def _run_interface(self, runtime):
dx, dy, dz = get_data_dims(self.inputs.image_file)
vx, vy, vz = get_vox_dims(self.inputs.image_file)
image_file = nb.load(self.inputs.image_file)
affine = image_file.get_affine()
out_filename = op.abspath(self.inputs.out_filename)
#Reads MRTrix tracks
header, streamlines = read_mrtrix_tracks(self.inputs.in_file, as_generator=True)
iflogger.info('MRTrix Header:')
iflogger.info(header)
# Writes to Trackvis
trk_header = nb.trackvis.empty_header()
trk_header['dim'] = [dx,dy,dz]
trk_header['voxel_size'] = [vx,vy,vz]
trk_header['n_count'] = header['count']
if isdefined(self.inputs.matrix_file) and isdefined(self.inputs.registration_image_file):
iflogger.info('Applying transformation from matrix file {m}'.format(m=self.inputs.matrix_file))
xfm = np.genfromtxt(self.inputs.matrix_file)
iflogger.info(xfm)
registration_image_file = nb.load(self.inputs.registration_image_file)
reg_affine = registration_image_file.get_affine()
r_dx, r_dy, r_dz = get_data_dims(self.inputs.registration_image_file)
r_vx, r_vy, r_vz = get_vox_dims(self.inputs.registration_image_file)
iflogger.info('Using affine from registration image file {r}'.format(r=self.inputs.registration_image_file))
iflogger.info(reg_affine)
trk_header['vox_to_ras'] = reg_affine
trk_header['dim'] = [r_dx,r_dy,r_dz]
trk_header['voxel_size'] = [r_vx,r_vy,r_vz]
affine = np.dot(affine,np.diag(1./np.array([vx, vy, vz, 1])))
transformed_streamlines = transform_to_affine(streamlines, trk_header, affine)
aff = affine_from_fsl_mat_file(xfm, [vx,vy,vz], [r_vx,r_vy,r_vz])
iflogger.info(aff)
axcode = aff2axcodes(reg_affine)
trk_header['voxel_order'] = axcode[0]+axcode[1]+axcode[2]
final_streamlines = move_streamlines(transformed_streamlines, aff)
trk_tracks = ((ii,None,None) for ii in final_streamlines)
trk.write(out_filename, trk_tracks, trk_header)
iflogger.info('Saving transformed Trackvis file as {out}'.format(out=out_filename))
iflogger.info('New TrackVis Header:')
iflogger.info(trk_header)
else:
iflogger.info('Applying transformation from scanner coordinates to {img}'.format(img=self.inputs.image_file))
axcode = aff2axcodes(affine)
trk_header['voxel_order'] = axcode[0]+axcode[1]+axcode[2]
trk_header['vox_to_ras'] = affine
transformed_streamlines = transform_to_affine(streamlines, trk_header, affine)
trk_tracks = ((ii,None,None) for ii in transformed_streamlines)
trk.write(out_filename, trk_tracks, trk_header)
iflogger.info('Saving Trackvis file as {out}'.format(out=out_filename))
iflogger.info('TrackVis Header:')
iflogger.info(trk_header)
return runtime
def _run_interface(self, runtime):
filename = os.path.basename(self.inputs.gibbs_output)
dx, dy, dz = get_data_dims(self.inputs.gibbs_input)
vx, vy, vz = get_vox_dims(self.inputs.gibbs_input)
image_file = nib.load(self.inputs.gibbs_input)
affine = image_file.get_affine()
import numpy as np
#Reads MITK tracks
fib, hdr = nib.trackvis.read(self.inputs.gibbs_output)
trk_header = nib.trackvis.empty_header()
trk_header['dim'] = [dx, dy, dz]
trk_header['voxel_size'] = [vx, vy, vz]
trk_header['origin'] = [0, 0, 0]
axcode = nib.orientations.aff2axcodes(affine)
if axcode[0] != str(hdr['voxel_order'])[0]:
flip_x = -1
else:
flip_x = 1
if axcode[1] != str(hdr['voxel_order'])[1]:
flip_y = -1
else:
flip_y = 1
if axcode[2] != str(hdr['voxel_order'])[2]:
flip_z = -1
else:
flip_z = 1
trk_header['voxel_order'] = axcode[0] + axcode[1] + axcode[2]
new_fib = []
for i in range(len(fib)):
temp_fib = fib[i][0].copy()
