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 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__)))
surface = opts.surface[0]
FWHM = opts.fwhm[0]
#load surface 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] # num_subjects
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)
#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 or opts.triangularmesh:
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 range(adjac_lh.shape[0]):
vdensity_lh[i] = len(adjac_lh[i])
for j in range(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)
#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")
binmgh_lh = np.squeeze(
nib.freesurfer.mghformat.load(opts.binmask[0]).get_data())
binmgh_rh = np.squeeze(
nib.freesurfer.mghformat.load(opts.binmask[1]).get_data())
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]
if opts.input:
#load variables
arg_predictor = opts.input[0]
arg_covars = opts.input[1]
pred_x = np.genfromtxt(arg_predictor, delimiter=',')
covars = np.genfromtxt(arg_covars, delimiter=',')
#step1
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
if opts.regressors:
arg_predictor = opts.regressors[0]
pred_x = np.genfromtxt(arg_predictor, delimiter=',')
merge_y = np.hstack((data_lh.T, data_rh.T))
#save variables
if not os.path.exists("python_temp_%s" % (surface)):
os.mkdir("python_temp_%s" % (surface))
np.save("python_temp_%s/pred_x" % (surface), pred_x)
np.save("python_temp_%s/num_subjects" % (surface), n)
np.save("python_temp_%s/all_vertex" % (surface), all_vertex)
np.save("python_temp_%s/num_vertex" % (surface), num_vertex)
np.save("python_temp_%s/num_vertex_lh" % (surface), num_vertex_lh)
np.save("python_temp_%s/num_vertex_rh" % (surface), num_vertex_rh)
np.save("python_temp_%s/bin_mask_lh" % (surface), bin_mask_lh)
np.save("python_temp_%s/bin_mask_rh" % (surface), bin_mask_rh)
np.save("python_temp_%s/affine_mask_lh" % (surface), affine_mask_lh)
np.save("python_temp_%s/affine_mask_rh" % (surface), affine_mask_rh)
np.save("python_temp_%s/adjac_lh" % (surface), adjac_lh)
np.save("python_temp_%s/adjac_rh" % (surface), adjac_rh)
np.save("python_temp_%s/merge_y" % (surface),
merge_y.astype(np.float32, order="C"))
np.save('python_temp_%s/optstfce' % (surface), opts.tfce)
np.save('python_temp_%s/vdensity_lh' % (surface), vdensity_lh)
np.save('python_temp_%s/vdensity_rh' % (surface), vdensity_rh)
#write TFCE images
if not os.path.exists("output_%s" % (surface)):
os.mkdir("output_%s" % (surface))
os.chdir("output_%s" % (surface))
#step2
X = np.column_stack([np.ones(n), pred_x])
k = len(X.T)
if opts.vertexregressor:
img_x_lh = np.squeeze(
nib.freesurfer.mghformat.load("../%s" %
opts.vertexregressor[0]).get_data())
img_x_rh = np.squeeze(
nib.freesurfer.mghformat.load("../%s" %
opts.vertexregressor[1]).get_data())
img_x_lh = img_x_lh[bin_mask_lh]
img_x_rh = img_x_rh[bin_mask_rh]
img_x = np.hstack((img_x_lh.T, img_x_rh.T))
img_x_lh = img_x_rh = None
merge_y = np.hstack((data_lh.T, data_rh.T))
tvals, timage = image_regression(merge_y.T.astype(np.float32),
img_x.T.astype(np.float32), pred_x,
covars)
VIF = image_reg_VIF(merge_y, np.column_stack((pred_x, covars)))
tvals = tvals.T
timage = timage.T
write_vertStat_img('tstat_imgcovar', timage[: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('tstat_imgcovar', timage[num_vertex_lh:],
outdata_mask_rh, affine_mask_rh, surface, 'rh',
bin_mask_rh, calcTFCE_rh, bin_mask_rh.shape[0],
vdensity_rh)
write_vertStat_img('negtstat_imgcovar', -timage[: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('negtstat_imgcovar', -timage[num_vertex_lh:],
outdata_mask_rh, affine_mask_rh, surface, 'rh',
bin_mask_rh, calcTFCE_rh, bin_mask_rh.shape[0],
vdensity_rh)
write_vertStat_img('VIF_imgcovar',
VIF[:num_vertex_lh],
outdata_mask_lh,
affine_mask_lh,
surface,
'lh',
bin_mask_lh,
calcTFCE_lh,
bin_mask_lh.shape[0],
vdensity_lh,
TFCE=False)
write_vertStat_img('VIF_imgcovar',
VIF[num_vertex_lh:],
outdata_mask_rh,
affine_mask_rh,
surface,
'rh',
bin_mask_rh,
calcTFCE_rh,
bin_mask_rh.shape[0],
vdensity_rh,
TFCE=False)
else:
invXX = np.linalg.inv(np.dot(X.T, X))
tvals = tval_int(X, invXX, merge_y, n, k, num_vertex)
for j in range(k - 1):
tnum = j + 1
write_vertStat_img('tstat_con%d' % tnum, tvals[tnum, :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('tstat_con%d' % tnum, tvals[tnum, num_vertex_lh:],
outdata_mask_rh, affine_mask_rh, surface, 'rh',
bin_mask_rh, calcTFCE_rh, bin_mask_rh.shape[0],
vdensity_rh)
write_vertStat_img('negtstat_con%d' % tnum,
(tvals[tnum, :num_vertex_lh] * -1), outdata_mask_lh,
affine_mask_lh, surface, 'lh', bin_mask_lh,
calcTFCE_lh, bin_mask_lh.shape[0], vdensity_lh)
write_vertStat_img('negtstat_con%d' % tnum,
(tvals[tnum, num_vertex_lh:] * -1), outdata_mask_rh,
affine_mask_rh, surface, 'rh', bin_mask_rh,
calcTFCE_rh, bin_mask_rh.shape[0], vdensity_rh)
def run(opts):
arg_perm_start = int(opts.range[0])
arg_perm_stop = int(opts.range[1]) + 1
surface = str(opts.surface[0])
if opts.exchangeblock:
block_list = np.genfromtxt(opts.exchangeblock[0], dtype=np.str)
indexer = np.array(range(len(block_list)))
#load variables
ny = np.load("python_temp_%s/merge_y.npy" % (surface))
num_vertex = np.load("python_temp_%s/num_vertex.npy" % (surface))
num_vertex_lh = np.load("python_temp_%s/num_vertex_lh.npy" % (surface))
all_vertex = np.load("python_temp_%s/all_vertex.npy" % (surface))
bin_mask_lh = np.load("python_temp_%s/bin_mask_lh.npy" % (surface))
bin_mask_rh = np.load("python_temp_%s/bin_mask_rh.npy" % (surface))
n = np.load("python_temp_%s/num_subjects.npy" % (surface))
pred_x = np.load("python_temp_%s/pred_x.npy" % (surface))
adjac_lh = np.load("python_temp_%s/adjac_lh.npy" % (surface))
adjac_rh = np.load("python_temp_%s/adjac_rh.npy" % (surface))
optstfce = np.load('python_temp_%s/optstfce.npy' % (surface))
vdensity_lh = np.load('python_temp_%s/vdensity_lh.npy' % (surface))
vdensity_rh = np.load('python_temp_%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 T values and write max TFCE values
