TEST: Verify SignalExtraction implementation is equivalent, faster

Author: effigies

fmriprep/interfaces/tests/__init__.py

@@ -0,0 +1,53 @@
+import time
+import numpy as np
+import nibabel as nb
+from nipype.interfaces import nilearn as nl
+from .. import images as im
+
+import pytest
+
+
+@pytest.mark.parametrize('nvols, nmasks, ext, factor', [
+    (1000, 10, '.nii', 2),
+    (1000, 10, '.nii.gz', 5),
+    (200, 3, '.nii', 1.1),
+    (200, 3, '.nii.gz', 2),
+    (200, 10, '.nii', 1.1),
+    (200, 10, '.nii.gz', 2),
+    ])
+def test_signal_extraction_equivalence(tmpdir, nvols, nmasks, ext, factor):
+    orig_dir = tmpdir.chdir()
+
+    vol_shape = (64, 64, 40)
+
+    img_fname = 'img' + ext
+    masks_fname = 'masks' + ext
+
+    random_data = np.random.random(size=vol_shape + (nvols,)) * 2000
+    random_mask_data = np.random.random(size=vol_shape + (nmasks,)) < 0.2
+
+    nb.Nifti1Image(random_data, np.eye(4)).to_filename(img_fname)
+    nb.Nifti1Image(random_mask_data.astype(np.uint8), np.eye(4)).to_filename(masks_fname)
+
+    se1 = nl.SignalExtraction(in_file=img_fname, label_files=masks_fname,
+                              class_labels=['a%d' % i for i in range(nmasks)],
+                              out_file='nlsignals.tsv')
+    se2 = im.SignalExtraction(in_file=img_fname, label_files=masks_fname,
+                              class_labels=['a%d' % i for i in range(nmasks)],
+                              out_file='imsignals.tsv')
+
+    tic = time.time()
+    se1.run()
+    toc = time.time()
+    se2.run()
+    toc2 = time.time()
+
+    tab1 = np.loadtxt('nlsignals.tsv', skiprows=1)
+    tab2 = np.loadtxt('imsignals.tsv', skiprows=1)
+
+    assert np.allclose(tab1, tab2)
+
+    t1 = toc - tic
+    t2 = toc2 - toc
+
+    assert t2 < t1 / factor