for j in range(len(fib[i][0])):
temp_fib[j] = [
flip_x * (fib[i][0][j][0] - hdr['origin'][0]) + vx / 2,
flip_y * (fib[i][0][j][1] - hdr['origin'][1]) + vy / 2,
flip_z * (fib[i][0][j][2] - hdr['origin'][2]) + vz / 2
]
new_fib.append((temp_fib, None, None))
nib.trackvis.write(os.path.abspath(filename),
new_fib,
trk_header,
points_space='voxmm')
iflogger.info('file written to %s' % os.path.abspath(filename))
return runtime
def _run_interface(self, runtime):
filename = os.path.basename(self.inputs.gibbs_output)
dx, dy, dz = get_data_dims(self.inputs.gibbs_input)
vx, vy, vz = get_vox_dims(self.inputs.gibbs_input)
image_file = nib.load(self.inputs.gibbs_input)
affine = image_file.get_affine()
import numpy as np
#Reads MITK tracks
fib, hdr = nib.trackvis.read(self.inputs.gibbs_output)
trk_header = nib.trackvis.empty_header()
trk_header['dim'] = [dx,dy,dz]
trk_header['voxel_size'] = [vx,vy,vz]
trk_header['origin'] = [0 ,0 ,0]
axcode = nib.orientations.aff2axcodes(affine)
if axcode[0] != str(hdr['voxel_order'])[0]:
flip_x = -1
else:
flip_x = 1
if axcode[1] != str(hdr['voxel_order'])[1]:
flip_y = -1
else:
flip_y = 1
if axcode[2] != str(hdr['voxel_order'])[2]:
flip_z = -1
else:
flip_z = 1
trk_header['voxel_order'] = axcode[0]+axcode[1]+axcode[2]
new_fib = []
for i in range(len(fib)):
temp_fib = fib[i][0].copy()
for j in range(len(fib[i][0])):
temp_fib[j] = [flip_x*(fib[i][0][j][0]-hdr['origin'][0])+vx/2,flip_y*(fib[i][0][j][1]-hdr['origin'][1])+vy/2, flip_z*(fib[i][0][j][2]-hdr['origin'][2])+vz/2]
new_fib.append((temp_fib,None,None))
nib.trackvis.write(os.path.abspath(filename), new_fib, trk_header, points_space = 'voxmm')
iflogger.info('file written to %s' % os.path.abspath(filename))
return runtime
def _run_interface(self, runtime):
dx, dy, dz = get_data_dims(self.inputs.image_file)
vx, vy, vz = get_vox_dims(self.inputs.image_file)
image_file = nb.load(self.inputs.image_file)
affine = image_file.get_affine()
out_filename = op.abspath(self.inputs.out_filename)
#Reads MRTrix tracks
header, streamlines = read_mrtrix_tracks(self.inputs.in_file,
as_generator=True)
iflogger.info('MRTrix Header:')
iflogger.info(header)
# Writes to Trackvis
trk_header = nb.trackvis.empty_header()
trk_header['dim'] = [dx, dy, dz]
trk_header['voxel_size'] = [vx, vy, vz]
trk_header['n_count'] = header['count']
if isdefined(self.inputs.matrix_file) and isdefined(
self.inputs.registration_image_file):
iflogger.info(
'Applying transformation from matrix file {m}'.format(
m=self.inputs.matrix_file))
xfm = np.genfromtxt(self.inputs.matrix_file)
iflogger.info(xfm)
registration_image_file = nb.load(
self.inputs.registration_image_file)
reg_affine = registration_image_file.get_affine()
r_dx, r_dy, r_dz = get_data_dims(
self.inputs.registration_image_file)
r_vx, r_vy, r_vz = get_vox_dims(
self.inputs.registration_image_file)
iflogger.info(
'Using affine from registration image file {r}'.format(
r=self.inputs.registration_image_file))
iflogger.info(reg_affine)
trk_header['vox_to_ras'] = reg_affine
trk_header['dim'] = [r_dx, r_dy, r_dz]
trk_header['voxel_size'] = [r_vx, r_vy, r_vz]
affine = np.dot(affine, np.diag(1. / np.array([vx, vy, vz, 1])))
transformed_streamlines = transform_to_affine(
streamlines, trk_header, affine)
aff = affine_from_fsl_mat_file(xfm, [vx, vy, vz],
[r_vx, r_vy, r_vz])
iflogger.info(aff)
axcode = aff2axcodes(reg_affine)
trk_header['voxel_order'] = axcode[0] + axcode[1] + axcode[2]
final_streamlines = move_streamlines(transformed_streamlines, aff)
