def run(opts):
#check if ICA has been run
if not os.path.exists("ICA_temp"):
print("ICA_temp not found")
exit()
#load mask
if opts.bothhemi:
lh_mask, lh_mask_data = loadmgh('ICA_temp/lh_mask.mgh')
rh_mask, rh_mask_data = loadmgh('ICA_temp/rh_mask.mgh')
lh_mask_index = (lh_mask_data != 0)
rh_mask_index = (rh_mask_data != 0)
midpoint = lh_mask_data[lh_mask_data == 1].shape[0]
else:
if opts.voxel:
mask, mask_data = loadnifti('ICA_temp/mask.nii.gz')
if opts.vertex:
mask, mask_data = loadmgh('ICA_temp/mask.mgh')
mask_index = mask_data > .99
#load data
rmcomps = np.genfromtxt(opts.input[0], delimiter=",", dtype='int')
dump_ica = pickle.load(open("ICA_temp/icasave.p", "rb"))
fitcomps = np.load('ICA_temp/signals.npy').T
selected = rmcomps - 1
#zero out unwanted components
fitcomps[:, selected] = 0
#rebuild image
X_rec = dump_ica.inverse_transform(fitcomps)
#save image
outname = opts.output[0]
outname = outname.split('.gz', 1)[0]
outname = outname.split('.nii', 1)[0]
outname = outname.split('.mgh', 1)[0]
if opts.voxel:
savenifti(X_rec, mask, mask_index, '%s.nii.gz' % outname)
if opts.vertex:
savemgh(X_rec, mask, mask_index, '%s.mgh' % outname)
if opts.bothhemi:
savemgh(X_rec[:midpoint], lh_mask, lh_mask_index,
'lh.%s.mgh' % outname)
savemgh(X_rec[midpoint:], rh_mask, rh_mask_index,
'rh.%s.mgh' % outname)
if opts.clean:
os.system("rm -rf ICA_temp")
def run(opts):
if opts.inputtxt:
txtname = opts.inputtxt[0]
if txtname.endswith(('.mgh', '.mgz')):
print "Error: mgh file input detected. Please use -s option."
quit()
surfVals = np.loadtxt(txtname, dtype=np.float, delimiter=',')
if opts.transpose:
print "Transposing array"
surfVals = surfVals.T
numVert = len(surfVals)
print "Reading in %d subjects and %d vertices. If this is incorrect re-run the script is the --transpose option." % (
surfVals.shape[1], surfVals.shape[0])
if surfVals.ndim == 1:
outsurf = np.zeros((numVert, 1, 1))
outsurf[:, 0, 0] = surfVals
if surfVals.ndim == 2:
outsurf = np.zeros((numVert, 1, 1, surfVals.shape[1]))
outsurf[:, 0, 0, :] = surfVals
txtname = os.path.splitext(txtname)[0]
nib.save(
nib.freesurfer.mghformat.MGHImage(outsurf.astype(np.float32,
order="C"),
affine=None), '%s.mgh' % txtname)
if opts.inputsurface:
surfname = opts.inputsurface[0]
if surfname.endswith(('.txt', '.csv')):
print "Error: text file input detected. Please use -i option."
quit()
img, imgdata = loadmgh(surfname)
if imgdata.ndim == 3:
imgdata = imgdata[:, 0, 0]
if imgdata.ndim == 4:
imgdata = imgdata[:, 0, 0, :]
if opts.transpose:
print "Transposing array"
imgdata = imgdata.T
surfname = os.path.splitext(surfname)[0]
np.savetxt(("%s.csv" % surfname), imgdata, delimiter=",", fmt='%10.8f')
def run(opts):
scriptwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
img, data = loadmgh(opts.input[0])
outname = str(opts.output[0])
hemi = str(opts.hemi[0])
fwhm = float(opts.geodesicfwhm[0])
sigma = fwhm / np.sqrt(8 * np.log(2))
# load distances and indices
dist = np.load(opts.distanceslist[0])
basenamefwhm = opts.distanceslist[0].split('_distances.npy', 1)[0]
indices = np.load('%s_indices.npy' % basenamefwhm)
cortex_index = np.load('%s/adjacency_sets/%s_cortex_mask_index.npy' %
(scriptwd, hemi))
distmask_index_start = np.in1d(indices[:, 0], cortex_index)
distmask_index_end = np.in1d(indices[:, 1], cortex_index)
distmask_index = distmask_index_start & distmask_index_end
# mask out non-cortex values
indices_masked = indices[distmask_index]
dist_masked = dist[distmask_index]
if opts.correct_surface:
multipler = opts.correct_surface[0]
y = data[cortex_index].flatten()
(mu, sigma) = norm.fit(y)
cthresh = multipler * sigma + mu
print("The upper threshold is: %1.4f" % cthresh)
data[data > cthresh] = cthresh
smoothed = calc_gd_fwhm(indices_masked.astype(np.int32, order='c'),
dist_masked.astype(np.float32, order='c'),
data[:, 0, 0].astype(np.float32, order="c"), sigma)
outdata = np.zeros_like(data)
outdata[cortex_index, 0, 0] = smoothed[cortex_index]
nib.save(nib.freesurfer.mghformat.MGHImage(outdata, img.affine), outname)