def structural_plotting(conn_matrix_symm, label_names, atlas_select, ID, bedpostx_dir, network, parc, roi, coords,
dir_path, conn_model, thr, node_size, smooth):
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import seaborn as sns
import pkg_resources
from matplotlib import colors
from nilearn import plotting as niplot
from pynets.plotting import plot_gen, plot_graphs
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
edge_threshold = 0.10
connectome_fdt_thresh = 90
dpi_resolution = 500
bpx_trx = False
# # Auto-set INPUTS# #
try:
FSLDIR = os.environ['FSLDIR']
except NameError:
print('FSLDIR environment variable not set!')
nodif_brain_mask_path = "%s%s" % (bedpostx_dir, '/nodif_brain_mask.nii.gz')
if parc is True:
node_size = 'parc'
if network:
probtrackx_output_dir_path = "%s%s%s%s%s%s" % (dir_path, '/probtrackx_', str(node_size), '%s' % ("mm_" if node_size != 'parc' else "_"), "%s" % ("%s%s" % (smooth, 'fwhm_') if float(smooth) > 0 else 'nosm_'), network)
else:
probtrackx_output_dir_path = "%s%s%s%s%s" % (dir_path, '/probtrackx_WB_', str(node_size), '%s' % ("mm_" if node_size != 'parc' else "_"), "%s" % ("%s%s" % (smooth, 'fwhm') if float(smooth) > 0 else 'nosm'))
# # Auto-set INPUTS# #
# G_pre=nx.from_numpy_matrix(conn_matrix_symm)
# if pruning is True:
# [G, pruned_nodes] = 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 adj. matrix based on determined inputs
plot_graphs.plot_conn_mat_struct(conn_matrix_symm, conn_model, atlas_select, dir_path, ID, network, label_names, roi,
thr, node_size, smooth)
if bpx_trx is True:
# Prepare glass brain figure
fdt_paths_loc = "%s%s" % (probtrackx_output_dir_path, '/fdt_paths.nii.gz')
# Create transform matrix between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'), name='coregister')
flirt.inputs.reference = "%s%s" % (FSLDIR, '/data/standard/MNI152_T1_1mm_brain.nii.gz')
flirt.inputs.in_file = nodif_brain_mask_path
flirt.inputs.out_matrix_file = "%s%s" % (bedpostx_dir, '/xfms/diff2MNI.mat')
flirt.inputs.out_file = '/tmp/out_flirt.nii.gz'
flirt.run()
# Apply transform between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'), name='coregister')
flirt.inputs.reference = "%s%s" % (FSLDIR, '/data/standard/MNI152_T1_1mm_brain.nii.gz')
flirt.inputs.in_file = nodif_brain_mask_path
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = "%s%s" % (bedpostx_dir, '/xfms/diff2MNI.mat')
flirt.inputs.out_file = "%s%s" % (bedpostx_dir, '/xfms/diff2MNI_affine.nii.gz')
flirt.inputs.out_matrix_file = '/tmp/out_flirt.mat'
flirt.run()
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'), name='coregister')
flirt.inputs.reference = "%s%s" % (FSLDIR, '/data/standard/MNI152_T1_1mm_brain.nii.gz')
flirt.inputs.in_file = fdt_paths_loc
out_file_MNI = "%s%s" % (fdt_paths_loc.split('.nii')[0], '_MNI.nii.gz')
flirt.inputs.out_file = out_file_MNI
flirt.inputs.out_matrix_file = '/tmp/out_flirt.mat'
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = "%s%s" % (bedpostx_dir, '/xfms/diff2MNI.mat')
flirt.run()
fdt_paths_MNI_loc = "%s%s" % (probtrackx_output_dir_path, '/fdt_paths_MNI.nii.gz')
else:
fdt_paths_MNI_loc = None
colors.Normalize(vmin=-1, vmax=1)
clust_pal = sns.color_palette("Blues_r", 4)
clust_colors = colors.to_rgba_array(clust_pal)
# Plotting with glass brain
ch2better_loc = pkg_resources.resource_filename("pynets", "templates/ch2better.nii.gz")
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)), [(0, 0, 0)], node_size=0.0001, black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.5, cmap=plt.cm.gray)
[z_min, z_max] = -np.abs(conn_matrix_symm).max(), np.abs(conn_matrix_symm).max()
connectome.add_graph(conn_matrix_symm, coords, edge_threshold=edge_threshold, node_color=clust_colors,
edge_cmap=plt.cm.binary, edge_vmax=z_max, edge_vmin=z_min, node_size=4)
if bpx_trx is True and fdt_paths_MNI_loc:
connectome.add_overlay(img=fdt_paths_MNI_loc, threshold=connectome_fdt_thresh, cmap=niplot.cm.cyan_copper_r,
alpha=0.6)
