def test_mask_files():
with InTemporaryDirectory():
# Make a 4D file from the anatomical example
img = nii.load(anatfile)
arr = img.get_data()
a2 = np.zeros(arr.shape + (2,))
a2[:, :, :, 0] = arr
a2[:, :, :, 1] = arr
img = nii.Nifti1Image(a2, np.eye(4))
a_fname = "fourd_anat.nii"
nii.save(img, a_fname)
# check 4D mask
msk1 = nnm.compute_mask_files(a_fname)
# and mask from identical list of 3D files
msk2 = nnm.compute_mask_files([anatfile, anatfile])
yield assert_array_equal, msk1, msk2
# Example usage
# python compute_mask.py swa4D.nii.gz mask.nii.gz
# python compute_mask.py swa*.nii mask.nii.gz
from nipy.neurospin.utils.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)
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'], False, infTh, supTh)
# step 4. Create Contrast Files
print "Creating Contrasts"
clist = contrast_tools.ContrastList(misc=misc)
d = clist.dic
d["SStSSp_minus_DStDSp"] = d["SSt-SSp"] - d["DSt-DSp"]
d["DStDSp_minus_SStSSp"] = d["DSt-DSp"] - d["SSt-SSp"]
d["DSt_minus_SSt"] = d["DSt-SSp"] + d["DSt-DSp"]\
- d["SSt-SSp"] - d["SSt-DSp"]
d["DSp_minus_SSp"] = d["DSt-DSp"] - d["DSt-SSp"]\
- d["SSt-SSp"] + d["SSt-DSp"]
d["DSt_minus_SSt_for_DSp"] = d["DSt-DSp"] - d["SSt-DSp"]
d["DSp_minus_SSp_for_DSt"] = d["DSt-DSp"] - d["DSt-SSp"]
if d.has_key("FirstSt"):
d["Deactivation"] = d["FirstSt"] - d["SSt-SSp"]\
paradigm = dm.load_protocol_from_csv_file(paradigm_file, session=0)
design_matrix = dm.DesignMatrix( frametimes, paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut,
cond_ids= conditions)
design_matrix.show()
# design_matrix.save(...)
########################################
# Mask the data
########################################
mask_path = op.join(swd, 'mask.nii')
mask_array = compute_mask_files( data_path, mask_path, False, 0.4, 0.9)
########################################
# Perform a GLM analysis
########################################
fmri_image = load(data_path)
Y = fmri_image.get_data()[mask_array]
model = "ar1"
method = "kalman"
my_glm = nipy.neurospin.glm.glm()
glm = my_glm.fit(Y.T, design_matrix.matrix,
method="kalman", model="ar1")
#########################################
# Specify the contrasts
def ComputeMask(fmriFiles, outputFile, infT=0.4, supT=0.9):
"""
Perform the mask computation, see the api of compute_mask_files
"""
compute_mask_files( fmriFiles, outputFile, False, infT, supT, cc=1)
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(
paths["fmri"][sess],
design_matrices[sess],
