def plot_all(conn_matrix, conn_model, atlas_select, dir_path, ID, network, label_names, mask, coords, edge_threshold, plot_switch):
from nilearn import plotting as niplot
pruning=True
dpi_resolution=1000
if plot_switch == True:
import pkg_resources
import networkx as nx
from pynets import plotting
import matplotlib.pyplot as plt
from pynets.netstats import most_important
G_pre=nx.from_numpy_matrix(conn_matrix)
if pruning == True:
[G, pruned_nodes, pruned_edges] = most_important(G_pre)
else:
G = G_pre
conn_matrix = nx.to_numpy_array(G)
pruned_nodes.sort(reverse = True)
for j in pruned_nodes:
del label_names[label_names.index(label_names[j])]
del coords[coords.index(coords[j])]
pruned_edges.sort(reverse = True)
for j in pruned_edges:
del label_names[label_names.index(label_names[j])]
del coords[coords.index(coords[j])]
##Plot connectogram
if len(conn_matrix) > 20:
try:
plotting.plot_connectogram(conn_matrix, conn_model, atlas_select, dir_path, ID, network, label_names)
except RuntimeError:
print('\n\n\nError: Connectogram plotting failed!')
else:
print('Error: Cannot plot connectogram for graphs smaller than 20 x 20!')
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_select, dir_path, ID, network, label_names, mask)
##Plot connectome
if mask != None:
if network != 'None':
out_path_fig=dir_path + '/' + ID + '_' + atlas_select + '_' + str(conn_model) + '_' + str(os.path.basename(mask).split('.')[0]) + '_' + str(network) + '_connectome_viz.png'
else:
out_path_fig=dir_path + '/' + ID + '_' + atlas_select + '_' + str(conn_model) + '_' + str(os.path.basename(mask).split('.')[0]) + '_connectome_viz.png'
else:
if network != 'None':
out_path_fig=dir_path + '/' + ID + '_' + atlas_select + '_' + str(conn_model) + '_' + str(network) + '_connectome_viz.png'
else:
out_path_fig=dir_path + '/' + ID + '_' + atlas_select + '_' + str(conn_model) + '_connectome_viz.png'
#niplot.plot_connectome(conn_matrix, coords, edge_threshold=edge_threshold, node_size=20, colorbar=True, output_file=out_path_fig)
ch2better_loc = pkg_resources.resource_filename("pynets", "templates/ch2better.nii.gz")
connectome = niplot.plot_connectome(np.zeros(shape=(1,1)), [(0,0,0)], black_bg=True, node_size=0.0001)
connectome.add_overlay(ch2better_loc, alpha=0.4, cmap=plt.cm.gray)
[z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
connectome.add_graph(conn_matrix, coords, edge_threshold = edge_threshold, edge_cmap = 'Blues', edge_vmax=z_max, edge_vmin=z_min, node_size=4)
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
pass
return
def plot_conn_mat_struct(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, roi, thr, node_size, smooth,
c_boot):
from pynets import plotting
if roi:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(os.path.basename(roi).split('.')[0]), '_struct_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 'struct_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(int(c_boot), 'nb_') if float(c_boot) > 0 else 'nb_'), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
plotting.plot_conn_mat(conn_matrix, label_names, out_path_fig)
return
def plot_conn_mat_struct(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, mask, thr, node_size):
from pynets import plotting
if mask:
if network:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), '_', network,
'_', str(os.path.basename(mask).split('.')[0]),
'_struct_adj_mat_', str(conn_model), '_', str(thr), '_',
str(node_size), '_network.png')
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), '_',
str(os.path.basename(mask).split('.')[0]), '_struct_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '.png')
else:
if network:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), '_', network,
'_struct_adj_mat_', str(conn_model), '_', str(thr), '_',
str(node_size), '_network.png')
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select),
'_struct_adj_mat_', str(conn_model), '_', str(thr), '_',
str(node_size), '.png')
plotting.plot_conn_mat(conn_matrix, label_names, out_path_fig)
return
def plot_conn_mat_func(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, mask, thr, node_size, smooth):
import networkx as nx
from pynets import plotting
from pynets.netstats import modularity_louvain_und_sign
if mask:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(os.path.basename(mask).split('.')[0]), '_func_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
str(os.path.basename(mask).split('.')[0]),
'_func_adj_mat_communities_', str(conn_model), '_', str(thr), '_',
str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" %
('_', network, '_') if network else "_"), 'func_adj_mat_',
str(conn_model), '_', str(thr), '_', str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
out_path_fig_comm = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
dir_path, '/', str(ID), '_', str(atlas_select), "%s" %
("%s%s%s" % ('_', network, '_') if network else "_"),
'func_adj_mat_communities_', str(conn_model), '_', str(thr), '_',
str(node_size), '%s' %
("mm_" if node_size != 'parc' else "_"), "%s" %
("%s%s" %
(smooth, 'fwhm.png') if float(smooth) > 0 else 'nosm.png'))
plotting.plot_conn_mat(conn_matrix, label_names, out_path_fig)
# Plot community adj. matrix
gamma = nx.density(nx.from_numpy_array(conn_matrix))
try:
[node_comm_aff_mat,
q] = modularity_louvain_und_sign(conn_matrix, gamma=float(gamma))
print("%s%s%s%s%s" % ('Found ', str(len(np.unique(node_comm_aff_mat))),
' communities with γ=', str(gamma), '...'))