if not os.path.exists("output_%s/perm_Tstat_%s" % (surface, surface)):
os.mkdir("output_%s/perm_Tstat_%s" % (surface, surface))
os.chdir("output_%s/perm_Tstat_%s" % (surface, surface))
X = np.column_stack([np.ones(n), pred_x])
k = len(X.T)
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))
if opts.specifyvars:
start = opts.specifyvars[0]
stop = opts.specifyvars[1] + 1
nx = X
nx[:, start:stop] = X[:, start:stop][np.random.permutation(
list(range(n)))]
elif opts.exchangeblock:
randindex = []
for block in np.random.permutation(list(np.unique(block_list))):
randindex.append(
np.random.permutation(indexer[block_list == block]))
randindex = np.concatenate(np.array(randindex))
nx = X[randindex]
else:
nx = X[np.random.permutation(list(range(n)))]
invXX = np.linalg.inv(np.dot(nx.T, nx))
tvals = tval_int(nx, invXX, ny, n, k, num_vertex)
if opts.specifyvars:
for j in range(stop - start):
tnum = j + 1
write_perm_maxTFCE_vertex('tstat_con%d' % tnum, tvals[tnum],
num_vertex_lh, bin_mask_lh,
bin_mask_rh, calcTFCE_lh,
calcTFCE_rh, vdensity_lh,
vdensity_rh)
write_perm_maxTFCE_vertex('tstat_con%d' % tnum,
(tvals[tnum] * -1), num_vertex_lh,
bin_mask_lh, bin_mask_rh,
calcTFCE_lh, calcTFCE_rh,
vdensity_lh, vdensity_rh)
else:
for j in range(k - 1):
tnum = j + 1
write_perm_maxTFCE_vertex('tstat_con%d' % tnum, tvals[tnum],
num_vertex_lh, bin_mask_lh,
bin_mask_rh, calcTFCE_lh,
calcTFCE_rh, vdensity_lh,
vdensity_rh)
write_perm_maxTFCE_vertex('tstat_con%d' % tnum,
(tvals[tnum] * -1), 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):
currentTime = int(time())
if opts.multisurfacefwecorrection:
#############################
###### FWER CORRECTION ######
#############################
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, columnids = read_tm_filetype(
'%s' % opts.tmifile[0], verbose=False)
# check file dimensions
if not image_array[0].shape[1] % 3 == 0:
print(
'Print file format is not understood. Please make sure %s is statistics file.'
% opts.tmifile[0])
quit()
else:
num_contrasts = int(image_array[0].shape[1] / 3)
# get surface coordinates in data array
position_array = create_position_array(masking_array)
if num_contrasts == 1:
# get lists for positive and negative contrasts
pos_range = [1]
neg_range = [2]
else:
# get lists for positive and negative contrasts
pos_range = list(
range(num_contrasts, num_contrasts + num_contrasts))
neg_range = list(
range(num_contrasts * 2, num_contrasts * 2 + num_contrasts))
# check that randomisation has been run
if not os.path.exists(
"%s/output_%s/perm_maxTFCE_surf0_tcon1.csv" %
(os.getcwd(), opts.tmifile[0])): # make this safer
print(
'Permutation folder not found. Please run --randomise first.')
quit()
#check permutation file lengths
num_surf = len(masking_array)
surface_range = list(range(num_surf))
num_perm = lowest_length(num_contrasts, surface_range, opts.tmifile[0])
if opts.setsurfacerange:
surface_range = list(
range(opts.setsurfacerange[0], opts.setsurfacerange[1] + 1))
elif opts.setsurface:
surface_range = opts.setsurface
if np.array(surface_range).max() > len(masking_array):
print(
"Error: range does note fit the surfaces contained in the tmi file. %s contains the following surfaces"
% opts.tmifile[0])
for i in range(len(surfname)):
print(("Surface %d : %s, %s" % (i, surfname[i], maskname[i])))
quit()
print("Reading %d contrast(s) from %d of %d surface(s)" %
((num_contrasts), len(surface_range), num_surf))
print("Reading %s permutations with an accuracy of p=0.05+/-%.4f" %
(num_perm, (2 * (np.sqrt(0.05 * 0.95 / num_perm)))))
# calculate the P(FWER) images from all surfaces
positive_data, negative_data = apply_mfwer(image_array,
num_contrasts,
surface_range,
num_perm,
num_surf,
opts.tmifile[0],
position_array,
pos_range,
neg_range,
weight='logmasksize')
# write out files
if opts.concatestats:
write_tm_filetype(opts.tmifile[0],
image_array=positive_data,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
adjacency_array=adjacency_array,
checkname=False,
tmi_history=tmi_history)
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, columnids = read_tm_filetype(
opts.tmifile[0], verbose=False)
write_tm_filetype(opts.tmifile[0],
image_array=np.column_stack(
(image_array[0], negative_data)),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
adjacency_array=adjacency_array,
checkname=False,
tmi_history=tmi_history)
else:
for i in range(len(opts.outtype)):
if opts.outtype[i] == 'tmi':
contrast_names = []
for j in range(num_contrasts):
contrast_names.append(("tstat_pFWER_con%d" % (j + 1)))
for k in range(num_contrasts):
contrast_names.append(
("negtstat_pFWER_con%d" % (k + 1)))
outdata = np.column_stack((positive_data, negative_data))
if opts.neglog:
for j in range(num_contrasts):
contrast_names.append(
("tstat_negLog_pFWER_con%d" % (j + 1)))
for k in range(num_contrasts):
contrast_names.append(
("negtstat_negLog_pFWER_con%d" % (k + 1)))
outdata = np.column_stack(
(outdata, -np.log10(1 - positive_data)))
outdata = np.column_stack(
(outdata, -np.log10(1 - negative_data)))
write_tm_filetype("pFWER_%s" % opts.tmifile[0],
image_array=outdata,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
columnids=np.array(contrast_names),
tmi_history=tmi_history)
else:
if opts.outtype[i] == 'mgh':
savefunc = savemgh_v2
if opts.outtype[i] == 'nii.gz':
savefunc = savenifti_v2
if opts.outtype[i] == 'auto':
savefunc = saveauto
for surf_count in surface_range:
start = position_array[surf_count]
end = position_array[surf_count + 1]
basename = strip_basename(maskname[surf_count])
if not os.path.exists("output_stats"):
os.mkdir("output_stats")
out_image = positive_data[start:end]
temp_image = negative_data[start:end]
for contrast in range(num_contrasts):
out_image[temp_image[:, contrast] != 0,
contrast] = temp_image[
temp_image[:, contrast] != 0,
contrast] * -1
if affine_array == []:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_pFWER" %
(surf_count, basename))
else:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_pFWER" %
(surf_count, basename),
