Fixed tests.

Author: chrisgorgo

nipype/algorithms/confounds.py

@@ -651,22 +651,22 @@ def compute_dvars(in_file, in_mask, remove_zerovariance=False,
     idx = np.where(mask > 0)
     mfunc = func[idx[0], idx[1], idx[2], :]
 
-    # Demean
-    mfunc = regress_poly(0, mfunc, remove_mean=True).astype(np.float32)
-
     if intensity_normalization != 0:
         mfunc = (mfunc / np.median(mfunc)) * intensity_normalization
 
-    # Robust standard deviation
-    func_sd = (np.percentile(mfunc, 75, axis=1) -
-               np.percentile(mfunc, 25, axis=1)) / 1.349
+    # Robust standard deviation (we are using "lower" interpolation
+    # because this is what FSL is doing
+    func_sd = (np.percentile(mfunc, 75, axis=1, interpolation="lower") -
+               np.percentile(mfunc, 25, axis=1, interpolation="lower")) / 1.349
 
     if remove_zerovariance:
         mfunc = mfunc[func_sd != 0, :]
         func_sd = func_sd[func_sd != 0]
 
     # Compute (non-robust) estimate of lag-1 autocorrelation
-    ar1 = np.apply_along_axis(AR_est_YW, 1, mfunc, 1)[:, 0]
+    ar1 = np.apply_along_axis(AR_est_YW, 1,
+                              regress_poly(0, mfunc, remove_mean=True).astype(
+                                  np.float32), 1)[:, 0]
 
     # Compute (predicted) standard deviation of temporal difference time series
     diff_sdhat = np.squeeze(np.sqrt(((1 - ar1) * 2).tolist())) * func_sd
@@ -681,11 +681,12 @@ def compute_dvars(in_file, in_mask, remove_zerovariance=False,
     # standardization
     dvars_stdz = dvars_nstd / diff_sd_mean
 
-    with warnings.catch_warnings(): # catch, e.g., divide by zero errors
+    with warnings.catch_warnings():  # catch, e.g., divide by zero errors
         warnings.filterwarnings('error')
 
         # voxelwise standardization
-        diff_vx_stdz = np.square(func_diff) / np.array([diff_sdhat] * func_diff.shape[-1]).T
+        diff_vx_stdz = np.square(
+            func_diff / np.array([diff_sdhat] * func_diff.shape[-1]).T)
         dvars_vx_stdz = np.sqrt(diff_vx_stdz.mean(axis=0))
 
     return (dvars_stdz, dvars_nstd, dvars_vx_stdz)

nipype/algorithms/tests/test_confounds.py

@@ -40,9 +40,26 @@ def test_dvars(tmpdir):
     dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
                          in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
                          save_all=True,
-                         intensity_normalization=False)
+                         intensity_normalization=0)
     os.chdir(str(tmpdir))
     res = dvars.run()
 
-    dv1 = np.loadtxt(res.outputs.out_std)
-    assert (np.abs(dv1 - ground_truth).sum()/ len(dv1)) < 0.05
+    dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
+    assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum()/ len(dv1)) < 0.05
+
+    assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) < 0.05
+
+    assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
+
+    dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
+                         in_mask=example_data(
+                             'ds003_sub-01_mc_brainmask.nii.gz'),
+                         save_all=True)
+    res = dvars.run()
+
+    dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
+    assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum() / len(dv1)) < 0.05
+
+    assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) > 0.05
+
+    assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05

nipype/testing/data/ds003_sub-01_mc.DVARS

@@ -1,19 +1,19 @@
-2.02915
-1.54871
-0.921419
-1.26058
-1.00079
-0.929074
-0.741207
-1.07913
-1.2969
-0.767387
-0.847059
-0.984061
-0.852897
-0.927778
-0.857544
-0.780098
-1.05496
-1.32099
-0.691529
+2.02915	5.2016	1.74221
+1.54871	3.97002	1.18108
+0.921419	2.362	0.784497
+1.26058	3.23142	0.734119
+1.00079	2.56548	0.787452
+0.929074	2.38163	0.828835
+0.741207	1.90004	0.746263
+1.07913	2.7663	0.779829
+1.2969	3.32452	0.73856
+0.767387	1.96715	0.772047
+0.847059	2.17138	0.774103
+0.984061	2.52258	0.88097
+0.852897	2.18635	0.794655
+0.927778	2.3783	0.756786
+0.857544	2.19826	0.796125
+0.780098	1.99973	0.731265
+1.05496	2.70434	0.788584
+1.32099	3.38628	0.831803
+0.691529	1.77269	0.738788