make compcor tests more sensitive to changes, simplify regress_poly()

Author: Shoshana Berleant

nipype/algorithms/compcor.py

@@ -3,6 +3,7 @@
 
 from nipype.pipeline import engine as pe
 from nipype.interfaces.utility import IdentityInterface
+from nipype.algorithms.tsnr import regress_poly
 
 import nibabel as nb
 import numpy as np
@@ -14,12 +15,15 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
                           desc='already realigned brain image (4D)')
     mask_file = File(exists=True, mandatory=False,
                      desc='mask file that determines ROI (3D)')
-    components_file = File('components_file.txt', exists=False, mandatory=False, usedefault=True,
+    components_file = File('components_file.txt', exists=False, mandatory=False,
+                           usedefault=True, 
                            desc='filename to store physiological components in')
     num_components = traits.Int(6, usedefault=True) # 6 for BOLD, 4 for ASL
+    regress_poly = traits.Range(low=1, default=1, usedefault=True)
 
 class CompCorOutputSpec(TraitedSpec):
-    components_file = File(desc='text file containing the noise components', exists=True)
+    components_file = File(exists=True,
+                           desc='text file containing the noise components')
 
 class CompCor(BaseInterface):
     '''

nipype/algorithms/tests/test_compcor.py

@@ -10,6 +10,7 @@
 import nibabel as nb
 import numpy as np
 import os
+from hashlib import sha1
 
 class TestCompCor(unittest.TestCase):
     ''' Note: Tests currently do a poor job of testing functionality '''
@@ -30,21 +31,25 @@ def test_compcor(self):
         mask[0,0,1] = 0
         mask_file = utils.save_toy_nii(mask, self.filenames['masknii'])
 
+        expected_sha1 = 'b0dd7f9ab7ba8f516712eb0204dacc9e397fc6aa'
+
         ccresult = self.run_cc(CompCor(realigned_file=self.realigned_file,
-                               mask_file=mask_file))
+                                       mask_file=mask_file),
+                               expected_sha1)
 
         accresult = self.run_cc(ACompCor(realigned_file=self.realigned_file,
-                             mask_file=mask_file,
-                             components_file='acc_components_file'))
+                                         mask_file=mask_file,
+                                         components_file='acc_components_file'),
+                                expected_sha1)
 
         assert_equal(os.path.getsize(ccresult.outputs.components_file),
                      os.path.getsize(accresult.outputs.components_file))
 
     def test_tcompcor(self):
         ccinterface = TCompCor(realigned_file=self.realigned_file)
-        self.run_cc(ccinterface)
+        self.run_cc(ccinterface, '12e54c07281a28ac0da3b934dce5c9d27626848a')
 
-    def run_cc(self, ccinterface):
+    def run_cc(self, ccinterface, expected_components_data_sha1):
         # run
         ccresult = ccinterface.run()
 
@@ -55,6 +60,13 @@ def run_cc(self, ccinterface):
         assert_true(os.path.getsize(expected_file) > 0)
         assert_equal(ccinterface.inputs.num_components, 6)
 
+        with open(ccresult.outputs.components_file, 'r') as components_file:
+            components_data = [line for line in components_file]
+            num_got_components = len(components_data)
+            assert_true(num_got_components == ccinterface.inputs.num_components
+                        or num_got_components == self.fake_data.shape[3])
+            print(str(components_data), "comdata")
+            assert_equal(sha1(str(components_data)).hexdigest(), expected_components_data_sha1)
         return ccresult
 
     def tearDown(self):

nipype/algorithms/tsnr.py

@@ -69,8 +69,9 @@ def _run_interface(self, runtime):
             data = data.astype(np.float32)
 
         if isdefined(self.inputs.regress_poly):
-            img, data = regress_poly(self.inputs.regress_poly, header, img, data,
-                                     self.inputs.detrended_file)
+            data = regress_poly(self.inputs.regress_poly, data)
+            img = nb.Nifti1Image(data, img.get_affine(), header)
+            nb.save(img, op.abspath(self.inputs.detrended_file))
 
         meanimg = np.mean(data, axis=3)
         stddevimg = np.std(data, axis=3)
@@ -93,8 +94,8 @@ def _list_outputs(self):
             outputs['detrended_file'] = op.abspath(self.inputs.detrended_file)
         return outputs
 
-def regress_poly(degree, header, img, data, filename):
-    timepoints = img.shape[-1]
+def regress_poly(degree, data):
+    timepoints = data.shape[-1]
     X = np.ones((timepoints, 1))
     for i in range(degree):
         X = np.hstack((X, legendre(
@@ -105,6 +106,4 @@ def regress_poly(degree, header, img, data, filename):
                                      betas[1:, :, :, :], 0, 3)),
                           0, 4)
     regressed_data = data - datahat
-    regressed_img = nb.Nifti1Image(regressed_data, img.get_affine(), header)
-    nb.save(regressed_img, op.abspath(filename))
-    return regressed_img, regressed_data
+    return regressed_data