affine_array[surf_count])
if opts.neglog:
out_image = -np.log10(1 - positive_data[start:end,
contrast])
temp_image = np.log10(1 - negative_data[start:end,
contrast])
for contrast in range(num_contrasts):
out_image[temp_image[:, contrast] != 0,
contrast] = temp_image[
temp_image[:, contrast] != 0,
contrast]
if affine_array == []:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_negLog_pFWER" %
(surf_count, basename))
else:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_negLog_pFWER" %
(surf_count, basename),
affine_array[surf_count])
if opts.outputply:
colorbar = True
if not os.path.exists("output_ply"):
os.mkdir("output_ply")
for contrast in range(num_contrasts):
for surf_count in surface_range:
start = position_array[surf_count]
end = position_array[surf_count + 1]
basename = strip_basename(maskname[surf_count])
if masking_array[surf_count].shape[2] > 1:
img_data = np.zeros((masking_array[surf_count].shape))
combined_data = positive_data[start:end, contrast]
combined_data[combined_data <= 0] = negative_data[
start:end, contrast][combined_data <= 0] * -1
combined_data[np.abs(combined_data) < float(
opts.outputply[0])] = 0
img_data[masking_array[surf_count]] = combined_data
v, f, values = convert_voxel(
img_data,
affine=affine_array[surf_count],
absthreshold=float(opts.outputply[0]))
if not v == []:
out_color_array = paint_surface(
opts.outputply[0],
opts.outputply[1],
opts.outputply[2],
values,
save_colorbar=colorbar)
negvalues = values * -1
index = negvalues > float(opts.outputply[0])
out_color_array2 = paint_surface(
opts.outputply[0],
opts.outputply[1],
opts.outputply[3],
negvalues,
save_colorbar=colorbar)
out_color_array[index, :] = out_color_array2[
index, :]
save_ply(
v, f, "output_ply/%d_%s_pFWE_tcon%d.ply" %
(surf_count, basename, contrast + 1),
out_color_array)
colorbar = False
else:
print("No output for %d %s T-contrast %d" %
(surf_count, basename, contrast + 1))
else:
img_data = np.zeros(
(masking_array[surf_count].shape[0]))
img_data[masking_array[surf_count][:, 0, 0] ==
True] = positive_data[start:end, contrast]
out_color_array = paint_surface(opts.outputply[0],
opts.outputply[1],
opts.outputply[2],
img_data,
save_colorbar=colorbar)
img_data[masking_array[surf_count][:, 0, 0] ==
True] = negative_data[start:end, contrast]
index = img_data > float(opts.outputply[0])
out_color_array2 = paint_surface(
opts.outputply[0],
opts.outputply[1],
opts.outputply[3],
img_data,
save_colorbar=colorbar)
out_color_array[index, :] = out_color_array2[index, :]
save_ply(
vertex_array[surf_count], face_array[surf_count],
"output_ply/%d_%s_pFWE_tcon%d.ply" %
(surf_count, basename, contrast + 1),
out_color_array)
colorbar = False
elif opts.mediationmfwe: # temporary solution -> maybe a general function instead of bulky code
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, columnids = read_tm_filetype(
'%s' % opts.tmifile[0], verbose=False)
# check file dimensions
if not image_array[0].shape[1] % 2 == 0:
print(
'Print file format is not understood. Please make sure %s is statistics file.'
% opts.tmifile[0])
quit()
# get surface coordinates in data array
position_array = create_position_array(masking_array)
# check that randomisation has been run
if not os.path.exists("%s/output_%s/perm_maxTFCE_surf0_%s_zstat.csv" %
(os.getcwd(), opts.tmifile[0],
opts.mediationmfwe[0])): # make this safer
print(
'Permutation folder not found. Please run --randomise first.')
quit()
#check permutation file lengths
num_surf = len(masking_array)
surface_range = list(range(num_surf))
num_perm = lowest_length(1,
surface_range,
opts.tmifile[0],
medtype=opts.mediationmfwe[0])
if opts.setsurfacerange:
surface_range = list(
range(opts.setsurfacerange[0], opts.setsurfacerange[1] + 1))
elif opts.setsurface:
surface_range = opts.setsurface
if np.array(surface_range).max() > len(masking_array):
print(
"Error: range does note fit the surfaces contained in the tmi file. %s contains the following surfaces"
% opts.tmifile[0])
for i in range(len(surfname)):
print(("Surface %d : %s, %s" % (i, surfname[i], maskname[i])))
quit()
print("Reading %d contrast(s) from %d of %d surface(s)" %
(1, len(surface_range), num_surf))
print("Reading %s permutations with an accuracy of p=0.05+/-%.4f" %
(num_perm, (2 * (np.sqrt(0.05 * 0.95 / num_perm)))))
# calculate the P(FWER) images from all surfaces
positive_data = apply_mfwer(image_array,
1,
surface_range,
num_perm,
num_surf,
opts.tmifile[0],
position_array, [1],
weight='logmasksize',
mediation=True,
medtype=opts.mediationmfwe[0])
if opts.outtype[0] == 'tmi':
contrast_names = []
contrast_names.append(("zstat_pFWER"))
outdata = positive_data
if opts.neglog:
contrast_names.append(("zstat_negLog_pFWER"))
outdata = np.column_stack(
(outdata, -np.log10(1 - positive_data)))
write_tm_filetype("pFWER_%s" % (opts.tmifile[0]),
image_array=outdata,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
columnids=np.array(contrast_names),
tmi_history=tmi_history)
else:
##################################
###### STATISTICAL ANALYSIS ######
##################################
# read tmi file
if opts.randomise:
_, image_array, masking_array, _, _, _, _, _, adjacency_array, _, _ = read_tm_filetype(
opts.tmifile[0])
_ = None
else:
element, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, _ = read_tm_filetype(
opts.tmifile[0])
# get surface coordinates in data array
position_array = create_position_array(masking_array)
if opts.setadjacencyobjs:
if len(opts.setadjacencyobjs) == len(masking_array):
adjacent_range = np.array(opts.setadjacencyobjs, dtype=np.int)
else:
print(
"Error: # of masking arrays (%d) must and list of matching adjacency (%d) must be equal."
% (len(masking_array), len(opts.setadjacencyobjs)))
quit()
else:
adjacent_range = list(range(len(adjacency_array)))
calcTFCE = []
if opts.assigntfcesettings:
if not len(opts.assigntfcesettings) == len(masking_array):
print(
"Error: # of masking arrays (%d) must and list of matching tfce setting (%d) must be equal."
