fix conflict in requirements

.github/workflows/test.yml

@@ -48,7 +48,8 @@ jobs:
 
       - name: Install Scilpy
         run: |
-          python -m pip install --upgrade pip setuptools wheel
+          python -m pip install --upgrade pip wheel
+          python -m pip install --upgrade "setuptools<71.0.0" 
           python -m pip install -e .
 
       - name: Run tests

requirements.txt

@@ -4,26 +4,28 @@ bz2file==0.98.*
 coloredlogs==15.0.*
 cvxpy==1.4.*
 cycler==0.11.*
-Cython==0.29.*, !=0.29.29
+#Cython==0.29.*, !=0.29.29
+Cython==3.0.*
 dipy==1.9.*
 deepdiff==6.3.0
-dmri-amico==2.0.*
-dmri-commit==2.1.*
+dmri-amico==2.0.3
+dmri-commit==2.3.0
 docopt==0.6.*
 dvc==3.48.*
 dvc-http==2.32.*
 formulaic==0.3.*
 fury==0.10.*
 future==0.18.*
 GitPython==3.1.*
-h5py==3.7.*
+h5py==3.10.*
 joblib==1.2.*
 kiwisolver==1.4.*
 matplotlib==3.6.*
+PyMCubes==0.1.*
 nibabel==5.2.*
 nilearn==0.9.*
 nltk==3.8.*
-numpy==1.23.*
+numpy==1.25.*
 openpyxl==3.0.*
 packaging == 23.2.*
 Pillow==10.2.*
@@ -40,9 +42,9 @@ pytz==2022.6.*
 requests==2.28.*
 scikit-learn==1.2.*
 scikit-image==0.22.*
-scipy==1.9.*
+scipy==1.11.*
 six==1.16.*
 spams==2.6.*
 statsmodels==0.13.*
-trimeshpy==0.0.3
+trimeshpy==0.0.4
 vtk==9.2.*

scilpy/image/labels.py

@@ -6,6 +6,7 @@
 import os
 
 import numpy as np
+from scipy import ndimage as ndi
 from scipy.spatial import cKDTree
 
 
@@ -67,6 +68,67 @@ def get_binary_mask_from_labels(atlas, label_list):
     return mask
 
 
+def get_labels_from_mask(mask_data, labels=None, background_label=0):
+    """
+    Get labels from a binary mask which contains multiple blobs. Each blob
+    will be assigned a label, by default starting from 1. Background will
+    be assigned the background_label value.
+
+    Parameters
+    ----------
+    mask_data: np.ndarray
+        The mask data.
+    labels: list, optional
+        Labels to assign to each blobs in the mask. Excludes the background
+        label.
+    background_label: int
+        Label for the background.
+
+    Returns
+    -------
+    label_map: np.ndarray
+        The labels.
+    """
+    # Get the number of structures and assign labels to each blob
+    label_map, nb_structures = ndi.label(mask_data)
+    # Assign labels to each blob if provided
+    if labels:
+        # Only keep the first nb_structures labels if the number of labels
+        # provided is greater than the number of blobs in the mask.
+        if len(labels) > nb_structures:
+            logging.warning("Number of labels ({}) does not match the number "
+                            "of blobs in the mask ({}). Only the first {} "
+                            "labels will be used.".format(
+                                len(labels), nb_structures, nb_structures))
+        # Cannot assign fewer labels than the number of blobs in the mask.
+        elif len(labels) < nb_structures:
+            raise ValueError("Number of labels ({}) is less than the number of"
+                             " blobs in the mask ({}).".format(
+                                 len(labels), nb_structures))
+
+        # Copy the label map to avoid scenarios where the label list contains
+        # labels that are already present in the label map
+        custom_label_map = label_map.copy()
+        # Assign labels to each blob
+        for idx, label in enumerate(labels[:nb_structures]):
+            custom_label_map[label_map == idx + 1] = label
+        label_map = custom_label_map
+
+    logging.info('Assigned labels {} to the mask.'.format(
+        np.unique(label_map[label_map != background_label])))
+
+    if background_label != 0 and background_label in label_map:
+        logging.warning("Background label {} corresponds to a label "
+                        "already in the map. This will cause issues.".format(
+                            background_label))
+
+    # Assign background label
+    if background_label:
+        label_map[label_map == 0] = background_label
+
+    return label_map
+
+
 def get_lut_dir():
     """
     Return LUT directory in scilpy repository
@@ -379,3 +441,39 @@ def get_stats_in_label(map_data, label_data, label_lut):
                                   'mean': float(mean_seed),
                                   'std': float(std_seed)}
     return out_dict
+
+
+def merge_labels_into_mask(atlas, filtering_args):
+    """
+    Merge labels into a mask.
+
+    Parameters
+    ----------
+    atlas: np.ndarray
+        Atlas with labels as a numpy array (uint16) to merge.
+
+    filtering_args: str
+        Filtering arguments from the command line.
+
+    Return
+    ------
+    mask: nibabel.nifti1.Nifti1Image
+        Mask obtained from the combination of multiple labels.
+    """
+    mask = np.zeros(atlas.shape, dtype=np.uint16)
+
+    if ' ' in filtering_args:
+        values = filtering_args.split(' ')
+        for filter_opt in values:
+            if ':' in filter_opt:
+                vals = [int(x) for x in filter_opt.split(':')]
+                mask[(atlas >= int(min(vals))) & (atlas <= int(max(vals)))] = 1
+            else:
+                mask[atlas == int(filter_opt)] = 1
+    elif ':' in filtering_args:
+        values = [int(x) for x in filtering_args.split(':')]
+        mask[(atlas >= int(min(values))) & (atlas <= int(max(values)))] = 1
+    else:
+        mask[atlas == int(filtering_args)] = 1
+
+    return mask