# Plot connectome
if roi:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/', ID, '_', str(atlas_select), '_', str(conn_model), '_', str(op.basename(roi).split('.')[0]), "%s" % ("%s%s%s" % ('_', network, '_') if network else "_"), str(thr), '_', str(node_size), '%s' % ("mm_" if node_size != 'parc' else "_"), "%s" % ("%s%s" % (smooth, 'fwhm_') if float(smooth) > 0 else 'nosm_'), 'struct_glass_viz.png')
coord_path = "%s%s%s%s" % (dir_path, '/struct_coords_', op.basename(roi).split('.')[0], '_plotting.pkl')
labels_path = "%s%s%s%s" % (dir_path, '/struct_labelnames_', op.basename(roi).split('.')[0], '_plotting.pkl')
else:
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (dir_path, '/', ID, '_', str(atlas_select), '_', str(conn_model), "%s" % ("%s%s%s" % ('_', network, '_') if network else "_"), str(thr), '_', str(node_size), '%s' % ("mm_" if node_size != 'parc' else "_"), "%s" % ("%s%s" % (smooth, 'fwhm_') if float(smooth) > 0 else 'nosm_'), 'struct_glass_viz.png')
coord_path = "%s%s" % (dir_path, '/struct_coords_plotting.pkl')
labels_path = "%s%s" % (dir_path, '/struct_labelnames_plotting.pkl')
# Save coords to pickle
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
with open(labels_path, 'wb') as f:
pickle.dump(label_names, f, protocol=2)
connectome.savefig(out_path_fig, dpi=dpi_resolution)
# connectome.savefig(out_path_fig, dpi=dpi_resolution, facecolor ='k', edgecolor ='k')
connectome.close()
return
def plot_all_struct(conn_matrix, conn_model, atlas, dir_path, ID, network, labels, roi, coords, thr,
node_size, edge_threshold, prune, uatlas, target_samples, norm, binary, track_type, directget,
max_length):
"""
Plot adjacency matrix, connectogram, and glass brain for functional connectome.
Parameters
----------
conn_matrix : array
NxN matrix.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
coords : list
List of (x, y, z) tuples corresponding to an a-priori defined set (e.g. a coordinate atlas).
thr : float
A value, between 0 and 1, to threshold the graph using any variety of methods
triggered through other options.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
edge_threshold : float
The actual value, between 0 and 1, that the graph was thresholded (can differ from thr if target was not
successfully obtained.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
target_samples : int
Total number of streamline samples specified to generate streams.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
max_length : int
Maximum fiber length threshold in mm to restrict tracking.
"""
import matplotlib
matplotlib.use('agg')
import os
import os.path as op
from matplotlib import pyplot as plt
from nilearn import plotting as niplot
import pkg_resources
import networkx as nx
from matplotlib import colors
import seaborn as sns
from pynets.core import thresholding
from pynets.plotting import plot_gen, plot_graphs
from pynets.stats.netstats import most_important, prune_disconnected
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
ch2better_loc = pkg_resources.resource_filename("pynets", "templates/ch2better.nii.gz")
coords = list(coords)
labels = list(labels)
if len(coords) > 0:
dpi_resolution = 500
if '\'b' in atlas:
atlas = atlas.decode('utf-8')
if (prune == 1 or prune == 2) and len(coords) == conn_matrix.shape[0]:
G_pre = nx.from_numpy_matrix(np.abs(conn_matrix))
if prune == 1:
[G, pruned_nodes] = prune_disconnected(G_pre)
elif prune == 2:
[G, pruned_nodes] = most_important(G_pre)
else:
G = G_pre
pruned_nodes = []
pruned_nodes.sort(reverse=True)
coords_pre = list(coords)
labels_pre = list(labels)
if len(pruned_nodes) > 0:
for j in pruned_nodes:
labels_pre.pop(j)
coords_pre.pop(j)
conn_matrix = nx.to_numpy_array(G)
labels = labels_pre
coords = coords_pre
else:
print('No nodes to prune for plot...')
coords = list(tuple(x) for x in coords)
namer_dir = dir_path + '/figures'
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
# Plot connectogram
if len(conn_matrix) > 20:
try:
plot_gen.plot_connectogram(conn_matrix, conn_model, atlas, namer_dir, ID, network, labels)
except RuntimeWarning:
print('\n\n\nWarning: Connectogram plotting failed!')
else:
print('Warning: Cannot plot connectogram for graphs smaller than 20 x 20!')