% (len(masking_array), len(opts.assigntfcesettings)))
quit()
if not len(opts.tfce) % 2 == 0:
print("Error. The must be an even number of input for --tfce")
quit()
tfce_settings_mask = []
for i in np.unique(opts.assigntfcesettings):
tfce_settings_mask.append(
(np.array(opts.assigntfcesettings) == int(i)))
pointer = int(i * 2)
adjacency = merge_adjacency_array(
np.array(adjacent_range)[tfce_settings_mask[int(i)]],
np.array(adjacency_array)[tfce_settings_mask[int(i)]])
calcTFCE.append((CreateAdjSet(float(opts.tfce[pointer]),
float(opts.tfce[pointer + 1]),
adjacency)))
del adjacency
else:
adjacency = merge_adjacency_array(adjacent_range, adjacency_array)
calcTFCE.append((CreateAdjSet(float(opts.tfce[0]),
float(opts.tfce[1]), adjacency)))
# make mega mask
fullmask = create_full_mask(masking_array)
if not opts.noweight:
# correction for vertex density
vdensity = []
#np.ones_like(masking_array)
for i in range(len(masking_array)):
temp_vdensity = np.zeros(
(adjacency_array[adjacent_range[i]].shape[0]))
for j in range(adjacency_array[adjacent_range[i]].shape[0]):
temp_vdensity[j] = len(
adjacency_array[adjacent_range[i]][j])
if masking_array[i].shape[2] == 1:
temp_vdensity = temp_vdensity[masking_array[i][:, 0,
0] == True]
vdensity = np.hstack(
(vdensity,
np.array((1 - (temp_vdensity / temp_vdensity.max()) +
(temp_vdensity.mean() / temp_vdensity.max())),
dtype=np.float32)))
del temp_vdensity
else:
vdensity = 1
#load regressors
if opts.input:
for i, arg_pred in enumerate(opts.input):
if i == 0:
pred_x = np.genfromtxt(arg_pred, delimiter=',')
else:
pred_x = np.column_stack(
[pred_x,
np.genfromtxt(arg_pred, delimiter=',')])
if opts.covariates:
covars = np.genfromtxt(opts.covariates[0], delimiter=',')
x_covars = np.column_stack([np.ones(len(covars)), covars])
if opts.subset:
masking_variable = np.isfinite(
np.genfromtxt(str(opts.subset[0]), delimiter=','))
if opts.covariates:
merge_y = resid_covars(x_covars,
image_array[0][:, masking_variable])
else:
merge_y = image_array[0][:, masking_variable].T
print("Check dimensions") # CHECK
print(merge_y.shape)
else:
if opts.covariates:
merge_y = resid_covars(x_covars, image_array[0])
else:
merge_y = image_array[0].T
if opts.inputmediation:
medtype = opts.inputmediation[0]
pred_x = np.genfromtxt(opts.inputmediation[1], delimiter=',')
depend_y = np.genfromtxt(opts.inputmediation[2], delimiter=',')
if opts.covariates:
covars = np.genfromtxt(opts.covariates[0], delimiter=',')
x_covars = np.column_stack([np.ones(len(covars)), covars])
merge_y = resid_covars(x_covars, image_array[0])
else:
merge_y = image_array[0].T
if opts.regressors:
arg_predictor = opts.regressors[0]
pred_x = np.genfromtxt(arg_predictor, delimiter=',')
if opts.subset:
masking_variable = np.isfinite(
np.genfromtxt(str(opts.subset[0]), delimiter=','))
merge_y = image_array[0][:, masking_variable].T
else:
merge_y = image_array[0].T
# cleanup
image_array = None
adjacency_array = None
adjacency = None
if opts.analysisname:
outname = opts.analysisname[0]
else:
outname = opts.tmifile[0][:-4]
# make output folder
if not os.path.exists("output_%s" % (outname)):
os.mkdir("output_%s" % (outname))
os.chdir("output_%s" % (outname))
if opts.randomise:
randTime = int(time())
mapped_y = merge_y.astype(np.float32,
order="C") # removed memory mapping
merge_y = None
if not outname.endswith('tmi'):
outname += '.tmi'
if opts.inputmediation:
outname = 'med_stats_' + outname
else:
outname = 'stats_' + outname
if not os.path.exists("output_%s" % (outname)):
os.mkdir("output_%s" % (outname))
os.chdir("output_%s" % (outname))
for i in range(opts.randomise[0], (opts.randomise[1] + 1)):
if opts.assigntfcesettings:
calc_mixed_tfce(opts.assigntfcesettings,
mapped_y,
masking_array,
position_array,
vdensity,
pred_x,
calcTFCE,
perm_number=i,
randomise=True)
elif opts.inputmediation:
calculate_mediation_tfce(medtype,
mapped_y,
masking_array,
pred_x,
depend_y,
calcTFCE[0],
vdensity,
position_array,
fullmask,
perm_number=i,
randomise=True)
else:
calculate_tfce(mapped_y,
masking_array,
pred_x,
calcTFCE[0],
vdensity,
position_array,
fullmask,
perm_number=i,
randomise=True)
print(("Total time took %.1f seconds" % (time() - currentTime)))
print(("Randomization took %.1f seconds" % (time() - randTime)))
else:
# Run TFCE
if opts.assigntfcesettings:
tvals, tfce_tvals, neg_tfce_tvals = calc_mixed_tfce(
opts.assigntfcesettings, merge_y, masking_array,
position_array, vdensity, pred_x, calcTFCE)
elif opts.inputmediation:
SobelZ, tfce_SobelZ = calculate_mediation_tfce(
medtype, merge_y, masking_array, pred_x, depend_y,
calcTFCE[0], vdensity, position_array, fullmask)
else:
tvals, tfce_tvals, neg_tfce_tvals = calculate_tfce(
merge_y, masking_array, pred_x, calcTFCE[0], vdensity,
position_array, fullmask)
if opts.outtype[0] == 'tmi':
if not outname.endswith('tmi'):
outname += '.tmi'
if opts.inputmediation:
outname = 'med_stats_' + outname
else:
outname = 'stats_' + outname
if opts.inputmediation:
contrast_names = []
contrast_names.append(("SobelZ"))
contrast_names.append(("SobelZ_tfce"))
outdata = np.column_stack((SobelZ.T, tfce_SobelZ.T))
else:
if tvals.ndim == 1:
num_contrasts = 1
else:
num_contrasts = tvals.shape[0]
contrast_names = []
for i in range(num_contrasts):
contrast_names.append(("tstat_con%d" % (i + 1)))
for j in range(num_contrasts):
contrast_names.append(("tstat_tfce_con%d" % (j + 1)))
for k in range(num_contrasts):
contrast_names.append(
("negtstat_tfce_con%d" % (k + 1)))
outdata = np.column_stack((tvals.T, tfce_tvals.T))
outdata = np.column_stack((outdata, neg_tfce_tvals.T))
# write tstat
write_tm_filetype(outname,
image_array=outdata,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
columnids=np.array(contrast_names),
tmi_history=[])
else:
print("not implemented yet")
def run(opts):
try:
sopts = np.load("tmi_temp/opts.npy").tolist()
except:
print("Error: tmi_temp was not found. Run mmr-lr first, or change directory to where tmi_temp is located.") # this error should never happen.