plotting.plot_community_conn_mat(conn_matrix, label_names,
out_path_fig_comm, node_comm_aff_mat)
except:
print(
'WARNING: Louvain community detection failed. Cannot plot community matrix...'
)
return
def test_plot_conn_mat_rois_lt_100():
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/rxh180012/PyNets-development/tests/examples'
dir_path = base_dir + '/997'
conn_matrix = np.genfromtxt(
dir_path +
'/whole_brain_cluster_labels_PCA200/997_Default_est_sps_0.95.txt')
labels_file_path = dir_path + '/whole_brain_cluster_labels_PCA200/Default_func_labelnames_wb.pkl'
labels_file = open(labels_file_path, 'rb')
label_names = pickle.load(labels_file)
out_path_fig = '/tmp/'
start_time = time.time()
plotting.plot_conn_mat(conn_matrix, label_names, out_path_fig)
print("%s%s%s" % ('plot_timeseries --> finished: ',
str(np.round(time.time() - start_time, 1)), 's'))
def test_plot_conn_mat_nonet_mask():
##Set example inputs##
base_dir = str(Path(__file__).parent / "examples")
#base_dir = '/Users/PSYC-dap3463/Applications/PyNets/tests/examples'
dir_path = base_dir + '/997'
network = None
ID = '997'
thr = 0.95
node_size = 2
conn_model = 'sps'
atlas_select = 'whole_brain_cluster_labels_PCA200'
mask = None
conn_matrix = np.genfromtxt(
dir_path + '/whole_brain_cluster_labels_PCA200/997_est_sps_0.94.txt')
labels_file_path = dir_path + '/whole_brain_cluster_labels_PCA200/WB_func_labelnames_wb.pkl'
labels_file = open(labels_file_path, 'rb')
label_names = pickle.load(labels_file)
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_select, dir_path, ID,
network, label_names, mask, thr, node_size)
def wb_connectome_with_us_atlas_coords(input_file, ID, atlas_select, NETWORK,
node_size, mask, thr, parlistfile,
all_nets, conn_model, dens_thresh, conf,
adapt_thresh, plot_switch,
bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Test if atlas_select is a nilearn atlas
if atlas_select in nilearn_atlases:
try:
parlistfile = getattr(datasets, 'fetch_%s' % atlas_select)().maps
try:
label_names = getattr(datasets,
'fetch_%s' % atlas_select)().labels
except:
label_names = None
try:
networks_list = getattr(datasets,
'fetch_%s' % atlas_select)().networks
except:
networks_list = None
except:
print(
'PyNets is not ready for multi-scale atlases like BASC just yet!'
)
sys.exit()
##Fetch user-specified atlas coords
[coords, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
atlas_select = atlas_name
try:
label_names
except:
label_names = np.arange(len(coords) +
1)[np.arange(len(coords) + 1) != 0].tolist()
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary if all_nets option triggered
if all_nets != None:
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Describe user atlas coords
print('\n' + atlas_name + ' comes with {0} '.format(par_max) + 'parcels' +
'\n')
print('\n' + 'Stacked atlas coordinates in array of shape {0}.'.format(
coords.shape) + '\n')
##Mask coordinates
if mask is not None:
[coords, label_names] = nodemaker.coord_masker(mask, coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_wb_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(coords, f)
labels_path = dir_path + '/labelnames_wb_' + str(thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, coords, node_size)
##extract time series from whole brain parcellaions:
parcellation = nib.load(parlistfile)
parcel_masker = input_data.NiftiLabelsMasker(labels_img=parcellation,
background_label=0,
memory='nilearn_cache',
memory_level=5,
standardize=True)
ts_within_parcels = parcel_masker.fit_transform(func_file, confounds=conf)
print('\n' +
'Time series has {0} samples'.format(ts_within_parcels.shape[0]) +
'\n')
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_whole_brain_ts_within_parcels.txt'
np.savetxt(out_path_ts, ts_within_parcels)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_parcels,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_parcels,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_parcels,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
atlast_graph_title = plotting.plot_conn_mat(conn_matrix, conn_model,
atlas_name, dir_path, ID,
NETWORK, label_names, mask)
##Plot connectome viz for all Yeo networks
if all_nets != False:
plotting.plot_membership(membership_plotting, conn_matrix,
conn_model, coords, edge_threshold,
atlas_name, dir_path)
else:
out_path_fig = dir_path + '/' + ID + '_connectome_viz.png'
niplot.plot_connectome(conn_matrix,
coords,
title=atlast_graph_title,
edge_threshold=edge_threshold,
node_size=20,
colorbar=True,
output_file=out_path_fig)
return est_path, thr
def network_connectome(input_file, ID, atlas_select, NETWORK, node_size, mask,
thr, parlistfile, all_nets, conn_model, dens_thresh,
conf, adapt_thresh, plot_switch, bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Test if atlas_select is a nilearn atlas
if atlas_select in nilearn_atlases:
atlas = getattr(datasets, 'fetch_%s' % atlas_select)()
try:
parlistfile = atlas.maps
try:
label_names = atlas.labels
except:
label_names = None
try:
networks_list = atlas.networks
except:
networks_list = None
except RuntimeError:
print('Error, atlas fetching failed.')