# Plot adj. matrix based on determined inputs
if not node_size or node_size == 'None':
node_size = 'parc'
plot_graphs.plot_conn_mat_struct(conn_matrix, conn_model, atlas, namer_dir, ID, network, labels, roi, thr,
node_size, target_samples, track_type, directget, max_length)
# Plot connectome
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (namer_dir, '/', ID, '_modality-dwi_',
'%s' % ("%s%s%s" % ('rsn-', network, '_') if
network is not None else ''),
'%s' % ("%s%s%s" % ('roi-',
op.basename(roi).split(
'.')[0],
'_') if roi is not
None else ''),
'est-', conn_model, '_',
'%s' % (
"%s%s%s" % ('nodetype-spheres-', node_size,
'mm_')
if ((node_size != 'parc') and
(node_size is not None))
else 'nodetype-parc_'),
"%s" % ("%s%s%s" % (
'samples-', int(target_samples),
'streams_')
if float(target_samples) > 0 else '_'),
'tt-', track_type, '_dg-', directget,
'_ml-', max_length,
'_thr-', thr, '_glass_viz.png')
if roi:
# Save coords to pickle
coord_path = "%s%s%s%s" % (namer_dir, '/coords_', op.basename(roi).split('.')[0], '_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s%s%s" % (namer_dir, '/labelnames_', op.basename(roi).split('.')[0], '_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
else:
# Save coords to pickle
coord_path = "%s%s" % (namer_dir, '/coords_plotting.pkl')
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = "%s%s" % (namer_dir, '/labelnames_plotting.pkl')
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)), [(0, 0, 0)], node_size=0.0001, black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
#connectome.add_overlay(ch2better_loc, alpha=0.35, cmap=plt.cm.gray)
conn_matrix = np.array(np.array(thresholding.autofix(conn_matrix)))
[z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
if node_size == 'parc':
node_size_plot = int(6)
else:
node_size_plot = int(node_size)
if len(coords) != conn_matrix.shape[0]:
raise RuntimeWarning('\nWARNING: Number of coordinates does not match conn_matrix dimensions.')
else:
norm = colors.Normalize(vmin=-1, vmax=1)
clust_pal = sns.color_palette("Blues_r", conn_matrix.shape[0])
clust_colors = colors.to_rgba_array(clust_pal)
fa_path = dir_path + '/../reg_dmri/dmri_tmp/DSN/Warped.nii.gz'
if os.path.isfile(fa_path):
connectome.add_overlay(img=fa_path,
threshold=0.01, alpha=0.25, cmap=plt.cm.copper)
connectome.add_graph(conn_matrix, coords, edge_threshold=edge_threshold, edge_cmap=plt.cm.binary,
edge_vmax=float(z_max), edge_vmin=float(z_min), node_size=node_size_plot,
node_color=clust_colors)
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
raise RuntimeError('\nERROR: no coordinates to plot! Are you running plotting outside of pynets\'s internal '
'estimation schemes?')
plt.close('all')
return
def plot_all_struct(conn_matrix, conn_model, atlas, dir_path, ID, network,
labels, roi, coords, thr, node_size, edge_threshold, prune,
uatlas, target_samples, norm, binary, track_type,
directget, min_length):
"""
Plot adjacency matrix, connectogram, and glass brain for functional connectome.
Parameters
----------
conn_matrix : array
NxN matrix.
conn_model : str
Connectivity estimation model (e.g. corr for correlation, cov for covariance, sps for precision covariance,
partcorr for partial correlation). sps type is used by default.
atlas : str
Name of atlas parcellation used.
dir_path : str
Path to directory containing subject derivative data for given run.
ID : str
A subject id or other unique identifier.
network : str
Resting-state network based on Yeo-7 and Yeo-17 naming (e.g. 'Default') used to filter nodes in the study of
brain subgraphs.
labels : list
List of string labels corresponding to ROI nodes.
roi : str
File path to binarized/boolean region-of-interest Nifti1Image file.
coords : list
List of (x, y, z) tuples corresponding to an a-priori defined set (e.g. a coordinate atlas).
thr : float
A value, between 0 and 1, to threshold the graph using any variety of methods
triggered through other options.
node_size : int
Spherical centroid node size in the case that coordinate-based centroids
are used as ROI's.
edge_threshold : float
The actual value, between 0 and 1, that the graph was thresholded (can differ from thr if target was not
successfully obtained.
prune : bool
Indicates whether to prune final graph of disconnected nodes/isolates.
uatlas : str
File path to atlas parcellation Nifti1Image in MNI template space.
target_samples : int
Total number of streamline samples specified to generate streams.
norm : int
Indicates method of normalizing resulting graph.
