replacer_dict_fname = get_jumeg_path() + '/data/replacer_dictionaries.yaml'
with open(replacer_dict_fname, 'r') as f:
replacer_dict = yaml.safe_load(f)['replacer_dict_aparc']
# load the distances matrix
con = np.load(label_distances_fname)
node_order_size = con.shape[0]
# forget long range connections, plot short neighbouring connections
neighbor_range = 30. # millimetres
con[con > neighbor_range] = 0.
plot_grouped_connectivity_circle(yaml_fname, con, label_names,
labels_mode='cortex_only',
node_order_size=node_order_size,
replacer_dict=replacer_dict,
out_fname='label_com_distances_circle_%0.1f_%s.png' % (neighbor_range, parc),
colorbar_pos=(0.1, 0.1),
n_lines=None, colorbar=True,
colormap='Reds')
# compute the distances between COM's of the labels
_, coords, _, _ = get_label_distances(subject, subjects_dir, parc=parc)
# compute the degree
degs = mne.connectivity.degree(con, threshold_prop=1)
# show the label ROIs using Nilearn plotting
fig = plotting.plot_connectome(np.zeros((node_order_size, node_order_size)),
coords, node_size=20, edge_threshold='99%',
node_color='cornflowerblue',
display_mode='ortho',
Plot grouped connectivity circle with these grouped labels
for the Destriux atlas.
"""
from jumeg import get_jumeg_path
from jumeg.connectivity import (plot_grouped_connectivity_circle,
generate_random_connectivity_matrix)
import yaml
grouping_yaml_fname = get_jumeg_path(
) + '/data/destriux_aparc_cortex_based_grouping.yaml'
label_names_yaml_fname = get_jumeg_path() + '/data/destriux_label_names.yaml'
with open(label_names_yaml_fname, 'r') as f:
label_names = yaml.safe_load(f)['label_names']
# make a random matrix with 148 nodes
con = generate_random_connectivity_matrix(size=(148, 148), symmetric=True)
con[con < 0.5] = 0.
# plot grouped connnectivity
plot_grouped_connectivity_circle(grouping_yaml_fname,
con,
label_names,
labels_mode=None,
colorbar_pos=(0.1, 0.1),
replacer_dict=None,
out_fname='fig_destriux_circle.png',
n_lines=50,
colorbar=True)
'''
Read grouped aparc labels from yaml file.
Plot grouped connectivity circle with these grouped labels.
'''
import numpy as np
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_grouped_connectivity_circle
import pickle
labels_fname = get_jumeg_path() + '/examples/label_names.list'
yaml_fname = get_jumeg_path() + '/examples/aparc_cortex_based_grouping.yaml'
with open('label_names.list', 'r') as f:
label_names = pickle.load(f)
# make a random matrix with 68 nodes
# use simple seed for reproducibility
np.random.seed(42)
con = np.random.random((68, 68))
con[con < 0.5] = 0.
indices = (np.array((1, 2, 3)), np.array((5, 6, 7)))
plot_grouped_connectivity_circle(yaml_fname,
con,
label_names,
labels_mode='cortex_only',
colorbar_pos=(0.1, 0.1),
n_lines=10,
colorbar=True)
con[16, 31] = 0.4 # Auditory
con[50, 14] = con[50, 15] = con[50,
28] = con[14,
15] = con[14,
28] = con[15,
28] = 0.5 # DMN
con[22, 23] = 0.6 # Visual
con[14, 60] = 0.7 # Left FP
con[62, 63] = 0.8 # Sensorimotor
con[15, 61] = 0.9 # Right FP
con[16, 17] = 1 # Frontoinsular
con += con.T # since we only add the combinations
# plot the connectivity circle showing standard RSNs
from matplotlib.colors import ListedColormap
cmap = ListedColormap(['m', 'k', 'b', 'y', 'r', 'c', 'g'])
yaml_fname = get_jumeg_path(
) + '/data/desikan_aparc_cortex_based_grouping.yaml'
plot_grouped_connectivity_circle(yaml_fname,
con,
aparc_names,
n_lines=12,
labels_mode=None,
colormap=cmap,
colorbar=True,
replacer_dict=None,
indices=None,
out_fname='fig_rsn_circle_plot.png')
con, coords, _, _ = get_label_distances(subject, subjects_dir, parc=parc)
# forget long range connections, plot short neighbouring connections
neighbor_range = 30. # millimetres
con[con > neighbor_range] = 0.