trk_tracks = ((ii, None, None) for ii in final_streamlines)
trk.write(out_filename, trk_tracks, trk_header)
iflogger.info('Saving transformed Trackvis file as {out}'.format(
out=out_filename))
iflogger.info('New TrackVis Header:')
iflogger.info(trk_header)
else:
iflogger.info(
'Applying transformation from scanner coordinates to {img}'.
format(img=self.inputs.image_file))
axcode = aff2axcodes(affine)
trk_header['voxel_order'] = axcode[0] + axcode[1] + axcode[2]
trk_header['vox_to_ras'] = affine
transformed_streamlines = transform_to_affine(
streamlines, trk_header, affine)
trk_tracks = ((ii, None, None) for ii in transformed_streamlines)
trk.write(out_filename, trk_tracks, trk_header)
iflogger.info(
'Saving Trackvis file as {out}'.format(out=out_filename))
iflogger.info('TrackVis Header:')
iflogger.info(trk_header)
return runtime
def _run_interface(self, runtime):
key = 'congraph'
edge_key = 'weight'
iflogger.info(
'T-value Threshold file: {t}'.format(t=self.inputs.t_value_threshold_file))
iflogger.info(
'Independent component to use: {i}'.format(i=self.inputs.component_index))
path, name, ext = split_filename(self.inputs.t_value_threshold_file)
if ext == '.mat':
t_value_dict = sio.loadmat(self.inputs.t_value_threshold_file)
t_values = t_value_dict['t_value_per_node']
t_value_per_node = t_values[:, self.inputs.component_index - 1]
number_of_ICs = np.shape(t_values)[1]
else:
iflogger.info(
"Please save the t-values as a Matlab file with key 't_value_per_node'")
functional = nb.load(self.inputs.in_file)
functionaldata = functional.get_data()
segmentation = nb.load(self.inputs.segmentation_file)
segmentationdata = segmentation.get_data()
rois = get_roi_list(self.inputs.segmentation_file)
number_of_nodes = len(rois)
iflogger.info(
'Found {roi} unique region values'.format(roi=number_of_nodes))
iflogger.info('Significance threshold: {p}'.format(
p=self.inputs.significance_threshold))
#number_of_images = self.inputs.number_of_images
# degrees_of_freedom = number_of_images - \
# number_of_ICs - 1
#sig = self.inputs.significance_threshold
#threshold = tinv((1-sig), degrees_of_freedom)
#threshold = 2*threshold
#iflogger.info('Weight threshold: {w}'.format(w=threshold))
iflogger.info(
'Functional image: {img}'.format(img=self.inputs.in_file))
iflogger.info(
'Segmentation image: {img}'.format(img=self.inputs.segmentation_file))
if not get_data_dims(self.inputs.in_file) == get_data_dims(self.inputs.segmentation_file):
iflogger.error(
'Image dimensions are not the same, please reslice the images to the same dimensions')
dx, dy, dz = get_data_dims(self.inputs.in_file)
iflogger.error('Functional image dimensions: {dimx}, {dimy}, {dimz}'.format(
dimx=dx, dimy=dy, dimz=dz))
dx, dy, dz = get_data_dims(self.inputs.segmentation_file)
iflogger.error('Segmentation image dimensions: {dimx}, {dimy}, {dimz}'.format(
dimx=dx, dimy=dy, dimz=dz))
stats = {}
if self.inputs.give_nodes_values:
func_mean = []
for idx, roi in enumerate(rois):
values = []
x, y, z = np.where(segmentationdata == roi)
for index in range(0, len(x)):
value = functionaldata[x[index]][y[index]][z[index]]
values.append(value)
func_mean.append(np.mean(values))
iflogger.info(
'Region ID: {id}, Mean Value: {avg}'.format(id=roi, avg=np.mean(values)))
stats[key] = func_mean
connectivity_matrix = np.zeros((number_of_nodes, number_of_nodes))
correlation_matrix = np.zeros((number_of_nodes, number_of_nodes))
anticorrelation_matrix = np.zeros((number_of_nodes, number_of_nodes))
iflogger.info('Drawing edges...')