tfce_settings = []
masking_array = np.load("tmi_temp/masking_array.npy")
for surf_num in range(len(masking_array)):
if sopts.assigntfcesettings:
pointer = int(sopts.assigntfcesettings[surf_num] * 2)
tfce_settings.append(([sopts.tfce[pointer],sopts.tfce[pointer+1]]))
if opts.outputstats:
# load npy objects for building the tmi file
maskname = np.load("tmi_temp/maskname.npy")
affine_array = np.load("tmi_temp/affine_array.npy")
vertex_array = np.load("tmi_temp/vertex_array.npy")
face_array = np.load("tmi_temp/face_array.npy")
surfname = np.load("tmi_temp/surfname.npy")
for surf_num in range(len(masking_array)):
print("Calculating stats for:\t %s" % maskname[surf_num])
adjacency = np.load("tmi_temp/%d_adjacency_temp.npy" % surf_num)
mask = np.load("tmi_temp/%d_mask_temp.npy" % surf_num)
data = np.load("tmi_temp/%d_data_temp.npy" % surf_num)
vdensity = np.load("tmi_temp/%d_vdensity_temp.npy" % surf_num)
if not sopts.assigntfcesettings:
calcTFCE = CreateAdjSet(float(sopts.tfce[0]), float(sopts.tfce[1]), adjacency)
if sopts.input:
for i, arg_pred in enumerate(sopts.input):
if i == 0:
pred_x = np.genfromtxt(arg_pred, delimiter=',')
else:
pred_x = np.column_stack([pred_x, np.genfromtxt(arg_pred, delimiter=',')])
if sopts.assigntfcesettings:
calcTFCE = CreateAdjSet(float(tfce_settings[surf_num][0]), float(tfce_settings[surf_num][1]), adjacency)
temp_tvals, temp_tfce_tvals, temp_neg_tfce_tvals = low_ram_calculate_tfce(data, mask, pred_x, calcTFCE, vdensity, set_surf_count = surf_num, randomise = False, no_intercept = True)
if surf_num == 0:
tvals = temp_tvals
tfce_tvals = temp_tfce_tvals
neg_tfce_tvals = temp_neg_tfce_tvals
if pred_x.ndim == 1: # get number of contrasts
num_contrasts = 1
else:
num_contrasts = pred_x.shape[1]
else:
tvals = np.concatenate((tvals, temp_tvals), 1)
tfce_tvals = np.concatenate((tfce_tvals, temp_tfce_tvals), 1)
neg_tfce_tvals = np.concatenate((neg_tfce_tvals, temp_neg_tfce_tvals), 1)
if sopts.inputmediation:
medtype = sopts.inputmediation[0]
pred_x = np.genfromtxt(sopts.inputmediation[1], delimiter=',')
depend_y = np.genfromtxt(sopts.inputmediation[2], delimiter=',')
if sopts.assigntfcesettings:
calcTFCE = CreateAdjSet(float(tfce_settings[surf_num][0]), float(tfce_settings[surf_num][1]), adjacency)
temp_zvals, temp_tfce_zvals = low_ram_calculate_mediation_tfce(medtype, data, mask, pred_x, depend_y, calcTFCE, vdensity, set_surf_count = surf_num, randomise = False, no_intercept = True)
if surf_num == 0:
zvals = temp_zvals
tfce_zvals = temp_tfce_zvals
if pred_x.ndim == 1: # get number of contrasts
num_contrasts = 1
else:
num_contrasts = pred_x.shape[1]
else:
zvals = np.concatenate((zvals, temp_zvals))
tfce_zvals = np.concatenate((tfce_zvals, temp_tfce_zvals))
if sopts.input:
data = np.column_stack((tvals.T, tfce_tvals.T))
data = np.column_stack((data, neg_tfce_tvals.T))
contrast_names = []
for i in range(num_contrasts):
contrast_names.append(("tstat_con%d" % (i+1)))
for j in range(num_contrasts):
contrast_names.append(("tstat_tfce_con%d" % (j+1)))
for k in range(num_contrasts):
contrast_names.append(("negtstat_tfce_con%d" % (k+1)))
if sopts.inputmediation:
data = np.column_stack((zvals.T, tfce_zvals.T))
contrast_names = []
contrast_names.append(("SobelZ"))
contrast_names.append(("SobelZ_tfce"))
outname = os.path.basename(str(opts.path[0]))[7:]
if not outname.endswith('tmi'):
outname += '.tmi'
write_tm_filetype("%s/%s" % (os.path.dirname(str(opts.path[0])), outname),
image_array = data,
masking_array = masking_array,
maskname = maskname,
affine_array = affine_array,
vertex_array = vertex_array,
face_array = face_array,
surfname = surfname,
checkname = True,
columnids = np.array(contrast_names),
tmi_history=[])
else:
currentTime=int(time())
surf_num = int(opts.surfacenumber[0])
p_range = np.array(opts.permutationrange)
adjacency = np.load("tmi_temp/%d_adjacency_temp.npy" % surf_num)
mask = np.load("tmi_temp/%d_mask_temp.npy" % surf_num)
data = np.load("tmi_temp/%d_data_temp.npy" % surf_num)
vdensity = np.load("tmi_temp/%d_vdensity_temp.npy" % surf_num)
if sopts.assigntfcesettings:
calcTFCE = CreateAdjSet(float(tfce_settings[surf_num][0]), float(tfce_settings[surf_num][1]), adjacency)
else:
calcTFCE = CreateAdjSet(float(sopts.tfce[0]), float(sopts.tfce[1]), adjacency)
if sopts.input:
for i, arg_pred in enumerate(sopts.input):
if i == 0:
pred_x = np.genfromtxt(arg_pred, delimiter=',')
else:
pred_x = np.column_stack([pred_x, np.genfromtxt(arg_pred, delimiter=',')])
for perm_number in range(p_range[0],int(p_range[1]+1)):
low_ram_calculate_tfce(data, mask, pred_x, calcTFCE, vdensity,
set_surf_count = surf_num,
perm_number = perm_number,
randomise = True,
no_intercept = True,
output_dir = str(opts.path[0]),
perm_seed = int(opts.seed[0]))
if sopts.inputmediation:
medtype = sopts.inputmediation[0]
pred_x = np.genfromtxt(sopts.inputmediation[1], delimiter=',')
depend_y = np.genfromtxt(sopts.inputmediation[2], delimiter=',')
for perm_number in range(p_range[0],int(p_range[1]+1)):
low_ram_calculate_mediation_tfce(medtype, data, mask, pred_x, depend_y, calcTFCE, vdensity,
set_surf_count = surf_num,
perm_number = perm_number,
randomise = True,
no_intercept = True,
output_dir = str(opts.path[0]),
perm_seed = int(opts.seed[0]))
print("Mask %d, Iteration %d -> %d took %i seconds." % (surf_num, p_range[0], p_range[1], (int(time()) - currentTime)))
def run(opts):
arg_perm_start = int(opts.range[0])
arg_perm_stop = int(opts.range[1]) + 1
if opts.exchangeblock:
block_list = np.genfromtxt(opts.exchangeblock[0], dtype=np.str)
indexer = np.array(range(len(block_list)))
np.seterr(divide="ignore",
invalid="ignore") #only necessary for ANTS skeleton
#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')
adjac = np.load('python_temp/adjac.npy')
ancova = np.load('python_temp/ancova.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 T values and write max TFCE values
if not os.path.exists('output/perm_Tstat'):
