merge master

.gitignore

@@ -71,4 +71,5 @@ target/
 *.swo
 
 # dwi_ml stuff
-.ipynb_config/
+.ipynb_config/
+.ipynb_checkpoints/

bash_utilities/scil_score_ismrm_Renauld2023.sh

@@ -31,7 +31,7 @@ fi
 
 
 echo '------------- SEGMENTATION ------------'
-scil_score_tractogram.py $tractogram $config_file_segmentation $out_dir --no_empty \
+scil_tractogram_segment_and_score.py $tractogram $config_file_segmentation $out_dir --no_empty \
     --gt_dir $scoring_data --reference $ref --json_prefix tmp_ --no_bbox_check;
 
 echo '------------- Merging CC sub-bundles ------------'
@@ -54,7 +54,7 @@ then
 fi
 
 echo '------------- FINAL SCORING ------------'
-scil_score_bundles.py -v $config_file_tractometry $out_dir \
-      --gt_dir $scoring_data --reference $ref --no_bbox_check
+scil_bundle_score_many_bundles_one_tractogram.py $config_file_tractometry $out_dir \
+      --gt_dir $scoring_data --reference $ref --no_bbox_check -v 
 
 cat $out_dir/results.json

dwi_ml/data/dataset/multi_subject_containers.py

@@ -208,6 +208,16 @@ def get_mri_data(self, subj_idx: int, group_idx: int,
 
         Contrary to get_volume_verify_cache, this does not send data to
         cache for later use.
+
+        Parameters
+        ----------
+        subj_idx: int
+            The subject id.
+        group_idx: int
+            The volume group idx.
+        load_it: bool
+            If data is lazy, get the volume as a LazyMRIData (False) or load it
+            as non-lazy (if True).
         """
         if self.subjs_data_list.is_lazy:
             if load_it:
@@ -271,7 +281,6 @@ def load(self, hdf_handle: h5py.File, subj_id=None):
                     hdf_handle, subj_id, ref_group_info)
 
                 # Add subject to the list
-                logger.debug("     Adding it to the list of subjects.")
                 subj_idx = self.subjs_data_list.add_subject(subj_data)
 
                 # Arrange streamlines
@@ -280,7 +289,6 @@ def load(self, hdf_handle: h5py.File, subj_id=None):
                 if subj_data.is_lazy:
                     subj_data.add_handle(hdf_handle)
 
-                logger.debug("     Counting streamlines")
                 for group in range(len(self.streamline_groups)):
                     subj_sft_data = subj_data.sft_data_list[group]
                     n_streamlines = len(subj_sft_data)
@@ -292,6 +300,7 @@ def load(self, hdf_handle: h5py.File, subj_id=None):
                 subj_data.hdf_handle = None
 
             # Arrange final data properties: Concatenate all subjects
+            logging.debug("All subjects added. Final verifications.")
             self.streamline_lengths_mm = \
                 [np.concatenate(lengths_mm[group], axis=0)
                  for group in range(len(self.streamline_groups))]
@@ -484,6 +493,7 @@ def load_data(self, load_training=True, load_validation=True,
                 self.streamline_groups = poss_strea_groups
                 self.streamlines_contain_connectivity = contains_connectivity
 
+            self.streamline_groups = list(self.streamline_groups)
             group_info = (self.volume_groups, self.nb_features,
                           self.streamline_groups,
                           self.streamlines_contain_connectivity)

dwi_ml/data/dataset/single_subject_containers.py

@@ -110,7 +110,8 @@ def init_single_subject_from_hdf(
             subject_mri_data_list.append(subject_mri_group_data)
 
         for group in streamline_groups:
-            logger.debug("        Loading subject's streamlines")
+            logger.debug("        Loading streamlines group '{}'"
+                         .format(group))
             sft_data = SFTData.init_sft_data_from_hdf_info(
                 hdf_file[subject_id][group])
             subject_sft_data_list.append(sft_data)

dwi_ml/data/dataset/streamline_containers.py

@@ -46,6 +46,27 @@ def _load_all_streamlines_from_hdf(hdf_group: h5py.Group):
     return streamlines
 
