def test_plot_connectivity_circle():
"""Test plotting connectivity circle
"""
node_order = ['frontalpole-lh', 'parsorbitalis-lh', 'lateralorbitofrontal-lh', 'rostralmiddlefrontal-lh', 'medialorbitofrontal-lh', 'parstriangularis-lh', 'rostralanteriorcingulate-lh', 'temporalpole-lh', 'parsopercularis-lh', 'caudalanteriorcingulate-lh', 'entorhinal-lh', 'superiorfrontal-lh', 'insula-lh', 'caudalmiddlefrontal-lh', 'superiortemporal-lh', 'parahippocampal-lh', 'middletemporal-lh', 'inferiortemporal-lh', 'precentral-lh', 'transversetemporal-lh', 'posteriorcingulate-lh', 'fusiform-lh', 'postcentral-lh', 'bankssts-lh', 'supramarginal-lh', 'isthmuscingulate-lh', 'paracentral-lh', 'lingual-lh', 'precuneus-lh', 'inferiorparietal-lh', 'superiorparietal-lh', 'pericalcarine-lh', 'lateraloccipital-lh', 'cuneus-lh', 'cuneus-rh', 'lateraloccipital-rh', 'pericalcarine-rh', 'superiorparietal-rh', 'inferiorparietal-rh', 'precuneus-rh', 'lingual-rh', 'paracentral-rh', 'isthmuscingulate-rh', 'supramarginal-rh', 'bankssts-rh', 'postcentral-rh', 'fusiform-rh', 'posteriorcingulate-rh', 'transversetemporal-rh', 'precentral-rh', 'inferiortemporal-rh', 'middletemporal-rh', 'parahippocampal-rh', 'superiortemporal-rh', 'caudalmiddlefrontal-rh', 'insula-rh', 'superiorfrontal-rh', 'entorhinal-rh', 'caudalanteriorcingulate-rh', 'parsopercularis-rh', 'temporalpole-rh', 'rostralanteriorcingulate-rh', 'parstriangularis-rh', 'medialorbitofrontal-rh', 'rostralmiddlefrontal-rh', 'lateralorbitofrontal-rh', 'parsorbitalis-rh', 'frontalpole-rh']
label_names = ['bankssts-lh', 'bankssts-rh', 'caudalanteriorcingulate-lh', 'caudalanteriorcingulate-rh', 'caudalmiddlefrontal-lh', 'caudalmiddlefrontal-rh', 'cuneus-lh', 'cuneus-rh', 'entorhinal-lh', 'entorhinal-rh', 'frontalpole-lh', 'frontalpole-rh', 'fusiform-lh', 'fusiform-rh', 'inferiorparietal-lh', 'inferiorparietal-rh', 'inferiortemporal-lh', 'inferiortemporal-rh', 'insula-lh', 'insula-rh', 'isthmuscingulate-lh', 'isthmuscingulate-rh', 'lateraloccipital-lh', 'lateraloccipital-rh', 'lateralorbitofrontal-lh', 'lateralorbitofrontal-rh', 'lingual-lh', 'lingual-rh', 'medialorbitofrontal-lh', 'medialorbitofrontal-rh', 'middletemporal-lh', 'middletemporal-rh', 'paracentral-lh', 'paracentral-rh', 'parahippocampal-lh', 'parahippocampal-rh', 'parsopercularis-lh', 'parsopercularis-rh', 'parsorbitalis-lh', 'parsorbitalis-rh', 'parstriangularis-lh', 'parstriangularis-rh', 'pericalcarine-lh', 'pericalcarine-rh', 'postcentral-lh', 'postcentral-rh', 'posteriorcingulate-lh', 'posteriorcingulate-rh', 'precentral-lh', 'precentral-rh', 'precuneus-lh', 'precuneus-rh', 'rostralanteriorcingulate-lh', 'rostralanteriorcingulate-rh', 'rostralmiddlefrontal-lh', 'rostralmiddlefrontal-rh', 'superiorfrontal-lh', 'superiorfrontal-rh', 'superiorparietal-lh', 'superiorparietal-rh', 'superiortemporal-lh', 'superiortemporal-rh', 'supramarginal-lh', 'supramarginal-rh', 'temporalpole-lh', 'temporalpole-rh', 'transversetemporal-lh', 'transversetemporal-rh']
node_angles = circular_layout(label_names, node_order, start_pos=90)
con = np.random.randn(68, 68)
plot_connectivity_circle(con, label_names, n_lines=300,
node_angles=node_angles, title='test')
def test_plot_connectivity_circle():
"""Test plotting connectivity circle
"""
node_order = [
'frontalpole-lh', 'parsorbitalis-lh', 'lateralorbitofrontal-lh',
'rostralmiddlefrontal-lh', 'medialorbitofrontal-lh',
'parstriangularis-lh', 'rostralanteriorcingulate-lh',
'temporalpole-lh', 'parsopercularis-lh', 'caudalanteriorcingulate-lh',
'entorhinal-lh', 'superiorfrontal-lh', 'insula-lh',
'caudalmiddlefrontal-lh', 'superiortemporal-lh', 'parahippocampal-lh',
'middletemporal-lh', 'inferiortemporal-lh', 'precentral-lh',
'transversetemporal-lh', 'posteriorcingulate-lh', 'fusiform-lh',
'postcentral-lh', 'bankssts-lh', 'supramarginal-lh',
'isthmuscingulate-lh', 'paracentral-lh', 'lingual-lh', 'precuneus-lh',
'inferiorparietal-lh', 'superiorparietal-lh', 'pericalcarine-lh',
'lateraloccipital-lh', 'cuneus-lh', 'cuneus-rh', 'lateraloccipital-rh',
'pericalcarine-rh', 'superiorparietal-rh', 'inferiorparietal-rh',
'precuneus-rh', 'lingual-rh', 'paracentral-rh', 'isthmuscingulate-rh',
'supramarginal-rh', 'bankssts-rh', 'postcentral-rh', 'fusiform-rh',
'posteriorcingulate-rh', 'transversetemporal-rh', 'precentral-rh',
'inferiortemporal-rh', 'middletemporal-rh', 'parahippocampal-rh',
'superiortemporal-rh', 'caudalmiddlefrontal-rh', 'insula-rh',
'superiorfrontal-rh', 'entorhinal-rh', 'caudalanteriorcingulate-rh',
'parsopercularis-rh', 'temporalpole-rh', 'rostralanteriorcingulate-rh',
'parstriangularis-rh', 'medialorbitofrontal-rh',
'rostralmiddlefrontal-rh', 'lateralorbitofrontal-rh',
'parsorbitalis-rh', 'frontalpole-rh'
]
label_names = [
'bankssts-lh', 'bankssts-rh', 'caudalanteriorcingulate-lh',
'caudalanteriorcingulate-rh', 'caudalmiddlefrontal-lh',
'caudalmiddlefrontal-rh', 'cuneus-lh', 'cuneus-rh', 'entorhinal-lh',
'entorhinal-rh', 'frontalpole-lh', 'frontalpole-rh', 'fusiform-lh',
'fusiform-rh', 'inferiorparietal-lh', 'inferiorparietal-rh',
'inferiortemporal-lh', 'inferiortemporal-rh', 'insula-lh', 'insula-rh',
'isthmuscingulate-lh', 'isthmuscingulate-rh', 'lateraloccipital-lh',
'lateraloccipital-rh', 'lateralorbitofrontal-lh',
'lateralorbitofrontal-rh', 'lingual-lh', 'lingual-rh',
'medialorbitofrontal-lh', 'medialorbitofrontal-rh',
'middletemporal-lh', 'middletemporal-rh', 'paracentral-lh',
'paracentral-rh', 'parahippocampal-lh', 'parahippocampal-rh',
'parsopercularis-lh', 'parsopercularis-rh', 'parsorbitalis-lh',
'parsorbitalis-rh', 'parstriangularis-lh', 'parstriangularis-rh',
'pericalcarine-lh', 'pericalcarine-rh', 'postcentral-lh',
'postcentral-rh', 'posteriorcingulate-lh', 'posteriorcingulate-rh',
'precentral-lh', 'precentral-rh', 'precuneus-lh', 'precuneus-rh',
'rostralanteriorcingulate-lh', 'rostralanteriorcingulate-rh',
'rostralmiddlefrontal-lh', 'rostralmiddlefrontal-rh',
'superiorfrontal-lh', 'superiorfrontal-rh', 'superiorparietal-lh',
'superiorparietal-rh', 'superiortemporal-lh', 'superiortemporal-rh',
'supramarginal-lh', 'supramarginal-rh', 'temporalpole-lh',
'temporalpole-rh', 'transversetemporal-lh', 'transversetemporal-rh'
]
node_angles = circular_layout(label_names, node_order, start_pos=90)
con = np.random.randn(68, 68)
plot_connectivity_circle(con,
label_names,
n_lines=300,
node_angles=node_angles,
title='test')
def plot_circular_connectivity(conmat, label_names, node_colors, node_order, vmin = 0.3, vmax = 1.0, nb_lines = 200, fname = "_def"):
import os
import numpy as np
from mne.viz import circular_layout, plot_connectivity_circle
import matplotlib.pyplot as plt
# Angles
node_angles = circular_layout(label_names, node_order, start_pos=90,
group_boundaries=[0, len(label_names) / 2])
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
fig,_ = plot_connectivity_circle(conmat,
label_names,
n_lines=nb_lines,
node_angles=node_angles,
node_colors = node_colors,
fontsize_names = 12,
title='All-to-All Connectivity' ,
show = False,
vmin = vmin,
vmax = vmax)
#plot_conmat_file = os.path.abspath('circle.png')
plot_conmat_file = os.path.abspath('circle_' + fname + '.eps')
fig.savefig(plot_conmat_file, facecolor='black')
plt.close(fig)
#fig1.close()
del fig
return plot_conmat_file
def _plot_circular_connectivity(conmat,
label_names,
node_colors=None,
node_order=[],
vmin=0.3,
vmax=1.0,
nb_lines=200,
fname="_def",
save_dir=None):
"""Plot circular connectivity."""
