def seed_voxel_masked_corrz(fourd, seed, maskf, outdir):
_, seedname, _ = pp.split_filename(seed)
outfile = os.path.join(outdir, '%s_corrz.nii.gz'%(seedname))
seedval = np.loadtxt(seed)
img = ni.load(fourd)
imgdim = img.get_shape()[:3]
maskdim = ni.load(maskf).get_shape()
if not maskdim == imgdim:
raise ValueError('dimension mismatch, mask: %s, data: %s'%(maskdim, imgdim))
dat = img.get_data()
maskdat = ni.load(maskf).get_data()
mask = maskdat > 0
masked_dat = dat[mask,:]
steps = [x * mask.shape[0]/10 for x in range(10)]
steps.append(-1)
allres = np.zeros(masked_dat.shape[0])
for i, stop in enumerate(steps):
start = steps[i]
clump = masked_dat[start:s,:]
tmpres = np.corrcoef(clump, seed)
allres[start:s] = tmpres[-1,:-1]
new = np.zeros(dat.shape[:3])
new[mask] = allres
newimg = ni.Nifti1Image(new, img.get_affine())
newimg.to_filename(outfile)
return outfile
def generate_seed_voxelcorrelation(fourd, seed, outdir):
_, seedname, _ = pp.split_filename(seed)
outfile = os.path.join(outdir, '%s_corr.nii.gz'%(seedname))
cmd = ' '.join(['3dfim+',
'-input',
fourd,
'-ideal_file',
seed,
'-out',
'Correlation',
'-bucket',
outfile])
cout = pp.CommandLine(cmd).run()
if not cout.runtime.returncode == 0:
print cout.runtime.stderr
return None
return outfile
def seed_voxel_corrz(fourd, seed, outdir):
""" uses numpy, nibabel to calc timeseries correlation
with seed values, ztransforms and saves to file in outdir"""
_, seedname, _ = pp.split_filename(seed)
outfile = os.path.join(outdir, '%s_corrz.nii.gz'%(seedname))
seedval = np.loadtxt(seed)
img = ni.load(fourd)
dat = img.get_data()
new = np.zeros(dat.shape[:3])
for i, slice in enumerate(dat):
for j, part in enumerate(slice):
res = np.corrcoef(part, seedval)
new[i,j,:] = res[-1,:-1]
new = np.arctanh(new)
new[np.isnan(new)] = 0
newimg = ni.Nifti1Image(new, img.get_affine())
newimg.to_filename(outfile)
return outfile
"""
if __name__ == '__main__':
### Change items here####
# get structurals
datadir = '/home/jagust/graph/data/spm_220'
anatomicals = pp.get_files(datadir,'B*/anat/B*_anat.nii')
#######################################################
# run vbm
vbm8_dict = {}
for anat in anatomicals:
# copy file to processing dir
pth, nme, ext = pp.split_filename(anat)
subid = nme.split('_anat')[0]
vbmdir,exists = pp.make_dir(pth, 'vbm8')
canat = pp.copy_file(anat, vbmdir)
if not exists: # only run if vbm directory is missing
out = pp.spm_vbm8(canat)
vbmd = pp.get_vbmfiles(canat)
vbm8_dict.update({subid: vbmd})
# run dartel on cohort
gms = pp.get_files(datadir, '*/anat/vbm8/rp1*.nii')
wms = pp.get_files(datadir, '*/anat/vbm8/rp2*.nii')
gms.sort()
wms.sort()
files = []
##################
datadir = '/home/jagust/graph/data/spm_220'
functionals = pp.get_files(datadir,'B*/func/B*_4d.nii*')
TR = 2.2 #set to TR = None if TR in file is correct
templatedir = '/home/jagust/graph/data/spm_220/template'
##### CHANGE TEMPLATE HERE!! ############
template_name = 'dartel_May_21_2013_14_27_6.nii'
#################################################
template = pp.os.path.join(templatedir,template_name)
ruwd = {}
for func in functionals:
# copy file to processing dir
pth, nme, ext = pp.split_filename(func)
subid = nme.split('_4d')[0]
realigndir,exists = pp.make_dir(pth, 'realign_unwarp')
slicetime_vars = pp.get_slicetime_vars(func, TR = TR)
if not exists: # only run if realign_unwarp directory is missing
cfunc = pp.copy_file(func, realigndir)
if pp.is_4d(cfunc):
# convert to list of 3d files
cfuncs = pp.fsl_split4d(cfunc, subid)
# remove 4d file
cfuncs = [x for x in cfuncs if not 'gz' in x]
#pp.remove_files(cfunc)
else:
cfuncs = cfunc
