Unmask

.travis.yml

@@ -3,6 +3,11 @@ language: python
 virtualenv:
   system_site_packages: true
 
+cache:
+  apt: true
+  directories:
+  - $HOME/.cache/pip
+
 env:
   global:
     - TEST_RUN_FOLDER="/tmp" # folder where the tests are run from

examples/unmasking/unmask_HCP.py

@@ -0,0 +1,69 @@
+import glob
+import json
+import os
+from os.path import join
+
+import numpy as np
+from modl.datasets.hcp import fetch_hcp_rest
+from modl.utils.system import get_data_dirs
+from nilearn.input_data import NiftiMasker
+from sklearn.externals.joblib import Parallel
+from sklearn.externals.joblib import delayed
+
+
+def _single_mask(masker, img, dest_data_dir, source_dir):
+    dest_file = img.replace(source_dir, dest_data_dir)
+    dest_file = dest_file.replace('.nii.gz', '')
+    dest_dir = os.path.abspath(os.path.join(dest_file, os.pardir))
+    if not os.path.exists(dest_dir):
+        os.makedirs(dest_dir)
+    print('Unmasking %s' % img)
+    data = masker.transform(img)
+    np.save(dest_file, data)
+    origin = dict(array=dest_file + '.npy', img=img)
+    with open(join(dest_dir, 'origin.json'), 'w+') as f:
+        json.dump(origin, f)
+
+
+def _gather(dest_data_dir):
+    data_dict = []
+    for this_dict in glob.glob(join(dest_data_dir, '**/origin.json'),
+                               recursive=True):
+        with open(this_dict, 'r') as f:
+            data_dict.append(json.load(f))
+    mapping = {}
+    for this_data in data_dict:
+        mapping[this_data['img']] = this_data['array']
+    with open(join(dest_data_dir, 'data.json'), 'w+') as f:
+        json.dump(data_dict, f)
+    with open(join(dest_data_dir, 'mapping.json'), 'w+') as f:
+        json.dump(mapping, f)
+
+
+def prepare_hcp_raw_data(data_dir=None,
+                         dest_data_dir='HCP_unmasked',
+                         n_jobs=1, smoothing_fwhm=3, n_subject=500):
+    data_dir = get_data_dirs(data_dir)[0]
+    source_dir = join(data_dir, 'HCP')
+    dest_data_dir = join(data_dir, dest_data_dir)
+    dataset = fetch_hcp_rest(data_dir=data_dir, n_subjects=n_subject)
+    imgs = dataset.func
+    imgs = [img for subject_imgs in imgs for img in subject_imgs]
+    try:
+        os.mkdir(dest_data_dir)
+    except OSError:
+        raise ValueError('%s already exist,'
+                         'please delete manually before proceeding'
+                         % dest_data_dir)
+    mask = join(data_dir, 'HCP_extra', 'mask_img.nii.gz')
+
+    masker = NiftiMasker(mask_img=mask,
+                         smoothing_fwhm=smoothing_fwhm,
+                         standardize=True).fit()
+    Parallel(n_jobs=n_jobs)(delayed(_single_mask)(masker, img,
+                                                  dest_data_dir, source_dir) for
+                            img in imgs)
+    _gather(dest_data_dir)
+
+if __name__ == '__main__':
+    prepare_hcp_raw_data(n_subject=2, dest_data_dir='HCP_unmasked_', n_jobs=10)