Rename legacy names in doc

scilpy/image/volume_math.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
 """
-Utility operations provided for scil_image_math.py
+Utility operations provided for scil_volume_math.py
 and scil_connectivity_math.py
 They basically act as wrappers around numpy to avoid installing MRtrix/FSL
 to apply simple operations on nibabel images or numpy arrays.

scilpy/tracking/tests/test_propagator.py

@@ -4,7 +4,7 @@
 def test_class_propagator():
     """
     We will not test the tracker / propagator : too big to be tested, and only
-    used in scil_compute_local_tracking_dev, which is intented for developping
-    and testing new parameters.
+    used in scil_tracking_local_dev, which is intented for developping and
+    testing new parameters.
     """
     pass

scilpy/tracking/tests/test_tracker.py

@@ -4,7 +4,7 @@
 def test_class_tracker():
     """
     We will not test the tracker / propagator : too big to be tested, and only
-    used in scil_compute_local_tracking_dev, which is intented for developping
-    and testing new parameters.
+    used in scil_tracking_local_dev, which is intented for developping and
+    testing new parameters.
     """
     pass

scilpy/tracking/utils.py

@@ -143,7 +143,7 @@ def add_out_options(p):
                        help='If set, save the seeds used for the tracking \n '
                             'in the data_per_streamline property.\n'
                             'Hint: you can then use '
-                            'scilpy_compute_seed_density_map.')
+                            'scil_tractogram_seed_density_map.')
     return out_g
 
 
@@ -220,7 +220,8 @@ def tracks_generator_wrapper():
                                           leave=False):
             if (scaled_min_length <= length(strl) <= scaled_max_length):
                 # Seeds are saved with origin `center` by our own convention.
-                # Other scripts (e.g. scil_compute_seed_density_map) expect so.
+                # Other scripts (e.g. scil_tractogram_seed_density_map) expect
+                # so.
                 dps = {}
                 if save_seeds:
                     dps['seeds'] = seed

scilpy/tractanalysis/todi.py

@@ -388,10 +388,10 @@ def compute_average_dir(self):
 
     def __enter__(self):
         # Necessary for a 'with' statement to scrap a todi_object after
-        # the scope of operation in the script scil_priors_from_streamlines.py
+        # the scope of operation in the script scil_bundle_generate_priors.py
         return self
 
     def __exit__(self, exception_type, exception_value, traceback):
         # Necessary for a 'with' statement to scrap a todi_object after
-        # the scope of operation in the script scil_priors_from_streamlines.py
+        # the scope of operation in the script scil_bundle_generate_priors.py
         pass

scilpy/utils/metrics_tools.py

@@ -216,7 +216,7 @@ def get_bundle_metrics_mean_std_per_point(streamlines, bundle_name,
                                           correlation_values=None,
                                           density_weighting=False):
     """
-    Compute the mean and std PER POiNT of the bundle for every given metric.
+    Compute the mean and std PER POINT of the bundle for every given metric.
 
     Parameters
     ----------
@@ -227,18 +227,18 @@ def get_bundle_metrics_mean_std_per_point(streamlines, bundle_name,
     metrics: sequence
         list of nibabel objects representing the metrics files
     labels: np.ndarray
-        List of labels obtained with scil_label_and_distance_maps.py
+        List of labels obtained with scil_bundle_label_map.py
     distance_values: np.ndarray
-        List of distances obtained with scil_compute_bundle_voxel_label_map.py
+        List of distances obtained with scil_bundle_label_map.py
     correlation_values: np.ndarray
-        List of correlations obtained with scil_compute_bundle_voxel_label_map.py
+        List of correlations obtained with scil_bundle_label_map.py
     density_weighting: bool
         If true, weight statistics by the number of streamlines passing through
         each voxel. [False]
 
     Returns
     -------
-    stats
+    stats: dict
     """
     # Computing infos on bundle
     unique_labels = np.unique(labels)[1:]