urllib
-from sklearn.datasets.base import Bunch
+                                    _fetch_files)
 from pynv import Client
 
 # Optional dependencies
@@ -160,132 +155,3 @@ def fetch_emotion_ratings(data_dir=None, resume=True, verbose=1):
                                           verbose=verbose)
     return Brain_Data(data=files, X=metadata)
 
-def fetch_localizer(subject_ids=None, get_anats=False, data_type='raw',
-                    data_dir=None, url=None, resume=True, verbose=1):
-    """ Download and load Brainomics Localizer dataset (94 subjects).
-    "The Functional Localizer is a simple and fast acquisition
-    procedure based on a 5-minute functional magnetic resonance
-    imaging (fMRI) sequence that can be run as easily and as
-    systematically as an anatomical scan. This protocol captures the
-    cerebral bases of auditory and visual perception, motor actions,
-    reading, language comprehension and mental calculation at an
-    individual level. Individual functional maps are reliable and
-    quite precise. The procedure is decribed in more detail on the
-    Functional Localizer page." This code is modified from
-    `fetch_localizer_contrasts` from nilearn.datasets.funcs.py.
-    (see http://brainomics.cea.fr/localizer/)
-    "Scientific results obtained using this dataset are described in
-    Pinel et al., 2007" [1]
-
-    Notes:
-    It is better to perform several small requests than a big one because the
-    Brainomics server has no cache (can lead to timeout while the archive
-    is generated on the remote server).  For example, download
-    n_subjects=np.array(1,10), then n_subjects=np.array(10,20), etc.
-
-    Args:
-        subject_ids: (list) List of Subject IDs (e.g., ['S01','S02'].
-                     If None is given, all 94 subjects are used.
-        get_anats: (boolean) Whether individual structural images should be
-                    fetched or not.
-        data_type: (string) type of data to download.
-                    Valid values are ['raw','preprocessed']
-        data_dir: (string, optional) Path of the data directory.
-                    Used to force data storage in a specified location.
-        url: (string, optional) Override download URL.
-             Used for test only (or if you setup a mirror of the data).
-        resume: (bool) Whether to resume download of a partly-downloaded file.
-        verbose: (int) Verbosity level (0 means no message).
-
-    Returns:
-        data: (Bunch)
-            Dictionary-like object, the interest attributes are :
-            - 'functional': string list
-                Paths to nifti contrast maps
-            - 'structural' string
-                Path to nifti files corresponding to the subjects structural images
-
-    References
-    ----------
-    Pinel, Philippe, et al.
-    "Fast reproducible identification and large-scale databasing of
-    individual functional cognitive networks."
-    BMC neuroscience 8.1 (2007): 91.
-
-    """
-
-    if subject_ids is None:
-        subject_ids = ['S%02d' % x for x in np.arange(1,95)]
-    elif not isinstance(subject_ids, (list)):
-        raise ValueError("subject_ids must be a list of subject ids (e.g., ['S01','S02'])")
-
-    if data_type == 'raw':
-        dat_type = "raw fMRI"
-        dat_label = "raw bold"
-        anat_type = "raw T1"
-        anat_label = "raw anatomy"
-    elif data_type == 'preprocessed':
-        dat_type = "preprocessed fMRI"
-        dat_label = "bold"
-        anat_type = "normalized T1"
-        anat_label = "anatomy"
-    else:
-        raise ValueError("Only ['raw','preprocessed'] data_types are currently supported.")
-
-    root_url = "http://brainomics.cea.fr/localizer/"
-    dataset_name = 'brainomics_localizer'
-    data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
-                                verbose=verbose)
-    fdescr = _get_dataset_descr(dataset_name)
-    opts = {'uncompress': True}
-
-    bold_files = []; anat_files = [];
-    for subject_id in subject_ids:
-        base_query = ("Any X,XT,XL,XI,XF,XD WHERE X is Scan, X type XT, "
-                  "X concerns S, "
-                  "X label XL, X identifier XI, "
-                  "X format XF, X description XD, "
-                  'S identifier = "%s", ' % (subject_id, ) +
-                  'X type IN(%(types)s), X label "%(label)s"')
-
-        file_tarball_url = "%sbrainomics_data.zip?rql=%s&vid=data-zip" % (root_url, _urllib.parse.quote(base_query % {"types": "\"%s\"" % dat_type,  "label": dat_label}, safe=',()'))
-        name_aux = str.replace(str.join('_', [dat_type, dat_label]), ' ', '_')
-        file_path = os.path.join("brainomics_data", subject_id, "%s.nii.gz" % name_aux)
-        bold_files.append(_fetch_files(data_dir, [(file_path, file_tarball_url, opts)], verbose=verbose))
-
-        if get_anats:
-            file_tarball_url = "%sbrainomics_data_anats.zip?rql=%s&vid=data-zip" % (root_url, _urllib.parse.quote(base_query % {"types": "\"%s\"" % anat_type, "label": anat_label}, safe=',()'))
-            if data_type == 'raw':
-                anat_name_aux = "raw_T1_raw_anat_defaced.nii.gz"
-            elif data_type == 'preprocessed':
-                anat_name_aux = "normalized_T1_anat_defaced.nii.gz"
-            file_path = os.path.join("brainomics_data", subject_id, anat_name_aux)
-            anat_files.append(_fetch_files(data_dir, [(file_path, file_tarball_url, opts)], verbose=verbose))
-
-    # Fetch subject characteristics (separated in two files)
-    if url is None:
-        url_csv = ("%sdataset/cubicwebexport.csv?rql=%s&vid=csvexport"
-                   % (root_url, _urllib.parse.quote("Any X WHERE X is Subject")))
-        url_csv2 = ("%sdataset/cubicwebexport2.csv?rql=%s&vid=csvexport"
-                    % (root_url,
-                       _urllib.parse.quote("Any X,XI,XD WHERE X is QuestionnaireRun, "
-                                    "X identifier XI, X datetime "
-                                    "XD", safe=',')))
-    else:
-        url_csv = "%s/cubicwebexport.csv" % url
-        url_csv2 = "%s/cubicwebexport2.csv" % url
-
-    filenames = [("cubicwebexport.csv", url_csv, {}),("cubicwebexport2.csv", url_csv2, {})]
-    csv_files = _fetch_files(data_dir, filenames, verbose=verbose)
-    metadata = pd.merge(pd.read_csv(csv_files[0], sep=';'), pd.read_csv(csv_files[1], sep=';'), on='"subject_id"')
-    metadata.to_csv(os.path.join(data_dir,'metadata.csv'))
-    for x in ['cubicwebexport.csv','cubicwebexport2.csv']:
-        os.remove(os.path.join(data_dir, x))
-
-    if not get_anats:
-        anat_files = None
-
-    return Bunch(functional=bold_files,
-                 structural=anat_files,
-                 ext_vars=metadata,
-                 description=fdescr)