cortex_colors = ['m', 'b', 'y', 'c', 'r', 'g', 'g', 'r', 'c', 'y', 'b', 'm']
out_fname = 'fig_label_com_distances_circle_%0.1f_%s.png' % (neighbor_range,
parc)
plot_grouped_connectivity_circle(yaml_cortex_fname,
con,
label_names,
replacer_dict=replacer_dict,
yaml_color_fname=None,
labels_mode='replace',
cortex_colors=cortex_colors,
out_fname=out_fname,
colorbar_pos=(0.1, 0.1),
n_lines=None,
colorbar=True,
colormap='Reds')
# compute the degree
degs = mne.connectivity.degree(con, threshold_prop=1)
# show the label ROIs and short range connections using nilearn glass brain
fig = plotting.plot_connectome(con,
coords,
node_size=degs,
edge_threshold='99%',
node_color='cornflowerblue',
'''
import numpy as np
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_grouped_connectivity_circle
import yaml
labels_fname = get_jumeg_path() + '/data/desikan_label_names.yaml'
yaml_fname = get_jumeg_path() + '/data/desikan_aparc_cortex_based_grouping.yaml'
replacer_dict_fname = get_jumeg_path() + '/data/replacer_dictionaries.yaml'
with open(labels_fname, 'r') as f:
label_names = yaml.safe_load(f)['label_names']
with open(replacer_dict_fname, 'r') as f:
replacer_dict = yaml.safe_load(f)['replacer_dict_aparc']
# make a random matrix with 68 nodes
# use simple seed for reproducibility
np.random.seed(42)
con = np.random.random((68, 68))
con[con < 0.5] = 0.
indices = (np.array((1, 2, 3)), np.array((5, 6, 7)))
plot_grouped_connectivity_circle(yaml_fname, con, label_names,
labels_mode='cortex_only',
replacer_dict=replacer_dict,
out_fname='example_grouped_con_circle.png',
colorbar_pos=(0.1, 0.1),
n_lines=10, colorbar=True)
with open(labels_fname, 'r') as f:
label_names = yaml.safe_load(f)['label_names']
with open(replacer_dict_fname, 'r') as f:
replacer_dict = yaml.safe_load(f)['replacer_dict_aparc']
# make a random matrix with 68 nodes
con = generate_random_connectivity_matrix(size=(68, 68), symmetric=True)
# plot simple connectivity circle with cortex based grouping and colors
plot_grouped_connectivity_circle(yaml_cortex_fname,
con,
label_names,
labels_mode='replace',
replacer_dict=replacer_dict,
out_fname='fig_grouped_con_circle_cortex.png',
colorbar_pos=(0.1, 0.1),
n_lines=10,
colorbar=True,
colormap='viridis')
# plot connectivity circle with cluster-based grouping but same node colors as above
plot_grouped_connectivity_circle(
yaml_cluster_fname,
con,
label_names,
labels_mode=None,
replacer_dict=None,
yaml_color_fname=yaml_cortex_fname,
out_fname='fig_grouped_con_circle_cluster.png',
colorbar_pos=(0.1, 0.1),
#!/usr/bin/env python
'''
Read grouped aparc labels from yaml file.
Plot grouped connectivity circle with these grouped labels.
'''
import numpy as np
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_grouped_connectivity_circle
import pickle
labels_fname = get_jumeg_path() + '/examples/label_names.list'
yaml_fname = get_jumeg_path() + '/examples/aparc_cortex_based_grouping.yaml'
with open('label_names.list', 'r') as f:
label_names = pickle.load(f)
# make a random matrix with 68 nodes
# use simple seed for reproducibility
np.random.seed(42)
con = np.random.random((68, 68))
con[con < 0.5] = 0.
plot_grouped_connectivity_circle(yaml_fname,
con,
label_names,
n_lines=10,
colorbar=True)
}, {
'parietal': parietal
}, {
'temporal': temporal
}, {
'insula': insula
}, {
'cingulate': cingulate
}, {
'frontal': frontal
}]
n_colors = len(label_groups)
cmap = plt.get_cmap('Pastel1')
cortex_colors = cmap.colors[:n_colors] + cmap.colors[:n_colors][::-1]
# plot simple connectivity circle with cortex based grouping and colors
plot_grouped_connectivity_circle(label_groups,
con,
label_names,
labels_mode='replace',
replacer_dict=replacer_dict,
cortex_colors=cortex_colors,
vmin=0.,
vmax=1.,
out_fname='fig_grouped_con_circle_cortex.png',
colorbar_pos=(0.1, 0.1),
n_lines=50,
colorbar=True,
colormap='viridis')
#!/usr/bin/env python
'''
Read grouped aparc labels from yaml file.
Plot grouped connectivity circle with these grouped labels.
'''
import numpy as np
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_grouped_connectivity_circle
import pickle
labels_fname = get_jumeg_path() + '/examples/label_names.list'
yaml_fname = get_jumeg_path() + '/examples/aparc_cortex_based_grouping.yaml'
with open('label_names.list', 'r') as f:
label_names = pickle.load(f)
# make a random matrix with 68 nodes
# use simple seed for reproducibility
np.random.seed(42)
con = np.random.random((68, 68))
con[con < 0.5] = 0.
plot_grouped_connectivity_circle(yaml_fname, con, label_names, n_lines=10, colorbar=True)