import matplotlib.pyplot as plt
assert len(conmat.shape) == 2, "Error, conmat should be 2D matrix"
assert conmat.shape[0] == conmat.shape[1], "Error, conmat should squared"
assert conmat.shape[0] == len(label_names), "Error, conmat and labels\
should have same length {} != {}".format(conmat.shape[0],
len(label_names))
# if not defined, use label_names
if len(node_order) == 0:
node_order = label_names
# Angles
node_angles = circular_layout(label_names,
node_order,
start_pos=90,
group_boundaries=[0, len(label_names) / 2])
# necessary to have symetric matrix
conmat = conmat + np.transpose(conmat)
# Plot the graph
fig, _ = plot_connectivity_circle(conmat,
label_names,
n_lines=nb_lines,
node_angles=node_angles.astype(int),
node_colors=None,
fontsize_names=12,
title='All-to-All Connectivity',
show=False,
vmin=vmin,
vmax=vmax)
# saving circle file
if save_dir is not None:
assert os.path.exists(save_dir), ("Error, do not use save_dir if it \
does not exists before")
plot_conmat_file = os.path.join(save_dir, 'circle_' + fname + '.png')
else:
plot_conmat_file = os.path.abspath('circle_' + fname + '.png')
fig.savefig(plot_conmat_file, facecolor='black')
plt.close(fig)
return plot_conmat_file
def plot_circular_connectivity(conmat,
label_names,
node_colors,
node_order,
vmin=0.3,
vmax=1.0,
nb_lines=200,
fname="_def"):
"""Plot circular connectivity"""
import os
import numpy as np
from mne.viz import circular_layout, plot_connectivity_circle
import matplotlib.pyplot as plt
# Angles
node_angles = circular_layout(label_names,
node_order,
start_pos=90,
group_boundaries=[0, len(label_names) / 2])
print(conmat)
print((node_angles.astype(int)))
conmat = conmat + np.transpose(conmat)
# Plot the graph using node colors from the FreeSurfer parcellation.
# We only show the 300 strongest connections.
fig, _ = plot_connectivity_circle(conmat,
label_names,
n_lines=nb_lines,
node_angles=node_angles.astype(int),
node_colors=None,
fontsize_names=12,
title='All-to-All Connectivity',
show=False,
vmin=vmin,
vmax=vmax)
# plt.show()
# print fig
# plot_conmat_file = os.path.abspath('circle.png')
plot_conmat_file = os.path.abspath('circle_' + fname + '.eps')
fig.savefig(plot_conmat_file, facecolor='black')
# fig.savefig(plot_conmat_file)
plt.close(fig)
# fig1.close()
del fig
return plot_conmat_file
def plot_seaborn(features, node_names=None, node_idx=None, **kwargs):
from mne.viz import circular_layout
node_angles = circular_layout(
node_names.tolist(),
node_names[node_idx].tolist(),
start_pos=90,
group_boundaries=[0, len(node_names) / 2. + 1])
matrix = copy_matrix(array_to_matrix(features.values[0]),
diagonal_filler=0.)
return plot_connectivity_seaborn(matrix,
node_names=node_names,
con_thresh=400,
node_angles=node_angles,
node_colors=sns.dark_palette(
kwargs['color']))
def test_plot_connectivity_circle():
"""Test plotting connectivity circle."""
node_order = ['aaa', 'bbb', 'ccc', 'ddd', 'eee', 'fff']
label_names = ['aaa', 'bbb', 'ccc', 'ddd', 'eee', 'fff']
group_boundaries = [0, 2, 4]
node_angles = circular_layout(label_names,
node_order,
start_pos=90,
group_boundaries=group_boundaries)
con = np.random.RandomState(0).randn(6, 6)
con[con < 0.7] = 0
fig, ax = plot_connectivity_circle(con,
label_names,
n_lines=60,
node_angles=node_angles,
title='test',
colormap='RdBu_r',
vmin=0,
vmax=2,
linewidth=.5,
facecolor='k')
plt.show()
pytest.raises(ValueError,
circular_layout,
label_names,
node_order,
group_boundaries=[-1])
pytest.raises(ValueError,
circular_layout,
label_names,
node_order,
group_boundaries=[20, 0])
def test_plot_connectivity_circle():
"""Test plotting connectivity circle."""
node_order = ['frontalpole-lh', 'parsorbitalis-lh',
'lateralorbitofrontal-lh', 'rostralmiddlefrontal-lh',
'medialorbitofrontal-lh', 'parstriangularis-lh',
'rostralanteriorcingulate-lh', 'temporalpole-lh',
'parsopercularis-lh', 'caudalanteriorcingulate-lh',
'entorhinal-lh', 'superiorfrontal-lh', 'insula-lh',
'caudalmiddlefrontal-lh', 'superiortemporal-lh',
'parahippocampal-lh', 'middletemporal-lh',
'inferiortemporal-lh', 'precentral-lh',
'transversetemporal-lh', 'posteriorcingulate-lh',
'fusiform-lh', 'postcentral-lh', 'bankssts-lh',
'supramarginal-lh', 'isthmuscingulate-lh', 'paracentral-lh',
'lingual-lh', 'precuneus-lh', 'inferiorparietal-lh',
'superiorparietal-lh', 'pericalcarine-lh',
'lateraloccipital-lh', 'cuneus-lh', 'cuneus-rh',
'lateraloccipital-rh', 'pericalcarine-rh',
'superiorparietal-rh', 'inferiorparietal-rh', 'precuneus-rh',
'lingual-rh', 'paracentral-rh', 'isthmuscingulate-rh',
'supramarginal-rh', 'bankssts-rh', 'postcentral-rh',
'fusiform-rh', 'posteriorcingulate-rh',
'transversetemporal-rh', 'precentral-rh',
'inferiortemporal-rh', 'middletemporal-rh',
'parahippocampal-rh', 'superiortemporal-rh',
'caudalmiddlefrontal-rh', 'insula-rh', 'superiorfrontal-rh',
'entorhinal-rh', 'caudalanteriorcingulate-rh',
'parsopercularis-rh', 'temporalpole-rh',
'rostralanteriorcingulate-rh', 'parstriangularis-rh',
'medialorbitofrontal-rh', 'rostralmiddlefrontal-rh',
'lateralorbitofrontal-rh', 'parsorbitalis-rh',
'frontalpole-rh']
label_names = ['bankssts-lh', 'bankssts-rh', 'caudalanteriorcingulate-lh',
'caudalanteriorcingulate-rh', 'caudalmiddlefrontal-lh',
'caudalmiddlefrontal-rh', 'cuneus-lh', 'cuneus-rh',
'entorhinal-lh', 'entorhinal-rh', 'frontalpole-lh',
'frontalpole-rh', 'fusiform-lh', 'fusiform-rh',
'inferiorparietal-lh', 'inferiorparietal-rh',
'inferiortemporal-lh', 'inferiortemporal-rh', 'insula-lh',
'insula-rh', 'isthmuscingulate-lh', 'isthmuscingulate-rh',
'lateraloccipital-lh', 'lateraloccipital-rh',
'lateralorbitofrontal-lh', 'lateralorbitofrontal-rh',
'lingual-lh', 'lingual-rh', 'medialorbitofrontal-lh',
'medialorbitofrontal-rh', 'middletemporal-lh',
'middletemporal-rh', 'paracentral-lh', 'paracentral-rh',
'parahippocampal-lh', 'parahippocampal-rh',
'parsopercularis-lh', 'parsopercularis-rh',
'parsorbitalis-lh', 'parsorbitalis-rh',
'parstriangularis-lh', 'parstriangularis-rh',
'pericalcarine-lh', 'pericalcarine-rh', 'postcentral-lh',
'postcentral-rh', 'posteriorcingulate-lh',
'posteriorcingulate-rh', 'precentral-lh', 'precentral-rh',
'precuneus-lh', 'precuneus-rh',
'rostralanteriorcingulate-lh',
'rostralanteriorcingulate-rh', 'rostralmiddlefrontal-lh',
'rostralmiddlefrontal-rh', 'superiorfrontal-lh',
'superiorfrontal-rh', 'superiorparietal-lh',
'superiorparietal-rh', 'superiortemporal-lh',
'superiortemporal-rh', 'supramarginal-lh',
'supramarginal-rh', 'temporalpole-lh', 'temporalpole-rh',
'transversetemporal-lh', 'transversetemporal-rh']
group_boundaries = [0, len(label_names) / 2]
node_angles = circular_layout(label_names, node_order, start_pos=90,
group_boundaries=group_boundaries)
con = np.random.RandomState(0).randn(68, 68)
plot_connectivity_circle(con, label_names, n_lines=300,
node_angles=node_angles, title='test',
)
pytest.raises(ValueError, circular_layout, label_names, node_order,
group_boundaries=[-1])
pytest.raises(ValueError, circular_layout, label_names, node_order,
group_boundaries=[20, 0])
plt.close('all')
def make_circle(threshmat, outpath, label_names, thr):