scilpy/image/tests/test_labels.py

@@ -10,8 +10,8 @@
 import pytest
 
 from scilpy.image.labels import (combine_labels, dilate_labels,
-                                 get_data_as_labels, get_lut_dir,
-                                 remove_labels, split_labels)
+                                 get_data_as_labels, get_labels_from_mask,
+                                 get_lut_dir, remove_labels, split_labels)
 from scilpy.tests.arrays import ref_in_labels, ref_out_labels
 
 
@@ -132,6 +132,54 @@ def test_get_data_as_labels_float():
         _ = get_data_as_labels(img)
 
 
+def test_get_labels_from_mask():
+    """ Test get_labels_from_mask with default labels. """
+    # ref_out_labels contains disjoint blobs with values 2,4,6
+    data = deepcopy(ref_out_labels)
+    data[data == 2] = 1
+    data[data == 4] = 2
+    data[data == 6] = 3
+    mask = data.astype(bool)
+
+    labels = get_labels_from_mask(mask)
+
+    assert_equal(labels, data)
+
+
+def test_get_labels_from_mask_custom_labels_raises():
+    """ Test get_labels_from_mask with custom labels. """
+    # ref_out_labels contains disjoint blobs with values 2,4,6
+    data = deepcopy(ref_out_labels)
+    mask = data.astype(bool)
+    labels = get_labels_from_mask(mask, [2, 4, 6, 8])
+
+    assert np.unique(labels).size == 4  # including background
+
+
+def test_get_labels_from_mask_custom_labels():
+    """ Test get_labels_from_mask with custom labels. """
+    # ref_out_labels contains disjoint blobs with values 2,4,6
+    data = deepcopy(ref_out_labels)
+    mask = data.astype(bool)
+
+    labels = get_labels_from_mask(mask, [2, 4, 6])
+
+    assert_equal(labels, data)
+
+
+def test_get_labels_from_mask_custom_background():
+    """ test get_labels_from_mask with custom background. """
+    # ref_out_labels contains disjoint blobs with values 2,4,6
+    data = deepcopy(ref_out_labels)
+    mask = data.copy().astype(bool)
+
+    data[data == 0] = 9
+
+    labels = get_labels_from_mask(mask, [2, 4, 6], background_label=9)
+
+    assert_equal(labels, data)
+
+
 def test_get_lut_dir():
     lut_dir = get_lut_dir()
     assert os.path.isdir(lut_dir)

scilpy/io/gradients.py

@@ -32,7 +32,7 @@ def fsl2mrtrix(fsl_bval_filename, fsl_bvec_filename, mrtrix_filename):
         logging.warning('WARNING: Your bvecs seem transposed. ' +
                         'Transposing them.')
 
-    shell_idx = [int(np.where(bval == bvals)[0]) for bval in shells]
+    shell_idx = [int(np.where(bval == bvals)[0][0]) for bval in shells]
     save_gradient_sampling_mrtrix(points, shell_idx, bvals,
                                   mrtrix_filename + '.b')
 
@@ -62,8 +62,7 @@ def mrtrix2fsl(mrtrix_filename, fsl_filename):
     shells = np.array(mrtrix_b[:, 3])
 
     bvals = np.unique(shells).tolist()
-    shell_idx = [int(np.where(bval == bvals)[0]) for bval in shells]
-
+    shell_idx = [int(np.where(bval == bvals)[0][0]) for bval in shells]
     save_gradient_sampling_fsl(points, shell_idx, bvals,
                                filename_bval=fsl_filename + '.bval',
                                filename_bvec=fsl_filename + '.bvec')

