Removing dependancy to nilearn (Inria-Empenn#241)

* Removing nilearn dependency from 98BT

* Removing nilearn dependency from UK24

* Removing last dependancies to nilearn + bug correction in conftest

Author: bclenet

narps_open/data/results/team_2T6S.py

@@ -7,8 +7,7 @@
 
 from shutil import copyfile
 
-from nibabel import load
-from nilearn.image import math_img
+from nibabel import load, save, Nifti1Image
 
 from narps_open.data.results import ResultsCollection
 
@@ -44,5 +43,4 @@ def rectify(self):
 
             # Change the signs of values inside the input image
             input_image = load(input_file)
-            output_image = math_img('img*-1', img = input_image)
-            output_image.to_filename(rectified_file)
+            save(Nifti1Image(input_image.get_fdata()*-1, input_image.affine), rectified_file)

narps_open/pipelines/team_98BT.py

@@ -424,9 +424,9 @@ def get_parameters_file(
         from os import makedirs
         from os.path import join
         from nibabel import load
+        from nibabel.processing import resample_from_to
         from numpy import mean
-        from pandas import read_table
-        from nilearn.image import iter_img, resample_to_img
+        from pandas import read_csv
 
         # Ignore all future warnings
         from warnings import simplefilter
@@ -435,22 +435,23 @@ def get_parameters_file(
         simplefilter(action = 'ignore', category = RuntimeWarning)
 
         # Load wc2 file and create a mask out of it
-        wc2 = load(wc2_file)
-        wc2_mask = wc2.get_fdata() > 0.6
-        wc2_mask = wc2_mask.astype(int)
+        wm_class_image = load(wc2_file)
+        wm_mask_data = wm_class_image.get_fdata() > 0.6
+        wm_mask_data = wm_mask_data.astype(int)
 
         # Compute the mean signal in white matter, for each slice of the functional data
         mean_wm = []
-        for current_slice in iter_img(load(func_file)):
-            slice_data = resample_to_img(
-                current_slice, wc2, interpolation = 'nearest', clip = True).get_fdata()
+        func_image = load(func_file)
+        for index in range(func_image.header.get_data_shape()[3]):
+            resampled_func = resample_from_to(
+                func_image.slicer[..., index], wm_class_image, order = 0, mode = 'nearest')
+
             # Append mean value of masked data
-            mean_wm.append(mean(slice_data * wc2_mask))
+            mean_wm.append(mean(resampled_func.get_fdata() * wm_mask_data))
 
         # Create new parameters file
-        data_frame = read_table(parameters_file, sep = '  ', header = None)
+        data_frame = read_csv(parameters_file, sep = '\t', dtype = str)
         data_frame['Mean_WM'] = mean_wm
-
         new_parameters_file = join(working_dir, 'parameters_files',
             f'parameters_file_sub-{subject_id}_run-{run_id}.tsv')
 

narps_open/pipelines/team_UK24.py

@@ -68,23 +68,21 @@ def get_average_values(
         """
         from os import makedirs
         from os.path import join, abspath
-
-        from nilearn.image import load_img, math_img, index_img, get_data
+        from nibabel import load
+        from numpy import mean
 
         # Load input images
-        mask_img = load_img(mask)
-        in_file_img = load_img(in_file)
+        mask_image = load(mask)
+        in_file_image = load(in_file)
+
+        # Create mask
+        mask_data = mask_image.get_fdata() > 0.0
+        mask_data = mask_data.astype(int)
 
         # Loop through time points
         average_values = []
-        for frame_index in range(in_file_img.shape[3]):
-            average_values.append(
-                get_data(math_img(
-                    'np.mean(image * (mask > 0.0))',
-                    image = index_img(in_file_img, frame_index),
-                    mask = mask_img
-                ))
-            )
+        for index in range(in_file_image.shape[3]):
+            average_values.append(mean(in_file_image.slicer[..., index].get_fdata() * mask_data))
 
         # Write confounds to a file
         out_file_name = abspath(f'sub-{subject_id}_run-{run_id}_' + out_file_suffix)

setup.py

@@ -17,7 +17,6 @@
     'importlib_resources>=5.10.2,<5.11',
     'tomli>=2.0.1,<2.1',
     'networkx>=2.0,<3.0', # a workaround to nipype's bug (issue 3530)
-    'nilearn>=0.10.0,<0.11',
     'nipype>=1.8.6,<1.9',
     'pandas>=1.5.2,<1.6',
     'niflow-nipype1-workflows>=0.0.5,<0.1.0'

tests/conftest.py

@@ -17,7 +17,7 @@
 from numpy import isclose
 
 from narps_open.pipelines import Pipeline
-from narps_open.runner import PipelineRunner
+from narps_open.runner import PipelineRunner, PipelineRunnerLevel
 from narps_open.utils import get_subject_id
 from narps_open.utils.correlation import get_correlation_coefficient
 from narps_open.utils.configuration import Configuration
@@ -113,7 +113,7 @@ def test_pipeline_execution(
 
