Adds a first draft of a label2vol function.

AFQ/surface.py

@@ -1,5 +1,89 @@
+import os.path
+import nibabel as nib
+from nilearn import surface
 import neuropythy as ny
-# import nilearn as ni
+import numpy as np
+import neuropythy as ny
+
+
+def roi_label2vol(sub_fs, label_roi_path, output_roi_path='default',
+                  hemi='left', do_plot=False, surface_size=163842):
+    """
+
+    Parameters
+    ----------
+    """
+    # sub_fs - a full path to a freesurfer subject ('FREESURFER_PATH/<subID>')
+    # label_roi - a full path ('PATH/<roi.label>')
+    if not os.path.exists(output_roi_path):
+        output_roi_path = str.replace(label_roi_path,'.label','.nii.gz')
+    elif output_roi_path == 'default':
+        output_roi_path = str.replace(label_roi_path,'.label','.nii.gz')
+
+    curv_roi_path = str.replace(output_roi_path,'.nii.gz','.curv')
+
+    # load label roi
+    label_roi = surface.load_surf_data(label_roi_path)
+    label_roi = label_roi.astype(int)
+
+    # hard coded hemisphere size from fsaverage (white_left/white_right)
+    # instead of loading fsaverage every time we call the function
+    surface_size = 163842
+
+    # convert label to curv
+    roi_map = np.zeros(surface_size, dtype=int)
+    roi_map[label_roi] = 1
+    # save surface ROI as curv file
+    nib.freesurfer.io.write_morph_data(str.replace(
+        output_roi_path, '.nii.gz','.curv'),roi_map)
+
+    hemisphere = hemi[0] + 'h'
+
+    import neuropythy.commands as nycmd
+    print('Running on ROI: ' + curv_roi_path)
+    print('Saving file to: ' + output_roi_path)
+    nycmd.surface_to_image.main([sub_fs, output_roi_path, '-v','--' +
+                                hemisphere, curv_roi_path])
+    # ---- alternative syntax ----#
+    # cur_sub= ny.freesurfer_subject('SUBJECT_PATH')
+    # cur_image = cur_sub.lh.to_image(roi_map)
+    # cur_image.to_filename('PATH')
+    # ----------------------------#
+
+    # ROIs get saved with a 4th dimension - we want to get rid of that
+    roi_image = nib.load(output_roi_path)
+    roi_data = roi_image.get_fdata()
+    roi_data = np.squeeze(roi_data)
+    new_roi_image = nib.Nifti1Image(roi_data,
+                                    roi_image.affine,
+                                    header=roi_image.header)
+    nib.save(new_roi_image, output_roi_path)
+
+    # plot ROIs for debugging
+    if do_plot:
+        from nilearn import plotting
+        from nilearn import datasets
+        import matplotlib.pyplot as plt
+        # Load fsaverage
+        fsaverage = datasets.fetch_surf_fsaverage(
+            'fsaverage',
+            data_dir='/home/groups/jyeatman/software')
+
+        fig, axes = plt.subplots(1,2,figsize=(8,6),subplot_kw={'projection': '3d'},constrained_layout=True)
+        plotting.plot_surf_roi(fsaverage['white_'+ hemi], roi_map=roi_map,
+                               hemi=hemi, view='ventral',
+                               bg_map=fsaverage['sulc_' + hemi],
+                               bg_on_data=False,
+                               axes=axes[0], title=None)
+        plotting.plot_surf_roi(fsaverage['white_'+ hemi], roi_map=roi_map,
+                               hemi=hemi, view='lateral',
+                               bg_map=fsaverage['sulc_'+ hemi],
+                               bg_on_data=False,
+                               axes=axes[1], title=None)
+        print()
+
+    return
+
 
 # GOAL: given a subject SUB1 with surface surf1, V vertices,
 # and a .trk file