os.mkdir('output/perm_Tstat')
os.chdir('output/perm_Tstat')
X = np.column_stack([np.ones(n), pred_x])
k = len(X.T)
if ancova == 1:
for iter_perm in range(arg_perm_start,
int((arg_perm_stop - 1) * 2 + 1)):
np.random.seed(int(iter_perm * 1000 + time()))
print("Permutation number: %d" % (iter_perm))
nx = X[np.random.permutation(list(range(n)))]
perm_fvals = calcF(nx, ny, n, k)
perm_fvals[perm_fvals < 0] = 0
perm_fvals = np.sqrt(perm_fvals)
print(perm_fvals.max())
print(perm_fvals.min())
write_perm_maxTFCE_voxel('fstat', perm_fvals, calcTFCE)
else:
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))
if opts.specifyvars:
start = opts.specifyvars[0]
stop = opts.specifyvars[1] + 1
nx = X
nx[:, start:stop] = X[:, start:stop][np.random.permutation(
list(range(n)))]
elif opts.exchangeblock:
randindex = []
for block in np.random.permutation(list(
np.unique(block_list))):
randindex.append(
np.random.permutation(indexer[block_list == block]))
randindex = np.concatenate(np.array(randindex))
nx = X[randindex]
else:
nx = X[np.random.permutation(list(range(n)))]
invXX = np.linalg.inv(np.dot(nx.T, nx))
perm_tvalues = tval_int(nx, invXX, ny, n, k, num_voxel)
perm_tvalues[np.isnan(
perm_tvalues)] = 0 #only necessary for ANTS skeleton
if opts.specifyvars:
for j in range(stop - start):
tnum = j + 1
write_perm_maxTFCE_voxel('tstat_con%d' % tnum,
perm_tvalues[tnum], calcTFCE)
write_perm_maxTFCE_voxel('tstat_con%d' % tnum,
(perm_tvalues[tnum] * -1),
calcTFCE)
else:
for j in range(k - 1):
tnum = j + 1
write_perm_maxTFCE_voxel('tstat_con%d' % tnum,
perm_tvalues[tnum], calcTFCE)
write_perm_maxTFCE_voxel('tstat_con%d' % tnum,
(perm_tvalues[tnum] * -1),
calcTFCE)
print(
("Finished. Randomization took %.1f seconds" % (time() - start_time)))
def run(opts):
if not os.path.exists("python_temp"):
print("python_temp missing!")
#load variables
raw_nonzero = np.load('python_temp/raw_nonzero.npy')
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')
n = raw_nonzero.shape[1]
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')
#step1
if opts.input:
pred_x = np.genfromtxt(opts.input[0], delimiter=',')
covars = np.genfromtxt(opts.input[1], delimiter=',')
x_covars = np.column_stack([np.ones(n), covars])
y = resid_covars(x_covars, raw_nonzero)
np.save('python_temp/covars', covars)
if opts.regressors:
pred_x = np.genfromtxt(opts.regressors[0], delimiter=',')
y = raw_nonzero.T
if opts.onesample:
pred_x = np.ones(n)
pred_x[:int(n / 2)] = -1
if opts.onesample[0] != 'none':
covars = np.genfromtxt(opts.onesample[0], delimiter=',')
x_covars = np.column_stack([np.ones(n), covars])
y = resid_covars(x_covars, raw_nonzero)
np.save('python_temp/covars', covars)
else:
y = raw_nonzero.T
ancova = 0
if opts.ftest:
ancova = 1
#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) # H=2, E=2, 26 neighbour connectivity
#save
np.save('python_temp/adjac', adjac)
np.save('python_temp/pred_x', pred_x)
np.save('python_temp/ancova', ancova)
np.save('python_temp/optstfce', opts.tfce)
np.save('python_temp/raw_nonzero_corr', y.T.astype(np.float32, order="C"))
if not os.path.exists('output'):
os.mkdir('output')
os.chdir('output')
X = np.column_stack([np.ones(n), pred_x])
k = len(X.T)
if opts.onesample:
if opts.onesample[0] == 'none':
tvalues, _ = stats.ttest_1samp(raw_nonzero, 0, axis=1)
write_voxelStat_img('tstat_intercept', tvalues, data_mask,
data_index, affine_mask, calcTFCE, imgext)
write_voxelStat_img('negtstat_intercept', (tvalues * -1),
data_mask, data_index, affine_mask, calcTFCE,
imgext)
else:
tvalues = tval_int(x_covars,
np.linalg.inv(np.dot(x_covars.T, x_covars)),
raw_nonzero.T, n, len(x_covars.T), num_voxel)
tvalues = tvalues[0]
write_voxelStat_img('tstat_intercept', tvalues, data_mask,
data_index, affine_mask, calcTFCE, imgext)
write_voxelStat_img('negtstat_intercept', (tvalues * -1),
data_mask, data_index, affine_mask, calcTFCE,
imgext)
exit()
if ancova == 0:
if opts.voxelregressor:
img_all_name = opts.voxelregressor[0]
_, file_ext = os.path.splitext(img_all_name)
if file_ext == '.gz':
_, file_ext = os.path.splitext(img_all_name)
if file_ext == '.mnc':
imgext = '.mnc'
image_x = nib.load(
'../%s' % img_all_name).get_data()[data_index].astype(
np.float32)
else:
imgext = '.nii.gz'
os.system("zcat ../%s > temp_4d.nii" % img_all_name)
image_x = nib.load(
'temp_4d.nii').get_data()[data_index].astype(
np.float32)
os.system("rm temp_4d.nii")
elif file_ext == '.nii':
imgext = '.nii.gz' # default to zipped images
image_x = nib.load('../%s' %
img_all_name).get_data()[data_index].astype(
np.float32)
else:
print('Error filetype for %s is not supported' % img_all_name)
quit()
tvalues, timage = image_regression(raw_nonzero.astype(np.float32),
image_x, pred_x, covars)
write_voxelStat_img('tstat_imgcovar', timage, data_mask,
data_index, affine_mask, calcTFCE, imgext)
write_voxelStat_img('negtstat_imgcovar', -timage, data_mask,
data_index, affine_mask, calcTFCE, imgext)
tvalues = tvalues.T
VIF = image_reg_VIF(image_x.T, np.column_stack((pred_x, covars)))
write_voxelStat_img('VIF_imgcovar',
VIF,
data_mask,
data_index,
affine_mask,
calcTFCE,
imgext,
TFCE=False)
else:
#multiple regression
invXX = np.linalg.inv(np.dot(X.T, X))
tvalues = tval_int(X, invXX, y, n, k, num_voxel)
tvalues[np.isnan(tvalues)] = 0 #only necessary for ANTS skeleton
#write TFCE images
for j in range(k - 1):
tnum = j + 1
write_voxelStat_img('tstat_con%d' % tnum, tvalues[tnum], data_mask,
data_index, affine_mask, calcTFCE, imgext)
write_voxelStat_img('negtstat_con%d' % tnum, (tvalues[tnum] * -1),
data_mask, data_index, affine_mask, calcTFCE,
imgext)
elif ancova == 1:
#anova
fvals = calcF(X, y, n, k) # sqrt to approximate the t-distribution
fvals[fvals < 0] = 0
write_voxelStat_img('fstat', np.sqrt(fvals), data_mask, data_index,
affine_mask, calcTFCE, imgext)
else:
print("Error")
exit()
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)
def run(opts):
currentTime = int(time())
