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[ENH] Remove warnings in tSNR calculation #1234

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Merged
merged 4 commits into from
Feb 3, 2016
Merged

[ENH] Remove warnings in tSNR calculation #1234

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7272302
Simplify tSNR, fix division warning
oesteban Oct 5, 2015
d3c97e0
missing update of the list_outputs
oesteban Oct 5, 2015
06b6ce7
add has_files False
oesteban Oct 6, 2015
7fe9d3c
Merge branch 'master' into enh/DivisionWarningsTSNR
oesteban Feb 1, 2016
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50 changes: 27 additions & 23 deletions nipype/algorithms/misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,6 +263,14 @@ class TSNRInputSpec(BaseInterfaceInputSpec):
in_file = InputMultiPath(File(exists=True), mandatory=True,
desc='realigned 4D file or a list of 3D files')
regress_poly = traits.Range(low=1, desc='Remove polynomials')
tsnr_file = File('tsnr.nii.gz', usedefault=True, hash_files=False,
desc='output tSNR file')
mean_file = File('mean.nii.gz', usedefault=True, hash_files=False,
desc='output mean file')
stddev_file = File('stdev.nii.gz', usedefault=True, hash_files=False,
desc='output tSNR file')
detrended_file = File('detrend.nii.gz', usedefault=True, hash_files=False,
desc='input file after detrending')


class TSNROutputSpec(TraitedSpec):
Expand All @@ -288,24 +296,18 @@ class TSNR(BaseInterface):
input_spec = TSNRInputSpec
output_spec = TSNROutputSpec

def _gen_output_file_name(self, suffix=None):
_, base, ext = split_filename(self.inputs.in_file[0])
if suffix in ['mean', 'stddev']:
return os.path.abspath(base + "_tsnr_" + suffix + ext)
elif suffix in ['detrended']:
return os.path.abspath(base + "_" + suffix + ext)
else:
return os.path.abspath(base + "_tsnr" + ext)

def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file[0])
header = img.header.copy()
vollist = [nb.load(filename) for filename in self.inputs.in_file]
data = np.concatenate([vol.get_data().reshape(
vol.shape[:3] + (-1,)) for vol in vollist], axis=3)
vol.get_shape()[:3] + (-1,)) for vol in vollist], axis=3)
data = data.nan_to_num()

if data.dtype.kind == 'i':
header.set_data_dtype(np.float32)
data = data.astype(np.float32)

if isdefined(self.inputs.regress_poly):
timepoints = img.shape[-1]
X = np.ones((timepoints, 1))
Expand All @@ -318,26 +320,28 @@ def _run_interface(self, runtime):
betas[1:, :, :, :], 0, 3)),
0, 4)
data = data - datahat
img = nb.Nifti1Image(data, img.affine, header)
nb.save(img, self._gen_output_file_name('detrended'))
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)
tsnr = meanimg / stddevimg
img = nb.Nifti1Image(tsnr, img.affine, header)
nb.save(img, self._gen_output_file_name())
img = nb.Nifti1Image(meanimg, img.affine, header)
nb.save(img, self._gen_output_file_name('mean'))
img = nb.Nifti1Image(stddevimg, img.affine, header)
nb.save(img, self._gen_output_file_name('stddev'))
tsnr = np.zeros_like(meanimg)
tsnr[stddevimg > 1.e-3] = meanimg[stddevimg > 1.e-3] / stddevimg[stddevimg > 1.e-3]
img = nb.Nifti1Image(tsnr, img.get_affine(), header)
nb.save(img, op.abspath(self.inputs.tsnr_file))
img = nb.Nifti1Image(meanimg, img.get_affine(), header)
nb.save(img, op.abspath(self.inputs.mean_file))
img = nb.Nifti1Image(stddevimg, img.get_affine(), header)
nb.save(img, op.abspath(self.inputs.stddev_file))
return runtime

def _list_outputs(self):
outputs = self._outputs().get()
outputs['tsnr_file'] = self._gen_output_file_name()
outputs['mean_file'] = self._gen_output_file_name('mean')
outputs['stddev_file'] = self._gen_output_file_name('stddev')
for k in ['tsnr_file', 'mean_file', 'stddev_file']:
outputs[k] = op.abspath(getattr(self.inputs, k))

if isdefined(self.inputs.regress_poly):
outputs['detrended_file'] = self._gen_output_file_name('detrended')
outputs['detrended_file'] = op.abspath(self.inputs.detrended_file)
return outputs


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