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Added support for a list of 3d files for TSNR calculation. #360

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Merged
satra merged 3 commits into from
Apr 16, 2012
Merged

Added support for a list of 3d files for TSNR calculation. #360

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cdab694
Added support for a list of 3d files for TSNR calculation.
chrisgorgo Apr 13, 2012
d14a11c
Extension is now based on the input file name
chrisgorgo Apr 15, 2012
7616655
pep8
chrisgorgo Apr 15, 2012
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39 changes: 20 additions & 19 deletions nipype/algorithms/misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -447,8 +447,8 @@ def _list_outputs(self):


class TSNRInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True,
desc='realigned 4D file')
in_file = InputMultiPath(File(exists=True), mandatory=True,
desc='realigned 4D file or a list of 3D files')
regress_poly = traits.Int(min=1, desc='Remove polynomials')


Expand All @@ -475,39 +475,40 @@ class TSNR(BaseInterface):
input_spec = TSNRInputSpec
output_spec = TSNROutputSpec

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

def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file)
data = img.get_data()
img = nb.load(self.inputs.in_file[0])
vollist = [nb.load(filename) for filename in self.inputs.in_file]
data = np.concatenate([vol.get_data().reshape(vol.get_shape()[:3] + (-1,)) for vol in vollist], axis=3)
if isdefined(self.inputs.regress_poly):
timepoints = img.get_shape()[-1]
X = np.ones((timepoints,1))
X = np.ones((timepoints, 1))
for i in range(self.inputs.regress_poly):
X = np.hstack((X,legendre(i+1)(np.linspace(-1, 1, timepoints))[:, None]))
X = np.hstack((X, legendre(i + 1)(np.linspace(-1, 1, timepoints))[:, None]))
betas = np.dot(np.linalg.pinv(X), np.rollaxis(data, 3, 2))
datahat = np.rollaxis(np.dot(X[:,1:],
datahat = np.rollaxis(np.dot(X[:, 1:],
np.rollaxis(betas[1:, :, :, :], 0, 3)),
0, 4)
data = data - datahat
img = nb.Nifti1Image(data, img.get_affine(), img.get_header())
nb.save(img, self._gen_output_file_name('detrended'))
nb.save(img, self._gen_output_file_name('detrended'))
meanimg = np.mean(data, axis=3)
stddevimg = np.std(data, axis=3)
tsnr = meanimg/stddevimg
tsnr = meanimg / stddevimg
img = nb.Nifti1Image(tsnr, img.get_affine(), img.get_header())
nb.save(img, self._gen_output_file_name())
nb.save(img, self._gen_output_file_name())
img = nb.Nifti1Image(meanimg, img.get_affine(), img.get_header())
nb.save(img, self._gen_output_file_name('mean'))
nb.save(img, self._gen_output_file_name('mean'))
img = nb.Nifti1Image(stddevimg, img.get_affine(), img.get_header())
nb.save(img, self._gen_output_file_name('stddev'))
nb.save(img, self._gen_output_file_name('stddev'))
return runtime

def _list_outputs(self):
Expand Down