sys.exit()
if parlistfile == None and atlas_select not in nilearn_atlases:
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
if atlas_name == 'Power 2011 atlas':
##Reference RSN list
import pkgutil
import io
network_coords_ref = NETWORK + '_coords.csv'
atlas_coords = pkgutil.get_data("pynets",
"rsnrefs/" + network_coords_ref)
df = pd.read_csv(io.BytesIO(atlas_coords)).ix[:, 0:4]
i = 1
net_coords = []
ix_labels = []
for i in range(len(df)):
#print("ROI Reference #: " + str(i))
x = int(df.ix[i, 1])
y = int(df.ix[i, 2])
z = int(df.ix[i, 3])
#print("X:" + str(x) + " Y:" + str(y) + " Z:" + str(z))
net_coords.append((x, y, z))
ix_labels.append(i)
i = i + 1
#print(net_coords)
label_names = ix_labels
elif atlas_name == 'Dosenbach 2010 atlas':
coords = list(tuple(x) for x in coords)
##Get coord membership dictionary
[membership, membership_plotting
] = nodemaker.get_mem_dict(func_file, coords, networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
nets_avail = list(set(list(mem_df['index'])))
##Get network name equivalents
if NETWORK == 'DMN':
NETWORK = 'default'
elif NETWORK == 'FPTC':
NETWORK = 'fronto-parietal'
elif NETWORK == 'CON':
NETWORK = 'cingulo-opercular'
elif NETWORK not in nets_avail:
print('Error: ' + NETWORK + ' not available with this atlas!')
sys.exit()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:,
0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
####Add code for any special RSN reference lists for the nilearn atlases here#####
##If labels_names are not indices and NETWORK is specified, sub-list label names
if label_names != ix_labels:
try:
label_names = label_names.tolist()
except:
pass
label_names = [label_names[i] for i in ix_labels]
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##If masking, remove those coords that fall outside of the mask
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
else:
##Fetch user-specified atlas coords
[coords_all, atlas_name,
par_max] = nodemaker.get_names_and_coords_of_parcels(parlistfile)
coords = list(tuple(x) for x in coords_all)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_name
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Convert to membership dataframe
mem_df = membership.to_frame().reset_index()
##Get coords for network-of-interest
mem_df.loc[mem_df['index'] == NETWORK]
net_coords = mem_df.loc[mem_df['index'] == NETWORK][[0]].values[:, 0]
net_coords = list(tuple(x) for x in net_coords)
ix_labels = mem_df.loc[mem_df['index'] == NETWORK].index.values
try:
label_names = [label_names[i] for i in ix_labels]
except:
label_names = ix_labels
if mask != None:
[net_coords,
label_names] = nodemaker.coord_masker(mask, net_coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_' + NETWORK + '_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(net_coords, f)
labels_path = dir_path + '/labelnames_' + NETWORK + '_' + str(
thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, net_coords, node_size)
##Generate network parcels image (through refinement, this could be used
##in place of the 3 lines above)
#net_parcels_img_path = gen_network_parcels(parlistfile, NETWORK, labels)
#parcellation = nib.load(net_parcels_img_path)
#parcel_masker = input_data.NiftiLabelsMasker(labels_img=parcellation, background_label=0, memory='nilearn_cache', memory_level=5, standardize=True)
#ts_within_parcels = parcel_masker.fit_transform(func_file)
#net_ts = ts_within_parcels
##Grow ROIs
masker = input_data.NiftiSpheresMasker(seeds=net_coords,
radius=float(node_size),
allow_overlap=True,
memory_level=5,
memory='nilearn_cache',
verbose=2,
standardize=True)
ts_within_spheres = masker.fit_transform(func_file, confounds=conf)
net_ts = ts_within_spheres
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_' + NETWORK + '_net_ts.txt'
np.savetxt(out_path_ts, net_ts)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot network time-series