for idx_i, roi_i in enumerate(rois):
t_i = t_value_per_node[idx_i]
#iflogger.info('ROI:{i}, T-value: {t}'.format(i=roi_i, t=t_i))
for idx_j, roi_j in enumerate(rois):
t_j = t_value_per_node[idx_j]
#iflogger.info('...ROI:{j}, T-value: {t}'.format(j=roi_j, t=t_j))
if idx_j > idx_i:
if (t_i > 0 and t_j > 0) or (t_i < 0 and t_j < 0):
weight = abs(t_i) + abs(t_j) - abs(t_i - t_j)
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
# if weight > threshold:
connectivity_matrix[idx_i, idx_j] = weight
correlation_matrix[idx_i, idx_j] = weight
#iflogger.info('Drawing a correlation edge for ROIs {i}-{j} at {id_i},{id_j}'.format(i=roi_i, j=roi_j, id_i=idx_i, id_j=idx_j))
elif (t_i < 0 and t_j > 0) or (t_i > 0 and t_j < 0):
weight = abs(t_i) + abs(t_j) - abs(t_i + t_j)
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
# if weight > threshold:
connectivity_matrix[idx_i, idx_j] = -weight
anticorrelation_matrix[idx_i, idx_j] = weight
#iflogger.info('Drawing an anticorrelation edge for ROIs {i}-{j} at {id_i},{id_j}'.format(i=roi_i, j=roi_j, id_i=idx_i, id_j=idx_j))
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
edges = len(np.nonzero(connectivity_matrix)[0])
cor_edges = len(np.nonzero(correlation_matrix)[0])
anticor_edges = len(np.nonzero(anticorrelation_matrix)[0])
iflogger.info('Total edges: {e}'.format(e=edges))
iflogger.info('Total correlation edges: {c}'.format(c=cor_edges))
iflogger.info(
'Total anticorrelation edges: {a}'.format(a=anticor_edges))
connectivity_matrix = connectivity_matrix + connectivity_matrix.T
correlation_matrix = correlation_matrix + correlation_matrix.T
anticorrelation_matrix = anticorrelation_matrix + \
anticorrelation_matrix.T
stats[edge_key] = connectivity_matrix
stats['correlation'] = correlation_matrix
stats['anticorrelation'] = anticorrelation_matrix
try:
gp = nx.read_gpickle(self.inputs.resolution_network_file)
except IndexError:
gp = nx.read_graphml(self.inputs.resolution_network_file)
nodedict = gp.node[gp.nodes()[0]]
if not nodedict.has_key('dn_position'):
iflogger.info("Creating node positions from segmentation")
G = nx.Graph()
for u, d in gp.nodes_iter(data=True):
G.add_node(int(u), d)
xyz = tuple(
np.mean(np.where(np.flipud(segmentationdata) == int(d["dn_correspondence_id"])), axis=1))
G.node[int(u)]['dn_position'] = xyz
ntwkname = op.abspath('nodepositions.pck')
nx.write_gpickle(G, ntwkname)
else:
ntwkname = self.inputs.resolution_network_file
try:
ntwkname = nx.read_gpickle(ntwkname)
except IndexError:
ntwkname = nx.read_graphml(ntwkname)
newntwk = ntwkname.copy()
newntwk = remove_all_edges(newntwk)
if self.inputs.give_nodes_values:
newntwk = add_node_data(stats[key], newntwk)
corntwk = add_node_data(stats[key], newntwk)
anticorntwk = add_node_data(stats[key], newntwk)
newntwk = add_edge_data(stats[edge_key], newntwk)
corntwk = add_edge_data(stats['correlation'], corntwk)
anticorntwk = add_edge_data(stats['anticorrelation'], anticorntwk)
else:
newntwk = add_edge_data(stats[edge_key], ntwkname)
corntwk = add_edge_data(stats['correlation'], ntwkname)
