Adds total spectra extraction

src/mibi_bin_tools/_extract_bin.pyx

@@ -413,6 +413,75 @@ cdef MAXINDEX_t _extract_total_counts(const char* filename):
     return counts
 
 
+cdef _extract_total_spectra(const char* filename, DTYPE_t low_range, DTYPE_t high_range):
+    """Extract total spectra from bin file
+
+    Args:
+        filename (const char*):
+            Name of bin file to extract
+        low_range (np.uint16_t):
+            The lowest time interval to consider
+        high_range (np.uint16_t):
+            The highest time interval to consider
+    """
+    cdef DTYPE_t num_x, num_y, num_trig, num_frames, desc_len, trig, num_pulses, pulse, time
+    cdef MAXINDEX_t data_start, pix 
+
+    # 10MB buffer
+    cdef MAXINDEX_t BUFFER_SIZE = 10 * 1024 * 1024
+    cdef char* file_buffer = <char*> malloc(BUFFER_SIZE * sizeof(char))
+    cdef MAXINDEX_t buffer_idx = 0
+
+    # open file
+    cdef FILE* fp
+    fp = fopen(filename, "rb")
+
+    # note, if cython has packed structs, this would be easier
+    # or even macros tbh
+    fseek(fp, 0x6, SEEK_SET)
+    fread(&num_x, sizeof(DTYPE_t), 1, fp)
+    fread(&num_y, sizeof(DTYPE_t), 1, fp)
+    fread(&num_trig, sizeof(DTYPE_t), 1, fp)
+    fread(&num_frames, sizeof(DTYPE_t), 1, fp)
+    fseek(fp, 0x2, SEEK_CUR)
+    fread(&desc_len, sizeof(DTYPE_t), 1, fp)
+
+    spectra_by_pixel = \
+        cvarray(
+            shape=(<MAXINDEX_t>(num_x) * <MAXINDEX_t>(num_y),
+                   <MAXINDEX_t>(high_range) - <MAXINDEX_t>(low_range) + 1),
+            itemsize=sizeof(SMALL_t),
+            format='B'
+        )
+    cdef SMALL_t[:, :] spectra_by_pixel_view = spectra_by_pixel
+    spectra_by_pixel_view[:, :] = 0
+
+    data_start = \
+        <MAXINDEX_t>(num_x) * <MAXINDEX_t>(num_y) * <MAXINDEX_t>(num_frames) * 8 + desc_len + 0x12
+
+    fseek(fp, data_start, SEEK_SET)
+    fread(file_buffer, sizeof(char), BUFFER_SIZE, fp)
+    for pix in range(<MAXINDEX_t>(num_x) * <MAXINDEX_t>(num_y)):
+        for trig in range(num_trig):
+            _check_buffer_refill(fp, file_buffer, &buffer_idx, 0x8 * sizeof(char), BUFFER_SIZE)
+            memcpy(&num_pulses, file_buffer + buffer_idx + 0x6, sizeof(time))
+            buffer_idx += 0x8
+            for pulse in range(num_pulses):
+                _check_buffer_refill(fp, file_buffer, &buffer_idx, 0x5 * sizeof(char), BUFFER_SIZE)
+                memcpy(&time, file_buffer + buffer_idx, sizeof(time))
+                buffer_idx += 0x5
+
+                if time >= low_range and time <= high_range:
+                    spectra_by_pixel_view[pix, <MAXINDEX_t>(time) - <MAXINDEX_t>(low_range)] += 1
+
+    fclose(fp)
+    free(file_buffer)
+
+    return np.asarray(spectra_by_pixel, dtype=np.uint8).reshape(
+        (<MAXINDEX_t>num_x, <MAXINDEX_t>num_y, <MAXINDEX_t>high_range - <MAXINDEX_t>low_range + 1)
+    )
+
+
 def c_extract_bin(char* filename, DTYPE_t[:] low_range,
                   DTYPE_t[:] high_range, SMALL_t[:] calc_intensity):
     return np.asarray(
@@ -437,3 +506,6 @@ def c_pulse_height_vs_positive_pixel(char* filename, DTYPE_t low_range, DTYPE_t
 def c_total_counts(char* filename):
     counts = _extract_total_counts(filename)
     return int(counts)
+
+def c_total_spectra(char* filename, DTYPE_t low_range, DTYPE_t high_range):
+    return _extract_total_spectra(filename, low_range, high_range)

src/mibi_bin_tools/bin_files.py

@@ -32,6 +32,27 @@ def _mass2tof(masses_arr: np.ndarray, mass_offset: float, mass_gain: float,
     return (mass_gain * np.sqrt(masses_arr) + mass_offset) / time_res
 