plotting.plot_timeseries(net_ts, NETWORK, ID, dir_path, atlas_name,
label_names)
##Plot connectome viz for specific Yeo networks
title = "Connectivity Projected on the " + NETWORK
out_path_fig = dir_path + '/' + ID + '_' + NETWORK + '_connectome_plot.png'
niplot.plot_connectome(conn_matrix,
net_coords,
edge_threshold=edge_threshold,
title=title,
display_mode='lyrz',
output_file=out_path_fig)
return est_path, thr
def wb_connectome_with_nl_atlas_coords(input_file, ID, atlas_select, NETWORK,
node_size, mask, thr, all_nets,
conn_model, dens_thresh, conf,
adapt_thresh, plot_switch,
bedpostx_dir):
nilearn_atlases = [
'atlas_aal', 'atlas_craddock_2012', 'atlas_destrieux_2009'
]
##Input is nifti file
func_file = input_file
##Fetch nilearn atlas coords
[coords, atlas_name, networks_list,
label_names] = nodemaker.fetch_nilearn_atlas_coords(atlas_select)
##Get subject directory path
dir_path = os.path.dirname(
os.path.realpath(func_file)) + '/' + atlas_select
if not os.path.exists(dir_path):
os.makedirs(dir_path)
##Get coord membership dictionary if all_nets option triggered
if all_nets != False:
try:
networks_list
except:
networks_list = None
[membership,
membership_plotting] = nodemaker.get_mem_dict(func_file, coords,
networks_list)
##Mask coordinates
if mask is not None:
[coords, label_names] = nodemaker.coord_masker(mask, coords,
label_names)
##Save coords and label_names to pickles
coord_path = dir_path + '/coords_wb_' + str(thr) + '.pkl'
with open(coord_path, 'wb') as f:
pickle.dump(coords, f)
labels_path = dir_path + '/labelnames_wb_' + str(thr) + '.pkl'
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f)
if bedpostx_dir is not None:
from pynets.diffconnectometry import run_struct_mapping
FSLDIR = os.environ['FSLDIR']
try:
FSLDIR
except NameError:
print('FSLDIR environment variable not set!')
est_path2 = run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path,
NETWORK, coords, node_size)
##Extract within-spheres time-series from funct file
spheres_masker = input_data.NiftiSpheresMasker(seeds=coords,
radius=float(node_size),
memory='nilearn_cache',
memory_level=5,
verbose=2,
standardize=True)
ts_within_spheres = spheres_masker.fit_transform(func_file, confounds=conf)
print('\n' +
'Time series has {0} samples'.format(ts_within_spheres.shape[0]) +
'\n')
##Save time series as txt file
out_path_ts = dir_path + '/' + ID + '_whole_brain_ts_within_spheres.txt'
np.savetxt(out_path_ts, ts_within_spheres)
##Fit connectivity model
if adapt_thresh is not False:
if os.path.isfile(est_path2) == True:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.adaptive_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
est_path2, dir_path)
else:
print('No structural mx found! Exiting...')
sys.exit(0)
elif dens_thresh is None:
edge_threshold = str(float(thr) * 100) + '%'
[conn_matrix,
est_path] = graphestimation.get_conn_matrix(ts_within_spheres,
conn_model, NETWORK, ID,
dir_path, thr)
conn_matrix = thresholding.threshold_proportional(
conn_matrix, float(thr), dir_path)
conn_matrix = thresholding.normalize(conn_matrix)
elif dens_thresh is not None:
[conn_matrix, est_path, edge_threshold,
thr] = thresholding.density_thresholding(ts_within_spheres,
conn_model, NETWORK, ID,
dens_thresh, dir_path)
if plot_switch == True:
##Plot connectogram
plotting.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
##Plot adj. matrix based on determined inputs
plotting.plot_conn_mat(conn_matrix, conn_model, atlas_name, dir_path,
ID, NETWORK, label_names, mask)
##Plot connectome viz for all Yeo networks
if all_nets != False:
plotting.plot_membership(membership_plotting, conn_matrix,
conn_model, coords, edge_threshold,
atlas_name, dir_path)
else:
out_path_fig = dir_path + '/' + ID + '_' + atlas_name + '_connectome_viz.png'
niplot.plot_connectome(conn_matrix,
coords,
title=atlas_name,
edge_threshold=edge_threshold,
node_size=20,
colorbar=True,
output_file=out_path_fig)
return est_path, thr
