def run(opts):
arg_perm_start = int(opts.range[0])
arg_perm_stop = int(opts.range[1]) + 1
medtype = str(opts.medtype[0])
surface = str(opts.surface[0])
#load variables
y = np.load("python_temp_med_%s/merge_y.npy" % (surface))
num_vertex = np.load("python_temp_med_%s/num_vertex.npy" % (surface))
num_vertex_lh = np.load("python_temp_med_%s/num_vertex_lh.npy" % (surface))
bin_mask_lh = np.load("python_temp_med_%s/bin_mask_lh.npy" % (surface))
bin_mask_rh = np.load("python_temp_med_%s/bin_mask_rh.npy" % (surface))
n = np.load("python_temp_med_%s/num_subjects.npy" % (surface))
pred_x = np.load("python_temp_med_%s/pred_x.npy" % (surface))
depend_y = np.load("python_temp_med_%s/depend_y.npy" % (surface))
adjac_lh = np.load("python_temp_med_%s/adjac_lh.npy" % (surface))
adjac_rh = np.load("python_temp_med_%s/adjac_rh.npy" % (surface))
all_vertex = np.load("python_temp_med_%s/all_vertex.npy" % (surface))
optstfce = np.load('python_temp_med_%s/optstfce.npy' % (surface))
vdensity_lh = np.load('python_temp_med_%s/vdensity_lh.npy' % (surface))
vdensity_rh = np.load('python_temp_med_%s/vdensity_rh.npy' % (surface))
#load TFCE fucntion
calcTFCE_lh = CreateAdjSet(float(optstfce[0]), float(optstfce[1]),
adjac_lh) # H=2, E=1
calcTFCE_rh = CreateAdjSet(float(optstfce[0]), float(optstfce[1]),
adjac_rh) # H=2, E=1
#permute Sobel Z
if not os.path.exists("output_med_%s/perm_SobelZ_%s" % (surface, medtype)):
os.mkdir("output_med_%s/perm_SobelZ_%s" % (surface, medtype))
os.chdir("output_med_%s/perm_SobelZ_%s" % (surface, medtype))
for iter_perm in range(arg_perm_start, arg_perm_stop):
np.random.seed(int(iter_perm * 1000 + time()))
print("Iteration number : %d" % (iter_perm))
indices_perm = np.random.permutation(list(range(n)))
if (medtype == 'M') or (medtype == 'I'):
pathA_nx = pred_x[indices_perm]
pathB_nx = depend_y
SobelZ = calc_sobelz(medtype, pathA_nx, pathB_nx, y, n, num_vertex)
else:
pathA_nx = pred_x[indices_perm]
pathB_nx = depend_y[indices_perm]
SobelZ = calc_sobelz(medtype, pathA_nx, pathB_nx, y, n, num_vertex)
write_perm_maxTFCE_vertex("Zstat_%s" % medtype, SobelZ, num_vertex_lh,
bin_mask_lh, bin_mask_rh, calcTFCE_lh,
calcTFCE_rh, vdensity_lh, vdensity_rh)
print(
("Finished. Randomization took %.1f seconds" % (time() - start_time)))
def run(opts):
arg_predictor = opts.input[0]
arg_depend = opts.input[1]
medtype = opts.medtype[0]
if not os.path.exists("python_temp"):
print("python_temp missing!")