anticorntwk = add_edge_data(stats['anticorrelation'], ntwkname)
if isdefined(self.inputs.out_network_file):
path, name, ext = split_filename(self.inputs.out_network_file)
if not ext == '.pck':
ext = '.pck'
out_network_file = op.abspath(name + ext)
else:
if isdefined(self.inputs.subject_id):
out_network_file = op.abspath(
self.inputs.subject_id + '_IC_' + str(self.inputs.component_index) + '.pck')
else:
out_network_file = op.abspath(
'IC_' + str(self.inputs.component_index) + '.pck')
path, name, ext = split_filename(out_network_file)
iflogger.info(
'Saving output network as {ntwk}'.format(ntwk=out_network_file))
nx.write_gpickle(newntwk, out_network_file)
out_correlation_network = op.abspath(name + '_correlation' + ext)
iflogger.info(
'Saving correlation network as {ntwk}'.format(ntwk=out_correlation_network))
nx.write_gpickle(corntwk, out_correlation_network)
out_anticorrelation_network = op.abspath(
name + '_anticorrelation' + ext)
iflogger.info('Saving anticorrelation network as {ntwk}'.format(
ntwk=out_anticorrelation_network))
nx.write_gpickle(anticorntwk, out_anticorrelation_network)
if isdefined(self.inputs.subject_id):
stats['subject_id'] = self.inputs.subject_id
if isdefined(self.inputs.out_stats_file):
path, name, ext = split_filename(self.inputs.out_stats_file)
if not ext == '.mat':
ext = '.mat'
out_stats_file = op.abspath(name + ext)
else:
if isdefined(self.inputs.subject_id):
out_stats_file = op.abspath(
self.inputs.subject_id + '_IC_' + str(self.inputs.component_index) + '.mat')
else:
out_stats_file = op.abspath(
'IC_' + str(self.inputs.component_index) + '.mat')
iflogger.info(
'Saving image statistics as {stats}'.format(stats=out_stats_file))
sio.savemat(out_stats_file, stats)
return runtime
def _run_interface(self, runtime):
key = 'congraph'
edge_key = 'weight'
iflogger.info('T-value Threshold file: {t}'.format(
t=self.inputs.t_value_threshold_file))
iflogger.info('Independent component to use: {i}'.format(
i=self.inputs.component_index))
path, name, ext = split_filename(self.inputs.t_value_threshold_file)
if ext == '.mat':
t_value_dict = sio.loadmat(self.inputs.t_value_threshold_file)
t_values = t_value_dict['t_value_per_node']
t_value_per_node = t_values[:, self.inputs.component_index - 1]
number_of_ICs = np.shape(t_values)[1]
else:
iflogger.info(
"Please save the t-values as a Matlab file with key 't_value_per_node'"
)
functional = nb.load(self.inputs.in_file)
functionaldata = functional.get_data()
segmentation = nb.load(self.inputs.segmentation_file)
segmentationdata = segmentation.get_data()
rois = get_roi_list(self.inputs.segmentation_file)
number_of_nodes = len(rois)
iflogger.info(
'Found {roi} unique region values'.format(roi=number_of_nodes))
iflogger.info('Significance threshold: {p}'.format(
p=self.inputs.significance_threshold))
#number_of_images = self.inputs.number_of_images
# degrees_of_freedom = number_of_images - \
# number_of_ICs - 1
#sig = self.inputs.significance_threshold
#threshold = tinv((1-sig), degrees_of_freedom)
#threshold = 2*threshold
#iflogger.info('Weight threshold: {w}'.format(w=threshold))
