#!/usr/bin/env python
import numpy as np
from jumeg.connectivity import plot_labelled_group_connectivity_circle
from jumeg import get_jumeg_path
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/examples/rsn_aparc_cortex_grouping.yaml'
# 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.
# plotting within a subplot
plot_labelled_group_connectivity_circle(yaml_fname, con, out_fname='rsn_circle.png',
show=True, n_lines=20, fontsize_names=6,
title='test RSN circ labels')
from mne.datasets import sample
from jumeg import get_jumeg_path
from jumeg.connectivity import (get_label_distances,
plot_grouped_connectivity_circle)
import yaml
from nilearn import plotting
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
subject = 'sample'
parc = 'aparc'
yaml_fname = get_jumeg_path() + '/data/desikan_%s_cortex_based_grouping.yaml' % parc
label_distances_fname = get_jumeg_path() + '/data/desikan_%s_label_com_distances.npy' % parc
labels_fname = get_jumeg_path() + '/data/desikan_label_names.yaml'
with open(labels_fname, 'r') as f:
label_names = yaml.safe_load(f)['label_names']
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
#!/usr/bin/env python
import numpy as np
from jumeg.connectivity import plot_labelled_group_connectivity_circle
from jumeg import get_jumeg_path
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/examples/rsn_aparc_cortex_grouping.yaml'
# 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.
# plotting within a subplot
plot_labelled_group_connectivity_circle(yaml_fname,
con,
out_fname='rsn_circle.png',
show=True,
n_lines=20,
fontsize_names=6,
title='test RSN circ labels')
'''
Script to show functionality to plot centrality indices along with
connectivity circle plot.
'''
import numpy as np
import os.path as op
import mne
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_degree_circle, plot_lines_and_blobs
import matplotlib.pyplot as plt
orig_labels_fname = get_jumeg_path() + '/data/desikan_label_names.yaml'
yaml_fname = get_jumeg_path(
) + '/data/desikan_aparc_cortex_based_grouping.yaml'
con_fname = get_jumeg_path() + '/data/sample,aparc-con.npy'
# real connectivity
con = np.load(con_fname)
con = con[0, :, :, 2] + con[0, :, :, 2].T
degrees = mne.connectivity.degree(con, threshold=0.2)
import bct
eigenvec_centrality = bct.eigenvector_centrality_und(con)
fig, ax = plot_lines_and_blobs(con,
degrees,
yaml_fname,
orig_labels_fname,
#!/usr/bin/env python3
"""Example showing how to plot a causality matrix on a circle plot."""
import numpy as np
from jumeg import get_jumeg_path
from jumeg.connectivity import plot_grouped_causality_circle
import yaml
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/data/desikan_aparc_cortex_based_grouping.yaml'
labels_fname = get_jumeg_path() + '/data/desikan_label_names.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 causality matrix
n_nodes = 68 # currently needs to be always this number
caus = np.random.random((n_nodes, n_nodes))
caus[np.diag_indices_from(caus)] = 0.
caus[caus < 0.7] = 0.
plot_grouped_causality_circle(caus, yaml_fname, label_names, n_lines=10,
labels_mode='replace', replacer_dict=replacer_dict,
out_fname='fig_causality_circle.png',
colormap='Blues', colorbar=True,
#!/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.
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, freqs, times, n_epochs, n_tapers = spectral_connectivity(
label_ts, method=con_methods, mode='multitaper', sfreq=sfreq, fmin=fmin,
fmax=fmax, faverage=True, mt_adaptive=True, n_jobs=4)
# con is a 3D array, get the connectivity for the first (and only) freq. band
# for each method
con_res = dict()
for method, c in zip(con_methods, con):
con_res[method] = c[:, :, 0]
# Now, we visualize the connectivity using a circular graph layout
# First, we reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
from jumeg import get_jumeg_path
yaml_fname = get_jumeg_path() + '/data/standard_garces_rsns_grouping.yaml'
import yaml
with open(yaml_fname, 'r') as f:
xlabels = yaml.safe_load(f)
# the yaml file has been hand curated to follow the same order as label_names
# if not the node order has to be changed appropriately
node_order = list()
node_order.extend(label_names)
group_bound = [len(list(key.values())[0]) for key in xlabels]
group_bound = [0] + group_bound
group_boundaries = [sum(group_bound[:i+1]) for i in range(len(group_bound))]
group_boundaries.pop()
#!/usr/bin/env python
"""Read grouped aparc labels from yaml file.
