def load_data(filenames):
mask = mask_utils.compute_mask_files(filenames)
series, _ = mask_utils.series_from_mask([filenames, ], mask)
series = series.squeeze()
series -= series.mean(axis=-1)[:, np.newaxis]
std = series.std(axis=-1)
std[std==0] = 1
series /= std[:, np.newaxis]
u, s, v = linalg.svd(series, full_matrices=False)
s[0] = 0
series = np.dot(u*s, v)
return mask, series
# vi: set ft=python sts=4 ts=4 sw=4 et:
# Example usage
# python compute_mask.py swa4D.nii.gz mask.nii.gz
# python compute_mask.py swa*.nii mask.nii.gz
from nipy.neurospin.mask import compute_mask_files
import sys
if __name__ == '__main__':
if len(sys.argv) < 3:
print """Usage : python compute_mask [ -s copyfilename ] inputfilename(s) outputfilename
inputfilename can be either a single (4D) file or a list of (3D) files
-s copyfilename : also save a copy of the orginal data as a single 4D file named copyfilename
# Example :
python compute_mask.py swa4D.nii.gz mask.nii.gz
python compute_mask.py swa*.nii mask.nii.gz
python compute_mask.py -s swaCopy4D.nii.gz swa*.nii ../masks/mask.nii.gz"""
sys.exit()
copyIn4Dfilename = None
if '-s' in sys.argv:
i = sys.argv.index('-s')
sys.argv.pop(i)
copyIn4Dfilename = sys.argv.pop(i)
if len(sys.argv) == 3:
inputFilename = sys.argv[1]
else:
inputFilename = sorted(sys.argv[1:-1])
outputFilename = sys.argv[-1]
compute_mask_files(inputFilename, outputFilename, copy_filename = copyIn4Dfilename)
# vi: set ft=python sts=4 ts=4 sw=4 et:
# Example usage
# python compute_mask.py swa4D.nii.gz mask.nii.gz
# python compute_mask.py swa*.nii mask.nii.gz
from nipy.neurospin.mask import compute_mask_files
import sys
if __name__ == '__main__':
if len(sys.argv) < 3:
print """Usage : python compute_mask [ -s copyfilename ] inputfilename(s) outputfilename
inputfilename can be either a single (4D) file or a list of (3D) files
-s copyfilename : also save a copy of the orginal data as a single 4D file named copyfilename
# Example :
python compute_mask.py swa4D.nii.gz mask.nii.gz
python compute_mask.py swa*.nii mask.nii.gz
python compute_mask.py -s swaCopy4D.nii.gz swa*.nii ../masks/mask.nii.gz"""
sys.exit()
copyIn4Dfilename = None
if '-s' in sys.argv:
i = sys.argv.index('-s')
sys.argv.pop(i)
copyIn4Dfilename = sys.argv.pop(i)
if len(sys.argv) == 3:
inputFilename = sys.argv[1]
else:
inputFilename = sorted(sys.argv[1:-1])
outputFilename = sys.argv[-1]
compute_mask_files(inputFilename, outputFilename)
misc["tasks"] = Conditions
misc["mask_url"] = paths['mask']
misc.write()
# step 2. Create one design matrix for each session
design_matrices = {}
for sess in Sessions:
design_matrices[sess] = glm_tools.design_matrix(
paths['misc'], paths['dmtx'][sess], sess, paths['paradigm'],
frametimes, hrf_model=hrf_model, drift_model=drift_model,
hfcut=hfcut, model=model_id)
# step 3. Compute the Mask
# fixme : it should be possible to provide a pre-computed mask
print "Computing the Mask"
mask_array = compute_mask_files(paths['fmri'].values()[0][0],
paths['mask'], True, infTh, supTh)
# step 4. Creating functional contrasts
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=ConfigObj(paths['misc']),
model=model_id)
generate_localizer_contrasts(clist)
contrast = clist.save_dic(paths['contrast_file'])
CompletePaths = glm_tools.generate_brainvisa_ouput_paths(
paths["contrasts"], contrast)
# step 5. Fit the glm for each session
glms = {}
for sess in Sessions:
print "Fitting GLM for session : %s" % sess
glms[sess] = glm_tools.glm_fit(