#load variables
raw_nonzero = np.load('python_temp/raw_nonzero.npy')
n = raw_nonzero.shape[1]
affine_mask = np.load('python_temp/affine_mask.npy')
data_mask = np.load('python_temp/data_mask.npy')
data_index = data_mask > 0.99
num_voxel = np.load('python_temp/num_voxel.npy')
pred_x = np.genfromtxt(arg_predictor, delimiter=",")
depend_y = np.genfromtxt(arg_depend, delimiter=",")
imgext = '.nii.gz' #default save type is nii.gz
# if not os.path.isfile('python_temp/imgext.npy'): # to maintain compability
# imgext = '.nii.gz'
# else:
# imgext = np.load('python_temp/imgext.npy')
#TFCE
adjac = create_adjac_voxel(data_index,
data_mask,
num_voxel,
dirtype=opts.tfce[2])
calcTFCE = CreateAdjSet(
float(opts.tfce[0]), float(opts.tfce[1]),
adjac) # i.e. default: H=2, E=2, 26 neighbour connectivity
#step1
if opts.covariates:
arg_covars = opts.covariates[0]
covars = np.genfromtxt(arg_covars, delimiter=",")
x_covars = np.column_stack([np.ones(n), covars])
y = resid_covars(x_covars, raw_nonzero)
else:
y = raw_nonzero.T
#save
np.save('python_temp/pred_x', pred_x)
np.save('python_temp/depend_y', depend_y)
np.save('python_temp/adjac', adjac)
np.save('python_temp/medtype', medtype)
np.save('python_temp/optstfce', opts.tfce)
np.save('python_temp/raw_nonzero_corr', y.T.astype(np.float32, order="C"))
#step2 mediation
SobelZ = calc_sobelz(medtype, pred_x, depend_y, y, n, num_voxel)
#write TFCE images
if not os.path.exists("output_med_%s" % medtype):
os.mkdir("output_med_%s" % medtype)
os.chdir("output_med_%s" % medtype)
write_voxelStat_img('SobelZ_%s' % medtype, SobelZ, data_mask, data_index,
affine_mask, calcTFCE, imgext)
def calculate_mediation_tfce(medtype,
merge_y,
masking_array,
pred_x,
depend_y,
calcTFCE,
vdensity,
position_array,
fullmask,
perm_number=None,
randomise=False,
verbose=False,
no_intercept=True):
n = len(pred_x)
if randomise:
np.random.seed(perm_number + int(float(str(time())[-6:]) * 100))
indices_perm = np.random.permutation(range(merge_y.shape[0]))
if (medtype == 'M') or (medtype == 'I'):
if randomise:
pathA_nx = pred_x[indices_perm]
else:
if randomise:
pathA_nx = pred_x[indices_perm]
pathB_nx = depend_y[indices_perm]
SobelZ = calc_sobelz(medtype, pred_x, depend_y, merge_y, merge_y.shape[0],
merge_y.shape[1])
SobelZ = SobelZ.astype(np.float32, order="C")
tfce_SobelZ = np.zeros_like(SobelZ).astype(np.float32, order="C")
zval_temp = np.zeros_like((fullmask)).astype(np.float32, order="C")
zval_temp[fullmask == 1] = SobelZ
zval_temp = zval_temp.astype(np.float32, order="C")
tfce_temp = np.zeros_like(zval_temp).astype(np.float32, order="C")
calcTFCE.run(zval_temp, tfce_temp)
zval_temp = zval_temp[fullmask == 1]
tfce_temp = tfce_temp[fullmask == 1]
for surf_count in range(len(masking_array)):
start = position_array[surf_count]
end = position_array[surf_count + 1]
tfce_SobelZ[:, start:end] = (tfce_temp[start:end] *
(zval_temp[start:end].max() / 100) *
vdensity[start:end])
if randomise:
os.system("echo %f >> perm_maxTFCE_surf%d_zstat.csv" %
(np.nanmax(tfce_SobelZ[:, start:end]), surf_count))
if verbose:
