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examples/forward/source_space_custom_atlas.py

@@ -9,7 +9,7 @@
 We showcase on the sample dataset how to apply the Yeo atlas during source reconstruction.
 You should replace the atlas with your own atlas and your own subject.
 
-Any atlas can be used instead of Yeo, provided each region contains a single label (ie: no probabilistic atlas). 
+Any atlas can be used instead of Yeo, provided each region contains a single label (ie: no probabilistic atlas).
 
 .. warning:: This tutorial uses FSL and FreeSurfer to perform MRI coregistrations. If you use a different software, replace the coregistration function appropriately.
 """
@@ -21,19 +21,26 @@
 
 # %%
 
-import mne
-import mne.datasets
+import os
 
 import nilearn.datasets
-import os
+
+import mne
+import mne.datasets
 
 # The atlas is in a template space. We download here as an example Yeo 2011's atlas, which is in the MNI152 1mm template space.
 # Replace this part with your atlas and the template space you used.
 
-nilearn.datasets.fetch_atlas_yeo_2011() # Download Yeo 2011
-yeo_path = os.path.join(nilearn.datasets.get_data_dirs()[0], "yeo_2011", "Yeo_JNeurophysiol11_MNI152")
-atlas_path = os.path.join(yeo_path, "Yeo2011_7Networks_MNI152_FreeSurferConformed1mm.nii.gz")
-atlas_template_T1_path = os.path.join(yeo_path, "FSL_MNI152_FreeSurferConformed_1mm.nii.gz")
+nilearn.datasets.fetch_atlas_yeo_2011()  # Download Yeo 2011
+yeo_path = os.path.join(
+    nilearn.datasets.get_data_dirs()[0], "yeo_2011", "Yeo_JNeurophysiol11_MNI152"
+)
+atlas_path = os.path.join(
+    yeo_path, "Yeo2011_7Networks_MNI152_FreeSurferConformed1mm.nii.gz"
+)
+atlas_template_T1_path = os.path.join(
+    yeo_path, "FSL_MNI152_FreeSurferConformed_1mm.nii.gz"
+)
 
 # The participant's T1 data. Here, we consider the sample dataset
 # The brain should be skull stripped. After freesurfer preprocessing, you can either use brain.mgz or antsdn.brain.mgz
@@ -47,7 +54,7 @@
 assert os.path.exists(atlas_template_T1_path)
 assert os.path.exists(T1_participant_path)
 
-#%%
+# %%
 # The first step is to put the atlas in subject space.
 # We show this step with FSL and freesurfer with linear coregistration. If your atlas is already in participant space,
 # you can skip this step. Coregistration is done in two steps: compute the atlas template to subject T1 transform and apply this transform
@@ -56,46 +63,63 @@
 # FSL does not know how to read .mgz, so we need to convert the T1 to nifti format
 # With FreeSurfer:
 T1_participant_nifti = T1_participant_path.replace("mgz", "nii.gz")
-os.system("mri_convert {} {}".format(T1_participant_path, T1_participant_nifti))
+os.system(f"mri_convert {T1_participant_path} {T1_participant_nifti}")
 
 # Compute template to subject anatomical transform
 template_to_anat_transform_path = os.path.join(mri_path, "template_to_anat.mat")
-os.system("flirt -in {} -ref {} -out {} -omat {}".format(atlas_template_T1_path, T1_participant_nifti, os.path.join(mri_path, "T1_atlas_coreg"), template_to_anat_transform_path))
+os.system(
+    "flirt -in {} -ref {} -out {} -omat {}".format(
+        atlas_template_T1_path,
+        T1_participant_nifti,
+        os.path.join(mri_path, "T1_atlas_coreg"),
+        template_to_anat_transform_path,
+    )
+)
 
 # Apply the transform to the atlas
 atlas_participant = os.path.join(mri_path, "yeo_atlas.nii.gz")
-os.system("flirt -in {} -ref {} -out {} -applyxfm -init {} -interp nearestneighbour".format(atlas_path, T1_participant_nifti, atlas_participant, template_to_anat_transform_path))
+os.system(
+    f"flirt -in {atlas_path} -ref {T1_participant_nifti} -out {atlas_participant} -applyxfm -init {template_to_anat_transform_path} -interp nearestneighbour"
+)
 
 # Convert resulting atlas from nifti to mgz
 # The filename must finish with aseg, to indicate to MNE that it is a proper atlas segmentation.
 atlas_converted = atlas_participant.replace(".nii.gz", "aseg.mgz")
-os.system("mri_convert {} {}".format(atlas_participant, atlas_converted))
+os.system(f"mri_convert {atlas_participant} {atlas_converted}")
 
-#%%
+# %%
 # With the atlas in participant space, we're still missing one ingredient.
 # We need a dictionary mapping label to region ID / value in the fMRI.
 # In FreeSurfer and atlases, these typically take the form of lookup tables.
 # You can also build the dictionary by hand.
 
 from mne._freesurfer import read_freesurfer_lut
-atlas_labels = read_freesurfer_lut(os.path.join(yeo_path, "Yeo2011_7Networks_ColorLUT.txt"))[0]
+
+atlas_labels = read_freesurfer_lut(
+    os.path.join(yeo_path, "Yeo2011_7Networks_ColorLUT.txt")
+)[0]
 print(atlas_labels)
 
 # Drop the key corresponding to outer region
 del atlas_labels["NONE"]
 
-#%%
+# %%
 # For the purpose of source reconstruction, let's create a volumetric source estimate and source reconstruction with e.g eLORETA.
 from mne.minimum_norm import apply_inverse, make_inverse_operator
 
-vol_src = mne.setup_volume_source_space("sample", subjects_dir=subjects_mri_dir,
-                                                surface=os.path.join(subject_mri_path, "bem", "inner_skull.surf"))
+vol_src = mne.setup_volume_source_space(
+    "sample",
+    subjects_dir=subjects_mri_dir,
+    surface=os.path.join(subject_mri_path, "bem", "inner_skull.surf"),
+)
 
 fif_path = os.path.join(data_path, "MEG", "sample")
 fname_trans = os.path.join(fif_path, "sample_audvis_raw-trans.fif")
 raw_fname = os.path.join(fif_path, "sample_audvis_filt-0-40_raw.fif")
 
-model = mne.make_bem_model(subject="sample", subjects_dir=subjects_mri_dir, ico=4, conductivity=(0.33,))  
+model = mne.make_bem_model(
+    subject="sample", subjects_dir=subjects_mri_dir, ico=4, conductivity=(0.33,)
+)
 bem_sol = mne.make_bem_solution(model)
 
 info = mne.io.read_info(raw_fname)
@@ -155,7 +179,9 @@
     verbose=True,
 )
 
-#%%
+# %%
 # Then, we can finally use our atlas!
-label_tcs = stc.extract_label_time_course(labels=(atlas_converted,atlas_labels),src=vol_src)
-label_tcs.shape
+label_tcs = stc.extract_label_time_course(
+    labels=(atlas_converted, atlas_labels), src=vol_src
+)
+label_tcs.shape