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',
#!/usr/bin/env python3
"""Script exposing circle plots with outer ring.
Author: Praveen Sripad <*****@*****.**>
"""
from jumeg.connectivity import (plot_generic_grouped_circle,
plot_fica_grouped_circle,
generate_random_connectivity_matrix)
from jumeg import get_jumeg_path
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/data/rsn_desikan_aparc_cortex_grouping.yaml'
# make a random matrix
con = generate_random_connectivity_matrix(size=(34, 34), symmetric=True)
# load the label names in the original order
# this should be same order as the connectivity matrix
labels_fname = get_jumeg_path() + '/data/fica_names.txt'
with open(labels_fname, 'r') as f:
orig_labels = [line.rstrip('\n') for line in f]
# plot the connectivity circle grouped
plot_generic_grouped_circle(yaml_fname,
con,
orig_labels,
node_order_size=34,
out_fname='fig_fica_circle.png',
show=False,
n_lines=20,
#!/usr/bin/env python3
"""
Example exposing the plot_labelled_group_connectivity_circle function.
Author: Praveen Sripad <*****@*****.**>
"""
from jumeg.connectivity import (plot_labelled_group_connectivity_circle,
generate_random_connectivity_matrix)
from jumeg import get_jumeg_path
import yaml
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/data/rsn_desikan_aparc_cortex_grouping.yaml'
label_names_yaml_fname = get_jumeg_path() + '/data/desikan_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 68 nodes
con = generate_random_connectivity_matrix(size=(68, 68), symmetric=True)
# plotting within a subplot
plot_labelled_group_connectivity_circle(yaml_fname,
con,
label_names,
out_fname='fig_rsn_circle.png',
show=False,
n_lines=20,
fontsize_names=6,
title='test RSN circ labels')
#!/usr/bin/env python
import numpy as np
from jumeg.connectivity import plot_generic_grouped_circle, plot_fica_grouped_circle
from jumeg import get_jumeg_path
# load the yaml grouping of Freesurfer labels
yaml_fname = get_jumeg_path() + '/examples/rsn_aparc_cortex_grouping.yaml'
# make a random matrix with 68 nodes
# use simple seed for reproducibility
np.random.seed(42)
con = np.random.random((34, 34))
con[con < 0.5] = 0.
# load the label names in the original order
# this should be same order as the connectivity matrix
labels_fname = get_jumeg_path() + '/examples/fica_names.txt'
with open(labels_fname, 'r') as f:
orig_labels = [line.rstrip('\n') for line in f]
# plot the connectivity circle grouped
plot_generic_grouped_circle(yaml_fname, con, orig_labels,
node_order_size=34,
out_fname='fica_circle.png',
show=False, n_lines=20,
title='Groups')
# plot the connectivity circle with outer group labels ring
plot_fica_grouped_circle(yaml_fname, con,
"""
Example how to create a custom label groups and plot grouped connectivity
circle with these labels.
Author: Praveen Sripad <*****@*****.**>
Christian Kiefer <*****@*****.**>
"""
import matplotlib.pyplot as plt
from jumeg import get_jumeg_path
from jumeg.connectivity import (plot_grouped_connectivity_circle,
generate_random_connectivity_matrix)
import yaml
labels_fname = get_jumeg_path() + '/data/desikan_label_names.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
con = generate_random_connectivity_matrix(size=(68, 68), symmetric=True)
# make groups based on lobes
occipital = ['lateraloccipital', 'lingual', 'cuneus', 'pericalcarine']
parietal = [
'superiorparietal', 'inferiorparietal', 'precuneus', 'postcentral',
#!/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)
