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,
figsize=(6, 6), show=False,
arrowstyle='->,head_length=1,head_width=1')
cau,
surr,
freqs,
nfft=nfft,
sfreq=epochs.info['sfreq'],
surr_thresh=surr_thresh)
print('Shape of causality matrix: ', caus.shape)
# get label names used for plotting
labels_fname = get_jumeg_path() + '/examples/label_names.list'
yaml_fname = get_jumeg_path() + '/examples/aparc_cortex_based_grouping.yaml'
with open(labels_fname, 'r') as f:
label_names = pickle.load(f)
plot_grouped_causality_circle(caus[0],
yaml_fname,
label_names,
n_lines=10,
labels_mode='cortex_only',
out_fname='causality_sample.png',
colormap='Blues',
colorbar=True,
figsize=(10, 6),
show=False)
t_end = time.time()
total_time_taken = t_end - t_start
print('Total time taken in minutes: %f' % (total_time_taken / 60.))
freqs,
nfft=nfft,
sfreq=epochs.info['sfreq'],
surr_thresh=surr_thresh)
print(('Shape of causality matrix: ', caus.shape))
# read the label names used for plotting
# with open(labels_fname, 'r') as f:
# label_names = pickle.load(f)
with open(labels_fname, 'r') as f:
label_names = yaml.safe_load(f)['label_names']
plot_grouped_causality_circle(caus[0],
yaml_fname,
label_names,
n_lines=10,
labels_mode=None,
replacer_dict=None,
out_fname='causality_sample.png',
colormap='Blues',
colorbar=True,
arrowstyle='->,head_length=1,head_width=1',
figsize=(10, 6),
show=False)
t_end = time.time()
total_time_taken = t_end - t_start
print(('Total time taken in minutes: %f' % (total_time_taken / 60.)))