## GET VMIN AND MAX FROM MATRIX
# maxval = threshmat.max()
# minval = np.min(threshmat[np.nonzero(threshmat)]) - 1
maxval = None
minval = None
## CHECK CIRCLE GRAPH COLOR TYPE ##
if color_type == 'DARK':
fc = 'black'
elif color_type == 'LIGHT':
fc = 'white'
## PREP FIGURE
fig = plt.figure(num=None, figsize=(30, 30), facecolor=fc)
## MAKE NODES ANGLES ##
node_angles = circular_layout(label_names,
node_order,
start_pos=sp,
group_boundaries=split)
## REMOVE NAMES IF TRUE ##
remove_names = True
if remove_names == True:
label_names = [''] * len(label_names)
## WRITE GRAPH TO FIG ##
if color_type == 'DARK':
plot_connectivity_circle(
threshmat,
label_names,
node_angles=node_angles,
node_colors=label_colors,
title=title,
fontsize_title=48,
textcolor='white',
facecolor='black',
node_edgecolor='black',
node_linewidth=.5,
linewidth=1.5,
fontsize_names=8,
colormap='hot',
vmin=minval,
vmax=maxval,
### make vmin and max the values from the min/max of matrix
colorbar_size=0.2,
colorbar_pos=(-1, .5),
fontsize_colorbar=28,
colorbar=True,
padding=6.0,
fig=fig,
subplot=111,
interactive=False,
show=False)
# ADD Left/Right Labels
plt.gcf().text(.22, .5, 'L', color='white', fontsize=48)
plt.gcf().text(.633, .5, 'R', color='white', fontsize=48)
elif color_type == 'LIGHT':
plot_connectivity_circle(
threshmat,
label_names,
node_angles=node_angles,
node_colors=label_colors,
title=title,
fontsize_title=48,
textcolor='black',
facecolor='white',
node_edgecolor='black',
node_linewidth=.5,
linewidth=1.5,
fontsize_names=8,
colormap='hot_r',
vmin=minval,
vmax=maxval, # colormap='YlGnBu'
### make vmin and max the values from the min/max of matrix
colorbar_size=0.2,
colorbar_pos=(-1, .5),
fontsize_colorbar=28,
colorbar=True,
padding=6.0,
fig=fig,
subplot=111,
interactive=False,
show=False)
# ADD Left/Right Labels
plt.gcf().text(.22, .5, 'L', color='black', fontsize=48)
plt.gcf().text(.633, .5, 'R', color='black', fontsize=48)
## SAVE FIGURE ##
out_fig = os.path.abspath(
os.path.join(out_path, file_name + '_THR' + str(thr)))
# have to re-set the facecolor before saving #
fig.savefig(out_fig, facecolor=fc)
# Reorder the labels based on their location
lh_labels = [label for (yp, label) in sorted(zip(label_ypos_lh, lh_labels))]
# For the right hemi
rh_labels = [
label[:-2] + 'rh' for label in lh_labels
if label != 'Brain-Stem' and label[:-2] + 'rh' in rh_labels
]
# Save the plot order
node_order = list()
node_order = lh_labels[::-1] + rh_labels
node_angles = circular_layout(label_names,
node_order,
start_pos=90,
group_boundaries=[0, len(label_names) // 2])
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
conmat = con[:, :, 0]
plot_connectivity_circle(conmat,
label_names,
n_lines=300,
node_angles=node_angles,
node_colors=node_colors,
title='All-to-All Connectivity left-Auditory '
'Condition (PLI)')
# Uncomment the following line to save the figure
'''
def plot_connectomics(matrix,
node_size,
save_path,
prename,
save=False,
**kwargs):
_plot_cfg = {
'threshold': 1.4,
'fontsize_title': 19,
'fontsize_colorbar': 13,
'fontsize_names': 13,
'colorbar_size': 0.3,
'colormap': 'hot',
'vmin': -3,
'vmax': 3,
'figure': plt.figure(figsize=(16, 16)),
'facecolor': 'black',
'dpi': 150,
'name': 'weights',
'title': 'Connectome',
'filetype': 'png',
'zscore': True
}
_plot_cfg.update(kwargs)
directory_ = save_path[save_path.rfind('/') + 1:]
#names_lr, colors_lr, index_, coords = get_plot_stuff(directory_)
names_lr = kwargs['node_names']
colors_lr = kwargs['node_colors']
index_ = kwargs['node_order']
coords = kwargs['node_coords']
networks = kwargs['networks']
matrix = matrix[index_][:, index_]
names_lr = names_lr[index_]
node_colors = colors_lr[index_]
node_size = node_size[index_]
f, _ = plot_connectivity_circle_edited(
matrix,
names_lr,
node_colors=node_colors,
node_size=node_size,
con_thresh=_plot_cfg['threshold'],
title=_plot_cfg['title'],
node_angles=circular_layout(
names_lr,
list(names_lr),
),
fontsize_title=_plot_cfg['fontsize_title'],
fontsize_names=_plot_cfg['fontsize_names'],
fontsize_colorbar=_plot_cfg['fontsize_colorbar'],
colorbar_size=_plot_cfg['colorbar_size'],
colormap=_plot_cfg['colormap'],
vmin=_plot_cfg['vmin'],
vmax=_plot_cfg['vmax'],
fig=_plot_cfg['figure'],
)
if save == True:
fname = "%s_features_%s.%s" % (prename, _plot_cfg['name'],
_plot_cfg['filetype'])
f.savefig(os.path.join(save_path, fname),
facecolor=_plot_cfg['facecolor'],
dpi=_plot_cfg['dpi'])
for d_ in ['x', 'y', 'z']:
fname = None
if save == True:
fname = "%s_connectome_feature_%s_%s.%s" % (
prename, _plot_cfg['name'], d_, _plot_cfg['filetype'])
fname = os.path.join(save_path, fname)
plot_connectome(matrix,
coords,
colors_lr,
node_size,
_plot_cfg['threshold'],
fname,
cmap=_plot_cfg['colormap'],
title=None,
display_=d_,
max_=_plot_cfg['vmax'],
min_=_plot_cfg['vmin'])
f = plot_matrix(matrix,
_,
networks,
threshold=_plot_cfg['threshold'],
zscore=_plot_cfg['zscore'])
if save == True:
fname = "%s_matrix_%s.%s" % (prename, _plot_cfg['name'],
_plot_cfg['filetype'])
f.savefig(os.path.join(save_path, fname),
facecolor=_plot_cfg['facecolor'],
dpi=_plot_cfg['dpi'])
except ValueError:
pass
# Reorder the labels based on their location
lh_labels = [label for (yp, label) in sorted(zip(label_ypos_lh, lh_labels))]
# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels
if label != 'Brain-Stem' and label[:-2] + 'rh' in rh_labels]
# Save the plot order
node_order = list()
node_order = lh_labels[::-1] + rh_labels
node_angles = circular_layout(label_names, node_order, start_pos=90,
group_boundaries=[0, len(label_names) // 2])
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
conmat = con[:, :, 0]
plot_connectivity_circle(conmat, label_names, n_lines=300,
node_angles=node_angles, node_colors=node_colors,
title='All-to-All Connectivity left-Auditory '
'Condition (PLI)')
# Uncomment the following line to save the figure
'''
import matplotlib.pyplot as plt
plt.savefig('circle.png', facecolor='black')
'''
def circular_brain_plot(forward,
solution_support,
stcs,
obj_fun,
label_name=False,
plot_circular=True,
plot_labels=True,
n_burnin=0,
vmin=0.,
vmax=10.,
plot_hist=False,
subplot=111,
title='',
fig=None,
colorbar=True):
indices = list()
vertices_lh = list()
vertices_rh = list()
support = np.unique(np.where(solution_support)[1])
indices.append(support)
for stc in stcs:
vertices_lh.append(stc.vertices[0])
vertices_rh.append(stc.vertices[1])
indices = np.unique(np.concatenate(np.array(indices)))
vertices_lh = np.unique(np.concatenate(np.array(vertices_lh)))
vertices_rh = np.unique(np.concatenate(np.array(vertices_rh)))
n_sources = solution_support.shape[1]
# Get n_used of the left hemi
n_used = forward['src'][0]['nuse']
n_rh = forward['src'][1]['nuse']
#
indices_lh = indices[np.where(indices < n_used)[0]]
indices_rh = indices[np.where(indices >= n_used)[0]]
indices_sym = np.sort(
np.concatenate(
np.array([indices, indices_lh + n_used, indices_rh - n_rh])))
#
n_support = len(indices_sym)
iindices = [list(indices_sym).index(ii) for ii in indices]
# Remove burnin
solution_support = solution_support[n_burnin:, :]
stcs = stcs[n_burnin:]