 
+def _tof2mass(tof_arr: np.ndarray, mass_offset: float, mass_gain: float,
+              time_res: float) -> np.ndarray:
+    """Convert array of time of flight values to equivalent m/z
+
+    Args:
+        tof_arr (array_like):
+            Array of time of flight values
+        mass_offset (float):
+            Mass offset for parabolic transformation
+        mass_gain (float):
+            Mass gain for parabolic transformation
+        time_res (float):
+            Time resolution for scaling parabolic transformation
+
+    Returns:
+        array_like:
+            Array of m/z values; indicies paried to `tof_range`
+    """
+    return (((time_res * tof_arr) - mass_offset) / mass_gain) ** 2
+
+
 def _set_tof_ranges(fov: Dict[str, Any], higher: np.ndarray, lower: np.ndarray,
                     time_res: float) -> None:
     """Converts and stores provided mass ranges as time of flight ranges within fov metadata
@@ -521,3 +542,65 @@ def get_total_counts(data_dir: str, include_fovs: Union[List[str], None] = None)
     outs = {name: _extract_bin.c_total_counts(bytes(bf, 'utf-8')) for name, bf in bin_files}
 
     return outs
+
+
+def get_total_spectra(data_dir: str, include_fovs: Union[List[str], None] = None,
+                      panel_df: pd.DataFrame = None, range_pad: float = 0.5):
+    """Retrieves total spectra for each field of view
+
+    Args:
+        data_dir (str | PathLike):
+            Directory containing bin files as well as accompanying json metadata files
+        include_fovs (List | None):
+            List of fovs to include. Includes all if None.
+        panel_df (pd.DataFrame | None):
+            If not None, get default callibration information
+        range_offset (float):
+            Mass padding below the lowest and highest masses to consider when binning.
+            The time-of-flight array go from TOF of (lowest mass - 0.5) to (highest_mass + 0.5).
+
+    Returns:
+        tuple (dict, dict, list):
+            dict of total spectra and the corresponding low and high ranges, with fov names as keys
+    """
+    if range_pad < 0:
+        raise ValueError("range_pad must be >= 0")
+
+    fov_metadata = _find_bin_files(data_dir, include_fovs)
+    if panel_df is not None:
+        for fov_info in fov_metadata.values():
+            _fill_fov_metadata(data_dir, fov_info, panel_df, False, 500e-6)
+
+    bin_metadata = list(fov_metadata.items())
+
+    # TODO: this assumes the panel_df is sorted
+    lowest_mass = panel_df.loc[0, "Stop"] - range_pad
+    highest_mass = panel_df.loc[panel_df.shape[0] - 1, "Stop"] + range_pad
+
+    # store the spectra, as well as the time intervals for each FOV
+    spectra = {}
+    tof_interval = {}
+    for fov_name, fov_info in bin_metadata:
+        # compute the low and high boundaries, this will differ per FOV
+        mass_offset = fov_info["mass_offset"]
+        mass_gain = fov_info["mass_gain"]
+        tof_boundaries = _mass2tof(
+            np.array([lowest_mass, highest_mass]), mass_offset, mass_gain, 500e-6
+        ).astype(np.uint16)
+
+        # set the boundaries
+        tof_interval[fov_name] = tof_boundaries
+
+        # extract the spectra on an individual basis per channel
+        spectra[fov_name] = _extract_bin.c_total_spectra(
+            bytes(os.path.join(data_dir, fov_info["bin"]), "utf-8"),
+            tof_boundaries[0],
+            tof_boundaries[1]
+        )
+
+        # generate equivalent m/z values
+        tof_arr = np.arange(tof_boundaries[0], tof_boundaries[1] + 1)
+        mass_arr = _tof2mass(tof_arr, mass_offset, mass_gain, 500e-6)
+        fov_info["mass_spectra_points"] = mass_arr
+
+    return spectra, tof_interval, fov_metadata

tests/bin_files_test.py

@@ -343,4 +343,23 @@ def test_get_total_counts(test_dir, fov):
         bytes(bf, 'utf-8'), np.array([0], np.uint16),
         np.array([-1], dtype=np.uint16), np.array([False], dtype=np.uint8)
     )
-    assert (total_counts['fov-1-scan-1'] == np.sum(total_ion_image[0, :, :, :]))
+    assert total_counts['fov-1-scan-1'] == np.sum(total_ion_image[0, :, :, :])
+
+
+@parametrize_with_cases('test_dir, fov', cases=FovMetadataTestFiles)
+@parametrize_with_cases('panel', cases=FovMetadataTestPanels, has_tag='specified')
+def test_get_total_spectra(test_dir, fov, panel):
+    # ensure range_pad is positive
+    with pytest.raises(ValueError):
+        bin_files.get_total_spectra(
+            test_dir,
+            [fov['json'].split('.')[0]],
+            panel,
+            range_pad=-0.1
+        )
+
+    bin_files.get_total_spectra(
+        test_dir,
+        [fov['json'].split('.')[0]],
+        panel
+    )