             # Get missing subjects
             missing_subjects = set()
-            for file in runner.get_missing_first_level_outputs():
+            for file in runner.get_missing_outputs(PipelineRunnerLevel.FIRST):
                 subject_id = get_subject_id(file)
                 if subject_id is not None:
                     missing_subjects.add(subject_id)
@@ -125,19 +125,19 @@ def test_pipeline_execution(
             # Start pipeline
             runner.subjects = missing_subjects
             try: # This avoids errors in the workflow to make the test fail
-                runner.start(True, False)
+                runner.start(PipelineRunnerLevel.FIRST)
             except(RuntimeError) as err:
                 print('RuntimeError: ', err)
 
     # Check missing files for the last time
-    missing_files = runner.get_missing_first_level_outputs()
+    missing_files = runner.get_missing_outputs(PipelineRunnerLevel.FIRST)
     if missing_files:
         print('Missing files:', missing_files)
         raise Exception('There are missing files for first level analysis.')
 
     # Start pipeline for the group level only
     runner.nb_subjects = nb_subjects
-    runner.start(False, True)
+    runner.start(PipelineRunnerLevel.SECOND)
 
     # Indices and keys to the unthresholded maps
     indices = list(range(1, 18, 2))

tests/pipelines/test_team_98BT.py

@@ -140,7 +140,7 @@ def test_subject_information():
         helpers.compare_float_2d_arrays(pmod.param, [
             [14.0, 34.0, 38.0, 10.0, 16.0],
             [6.0, 14.0, 19.0, 15.0, 17.0],
-            [1, 1, 0, 0, 0]
+            [1, 2, -2, -2, -1]
             ])
 
     @staticmethod

tests/test_data/pipelines/team_98BT/parameters_file.tsv

@@ -1,4 +1,3 @@
-"CSF	WhiteMatter	GlobalSignal	stdDVARS	non-stdDVARS	vx-wisestdDVARS	FramewiseDisplacement	tCompCor00	tCompCor01	tCompCor02	tCompCor03	tCompCor04	tCompCor05	aCompCor00	aCompCor01	aCompCor02	aCompCor03	aCompCor04	aCompCor05	Cosine00	Cosine01	Cosine02	Cosine03	Cosine04	Cosine05	NonSteadyStateOutlier00	X	Y	Z	RotX	RotY	RotZ"	261.948061726888
-"6551.281999999999	6476.4653	9874.576	n/a	n/a	n/a	n/a	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	-0.0	0.0"	241.43458658854166
-"6484.7285	6473.4890000000005	9830.212	1.09046686	52.78273392	1.05943739	0.13527900930999998	0.0263099209	-0.0673065879	0.0934882554	-0.0079328884	0.0338007737	-0.011491083999999999	-0.042411347099999996	0.027736422900000002	0.0453303087	-0.07022609490000001	0.0963618709	-0.0200867957	0.0665186088	0.0665174038	0.0665153954	0.0665125838	0.0665089688	0.06650455059999999	0.0	-0.00996895	-0.0313444	-3.00931e-06	0.00132687	-0.000384193	-0.00016819"	246.2539803059896
-"6441.5337	6485.7256	9821.212	1.07520139	52.04382706	1.03821933	0.12437666391	-0.0404820317	0.034150583	0.13661184210000002	0.0745358691	-0.0054829985999999995	-0.0217322686	0.046214115199999996	0.005774624	-0.043909359800000006	-0.075619539	0.17546891539999998	-0.0345256763	0.0665153954	0.06650455059999999	0.06648647719999999	0.0664611772	0.0664286533	0.0663889091	0.0	-2.56954e-05	-0.00923735	0.0549667	0.000997278	-0.00019745	-0.000398988"	246.21754170735676
+6551.281999999999	6476.4653	9874.576	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	-0.0	0.0	261.948061726888
+6484.7285	6473.4890000000005	9830.212	1.09046686	52.78273392	1.05943739	0.13527900930999998	0.0263099209	-0.0673065879	0.0934882554	-0.0079328884	0.0338007737	-0.011491083999999999	-0.042411347099999996	0.027736422900000002	0.0453303087	-0.07022609490000001	0.0963618709	-0.0200867957	0.0665186088	0.0665174038	0.0665153954	0.0665125838	0.0665089688	0.06650455059999999	0.0	-0.00996895	-0.0313444	-3.00931e-06	0.00132687	-0.000384193	-0.00016819	241.43458658854166
+6441.5337	6485.7256	9821.212	1.07520139	52.04382706	1.03821933	0.12437666391	-0.0404820317	0.034150583	0.13661184210000002	0.0745358691	-0.0054829985999999995	-0.0217322686	0.046214115199999996	0.005774624	-0.043909359800000006	-0.075619539	0.17546891539999998	-0.0345256763	0.0665153954	0.06650455059999999	0.06648647719999999	0.0664611772	0.0664286533	0.0663889091	0.0	-2.56954e-05	-0.00923735	0.0549667	0.000997278	-0.00019745	-0.000398988	246.2539803059896

tests/test_data/pipelines/team_98BT/uasub-001_task-MGT_run-01_bold_resampled-32.nii

@@ -1,4 +1,4 @@
-52.34728240966797
-52.014034271240234
-52.75069808959961
-52.45905303955078
+52.34728190104167
+52.014033203125
+52.75069986979167
+52.459052734375