# Plot histogram of the different objective function values
# obtained with the MCMC init after burnin.
if plot_hist:
out = plt.hist(obj_fun)
ymin, ymax = 0, out[0].max()
plt.vlines(obj_fun[0], ymin=ymin, ymax=ymax)
plt.ylim([ymin, ymax])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
plt.xlabel('Objective function values', fontsize=16)
plt.legend(['MM solution', 'MCMC initializations'], fontsize=16)
plt.show()
# ####
ind = np.unique(np.where(solution_support)[1])
# ####
# Take the results for all indices
data_support_res = solution_support[:, indices]
# Construct cooeccurrence matrix
cooecc = np.zeros((n_sources, n_sources))
ixgrid = np.ix_(indices, indices)
cooecc[ixgrid] = np.dot(data_support_res.T, data_support_res)
ixgrid = np.ix_(indices_sym, indices_sym)
cooecc = cooecc[ixgrid]
# Read labels
# data_path = sample.data_path()
subjects_dir = sample.data_path() + '/subjects'
labels = mne.read_labels_from_annot('sample',
parc='aparc.split-125',
subjects_dir=subjects_dir)
# First, we reorder the labels based on their location in the left hemi
label_names = [label.name for label in labels]
lh_labels = [name for name in label_names if name.endswith('lh')]
# Get the y-location of the label
label_ypos = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ypos.append(ypos)
# Reorder the labels based on their location
lh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))]
# Find the corresponding label name to the vertices
lh_names = list()
length = 0
for vertex in vertices_lh:
for label in labels[::2]:
if vertex in label.vertices:
lh_names.append(label.name)
if len(lh_names) != length + 1:
lh_names.append(labels[56].name)
length += 1
rh_names = list()
length = 0
for vertex in vertices_rh:
for label in labels[1::2]:
if vertex in label.vertices:
rh_names.append(label.name)
if len(rh_names) != length + 1:
rh_names.append(labels[57].name)
length += 1
names = lh_names + rh_names
names_sym = np.array([''] * n_support, dtype='U30')
names_sym[iindices] = names
for ii in np.where(names_sym == '')[0]:
if indices_sym[ii] < n_used:
ind = np.where(indices == indices_sym[ii] + n_rh)[0]
if ind.shape[0] == 0:
ind = np.where(indices == indices_sym[ii] - 2 + n_rh)[0]
name = np.array(names)[ind][0][:-3] + '-lh'
else:
ind = np.where(indices == indices_sym[ii] - n_used)[0]
if ind.shape[0] == 0:
ind = np.where(indices == indices_sym[ii] - 2 - n_used)[0]
name = np.array(names)[ind][0][:-3] + '-rh'
names_sym[ii] = name
names = names_sym
dipole_colors = list()
names_lh = list()
# For each found label find its color
for label in names:
if label[:-3] != 'n':
idx = label_names.index(label)
if labels[idx].color == (0., 0., 0., 1.):
labels[idx].color = (0.5, 0.5, 0.5, 1.)
dipole_colors.append(labels[idx].color)
names_lh.append(label[:-3])
else:
dipole_colors.append((0., 0., 0., 1.))
names_lh.append('none')
names_lh = names_lh[:n_support // 2]
seen_labels = list()
node_order = list()
# Find the good order for names and cooecc
for label in lh_labels:
if label not in seen_labels and label[:-3] in names_lh:
node_order.append(np.where(np.array(names_lh) == label[:-3])[0])
seen_labels.append(label)
lh_order = list(np.concatenate(np.array(node_order)))
node_order = lh_order[::-1]
# colors = list(np.array(dipole_colors)[node_order])
node_order.extend(list(np.array(lh_order) + len(lh_order)))
node_width = 2 * 180. / n_support
label_indices = range(n_support)
node_angles = circular_layout(label_indices,
node_order,
start_pos=90,
group_boundaries=[0,
len(label_indices) / 2])
dipole_colors = dipole_colors[:n_support // 2]
colors = np.concatenate(np.array([dipole_colors, dipole_colors]))
if not label_name:
names = np.array([''] * len(indices_sym), dtype=np.dtype((str, 35)))
if plot_circular:
fig, ax = plot_connectivity_circle(cooecc,
names,
n_lines=None,
node_angles=node_angles,
node_colors=colors,
node_width=node_width,
vmin=vmin,
vmax=vmax,
fig=fig,
subplot=subplot,
title=title,
padding=4,
textcolor='black',
facecolor='white',
colormap='viridis',
node_edgecolor=None,
colorbar=colorbar)
plot_bars_circular(n_support, node_angles, cooecc.diagonal(), ax)
if plot_labels:
brain_labels(labels, names_sym, subjects_dir)
node_names = list(vertices.name[3:])
node_order = ['V1', 'PCU', 'PCC', 'lIPL', 'rIPL', 'lHP', 'rHP', 'ACC']
# find V1
v1_pos = [i for i, x in enumerate(node_order) if (x == 'V1')]
if v1_pos[0] == 7:
v1_pos.append(0)
else:
v1_pos.append(v1_pos[0] + 1)
v1_pos.sort()
node_angles = circular_layout(node_names,
node_order,
start_pos=90,
group_boundaries=v1_pos)
indices = (From - 4, To - 4)
# undirected connectivity
con, ind = np.unique(conn, return_index=True)
indices = (From[ind] - 4, To[ind] - 4)
#color rgb
n_colors = [None] * len(node_order)
n = 0 # counter
for i in range(len(node_order)):
if node_order[i] == 'V1':
n_colors[i] = (0, 0, 0)
else:
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()
rsn_colors = ['m', 'b', 'y', 'c', 'r', 'g', 'w']
group_bound.pop(0)
label_colors = []
for ind, rep in enumerate(group_bound):
label_colors += [rsn_colors[ind]] * rep
assert len(label_colors) == len(node_order), 'Number of colours do not match'
from mne.viz.circle import circular_layout
node_angles = circular_layout(label_names, label_names, start_pos=90,
group_boundaries=group_boundaries)
# Plot the graph using node colors from the FreeSurfer parcellation.
plot_connectivity_circle(con_res['wpli'], label_names, n_lines=300,
node_angles=node_angles, node_colors=label_colors,
title='Connectivity between standard RSNs')
# plt.savefig('circle.png', facecolor='black')
def plot_connectivity(fc: Union[np.ndarray, pd.DataFrame], threshold: Optional[float] = None, plot_style: str = 'heatmap',
bg_style: str = 'whitegrid', node_order: Optional[list] = None, auto_cluster: bool = False,
**kwargs) -> plt.Axes:
"""Plot functional connectivity between nodes in backend.
Parameters
----------
fc
Pandas dataframe containing or numpy array containing the functional connectivities.
threshold
Connectivtiy threshold to be applied (only connectivities larger than the threshold will be shown).
plot_style
Can either be `heatmap` for plotting with seaborn.heatmap or `circular_graph` for plotting with
mne.viz.plot_connectivity_circle. Check out the respective function docstrings for information on
their arguments (can be passed to kwargs).
bg_style
Only relevant if plot_style == heatmap. Then this will define the style of the background of the plot.
node_order
Order in which the nodes should appear in the plot.
auto_cluster
If true, automatic cluster detection will be used to arange the nodes
kwargs
Additional arguments for the fc calculation or fc plotting that can be passed.
Returns
-------
plt.Axes
Handle of the axis the plot was created in.