if opts.multisurfacefwecorrection:
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, subjectids = read_tm_filetype(
'%s' % opts.tmifile[0], verbose=False)
# check file dimensions
if not image_array[0].shape[1] % 3 == 0:
print 'Print file format is not understood. Please make sure %s is statistics file.' % opts.tmifile[
0]
quit()
else:
num_contrasts = int(image_array[0].shape[1] / 3)
# get surface coordinates in data array
pointer = 0
position_array = [0]
for i in range(len(masking_array)):
pointer += len(masking_array[i][masking_array[i] == True])
position_array.append(pointer)
del pointer
if num_contrasts == 1:
# get lists for positive and negative contrasts
pos_range = [1]
neg_range = [2]
else:
# get lists for positive and negative contrasts
pos_range = range(num_contrasts, num_contrasts + num_contrasts)
neg_range = range(num_contrasts * 2,
num_contrasts * 2 + num_contrasts)
# check that randomisation has been run
if not os.path.exists(
"%s/output_%s/perm_maxTFCE_surf0_tcon1.csv" %
(os.getcwd(), opts.tmifile[0])): # make this safer
print 'Permutation folder not found. Please run --randomise first.'
quit()
#check permutation file lengths
num_surf = len(masking_array)
surface_range = range(num_surf)
num_perm = lowest_length(num_contrasts, surface_range, opts.tmifile[0])
if opts.setsurfacerange:
surface_range = range(opts.setsurfacerange[0],
opts.setsurfacerange[1] + 1)
elif opts.setsurface:
surface_range = opts.setsurface
if np.array(surface_range).max() > len(masking_array):
print "Error: range does note fit the surfaces contained in the tmi file. %s contains the following surfaces" % opts.tmifile[
0]
for i in range(len(surfname)):
print("Surface %d : %s, %s" % (i, surfname[i], maskname[i]))
quit()
print "Reading %d contrast(s) from %d of %d surface(s)" % (
(num_contrasts), len(surface_range), num_surf)
print "Reading %s permutations with an accuracy of p=0.05+/-%.4f" % (
num_perm, (2 * (np.sqrt(0.05 * 0.95 / num_perm))))
# calculate the P(FWER) images from all surfaces
positive_data, negative_data = apply_mfwer(image_array,
num_contrasts,
surface_range,
num_perm,
num_surf,
opts.tmifile[0],
position_array,
pos_range,
neg_range,
weight='logmasksize')
# write out files
if opts.concatestats:
write_tm_filetype(opts.tmifile[0],
image_array=positive_data,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
adjacency_array=adjacency_array,
checkname=False,
tmi_history=tmi_history)
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, subjectids = read_tm_filetype(
opts.tmifile[0], verbose=False)
write_tm_filetype(opts.tmifile[0],
image_array=np.column_stack(
(image_array[0], negative_data)),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
adjacency_array=adjacency_array,
checkname=False,
tmi_history=tmi_history)
else:
for i in range(len(opts.outtype)):
if opts.outtype[i] == 'tmi':
write_tm_filetype("tstats_pFWER_%s" % opts.tmifile[0],
image_array=positive_data,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
write_tm_filetype("negtstats_pFWER_%s" % opts.tmifile[0],
image_array=negative_data,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
if opts.neglog:
write_tm_filetype(
"tstats_negLog_pFWER_%s" % opts.tmifile[0],
image_array=-np.log10(1 - positive_data),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
write_tm_filetype(
"negtstats_negLog_pFWER_%s" % opts.tmifile[0],
image_array=-np.log10(1 - negative_data),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
else:
if opts.outtype[i] == 'mgh':
savefunc = savemgh_v2
if opts.outtype[i] == 'nii.gz':
savefunc = savenifti_v2
if opts.outtype[i] == 'auto':
savefunc = saveauto
for surf_count in surface_range:
start = position_array[surf_count]
end = position_array[surf_count + 1]
basename = strip_basename(maskname[surf_count])
if not os.path.exists("output_stats"):
os.mkdir("output_stats")
out_image = positive_data[start:end]
temp_image = negative_data[start:end]
for contrast in range(num_contrasts):
out_image[temp_image[:, contrast] != 0,
contrast] = temp_image[
temp_image[:, contrast] != 0,
contrast] * -1
if affine_array == []:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_pFWER" %
(surf_count, basename))
else:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_pFWER" %
(surf_count, basename),
affine_array[surf_count])
if opts.neglog:
out_image = -np.log10(1 - positive_data[start:end,
contrast])
temp_image = np.log10(1 - negative_data[start:end,
contrast])
for contrast in range(num_contrasts):
out_image[temp_image[:, contrast] != 0,
contrast] = temp_image[
temp_image[:, contrast] != 0,
contrast]
if affine_array == []:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_negLog_pFWER" %
(surf_count, basename))
else:
savefunc(
out_image, masking_array[surf_count],
"output_stats/%d_%s_negLog_pFWER" %
(surf_count, basename),
affine_array[surf_count])
if opts.outputply:
if not os.path.exists("output_ply"):
os.mkdir("output_ply")
for contrast in range(num_contrasts):
for surf_count in surface_range:
start = position_array[surf_count]
end = position_array[surf_count + 1]
basename = strip_basename(maskname[surf_count])
if masking_array[surf_count].shape[2] > 1:
img_data = np.zeros((masking_array[surf_count].shape))
combined_data = positive_data[start:end, contrast]
combined_data[combined_data <= 0] = negative_data[
start:end, contrast][combined_data <= 0] * -1
combined_data[np.abs(combined_data) < float(
opts.outputply[0])] = 0
img_data[masking_array[surf_count]] = combined_data
v, f, values = convert_voxel(
img_data,
affine=affine_array[surf_count],
absthreshold=float(opts.outputply[0]))
out_color_array = paint_surface(
opts.outputply[0], opts.outputply[1],
opts.outputply[2], values)
negvalues = values * -1
index = negvalues > float(opts.outputply[0])
out_color_array2 = paint_surface(
opts.outputply[0], opts.outputply[1],
opts.outputply[3], negvalues)
out_color_array[index, :] = out_color_array2[index, :]