print "Max Zstat tfce from all surfaces = %f" % tfce_SobelZ.max()
if randomise:
print "Interation number: %d" % perm_number
os.system("echo %s >> perm_maxTFCE_allsurf_zstat.csv" %
(','.join(["%0.2f" % i for i in tfce_SobelZ.max(axis=1)])))
SobelZ = None
tfce_SobelZ = None
zval_temp = None
tfce_temp = None
del calcTFCE
if not randomise:
return (SobelZ.astype(np.float32, order="C"),
tfce_SobelZ.astype(np.float32, order="C"))
def run(opts):
arg_perm_start = int(opts.range[0])
arg_perm_stop = int(opts.range[1]) + 1
medtype = str(opts.medtype[0])
#load variables
num_voxel = np.load('python_temp/num_voxel.npy')
n = np.load('python_temp/num_subjects.npy')
ny = np.load('python_temp/raw_nonzero_corr.npy').T
pred_x = np.load('python_temp/pred_x.npy')
depend_y = np.load("python_temp/depend_y.npy")
adjac = np.load('python_temp/adjac.npy')
optstfce = np.load('python_temp/optstfce.npy')
#load TFCE fucntion
calcTFCE = CreateAdjSet(float(optstfce[0]), float(optstfce[1]),
adjac) # H=2, E=2, 26 neighbour connectivity
#permute Sobel Z values and write max TFCE values
if not os.path.exists("output_med_%s/perm_SobelZ" % medtype):
os.mkdir("output_med_%s/perm_SobelZ" % medtype)
os.chdir("output_med_%s/perm_SobelZ" % medtype)
for iter_perm in xrange(arg_perm_start, arg_perm_stop):
np.random.seed(int(iter_perm * 1000 + time()))
print "Iteration number : %d" % (iter_perm)
indices_perm = np.random.permutation(range(n))
if (medtype == 'M') or (medtype == 'I'):
pathA_nx = pred_x[indices_perm]
pathB_nx = depend_y
SobelZ = calc_sobelz(medtype, pathA_nx, pathB_nx, ny, n, num_voxel)
else:
pathA_nx = pred_x[indices_perm]
pathB_nx = depend_y[indices_perm]
SobelZ = calc_sobelz(medtype, pathA_nx, pathB_nx, ny, n, num_voxel)
write_perm_maxTFCE_voxel('Zstat_%s' % medtype, SobelZ, calcTFCE)
print("Finished. Randomization took %.1f seconds" % (time() - start_time))
def run(opts):
scriptwd = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
arg_predictor = opts.input[0]
arg_depend = opts.input[1]
surface = opts.surface[0]
medtype = opts.medtype[0]
FWHM = opts.fwhm[0]
#load variables
pred_x = np.genfromtxt(arg_predictor, delimiter=",")
depend_y = np.genfromtxt(arg_depend, delimiter=",")
#load data
img_data_lh = nib.freesurfer.mghformat.load("lh.all.%s.%s.mgh" %
(surface, FWHM))
data_full_lh = img_data_lh.get_data()
data_lh = np.squeeze(data_full_lh)
affine_mask_lh = img_data_lh.get_affine()
n = data_lh.shape[1]
outdata_mask_lh = np.zeros_like(data_full_lh[:, :, :, 1])
img_data_rh = nib.freesurfer.mghformat.load("rh.all.%s.%s.mgh" %
(surface, FWHM))
data_full_rh = img_data_rh.get_data()
data_rh = np.squeeze(data_full_rh)
affine_mask_rh = img_data_rh.get_affine()
outdata_mask_rh = np.zeros_like(data_full_rh[:, :, :, 1])
if not os.path.exists("lh.mean.%s.%s.mgh" % (surface, FWHM)):
mean_lh = np.sum(data_lh, axis=1) / data_lh.shape[1]
outmean_lh = np.zeros_like(data_full_lh[:, :, :, 1])
outmean_lh[:, 0, 0] = mean_lh
nib.save(nib.freesurfer.mghformat.MGHImage(outmean_lh, affine_mask_lh),
"lh.mean.%s.%s.mgh" % (surface, FWHM))
mean_rh = np.sum(data_rh, axis=1) / data_rh.shape[1]