"""
import seaborn as sb
# turn fc into dataframe if necessary
if type(fc) is np.ndarray:
rows = kwargs.pop('yticklabels') if 'yticklabels' in kwargs.keys() else [str(i) for i in range(fc.shape[0])]
cols = kwargs.pop('xticklabels') if 'xticklabels' in kwargs.keys() else [str(i) for i in range(fc.shape[1])]
fc = pd.DataFrame(fc, index=[str(r) for r in rows], columns=[str(c) for c in cols])
# apply threshold
if threshold:
fc[fc < threshold] = 0.
# cluster the columns
#####################
if auto_cluster:
idx_r = [i for i in range(fc.shape[0])]
idx_c = [i for i in range(fc.shape[1])]
# Create a categorical color palette for node groups
col_pal_args = ['h', 's', 'l']
kwargs_tmp = {}
for key in kwargs.keys():
if key in col_pal_args:
kwargs_tmp[key] = kwargs.pop(key)
node_pal = sb.husl_palette(len(idx_c), **kwargs_tmp)
nodes = fc.columns.values
node_lut = dict(zip(map(str, nodes), node_pal))
# Convert the palette to vectors that will be drawn on the side of the fc plot
node_colors = pd.Series(nodes, index=fc.columns).map(node_lut)
elif node_order:
idx_c = [node_order.index(n) for n in fc.columns.values]
idx_r = [i for i in range(fc.shape[0])]
else:
idx_r = [i for i in range(fc.shape[0])]
idx_c = [i for i in range(fc.shape[1])]
fc = fc.iloc[idx_r, idx_c]
# plot the functional connectivities
####################################
# choose plot style
if plot_style == 'heatmap':
# seaborn plot
if 'xticklabels' not in kwargs:
kwargs['xticklabels'] = fc.columns.values[idx_c]
if 'yticklabels' not in kwargs:
kwargs['yticklabels'] = fc.index[idx_r]
sb.set_style(bg_style)
if auto_cluster:
ax = sb.clustermap(data=fc, row_colors=node_colors, col_colors=node_colors, **kwargs)
else:
ax = sb.heatmap(fc, **kwargs)
# ax.invert_yaxis()
elif plot_style == 'circular_graph':
# mne python plot
from mne.viz import circular_layout, plot_connectivity_circle
# get node order for node layout
node_names = fc.columns.values
if auto_cluster:
cluster_args = ['method', 'metric', 'z_score', 'standard_scale']
kwargs_tmp = {}
for key in kwargs.keys():
if key in cluster_args:
kwargs_tmp[key] = kwargs.pop(key)
clust_map = sb.clustermap(data=fc, row_colors=node_colors, col_colors=node_colors, **kwargs_tmp)
node_order = [node_names[idx] for idx in clust_map.dendrogram_row.reordered_ind]
elif not node_order:
node_order = list(node_names)
# create circular node layout
kwargs_tmp = {}
layout_args = ['start_pos', 'start_between', 'group_boundaries', 'group_sep']
for key in kwargs.keys():
if key in layout_args:
kwargs_tmp[key] = kwargs.pop(key)
node_angles = circular_layout(node_names, node_order, **kwargs_tmp)
# plot the circular graph
ax = plot_connectivity_circle(fc.values, node_names, node_angles=node_angles, **kwargs)
else:
raise ValueError(f'Plot style is not supported by this function: {plot_style}. Check the documentation of the '
f'argument `plot_style` for valid options.')
return ax
with open('/Users/pinheirochagas/Desktop/labels.csv', 'r') as f:
reader = csv.reader(f, delimiter=',')
labels = list(reader)
labels = labels[1:]
l1 = ['a' for i in range(len(labels))]
for i in range(len(labels)):
l1[i] = labels[i][0]
labels = l1
label_colors = [labels for label in labels]
labels[1][0]
node_angles = circular_layout(labels, labels, group_boundaries = boundaries)
fig = plt.figure(figsize=(20,20))
plot_connectivity_circle(corr_chans, labels,
node_angles=node_angles,
node_colors=[(.5,.5,.5, 1)],
facecolor = [0.1,0.1,0.1],
node_edgecolor=[0.1,0.1,0.1],
textcolor=[1,1,1],
fontsize_names = 10,
colormap='viridis',
vmin=np.min(corr_chans),
vmax = np.max(corr_chans),
linewidth = 1.5, fig=fig)
def make_circle(authors_df, label_names):
## Prep all labels, colors, etc
# Create node colors
types_colors = [ncol[0]] * len(type_labels)
mm_colors = [ncol[1]] * len(authors_df.loc[authors_df['GendCat'] == 'MM', 'CitationKey'])
mw_colors = [ncol[2]] * len(authors_df.loc[authors_df['GendCat'] == 'MW', 'CitationKey'])
wm_colors = [ncol[3]] * len(authors_df.loc[authors_df['GendCat'] == 'WM', 'CitationKey'])
ww_colors = [ncol[4]] * len(authors_df.loc[authors_df['GendCat'] == 'WW', 'CitationKey'])
u_colors = [ncol[5]] * len(authors_df.loc[authors_df['GendCat'].str.contains(r'U'), 'CitationKey'])
# LABEL COLORS GO IN THE ORIGINAL/MATRIX ORDER , not the reordered order!
label_colors = types_colors + mm_colors + mw_colors + wm_colors + ww_colors + u_colors
# MAKE NODE ORDER TO MAKE THE CIRCLE LOOK NICER - Node order is ONLY on the circle, not the matrix order.
pre_labels = ['Man - Man','Man - Woman','Spacer1']
post_labels = ['Spacer2','Woman - Man','Woman - Woman','Unknown']
# This node order is for the graph only! (ordered from starting point counter clockwise)
node_order = pre_labels + papers_label_list + post_labels
# now clean node order remove spacer labels only for graphing (angles needs all to be unique and match matrix)
cleaned_label_names = [' ' if 'Spacer' in i else i for i in label_names]
# Optional(?) clean paper titles for better looking graph
cleaned_titles = []
for paper in cleaned_label_names:
x = re.split('(\d+)',paper)
if len(x) == 3:
cleaned_titles.append(f'{x[0]} ({x[1]})')
else:
cleaned_titles.append(paper)
logger.debug(f'\n-Cleaned titles list: {cleaned_titles}\n')
## PREP FIGURE ##
fig = plt.figure(figsize=(30, 30), facecolor='black')
## MAKE NODES ANGLES ##
sp = 90
node_angles = circular_layout(label_names, node_order, start_pos=sp)
## Make custom colormap (if used)
cmap = colors.ListedColormap(['black',
ccol[0],ccol[1],
ccol[2],ccol[3],
ccol[4]])
boundaries = [0, 1, 2, 3, 4, 5]
norm = colors.BoundaryNorm(boundaries, cmap.N, clip=True)
# Make Custom/Manual Legend
MM_patch = mpatches.Patch(facecolor=ncol[1], label='Man / Man', linewidth = 1, edgecolor = 'black')
MW_patch = mpatches.Patch(facecolor=ncol[2], label='Man / Woman', linewidth = 1, edgecolor = 'black')
WM_patch = mpatches.Patch(facecolor=ncol[3], label='Woman / Man', linewidth = 1, edgecolor = 'black')
WW_patch = mpatches.Patch(facecolor=ncol[4], label='Woman / Woman', linewidth = 1, edgecolor = 'black')
U_patch = mpatches.Patch(facecolor=ncol[5], label='Unknown', linewidth = 1, edgecolor = 'black')
legend = plt.gcf().legend(handles=[MM_patch,MW_patch,WM_patch,WW_patch,U_patch],
loc=1, facecolor=args.lcol, framealpha=.98, prop={'size':35},
fancybox=True, title='Citation Type',title_fontsize=40)
# change legen color text?
# plt.setp(legend.get_texts(), color='g')
## Create Circle Graph
plot_connectivity_circle(cite_mat, cleaned_titles,
node_angles=node_angles,
node_colors=label_colors,
title=title, padding=4, fontsize_title=48,
textcolor='white', facecolor='black',
colormap=cmap, colorbar=False, fig=fig,
linewidth=4, fontsize_names=25,
subplot=111,
interactive=False, show=False
)
## SAVE FIGURE ##
out_fig = os.path.abspath(os.path.join(args.out_dir,png_name))
# have to re-set the facecolor before saving #
fig.savefig(out_fig, facecolor='black')
# Average across different k-values:
df_k_feat = df_feat.reset_index().groupby(['band','targets'])['features'].apply(lambda x: np.vstack(x).mean(0))
# This is a weighted average
ks = np.unique(df_feat.reset_index()['k'].values)[::-1]
fx = lambda x: np.average(np.vstack(x), 0, weights=ks)
df_k_feat = df_feat.reset_index().groupby(['band','targets'])['features'].apply(fx)
df = df_k_feat.reset_index()
# Connectome labels loading
labels = np.loadtxt("/home/robbis/ROI_MNI_V4.txt", dtype=np.str_)
node_names = labels.T[1][:99]
node_idx = np.argsort(np.array([node[-1] for node in node_names]))
node_angles = circular_layout(node_names.tolist(),
node_names[node_idx].tolist(),
start_pos=90,
group_boundaries=[0, len(node_names) / 2.+1])
# Plot of the connectivity circle of selected features
for band in np.unique(df['band'].values):
for condition in np.unique(df['targets'].values):
features = filter_dataframe(df, selection_dict={'band':[band],
'targets':[condition]}).reset_index()
matrix = array_to_matrix(features['features'].values[0], copy=True, diagonal_filler=0.)