 
+def _load_connectivity_info(hdf_group: h5py.Group):
+    connectivity_nb_blocs = None
+    connectivity_labels = None
+    if 'connectivity_matrix' in hdf_group:
+        contains_connectivity = True
+        if 'connectivity_nb_blocs' in hdf_group.attrs:
+            connectivity_nb_blocs = hdf_group.attrs['connectivity_nb_blocs']
+        elif 'connectivity_label_volume' in hdf_group:
+            connectivity_labels = np.asarray(
+                hdf_group['connectivity_label_volume'], dtype=int)
+        else:
+            raise ValueError(
+                "Information stored in the hdf5 is that it contains a "
+                "connectivity matrix, but we don't know how it was "
+                "created. Either 'connectivity_nb_blocs' or "
+                "'connectivity_labels' should be set.")
+    else:
+        contains_connectivity = False
+    return contains_connectivity, connectivity_nb_blocs, connectivity_labels
+
+
 class _LazyStreamlinesGetter(object):
     def __init__(self, hdf_group):
         self.hdf_group = hdf_group
@@ -141,27 +162,38 @@ class SFTDataAbstract(object):
     """
     def __init__(self, space_attributes: Tuple, space: Space, origin: Origin,
                  contains_connectivity: bool,
-                 connectivity_nb_blocs: List):
+                 connectivity_nb_blocs: List = None,
+                 connectivity_labels: np.ndarray = None):
         """
-        Params
-        ------
-        group: str
-            The current streamlines group id, as loaded in the hdf5 file (it
-            had type "streamlines"). Probabaly 'streamlines'.
+        The lazy/non-lazy versions will have more parameters, such as the
+        streamlines, the connectivity_matrix. In the case of the lazy version,
+        through the LazyStreamlinesGetter.
+
+        Parameters
+        ----------
         space_attributes: Tuple
             The space attributes consist of a tuple:
              (affine, dimensions, voxel_sizes, voxel_order)
         space: Space
             The space from dipy's Space format.
-        subject_id: str:
-            The subject's name
+        origin: Origin
+            The origin from dipy's Origin format.
+        contains_connectivity: bool
+            If true, will search for either the connectivity_nb_blocs or the
+            connectivity_from_labels information.
+        connectivity_nb_blocs: List
+            The information how to recreate the connectivity matrix.
+        connectivity_labels: np.ndarray
+            The 3D volume stating how to recreate the labels.
+            (toDo: Could be managed to be lazy)
         """
         self.space_attributes = space_attributes
         self.space = space
         self.origin = origin
         self.is_lazy = None
         self.contains_connectivity = contains_connectivity
         self.connectivity_nb_blocs = connectivity_nb_blocs
+        self.connectivity_labels = connectivity_labels
 
     def __len__(self):
         raise NotImplementedError
@@ -195,7 +227,7 @@ def get_connectivity_matrix_and_info(self, ind=None):
         (_, ref_volume_shape, _, _) = self.space_attributes
 
         return (self._access_connectivity_matrix(ind), ref_volume_shape,
-                self.connectivity_nb_blocs)
+                self.connectivity_nb_blocs, self.connectivity_labels)
 
     def _access_connectivity_matrix(self, ind):
         raise NotImplementedError
@@ -277,15 +309,14 @@ def init_sft_data_from_hdf_info(cls, hdf_group: h5py.Group):
         streamlines = _load_all_streamlines_from_hdf(hdf_group)
         # Adding non-hidden parameters for nicer later access
         lengths_mm = hdf_group['euclidean_lengths']
-        if 'connectivity_matrix' in hdf_group:
-            contains_connectivity = True
-            connectivity_matrix = np.asarray(hdf_group['connectivity_matrix'],
-                                             dtype=int)
-            connectivity_nb_blocs = hdf_group.attrs['connectivity_nb_blocs']
+
+        contains_connectivity, connectivity_nb_blocs, connectivity_labels = \
+            _load_connectivity_info(hdf_group)
+        if contains_connectivity:
+            connectivity_matrix = np.asarray(
+                hdf_group['connectivity_matrix'], dtype=int)  # int or bool?
         else:
-            contains_connectivity = False
             connectivity_matrix = None
-            connectivity_nb_blocs = None
 
         space_attributes, space, origin = _load_space_attributes_from_hdf(hdf_group)
 
@@ -296,7 +327,8 @@ def init_sft_data_from_hdf_info(cls, hdf_group: h5py.Group):
                    space_attributes=space_attributes,
                    space=space, origin=origin,
                    contains_connectivity=contains_connectivity,
-                   connectivity_nb_blocs=connectivity_nb_blocs)
+                   connectivity_nb_blocs=connectivity_nb_blocs,
+                   connectivity_labels=connectivity_labels)
 
     def _get_streamlines_as_list(self, streamline_ids):
         if streamline_ids is not None:
@@ -336,22 +368,22 @@ def _access_connectivity_matrix(self, indxyz: Tuple = None):
 
     @classmethod
     def init_sft_data_from_hdf_info(cls, hdf_group: h5py.Group):
-        space_attributes, space, origin = _load_space_attributes_from_hdf(hdf_group)
-        if 'connectivity_matrix' in hdf_group:
-            contains_connectivity = True
-            connectivity_nb_blocs = hdf_group.attrs['connectivity_nb_blocs']
-        else:
-            contains_connectivity = False
-            connectivity_nb_blocs = None
+        space_attributes, space, origin = _load_space_attributes_from_hdf(
+            hdf_group)
+
+        contains_connectivity, connectivity_nb_blocs, connectivity_labels = \
+            _load_connectivity_info(hdf_group)
 
         streamlines = _LazyStreamlinesGetter(hdf_group)
 
         return cls(streamlines_getter=streamlines,
                    space_attributes=space_attributes,
                    space=space, origin=origin,
                    contains_connectivity=contains_connectivity,
-                   connectivity_nb_blocs=connectivity_nb_blocs)
+                   connectivity_nb_blocs=connectivity_nb_blocs,
+                   connectivity_labels=connectivity_labels)
 
     def _get_streamlines_as_list(self, streamline_ids):
-        streamlines = self.streamlines_getter.get_array_sequence(streamline_ids)
+        streamlines = self.streamlines_getter.get_array_sequence(
+            streamline_ids)
         return streamlines