binary : bool
Indicates whether to binarize resulting graph edges to form an
unweighted graph.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
"""
import matplotlib
matplotlib.use('agg')
import os
import yaml
import sys
import os.path as op
from matplotlib import pyplot as plt
from nilearn import plotting as niplot
import pkg_resources
import networkx as nx
from pynets.plotting import plot_gen, plot_graphs
from pynets.plotting.plot_gen import create_gb_palette
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
ch2better_loc = pkg_resources.resource_filename(
"pynets", "templates/ch2better.nii.gz")
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
'r') as stream:
hardcoded_params = yaml.load(stream)
try:
color_theme = hardcoded_params['plotting']['structural'][
'glassbrain']['color_theme'][0]
connectogram = hardcoded_params['plotting']['connectogram'][0]
glassbrain = hardcoded_params['plotting']['glassbrain'][0]
adjacency = hardcoded_params['plotting']['adjacency'][0]
dpi_resolution = hardcoded_params['plotting']['dpi'][0]
except KeyError:
print(
'ERROR: Plotting configuration not successfully extracted from runconfig.yaml'
)
sys.exit(0)
stream.close()
if not isinstance(coords, list):
coords = list(tuple(x) for x in coords)
if not isinstance(labels, list):
labels = list(labels)
if len(coords) > 0:
if '\'b' in atlas:
atlas = atlas.decode('utf-8')
namer_dir = dir_path + '/figures'
if not os.path.isdir(namer_dir):
os.makedirs(namer_dir, exist_ok=True)
# Plot connectogram
if connectogram is True:
if len(conn_matrix) > 20:
try:
plot_gen.plot_connectogram(conn_matrix, conn_model, atlas,
namer_dir, ID, network, labels)
except RuntimeWarning:
print('\n\n\nWarning: Connectogram plotting failed!')
else:
print(
'Warning: Cannot plot connectogram for graphs smaller than 20 x 20!'
)
# Plot adj. matrix based on determined inputs
if not node_size or node_size == 'None':
node_size = 'parc'
if adjacency is True:
plot_graphs.plot_conn_mat_struct(conn_matrix, conn_model, atlas,
namer_dir, ID, network, labels,
roi, thr, node_size,
target_samples, track_type,
directget, min_length)
if glassbrain is True:
views = ['x', 'y', 'z']
# Plot connectome
out_path_fig = "%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s%s" % (
namer_dir, '/', ID, '_modality-dwi_', '%s' %
("%s%s%s" %
('rsn-', network, '_') if network is not None else ''), '%s' %
("%s%s%s" % ('roi-', op.basename(roi).split('.')[0], '_')
if roi is not None else ''), 'est-', conn_model, '_', '%s' %
("%s%s%s" % ('nodetype-spheres-', node_size, 'mm_') if
((node_size != 'parc') and
(node_size is not None)) else 'nodetype-parc_'), "%s" %
("%s%s%s" % ('samples-', int(target_samples), 'streams_')
if float(target_samples) > 0 else '_'), 'tt-', track_type,
'_dg-', directget, '_ml-', min_length, '_thr-', thr,
'_glass_viz.png')
connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)),
[(0, 0, 0)],
node_size=0.0001,
black_bg=True)
connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
[
conn_matrix, clust_pal_edges, clust_pal_nodes, node_sizes,
edge_sizes, _, _, coords, labels
] = create_gb_palette(conn_matrix, color_theme, coords, labels)
if roi:
# Save coords to pickle
coord_path = f"{namer_dir}{'/coords_'}{op.basename(roi).split('.')[0]}{'_plotting.pkl'}"
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = f"{namer_dir}{'/labelnames_'}{op.basename(roi).split('.')[0]}{'_plotting.pkl'}"
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
else:
# Save coords to pickle
coord_path = f"{namer_dir}{'/coords_plotting.pkl'}"
with open(coord_path, 'wb') as f:
pickle.dump(coords, f, protocol=2)
# Save labels to pickle
labels_path = f"{namer_dir}{'/labelnames_plotting.pkl'}"
with open(labels_path, 'wb') as f:
pickle.dump(labels, f, protocol=2)
connectome.add_graph(conn_matrix,
coords,
edge_cmap=clust_pal_edges,
edge_vmax=float(1),
edge_vmin=float(1),
node_size=node_sizes,
node_color=clust_pal_nodes,
edge_kwargs={
'alpha': 0.50,
"lineStyle": 'dashed'
})
for view in views:
mod_lines = []
for line, edge_size in list(
zip(connectome.axes[view].ax.lines, edge_sizes)):
line.set_lw(edge_size)
mod_lines.append(line)
connectome.axes[view].ax.lines = mod_lines
connectome.savefig(out_path_fig, dpi=dpi_resolution)
else:
raise RuntimeError(
'\nERROR: no coordinates to plot! Are you running plotting outside of pynets\'s '
'internal estimation schemes?')
plt.close('all')
return