f, a = plot_connectivity_circle_edited(matrix,
node_names=node_names,
con_thresh=70,
node_angles=node_angles,
colormap='magma',
def test_plot_connectivity_circle():
"""Test plotting connectivity circle."""
node_order = ['frontalpole-lh', 'parsorbitalis-lh',
'lateralorbitofrontal-lh', 'rostralmiddlefrontal-lh',
'medialorbitofrontal-lh', 'parstriangularis-lh',
'rostralanteriorcingulate-lh', 'temporalpole-lh',
'parsopercularis-lh', 'caudalanteriorcingulate-lh',
'entorhinal-lh', 'superiorfrontal-lh', 'insula-lh',
'caudalmiddlefrontal-lh', 'superiortemporal-lh',
'parahippocampal-lh', 'middletemporal-lh',
'inferiortemporal-lh', 'precentral-lh',
'transversetemporal-lh', 'posteriorcingulate-lh',
'fusiform-lh', 'postcentral-lh', 'bankssts-lh',
'supramarginal-lh', 'isthmuscingulate-lh', 'paracentral-lh',
'lingual-lh', 'precuneus-lh', 'inferiorparietal-lh',
'superiorparietal-lh', 'pericalcarine-lh',
'lateraloccipital-lh', 'cuneus-lh', 'cuneus-rh',
'lateraloccipital-rh', 'pericalcarine-rh',
'superiorparietal-rh', 'inferiorparietal-rh', 'precuneus-rh',
'lingual-rh', 'paracentral-rh', 'isthmuscingulate-rh',
'supramarginal-rh', 'bankssts-rh', 'postcentral-rh',
'fusiform-rh', 'posteriorcingulate-rh',
'transversetemporal-rh', 'precentral-rh',
'inferiortemporal-rh', 'middletemporal-rh',
'parahippocampal-rh', 'superiortemporal-rh',
'caudalmiddlefrontal-rh', 'insula-rh', 'superiorfrontal-rh',
'entorhinal-rh', 'caudalanteriorcingulate-rh',
'parsopercularis-rh', 'temporalpole-rh',
'rostralanteriorcingulate-rh', 'parstriangularis-rh',
'medialorbitofrontal-rh', 'rostralmiddlefrontal-rh',
'lateralorbitofrontal-rh', 'parsorbitalis-rh',
'frontalpole-rh']
label_names = ['bankssts-lh', 'bankssts-rh', 'caudalanteriorcingulate-lh',
'caudalanteriorcingulate-rh', 'caudalmiddlefrontal-lh',
'caudalmiddlefrontal-rh', 'cuneus-lh', 'cuneus-rh',
'entorhinal-lh', 'entorhinal-rh', 'frontalpole-lh',
'frontalpole-rh', 'fusiform-lh', 'fusiform-rh',
'inferiorparietal-lh', 'inferiorparietal-rh',
'inferiortemporal-lh', 'inferiortemporal-rh', 'insula-lh',
'insula-rh', 'isthmuscingulate-lh', 'isthmuscingulate-rh',
'lateraloccipital-lh', 'lateraloccipital-rh',
'lateralorbitofrontal-lh', 'lateralorbitofrontal-rh',
'lingual-lh', 'lingual-rh', 'medialorbitofrontal-lh',
'medialorbitofrontal-rh', 'middletemporal-lh',
'middletemporal-rh', 'paracentral-lh', 'paracentral-rh',
'parahippocampal-lh', 'parahippocampal-rh',
'parsopercularis-lh', 'parsopercularis-rh',
'parsorbitalis-lh', 'parsorbitalis-rh',
'parstriangularis-lh', 'parstriangularis-rh',
'pericalcarine-lh', 'pericalcarine-rh', 'postcentral-lh',
'postcentral-rh', 'posteriorcingulate-lh',
'posteriorcingulate-rh', 'precentral-lh', 'precentral-rh',
'precuneus-lh', 'precuneus-rh',
'rostralanteriorcingulate-lh',
'rostralanteriorcingulate-rh', 'rostralmiddlefrontal-lh',
'rostralmiddlefrontal-rh', 'superiorfrontal-lh',
'superiorfrontal-rh', 'superiorparietal-lh',
'superiorparietal-rh', 'superiortemporal-lh',
'superiortemporal-rh', 'supramarginal-lh',
'supramarginal-rh', 'temporalpole-lh', 'temporalpole-rh',
'transversetemporal-lh', 'transversetemporal-rh']
group_boundaries = [0, len(label_names) / 2]
node_angles = circular_layout(label_names, node_order, start_pos=90,
group_boundaries=group_boundaries)
con = np.random.RandomState(0).randn(68, 68)
plot_connectivity_circle(con, label_names, n_lines=300,
node_angles=node_angles, title='test',
)
pytest.raises(ValueError, circular_layout, label_names, node_order,
group_boundaries=[-1])
pytest.raises(ValueError, circular_layout, label_names, node_order,
group_boundaries=[20, 0])
plt.close('all')
print("E2E prediction results:")
test_metrics_0 = regression_metrics(y_pred_0, y_true_0)
print("class 0: {0}".format(test_metrics_0))
test_metrics_1 = regression_metrics(y_pred_1, y_true_1)
print("class 1: {0}".format(test_metrics_1))
# Saliency map is the gradient of the maximum score value with respect to
# the input image.
model.model.eval()
X = torch.from_numpy(x_test)
X.requires_grad_()
scores = model.model(X)
scores.backward(torch.ones(scores.shape, dtype=torch.float32))
saliency, _ = torch.max(X.grad.data.abs(), dim=1)
saliency = np.mean(saliency.numpy(), axis=0)
hemi_size = len(labels) // 2
node_order = labels[:hemi_size]
node_order.extend(labels[hemi_size:][::-1])
node_angles = circular_layout(labels,
node_order,
start_pos=90,
group_boundaries=[0, hemi_size])
plot_connectivity_circle(saliency,
labels,
n_lines=300,
node_angles=node_angles,
title="Partial derivatives mapped on a circle plot")
plt.show()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ypos.append(ypos)
# Reorder the labels based on their location
lh_labels = [label for (ypos, label) in sorted(zip(label_ypos, lh_labels))]
# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels]
# Save the plot order and create a circular layout
node_order = list()
node_order.extend(lh_labels[::-1]) # reverse the order
node_order.extend(rh_labels)
node_angles = circular_layout(label_names, node_order, start_pos=90)
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
plot_connectivity_circle(con,
label_names,
n_lines=300,
node_angles=node_angles,
node_colors=label_colors,
title='All-to-All Connectivity left-Auditory '
'Condition')
import matplotlib.pyplot as plt
plt.savefig('circle.png', facecolor='black')
plt.show()
def plot_tab_circular_connectivity(list_list_conmat,
all_elec_labels,
plot_filename,
coh_low_thresh=0.0,
coh_high_thresh=1.0,
color_bar='gist_rainbow',
column_labels=[],
row_labels=[]):
"""Plot tab circular conectivity."""