scilpy/io/image.py

@@ -32,42 +32,6 @@ def load_img(arg):
     return img, dtype
 
 
-def merge_labels_into_mask(atlas, filtering_args):
-    """
-    Merge labels into a mask.
-
-    Parameters
-    ----------
-    atlas: np.ndarray
-        Atlas with labels as a numpy array (uint16) to merge.
-
-    filtering_args: str
-        Filtering arguments from the command line.
-
-    Return
-    ------
-    mask: nibabel.nifti1.Nifti1Image
-        Mask obtained from the combination of multiple labels.
-    """
-    mask = np.zeros(atlas.shape, dtype=np.uint16)
-
-    if ' ' in filtering_args:
-        values = filtering_args.split(' ')
-        for filter_opt in values:
-            if ':' in filter_opt:
-                vals = [int(x) for x in filter_opt.split(':')]
-                mask[(atlas >= int(min(vals))) & (atlas <= int(max(vals)))] = 1
-            else:
-                mask[atlas == int(filter_opt)] = 1
-    elif ':' in filtering_args:
-        values = [int(x) for x in filtering_args.split(':')]
-        mask[(atlas >= int(min(values))) & (atlas <= int(max(values)))] = 1
-    else:
-        mask[atlas == int(filtering_args)] = 1
-
-    return mask
-
-
 def assert_same_resolution(images):
     """
     Check the resolution of multiple images.

scilpy/io/utils.py

@@ -100,6 +100,13 @@ def link_bundles_and_reference(parser, args, input_tractogram_list):
     return bundles_references_tuple
 
 
+def check_tract_trk(parser, filename):
+
+    _, ext = os.path.splitext(filename)
+    if ext != '.trk':
+        parser.error('File {} is not a .trk file.'.format(filename))
+
+
 def check_tracts_same_format(parser, filename_list):
     _, ref_ext = os.path.splitext(filename_list[0])
 
@@ -667,7 +674,7 @@ def add_compression_arg(p, additional_msg=''):
         compress arg.
     """
     p.add_argument('--compress', dest='compress_th', nargs='?', const=0.1,
-                   type=ranged_type(float, 0, None),
+                   type=ranged_type(float, 0, None, min_excluded=True),
                    help='If set, compress the resulting streamline. Value is '
                         'the maximum \ncompression distance in mm.'
                         + additional_msg + '[%(const)s]')
@@ -1049,9 +1056,12 @@ def parser_color_type(arg):
     return f
 
 
-def ranged_type(value_type, min_value=None, max_value=None):
+def ranged_type(value_type, min_value=None, max_value=None,
+                min_excluded=False, max_excluded=False):
     """Return a function handle of an argument type function for ArgumentParser
     checking a range: `min_value` <= arg <= `max_value`.
+    With min_excluded and max_excluded, the verification becomes
+    `min_value` < arg < `max_value`.
 
     Parameters
     ----------
@@ -1061,6 +1071,10 @@ def ranged_type(value_type, min_value=None, max_value=None):
         Minimum acceptable argument value.
     max_value : scalar
        Maximum acceptable argument value.
+    min_excluded: bool
+        If true, the accepted range is ]min_value, max_value].
+    max_excluded: bool
+        If true, the accepted range is [min_value, max_value[.
 
     Returns
     -------
@@ -1076,16 +1090,40 @@ def range_checker(arg: str):
             f = value_type(arg)
         except ValueError:
             raise argparse.ArgumentTypeError(f"must be a valid {value_type}")
+
+        smaller = np.less
+        bigger = np.greater
+        if min_excluded:
+            smaller = np.less_equal
+        if max_excluded:
+            bigger = np.greater_equal
+
         if min_value is not None and max_value is not None:
-            if f < min_value or f > max_value:
-                raise argparse.ArgumentTypeError(
-                    f"must be within [{min_value}, {max_value}]")
+            if smaller(f, min_value) or bigger(f, max_value):
+                if min_excluded and max_excluded:
+                    raise argparse.ArgumentTypeError(
+                        f"must be within ]{min_value}, {max_value}[")
+                elif min_excluded:
+                    raise argparse.ArgumentTypeError(
+                        f"must be within ]{min_value}, {max_value}")
+                elif max_excluded:
+                    raise argparse.ArgumentTypeError(
+                        f"must be within [{min_value}, {max_value}[")
+                else:
+                    raise argparse.ArgumentTypeError(
+                        f"must be within [{min_value}, {max_value}]")
         elif min_value is not None:
             if f < min_value:
-                raise argparse.ArgumentTypeError(f"must be >= {min_value}")
+                if min_excluded:
+                    raise argparse.ArgumentTypeError(f"must be > {min_value}")
+                else:
+                    raise argparse.ArgumentTypeError(f"must be >= {min_value}")
         elif max_value is not None:
             if f > max_value:
-                raise argparse.ArgumentTypeError(f"must be <= {max_value}")
+                if max_excluded:
+                    raise argparse.ArgumentTypeError(f"must be < {max_value}")
+                else:
+                    raise argparse.ArgumentTypeError(f"must be <= {max_value}")
         return f
 
     # Return handle to checking function