save_ply(
v, f, "output_ply/%d_%s_pFWE_tcon%d.ply" %
(surf_count, basename, contrast + 1),
out_color_array)
else:
img_data = np.zeros(
(masking_array[surf_count].shape[0]))
img_data[masking_array[surf_count][:, 0, 0] ==
True] = positive_data[start:end, contrast]
out_color_array = paint_surface(
opts.outputply[0], opts.outputply[1],
opts.outputply[2], img_data)
img_data[masking_array[surf_count][:, 0, 0] ==
True] = negative_data[start:end, contrast]
index = img_data > float(opts.outputply[0])
out_color_array2 = paint_surface(
opts.outputply[0], opts.outputply[1],
opts.outputply[3], img_data)
out_color_array[index, :] = out_color_array2[index, :]
save_ply(
vertex_array[surf_count], face_array[surf_count],
"output_ply/%d_%s_pFWE_tcon%d.ply" %
(surf_count, basename, contrast + 1),
out_color_array)
else:
# read tmi file
if opts.randomise:
_, image_array, masking_array, _, _, _, _, _, adjacency_array, _, _ = read_tm_filetype(
opts.tmifile[0])
_ = None
else:
element, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, _ = read_tm_filetype(
opts.tmifile[0])
# get surface coordinates in data array
pointer = 0
position_array = [0]
for i in range(len(masking_array)):
pointer += len(masking_array[i][masking_array[i] == True])
position_array.append(pointer)
del pointer
if opts.setadjacencyobjs:
if len(opts.setadjacencyobjs) == len(masking_array):
adjacent_range = opts.setadjacencyobjs
else:
print "Error: # of masking arrays %d must and list of matching adjacency %d must be equal." % (
len(masking_array), len(opts.setadjacencyobjs))
quit()
else:
adjacent_range = range(len(adjacency_array))
calcTFCE = []
if opts.setfcesettings:
if len(opts.setfcesettings) == len(masking_array):
print "Error: # of masking arrays %d must and list of matching tfce setting %d must be equal." % (
len(masking_array), len(opts.setfcesettings))
quit()
if len(opts.tfce) % 2 != 0:
print "Error. The must be an even number of input for --tfce"
quit()
tfce_settings_mask = []
for i in range(len(opts.tfce) / 2):
tfce_settings_mask.append((opts.setfcesettings == int(i)))
pointer = int(i * 2)
adjacency = merge_adjacency_array(
adjacent_range[tfce_settings_mask[i]],
adjacency_array[tfce_settings_mask[i]])
calcTFCE.append((CreateAdjSet(float(opts.tfce[pointer]),
float(opts.tfce[pointer + 1]),
adjacency)))
del adjacency
else:
adjacency = merge_adjacency_array(adjacent_range, adjacency_array)
calcTFCE.append((CreateAdjSet(float(opts.tfce[0]),
float(opts.tfce[1]), adjacency)))
# make mega mask
fullmask = []
for i in range(len(masking_array)):
if masking_array[i].shape[
2] == 1: # check if vertex or voxel image
fullmask = np.hstack((fullmask, masking_array[i][:, 0, 0]))
else:
fullmask = np.hstack(
(fullmask, masking_array[i][masking_array[i] == True]))
if not opts.noweight:
# correction for vertex density
vdensity = []
#np.ones_like(masking_array)
for i in range(len(masking_array)):
temp_vdensity = np.zeros(
(adjacency_array[adjacent_range[i]].shape[0]))
for j in xrange(adjacency_array[adjacent_range[i]].shape[0]):
temp_vdensity[j] = len(
adjacency_array[adjacent_range[i]][j])
if masking_array[i].shape[2] == 1:
temp_vdensity = temp_vdensity[masking_array[i][:, 0,
0] == True]
vdensity = np.hstack(
(vdensity,
np.array((1 - (temp_vdensity / temp_vdensity.max()) +
(temp_vdensity.mean() / temp_vdensity.max())),
dtype=np.float32)))
del temp_vdensity
else:
vdensity = 1
#load regressors
if opts.input:
arg_predictor = opts.input[0]
arg_covars = opts.input[1]
pred_x = np.genfromtxt(arg_predictor, delimiter=',')
covars = np.genfromtxt(arg_covars, delimiter=',')
x_covars = np.column_stack([np.ones(len(covars)), covars])
merge_y = resid_covars(x_covars, image_array[0])
if opts.regressors:
arg_predictor = opts.regressors[0]
pred_x = np.genfromtxt(arg_predictor, delimiter=',')
merge_y = image_array[0].T
# cleanup
image_array = None
adjacency_array = None
adjacency = None
if opts.analysisname:
outname = opts.analysisname[0]
else:
outname = opts.tmifile[0][:-4]
# make output folder
if not os.path.exists("output_%s" % (outname)):
os.mkdir("output_%s" % (outname))
os.chdir("output_%s" % (outname))
if opts.randomise:
randTime = int(time())
mapped_y = merge_y.astype(np.float32,
order="C") # removed memory mapping
merge_y = None
if not outname.endswith('tmi'):
outname += '.tmi'
outname = 'stats_' + outname
if not os.path.exists("output_%s" % (outname)):
os.mkdir("output_%s" % (outname))
os.chdir("output_%s" % (outname))
for i in range(opts.randomise[0], (opts.randomise[1] + 1)):
calculate_tfce(mapped_y,
masking_array,
pred_x,
calcTFCE[0],
vdensity,
position_array,
fullmask,
perm_number=i,
randomise=True)
print("Total time took %.1f seconds" % (time() - currentTime))
print("Randomization took %.1f seconds" % (time() - randTime))
else:
# Run TFCE
tvals, tfce_tvals, neg_tfce_tvals = calculate_tfce(
merge_y, masking_array, pred_x, calcTFCE[0], vdensity,
position_array, fullmask)
if opts.outtype[0] == 'tmi':
if not outname.endswith('tmi'):
outname += '.tmi'
outname = 'stats_' + outname
# write tstat
write_tm_filetype(outname,
image_array=tvals.T,
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=[])
# read the tmi back in.
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, _, tmi_history, subjectids = read_tm_filetype(
outname, verbose=False)
write_tm_filetype(outname,
image_array=np.column_stack(
(image_array[0], tfce_tvals.T)),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
_, image_array, masking_array, maskname, affine_array, vertex_array, face_array, surfname, adjacency_array, tmi_history, subjectids = read_tm_filetype(
outname, verbose=False)
write_tm_filetype(outname,
image_array=np.column_stack(
(image_array[0], neg_tfce_tvals.T)),
masking_array=masking_array,
maskname=maskname,
affine_array=affine_array,
vertex_array=vertex_array,
face_array=face_array,
surfname=surfname,
checkname=False,
tmi_history=tmi_history)
else:
print "not implemented yet"