nb_lines = len(list_list_conmat)
print(nb_lines)
print(len(list_list_conmat[0]))
if len(list_list_conmat) != 1:
for i, j in combinations(list(range(nb_lines)), 2):
assert len(list_list_conmat[i]) == len(
list_list_conmat[j]), ("Error, not all the same length {} != "
"{}".format(len(list_list_conmat[i]),
len(list_list_conmat[j])))
nb_cols = len(list_list_conmat[0])
# for i in
# print list_list_conmat[0][0].shape
# Angles
bounds = [0, len(all_elec_labels) / 2]
all_node_angles = circular_layout(all_elec_labels,
node_order=all_elec_labels,
start_pos=90,
group_boundaries=bounds)
# print all_node_angles
fig, axes = plt.subplots(nrows=nb_lines,
ncols=nb_cols,
figsize=(4 * nb_cols, 4 * nb_lines),
facecolor='black')
# fig = plt.figure(num=None, figsize=(4*nb_cols, 4*nb_lines),
# facecolor='black')
if len(column_labels) == 0:
column_labels = ['Column {}'.format(col) for col in range(nb_cols)]
if len(row_labels) == 0:
row_labels = ['Line {}'.format(row) for row in range(nb_lines)]
assert len(column_labels) == nb_cols, (
"Error, specifying invalid number of column labels")
assert len(row_labels) == nb_lines, (
"Error, specifying invalid number of line labels")
for index_sess, list_conmat in enumerate(list_list_conmat):
print(len(list_conmat))
for index_win, np_all_mean_con_mats in enumerate(list_conmat):
print(np_all_mean_con_mats.shape)
kw = dict(textcolor="black",
facecolor="white",
n_lines=None,
node_angles=all_node_angles,
fontsize_names=15,
show=False,
colormap=color_bar,
vmin=coh_low_thresh,
vmax=coh_high_thresh,
fig=fig,
subplot=(nb_lines, nb_cols,
1 + index_win + nb_cols * index_sess))
if index_win == len(list_conmat) - 1:
fig, ax = plot_connectivity_circle(np_all_mean_con_mats,
all_elec_labels,
colorbar_size=0.5,
**kw)
# (nb_lines,nb_cols,1+index_win+nb_cols*index_sess))
else:
fig, ax = plot_connectivity_circle(np_all_mean_con_mats,
all_elec_labels,
colorbar=False,
**kw)
if index_win == 0:
ax.set_ylabel(row_labels[index_sess],
rotation=0,
size='large',
fontsize=25)
if index_sess == 0:
ax.set_title(column_labels[index_win],
fontdict={'fontsize': 25})
# saving
print(plot_filename)
fig.savefig(plot_filename, facecolor='white')
plt.close(fig)
# fig1.close()
del fig
labels = labels_lr[:99]
node_idx = np.lexsort((labels.T[-1], [l[-1] for l in labels.T[1]]))
node_idx = np.hstack((node_idx[:49], node_idx[49:][::-1]))
labels_ord = labels[node_idx]
coords_lr_ord = coords_lr[node_idx]
names = labels_ord.T[1]
names = np.array([n.replace("_", " ") for n in names])
node_angles = circular_layout(names.tolist(),
names.tolist(),
start_between=False,
start_pos=90,
group_boundaries=[0, 49, len(names) / 2.+1],
group_sep=3.)
node_network = labels_ord.T[3]
networks, count = np.unique(node_network, return_counts=True)
color_network = sns.color_palette("Paired", len(networks)+1)
colors_ = dict(zip(networks, color_network[1:]))
colors = [colors_[n] for n in node_network]
########################################################
path = "/scratch/work/guidotr1/data/derivatives"
path = "/media/robbis/Seagate_Pt1/data/working_memory/data/derivatives/"
dataframes = []
def test_plot_connectivity_circle():
"""Test plotting connectivity circle
"""
node_order = [
"frontalpole-lh",
"parsorbitalis-lh",
"lateralorbitofrontal-lh",
"rostralmiddlefrontal-lh",
"medialorbitofrontal-lh",
"parstriangularis-lh",
"rostralanteriorcingulate-lh",
"temporalpole-lh",
"parsopercularis-lh",
"caudalanteriorcingulate-lh",
"entorhinal-lh",
"superiorfrontal-lh",
"insula-lh",
"caudalmiddlefrontal-lh",
"superiortemporal-lh",
"parahippocampal-lh",
"middletemporal-lh",
"inferiortemporal-lh",
"precentral-lh",
"transversetemporal-lh",
"posteriorcingulate-lh",
"fusiform-lh",
"postcentral-lh",
"bankssts-lh",
"supramarginal-lh",
"isthmuscingulate-lh",
"paracentral-lh",
"lingual-lh",
"precuneus-lh",
"inferiorparietal-lh",
"superiorparietal-lh",
"pericalcarine-lh",
"lateraloccipital-lh",
"cuneus-lh",
"cuneus-rh",
"lateraloccipital-rh",
"pericalcarine-rh",
"superiorparietal-rh",
"inferiorparietal-rh",
"precuneus-rh",
"lingual-rh",
"paracentral-rh",
"isthmuscingulate-rh",
"supramarginal-rh",
"bankssts-rh",
"postcentral-rh",
"fusiform-rh",
"posteriorcingulate-rh",
"transversetemporal-rh",
"precentral-rh",
"inferiortemporal-rh",
"middletemporal-rh",
"parahippocampal-rh",
"superiortemporal-rh",
"caudalmiddlefrontal-rh",
"insula-rh",
"superiorfrontal-rh",
"entorhinal-rh",
"caudalanteriorcingulate-rh",
"parsopercularis-rh",
"temporalpole-rh",
"rostralanteriorcingulate-rh",
"parstriangularis-rh",
"medialorbitofrontal-rh",
"rostralmiddlefrontal-rh",
"lateralorbitofrontal-rh",
"parsorbitalis-rh",
"frontalpole-rh",
]
label_names = [
"bankssts-lh",
"bankssts-rh",
"caudalanteriorcingulate-lh",
"caudalanteriorcingulate-rh",
"caudalmiddlefrontal-lh",
"caudalmiddlefrontal-rh",
"cuneus-lh",
"cuneus-rh",
"entorhinal-lh",
"entorhinal-rh",
"frontalpole-lh",
"frontalpole-rh",
"fusiform-lh",
"fusiform-rh",
"inferiorparietal-lh",
"inferiorparietal-rh",
"inferiortemporal-lh",
"inferiortemporal-rh",
"insula-lh",
"insula-rh",
"isthmuscingulate-lh",
"isthmuscingulate-rh",
"lateraloccipital-lh",
"lateraloccipital-rh",
"lateralorbitofrontal-lh",
"lateralorbitofrontal-rh",
"lingual-lh",
"lingual-rh",
"medialorbitofrontal-lh",
"medialorbitofrontal-rh",
"middletemporal-lh",
"middletemporal-rh",
"paracentral-lh",
"paracentral-rh",
"parahippocampal-lh",
"parahippocampal-rh",
"parsopercularis-lh",
"parsopercularis-rh",
"parsorbitalis-lh",
"parsorbitalis-rh",
"parstriangularis-lh",
"parstriangularis-rh",
"pericalcarine-lh",
"pericalcarine-rh",
"postcentral-lh",
"postcentral-rh",
"posteriorcingulate-lh",
"posteriorcingulate-rh",
"precentral-lh",
"precentral-rh",
"precuneus-lh",
"precuneus-rh",
"rostralanteriorcingulate-lh",
"rostralanteriorcingulate-rh",
"rostralmiddlefrontal-lh",
"rostralmiddlefrontal-rh",
"superiorfrontal-lh",
"superiorfrontal-rh",
"superiorparietal-lh",
"superiorparietal-rh",
"superiortemporal-lh",
"superiortemporal-rh",
"supramarginal-lh",
"supramarginal-rh",
"temporalpole-lh",
"temporalpole-rh",
"transversetemporal-lh",
"transversetemporal-rh",
]
group_boundaries = [0, len(label_names) / 2]
node_angles = circular_layout(label_names, node_order, start_pos=90, group_boundaries=group_boundaries)
con = np.random.randn(68, 68)
plot_connectivity_circle(con, label_names, n_lines=300, node_angles=node_angles, title="test")
plt.close("all")
assert_raises(ValueError, circular_layout, label_names, node_order, group_boundaries=[-1])
assert_raises(ValueError, circular_layout, label_names, node_order, group_boundaries=[20, 0])
def plot_fica_grouped_circle(yaml_fname,
con,
node_order_size,
out_fname='grouped_circle.png',
title=None,
facecolor='white',
fontsize_names=6,
subplot=111,
include_legend=False,
n_lines=None,
fig=None,
show=True):
'''
Plot the connectivity circle grouped and ordered according to
groups in the yaml input file provided.
This is not specific to 'aparc' parcellation and does not split the labels
into left and right hemispheres.
Note: Currently requires fica_names.txt in jumeg/examples. This needs to
be removed.
'''
# load the label names in the original order
# TODO remove empty lines / strings if any
# TODO remove below code from here
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]
# read the yaml file with grouping
if op.isfile(yaml_fname):
with open(yaml_fname, 'r') as f:
labels = yaml.load(f)
else:
print '%s - File not found.' % yaml_fname
sys.exit()
cortex_colors = ['m', 'b', 'y', 'c', 'r', 'g']
# make list of label_names (without individual cortex locations)
label_names = list()
for lab in labels:
label_names.extend(labels[lab])
group_labels = labels.keys()
# Save the plot order and create a circular layout
node_order = label_names
assert len(node_order) == node_order_size, 'Node order length is correct.'
# save the group order
group_node_order = group_labels
from mne.viz.circle import circular_layout
group_node_angles = circular_layout(group_node_order,
group_node_order,
start_pos=75.)