outmean_rh = np.zeros_like(data_full_rh[:, :, :, 1])
outmean_rh[:, 0, 0] = mean_rh
nib.save(nib.freesurfer.mghformat.MGHImage(outmean_rh, affine_mask_rh),
"rh.mean.%s.%s.mgh" % (surface, FWHM))
else:
img_mean_lh = nib.freesurfer.mghformat.load("lh.mean.%s.%s.mgh" %
(surface, FWHM))
mean_full_lh = img_mean_lh.get_data()
mean_lh = np.squeeze(mean_full_lh)
img_mean_rh = nib.freesurfer.mghformat.load("rh.mean.%s.%s.mgh" %
(surface, FWHM))
mean_full_rh = img_mean_rh.get_data()
mean_rh = np.squeeze(mean_full_rh)
#create masks
if opts.fmri:
maskthresh = opts.fmri
print("fMRI threshold mask = %2.2f" % maskthresh)
bin_mask_lh = np.logical_or(mean_lh > maskthresh, mean_lh <
(-1 * maskthresh))
bin_mask_rh = np.logical_or(mean_rh > maskthresh, mean_rh <
(-1 * maskthresh))
elif opts.fsmask:
label = opts.fsmask
print("Loading fsaverage ?l.%s.label" % label)
index_lh, _, _ = convert_fslabel("%s/fsaverage/label/lh.%s.label" %
(os.environ["SUBJECTS_DIR"], label))
index_rh, _, _ = convert_fslabel("%s/fsaverage/label/rh.%s.label" %
(os.environ["SUBJECTS_DIR"], label))
bin_mask_lh = np.zeros_like(mean_lh)
bin_mask_lh[index_lh] = 1
bin_mask_lh = bin_mask_lh.astype(bool)
bin_mask_rh = np.zeros_like(mean_rh)
bin_mask_rh[index_rh] = 1
bin_mask_rh = bin_mask_rh.astype(bool)
elif opts.label:
label_lh = opts.label[0]
label_rh = opts.label[1]
index_lh, _, _ = convert_fslabel(label_lh)
index_rh, _, _ = convert_fslabel(label_rh)
bin_mask_lh = np.zeros_like(mean_lh)
bin_mask_lh[index_lh] = 1
bin_mask_lh = bin_mask_lh.astype(bool)
bin_mask_rh = np.zeros_like(mean_rh)
bin_mask_rh[index_rh] = 1
bin_mask_rh = bin_mask_rh.astype(bool)
elif opts.binmask:
print("Loading masks")
img_binmgh_lh = nib.freesurfer.mghformat.load(opts.binmask[0])
binmgh_lh = img_binmgh_lh.get_data()
binmgh_lh = np.squeeze(binmgh_lh)
img_binmgh_rh = nib.freesurfer.mghformat.load(opts.binmask[1])
binmgh_rh = img_binmgh_rh.get_data()
binmgh_rh = np.squeeze(binmgh_rh)
bin_mask_lh = binmgh_lh > .99
bin_mask_rh = binmgh_rh > .99
else:
bin_mask_lh = mean_lh > 0
bin_mask_rh = mean_rh > 0
data_lh = data_lh[bin_mask_lh]
num_vertex_lh = data_lh.shape[0]
data_rh = data_rh[bin_mask_rh]
num_vertex_rh = data_rh.shape[0]
num_vertex = num_vertex_lh + num_vertex_rh
all_vertex = data_full_lh.shape[0]
#TFCE
if opts.triangularmesh:
print "Creating adjacency set"
if opts.inputsurfs:
# 3 Neighbour vertex connectity
v_lh, faces_lh = nib.freesurfer.read_geometry(opts.inputsurfs[0])
v_rh, faces_rh = nib.freesurfer.read_geometry(opts.inputsurfs[1])
else:
v_lh, faces_lh = nib.freesurfer.read_geometry(
"%s/fsaverage/surf/lh.sphere" % os.environ["SUBJECTS_DIR"])
v_rh, faces_rh = nib.freesurfer.read_geometry(
"%s/fsaverage/surf/rh.sphere" % os.environ["SUBJECTS_DIR"])
adjac_lh = create_adjac_vertex(v_lh, faces_lh)
adjac_rh = create_adjac_vertex(v_rh, faces_rh)
elif opts.adjfiles:
print "Loading prior adjacency set"
arg_adjac_lh = opts.adjfiles[0]
arg_adjac_rh = opts.adjfiles[1]
adjac_lh = np.load(arg_adjac_lh)
adjac_rh = np.load(arg_adjac_rh)
elif opts.dist:
print "Loading prior adjacency set for %s mm" % opts.dist[0]
adjac_lh = np.load("%s/adjacency_sets/lh_adjacency_dist_%s.0_mm.npy" %
(scriptwd, str(opts.dist[0])))
adjac_rh = np.load("%s/adjacency_sets/rh_adjacency_dist_%s.0_mm.npy" %
(scriptwd, str(opts.dist[0])))
else:
print "Error"
if opts.noweight:
vdensity_lh = 1
vdensity_rh = 1
else:
# correction for vertex density
vdensity_lh = np.zeros((adjac_lh.shape[0]))
vdensity_rh = np.zeros((adjac_rh.shape[0]))
for i in xrange(adjac_lh.shape[0]):
vdensity_lh[i] = len(adjac_lh[i])
for j in xrange(adjac_rh.shape[0]):
vdensity_rh[j] = len(adjac_rh[j])
vdensity_lh = np.array((1 - (vdensity_lh / vdensity_lh.max()) +
(vdensity_lh.mean() / vdensity_lh.max())),
dtype=np.float32)
vdensity_rh = np.array((1 - (vdensity_rh / vdensity_rh.max()) +
(vdensity_rh.mean() / vdensity_rh.max())),
dtype=np.float32)
calcTFCE_lh = CreateAdjSet(float(opts.tfce[0]), float(opts.tfce[1]),
adjac_lh)
calcTFCE_rh = CreateAdjSet(float(opts.tfce[0]), float(opts.tfce[1]),
adjac_rh)
#save variables
if not os.path.exists("python_temp_med_%s" % surface):
os.mkdir("python_temp_med_%s" % surface)
np.save("python_temp_med_%s/pred_x" % surface, pred_x)
np.save("python_temp_med_%s/depend_y" % surface, depend_y)
np.save("python_temp_med_%s/num_subjects" % surface, n)
np.save("python_temp_med_%s/num_vertex" % surface, num_vertex)
np.save("python_temp_med_%s/num_vertex_lh" % (surface), num_vertex_lh)
np.save("python_temp_med_%s/num_vertex_rh" % (surface), num_vertex_rh)
np.save("python_temp_med_%s/all_vertex" % (surface), all_vertex)
np.save("python_temp_med_%s/bin_mask_lh" % (surface), bin_mask_lh)
np.save("python_temp_med_%s/bin_mask_rh" % (surface), bin_mask_rh)
np.save("python_temp_med_%s/adjac_lh" % (surface), adjac_lh)
np.save("python_temp_med_%s/adjac_rh" % (surface), adjac_rh)
np.save("python_temp_med_%s/optstfce" % (surface), opts.tfce)
np.save('python_temp_med_%s/vdensity_lh' % (surface), vdensity_lh)
np.save('python_temp_med_%s/vdensity_rh' % (surface), vdensity_rh)
#step1
if opts.covariates:
arg_covars = opts.covariates[0]
covars = np.genfromtxt(arg_covars, delimiter=",")
x_covars = np.column_stack([np.ones(n), covars])
y_lh = resid_covars(x_covars, data_lh)
y_rh = resid_covars(x_covars, data_rh)
merge_y = np.hstack((y_lh, y_rh))
del y_lh
del y_rh
else:
#no covariates
merge_y = np.hstack((data_lh.T, data_rh.T))
del data_lh
del data_rh
np.save("python_temp_med_%s/merge_y" % (surface),
merge_y.astype(np.float32, order="C"))
#step2 mediation
SobelZ = calc_sobelz(medtype, pred_x, depend_y, merge_y, n, num_vertex)
#write TFCE images
if not os.path.exists("output_med_%s" % surface):
os.mkdir("output_med_%s" % surface)
os.chdir("output_med_%s" % surface)
write_vertStat_img('SobelZ_%s' % (medtype), SobelZ[:num_vertex_lh],
outdata_mask_lh, affine_mask_lh, surface, 'lh',
bin_mask_lh, calcTFCE_lh, bin_mask_lh.shape[0],
vdensity_lh)
write_vertStat_img('SobelZ_%s' % (medtype), SobelZ[num_vertex_lh:],
outdata_mask_rh, affine_mask_rh, surface, 'rh',
bin_mask_rh, calcTFCE_rh, bin_mask_rh.shape[0],
vdensity_rh)