iflogger.info(
'Functional image: {img}'.format(img=self.inputs.in_file))
iflogger.info('Segmentation image: {img}'.format(
img=self.inputs.segmentation_file))
if not get_data_dims(self.inputs.in_file) == get_data_dims(
self.inputs.segmentation_file):
iflogger.error(
'Image dimensions are not the same, please reslice the images to the same dimensions'
)
dx, dy, dz = get_data_dims(self.inputs.in_file)
iflogger.error(
'Functional image dimensions: {dimx}, {dimy}, {dimz}'.format(
dimx=dx, dimy=dy, dimz=dz))
dx, dy, dz = get_data_dims(self.inputs.segmentation_file)
iflogger.error(
'Segmentation image dimensions: {dimx}, {dimy}, {dimz}'.format(
dimx=dx, dimy=dy, dimz=dz))
stats = {}
if self.inputs.give_nodes_values:
func_mean = []
for idx, roi in enumerate(rois):
values = []
x, y, z = np.where(segmentationdata == roi)
for index in range(0, len(x)):
value = functionaldata[x[index]][y[index]][z[index]]
values.append(value)
func_mean.append(np.mean(values))
iflogger.info('Region ID: {id}, Mean Value: {avg}'.format(
id=roi, avg=np.mean(values)))
stats[key] = func_mean
connectivity_matrix = np.zeros((number_of_nodes, number_of_nodes))
correlation_matrix = np.zeros((number_of_nodes, number_of_nodes))
anticorrelation_matrix = np.zeros((number_of_nodes, number_of_nodes))
iflogger.info('Drawing edges...')
for idx_i, roi_i in enumerate(rois):
t_i = t_value_per_node[idx_i]
#iflogger.info('ROI:{i}, T-value: {t}'.format(i=roi_i, t=t_i))
for idx_j, roi_j in enumerate(rois):
t_j = t_value_per_node[idx_j]
#iflogger.info('...ROI:{j}, T-value: {t}'.format(j=roi_j, t=t_j))
if idx_j > idx_i:
if (t_i > 0 and t_j > 0) or (t_i < 0 and t_j < 0):
weight = abs(t_i) + abs(t_j) - abs(t_i - t_j)
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
# if weight > threshold:
connectivity_matrix[idx_i, idx_j] = weight
correlation_matrix[idx_i, idx_j] = weight
#iflogger.info('Drawing a correlation edge for ROIs {i}-{j} at {id_i},{id_j}'.format(i=roi_i, j=roi_j, id_i=idx_i, id_j=idx_j))
elif (t_i < 0 and t_j > 0) or (t_i > 0 and t_j < 0):
weight = abs(t_i) + abs(t_j) - abs(t_i + t_j)
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
# if weight > threshold:
connectivity_matrix[idx_i, idx_j] = -weight
anticorrelation_matrix[idx_i, idx_j] = weight
#iflogger.info('Drawing an anticorrelation edge for ROIs {i}-{j} at {id_i},{id_j}'.format(i=roi_i, j=roi_j, id_i=idx_i, id_j=idx_j))
#iflogger.info('Weight = {w} T-values for ROIs {i}-{j}'.format(w=weight, i=t_i, j=t_j))
edges = len(np.nonzero(connectivity_matrix)[0])
cor_edges = len(np.nonzero(correlation_matrix)[0])
anticor_edges = len(np.nonzero(anticorrelation_matrix)[0])
iflogger.info('Total edges: {e}'.format(e=edges))
iflogger.info('Total correlation edges: {c}'.format(c=cor_edges))
iflogger.info(
'Total anticorrelation edges: {a}'.format(a=anticor_edges))
connectivity_matrix = connectivity_matrix + connectivity_matrix.T
correlation_matrix = correlation_matrix + correlation_matrix.T
anticorrelation_matrix = anticorrelation_matrix + \
anticorrelation_matrix.T
stats[edge_key] = connectivity_matrix
