Commit 611b903

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Merge pull request #3489 from effigies/fix/dvars_tolerance

FIX: Add tolerance parameter to ComputeDVARS

2 parents c388fae + 693bff7 commit 611b903Copy full SHA for 611b903

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+31

-12

lines changed

nipype/algorithms
- confounds.py
- tests
  - test_auto_ComputeDVARS.py

2 files changed

+31

-12

lines changed

`‎nipype/algorithms/confounds.py‎`

Lines changed: 28 additions & 12 deletions

Original file line number	Diff line number	Diff line change
`@@ -50,6 +50,11 @@ class ComputeDVARSInputSpec(BaseInterfaceInputSpec):`
`50`	`50`	`remove_zerovariance = traits.Bool(`
`51`	`51`	`True, usedefault=True, desc="remove voxels with zero variance"`
`52`	`52`	`)`
	`53`	`+ variance_tol = traits.Float(`
	`54`	`+ 1e-7,`
	`55`	`+ usedefault=True,`
	`56`	`+ desc="maximum variance to consider \"close to\" zero for the purposes of removal",`
	`57`	`+ )`
`53`	`58`	`save_std = traits.Bool(True, usedefault=True, desc="save standardized DVARS")`
`54`	`59`	`save_nstd = traits.Bool(False, usedefault=True, desc="save non-standardized DVARS")`
`55`	`60`	`save_vxstd = traits.Bool(`
`@@ -167,6 +172,7 @@ def _run_interface(self, runtime):`
`167`	`172`	`self.inputs.in_file,`
`168`	`173`	`self.inputs.in_mask,`
`169`	`174`	`remove_zerovariance=self.inputs.remove_zerovariance,`
	`175`	`+ variance_tol=self.inputs.variance_tol,`
`170`	`176`	`intensity_normalization=self.inputs.intensity_normalization,`
`171`	`177`	`)`
`172`	`178`
`@@ -994,8 +1000,19 @@ def _list_outputs(self):`
`994`	`1000`	`return self._results`
`995`	`1001`
`996`	`1002`
	`1003`	`+def _AR_est_YW(x, order, rxx=None):`
	`1004`	`+ """Retrieve AR coefficients while dropping the sig_sq return value"""`
	`1005`	`+ from nitime.algorithms import AR_est_YW`
	`1006`	`+`
	`1007`	`+ return AR_est_YW(x, order, rxx=rxx)[0]`
	`1008`	`+`
	`1009`	`+`
`997`	`1010`	`def compute_dvars(`
`998`		`- in_file, in_mask, remove_zerovariance=False, intensity_normalization=1000`
	`1011`	`+ in_file,`
	`1012`	`+ in_mask,`
	`1013`	`+ remove_zerovariance=False,`
	`1014`	`+ intensity_normalization=1000,`
	`1015`	`+ variance_tol=0.0,`
`999`	`1016`	`):`
`1000`	`1017`	`"""`
`1001`	`1018`	Compute the :abbr:`DVARS (D referring to temporal
`@@ -1027,36 +1044,35 @@ def compute_dvars(`
`1027`	`1044`	`"""`
`1028`	`1045`	`import numpy as np`
`1029`	`1046`	`import nibabel as nb`
`1030`		`- from nitime.algorithms import AR_est_YW`
`1031`	`1047`	`import warnings`
`1032`	`1048`
`1033`		`- func = nb.load(in_file).get_fdata(dtype=np.float32)`
`1034`		`- mask = np.asanyarray(nb.load(in_mask).dataobj).astype(np.uint8)`
	`1049`	`+ func = np.float32(nb.load(in_file).dataobj)`
	`1050`	`+ mask = np.bool_(nb.load(in_mask).dataobj)`
`1035`	`1051`
`1036`	`1052`	`if len(func.shape) != 4:`
`1037`	`1053`	`raise RuntimeError("Input fMRI dataset should be 4-dimensional")`
`1038`	`1054`
`1039`		`- idx = np.where(mask > 0)`
`1040`		`- mfunc = func[idx[0], idx[1], idx[2], :]`
	`1055`	`+ mfunc = func[mask]`
`1041`	`1056`
`1042`	`1057`	`if intensity_normalization != 0:`
`1043`	`1058`	`mfunc = (mfunc / np.median(mfunc)) * intensity_normalization`
`1044`	`1059`
`1045`	`1060`	`# Robust standard deviation (we are using "lower" interpolation`
`1046`	`1061`	`# because this is what FSL is doing`
`1047`	`1062`	`func_sd = (`
`1048`		`- np.percentile(mfunc, 75, axis=1, interpolation="lower")`
`1049`		`- - np.percentile(mfunc, 25, axis=1, interpolation="lower")`
	`1063`	`+ np.percentile(mfunc, 75, axis=1, method="lower")`
	`1064`	`+ - np.percentile(mfunc, 25, axis=1, method="lower")`
`1050`	`1065`	`) / 1.349`
`1051`	`1066`
`1052`	`1067`	`if remove_zerovariance:`
`1053`		`- mfunc = mfunc[func_sd != 0, :]`
`1054`		`- func_sd = func_sd[func_sd != 0]`
	`1068`	`+ zero_variance_voxels = func_sd > variance_tol`
	`1069`	`+ mfunc = mfunc[zero_variance_voxels, :]`
	`1070`	`+ func_sd = func_sd[zero_variance_voxels]`
`1055`	`1071`
`1056`	`1072`	`# Compute (non-robust) estimate of lag-1 autocorrelation`
`1057`	`1073`	`ar1 = np.apply_along_axis(`
`1058`		`- AR_est_YW, 1, regress_poly(0, mfunc, remove_mean=True)[0].astype(np.float32), 1`
`1059`		`- )[:, 0]`
	`1074`	`+ _AR_est_YW, 1, regress_poly(0, mfunc, remove_mean=True)[0].astype(np.float32), 1`
	`1075`	`+ )`
`1060`	`1076`
`1061`	`1077`	`# Compute (predicted) standard deviation of temporal difference time series`
`1062`	`1078`	`diff_sdhat = np.squeeze(np.sqrt(((1 - ar1) * 2).tolist())) * func_sd`

`‎nipype/algorithms/tests/test_auto_ComputeDVARS.py‎`

Lines changed: 3 additions & 0 deletions

Original file line number	Diff line number	Diff line change
`@@ -43,6 +43,9 @@ def test_ComputeDVARS_inputs():`
`43`	`43`	`usedefault=True,`
`44`	`44`	`),`
`45`	`45`	`series_tr=dict(),`
	`46`	`+ variance_tol=dict(`
	`47`	`+ usedefault=True,`
	`48`	`+ ),`
`46`	`49`	`)`
`47`	`50`	`inputs = ComputeDVARS.input_spec()`
`48`	`51`

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2 files changed

2 files changed

`‎nipype/algorithms/confounds.py‎`

`‎nipype/algorithms/tests/test_auto_ComputeDVARS.py‎`

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