stats['correlation'] = correlation_matrix
stats['anticorrelation'] = anticorrelation_matrix
try:
gp = nx.read_gpickle(self.inputs.resolution_network_file)
except IndexError:
gp = nx.read_graphml(self.inputs.resolution_network_file)
nodedict = gp.node[gp.nodes()[0]]
if not nodedict.has_key('dn_position'):
iflogger.info("Creating node positions from segmentation")
G = nx.Graph()
for u, d in gp.nodes_iter(data=True):
G.add_node(int(u), d)
xyz = tuple(
np.mean(np.where(
np.flipud(segmentationdata) == int(
d["dn_correspondence_id"])),
axis=1))
G.node[int(u)]['dn_position'] = xyz
ntwkname = op.abspath('nodepositions.pck')
nx.write_gpickle(G, ntwkname)
else:
ntwkname = self.inputs.resolution_network_file
try:
ntwkname = nx.read_gpickle(ntwkname)
except IndexError:
ntwkname = nx.read_graphml(ntwkname)
newntwk = ntwkname.copy()
newntwk = remove_all_edges(newntwk)
if self.inputs.give_nodes_values:
newntwk = add_node_data(stats[key], newntwk)
corntwk = add_node_data(stats[key], newntwk)
anticorntwk = add_node_data(stats[key], newntwk)
newntwk = add_edge_data(stats[edge_key], newntwk)
corntwk = add_edge_data(stats['correlation'], corntwk)
anticorntwk = add_edge_data(stats['anticorrelation'], anticorntwk)
else:
newntwk = add_edge_data(stats[edge_key], ntwkname)
corntwk = add_edge_data(stats['correlation'], ntwkname)
anticorntwk = add_edge_data(stats['anticorrelation'], ntwkname)
if isdefined(self.inputs.out_network_file):
path, name, ext = split_filename(self.inputs.out_network_file)
if not ext == '.pck':
ext = '.pck'
out_network_file = op.abspath(name + ext)
else:
if isdefined(self.inputs.subject_id):
out_network_file = op.abspath(
self.inputs.subject_id + '_IC_' +
str(self.inputs.component_index) + '.pck')
else:
out_network_file = op.abspath(
'IC_' + str(self.inputs.component_index) + '.pck')
path, name, ext = split_filename(out_network_file)
iflogger.info(
'Saving output network as {ntwk}'.format(ntwk=out_network_file))
nx.write_gpickle(newntwk, out_network_file)
out_correlation_network = op.abspath(name + '_correlation' + ext)
iflogger.info('Saving correlation network as {ntwk}'.format(
ntwk=out_correlation_network))
nx.write_gpickle(corntwk, out_correlation_network)
out_anticorrelation_network = op.abspath(name + '_anticorrelation' +
ext)
iflogger.info('Saving anticorrelation network as {ntwk}'.format(
ntwk=out_anticorrelation_network))
nx.write_gpickle(anticorntwk, out_anticorrelation_network)
if isdefined(self.inputs.subject_id):
stats['subject_id'] = self.inputs.subject_id
if isdefined(self.inputs.out_stats_file):
path, name, ext = split_filename(self.inputs.out_stats_file)
if not ext == '.mat':
ext = '.mat'
out_stats_file = op.abspath(name + ext)
else:
if isdefined(self.inputs.subject_id):
out_stats_file = op.abspath(self.inputs.subject_id + '_IC_' +
str(self.inputs.component_index) +
'.mat')
else:
out_stats_file = op.abspath('IC_' +
str(self.inputs.component_index) +
'.mat')
iflogger.info(
'Saving image statistics as {stats}'.format(stats=out_stats_file))
sio.savemat(out_stats_file, stats)
return runtime