# the respective no. of regions in each cortex
group_bound = [len(labels[key]) for key in labels.keys()]
group_bound = [0] + group_bound
# group_bound = [0] + group_bound[::-1] + group_bound
group_boundaries = [
sum(group_bound[:i + 1]) for i in range(len(group_bound))
]
# remove the first element of group_bound
# make label colours such that each cortex is of one colour
group_bound.pop(0)
label_colors = []
for ind, rep in enumerate(group_bound):
label_colors += [cortex_colors[ind]] * rep
assert len(label_colors) == len(node_order), 'Num. of colours do not match'
# remove the last total sum of the list
group_boundaries.pop()
# obtain the node angles
node_angles = circular_layout(orig_labels,
node_order,
start_pos=90,
group_boundaries=group_boundaries)
# the order of the node_colors must match that of orig_labels
# therefore below reordering is necessary
reordered_colors = [
label_colors[node_order.index(orig)] for orig in orig_labels
]
# Plot the graph using node_order and colours
_plot_connectivity_circle(con,
orig_labels,
n_lines=n_lines,
facecolor=facecolor,
textcolor='black',
group_node_order=group_node_order,
group_node_angles=group_node_angles,
group_colors=cortex_colors,
fontsize_groups=6,
node_angles=node_angles,
node_colors=reordered_colors,
fontsize_names=8,
node_edgecolor='white',
fig=fig,
colorbar=False,
show=show,
subplot=subplot,
title=title)
if include_legend:
import matplotlib.patches as mpatches
legend_patches = [
mpatches.Patch(color=col, label=key)
for col, key in zip(['g', 'r', 'c', 'y', 'b', 'm'], labels.keys())
]
plt.legend(handles=legend_patches,
loc=(0.02, 0.02),
ncol=1,
mode=None,
fontsize='small')
if out_fname:
plt.savefig(out_fname, facecolor='white', dpi=300)
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.exists(args.matrix):
parser.error("{0} is not a valid file path.".format(args.matrix))
if not os.path.splitext(args.matrix)[1] == ".npy":
parser.error("Connectivity matrix must be given in .npy format.")
if not os.path.exists(args.row_map):
parser.error("{0} is not a valid file path.".format(args.row_map))
if os.path.splitext(args.row_map)[1] != ".pkl":
parser.error("Name mapping must be given in a .pkl file.")
if args.save and os.path.exists(args.save) and not args.force:
parser.error("Output image: {0} already exists.\n".format(args.save) +
"Use -f to force overwriting.")
con_mat = np.load(args.matrix)
with open(args.row_map) as f:
row_name_map = pickle.load(f)
lh_tags = ['_lh_', 'Left']
rh_tags = ['_rh_', 'Right']
if args.clean_lbl:
label_names = [row_name_map[k]['lut_name'] for k in sorted(row_name_map)]
lh_tags = ['LH']
rh_tags = ['RH']
else:
label_names = [row_name_map[k]['free_name'] for k in sorted(row_name_map)]
lh_tags.append('-lh-')
rh_tags.append('-rh-')
lh_labels = [name for name in label_names if any(tag in name for tag in lh_tags)]
rh_labels = [name for name in label_names if any(tag in name for tag in rh_tags)]
# Validate if all labels were found
uncut_labels = set(label_names) - set(lh_labels) - set(rh_labels)
if len(uncut_labels) > 0:
raise ValueError("Some labels were not filtered as Left or Right.")
if args.sort_y:
# TODO choose color
# label_colors = [label.color for label in labels]
label_ypos = []
for name in lh_labels:
label_ypos.append(find_mean_y_pos(row_name_map, name))
lh_labels = [label for (ypos, label) in sorted(zip(label_ypos, lh_labels))]
rh_labels = [label for (ypos, label) in sorted(zip(label_ypos, rh_labels))]
node_order = list()
node_order.extend(lh_labels[::-1]) # reverse the order
node_order.extend(rh_labels)
node_angles = circular_layout(label_names, node_order, start_pos=90,
group_boundaries=[0, len(label_names) / 2])
fig, axes = plot_connectivity_circle(con_mat, label_names, linewidth=1.5,
interactive=True, vmin=0, vmax=1,
node_angles=node_angles,
title='All-to-all Connectivity')
# plot_connectivity_circle(
# node_colors=label_colors,
# fontsize_colorbar=6,
plt.show(block=True)
if args.save:
fig.savefig(args.save, facecolor='black')
def plot_fica_grouped_circle(yaml_fname, con, node_order_size,
out_fname='grouped_circle.png', title=None,
facecolor='white', fontsize_names=6,
subplot=111, include_legend=False,
n_lines=None, fig=None, show=True):
'''
Plot the connectivity circle grouped and ordered according to
groups in the yaml input file provided.
This is not specific to 'aparc' parcellation and does not split the labels
into left and right hemispheres.
Note: Currently requires fica_names.txt in jumeg/examples. This needs to
be removed.
'''
# load the label names in the original order
# TODO remove empty lines / strings if any
# TODO remove below code from here
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]
# read the yaml file with grouping
if op.isfile(yaml_fname):
with open(yaml_fname, 'r') as f:
labels = yaml.load(f)
else:
print '%s - File not found.' % yaml_fname
sys.exit()
cortex_colors = ['m', 'b', 'y', 'c', 'r', 'g']
# make list of label_names (without individual cortex locations)
label_names = list()
for lab in labels:
label_names.extend(labels[lab])
group_labels = labels.keys()
# Save the plot order and create a circular layout
node_order = label_names
assert len(node_order) == node_order_size, 'Node order length is correct.'
# save the group order
group_node_order = group_labels
from mne.viz.circle import circular_layout
group_node_angles = circular_layout(group_node_order, group_node_order,
start_pos=75.)
# the respective no. of regions in each cortex
group_bound = [len(labels[key]) for key in labels.keys()]
group_bound = [0] + group_bound
# group_bound = [0] + group_bound[::-1] + group_bound
group_boundaries = [sum(group_bound[:i+1])
for i in range(len(group_bound))]
# remove the first element of group_bound
# make label colours such that each cortex is of one colour
group_bound.pop(0)
label_colors = []
for ind, rep in enumerate(group_bound):
label_colors += [cortex_colors[ind]] * rep
assert len(label_colors) == len(node_order), 'Num. of colours do not match'
# remove the last total sum of the list
group_boundaries.pop()
# obtain the node angles
node_angles = circular_layout(orig_labels, node_order, start_pos=90,
group_boundaries=group_boundaries)
# the order of the node_colors must match that of orig_labels
# therefore below reordering is necessary
reordered_colors = [label_colors[node_order.index(orig)]
for orig in orig_labels]
# Plot the graph using node_order and colours
_plot_connectivity_circle(con, orig_labels, n_lines=n_lines,
facecolor=facecolor, textcolor='black',
group_node_order=group_node_order,
group_node_angles=group_node_angles,
group_colors=cortex_colors,
fontsize_groups=6, node_angles=node_angles,
node_colors=reordered_colors, fontsize_names=8,
node_edgecolor='white', fig=fig,
colorbar=False, show=show, subplot=subplot,
title=title)
if include_legend:
import matplotlib.patches as mpatches
legend_patches = [mpatches.Patch(color=col, label=key)
for col, key in zip(['g', 'r', 'c', 'y', 'b', 'm'],
labels.keys())]
plt.legend(handles=legend_patches, loc=(0.02, 0.02), ncol=1,
mode=None, fontsize='small')
if out_fname:
plt.savefig(out_fname, facecolor='white', dpi=300)
# Get the y-location of the label
label_ypos = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ypos.append(ypos)
# Reorder the labels based on their location
lh_labels = [label for (ypos, label) in sorted(zip(label_ypos, lh_labels))]
# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels]
# Save the plot order and create a circular layout
node_order = list()
node_order.extend(lh_labels[::-1]) # reverse the order
node_order.extend(rh_labels)
node_angles = circular_layout(label_names, node_order, start_pos=90)
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
plot_connectivity_circle(con, label_names, n_lines=300, node_angles=node_angles,
node_colors=label_colors,
title='All-to-All Connectivity left-Auditory '
'Condition')
import matplotlib.pyplot as plt
plt.savefig('circle.png', facecolor='black')
plt.show()
node_order = models_only
print 'test1', len(models_only)
print models_only
print 'test2', len(model_runs)
print model_runs
else:
label_names = model_runs
node_order = model_runs
label_colors = model_colors
node_angles = circular_layout(
label_names,
node_order,
start_pos=90,
#start_pos=95,
#group_boundaries=[0, len(label_names) / 2])
#group_boundaries=[0],
#group_boundaries=[0, len(label_names)-1],
#group_boundaries = gbdy,
)
figure = plt.figure(figsize=(20, 20), dpi=100, facecolor='black')
if OnePerMod:
ftsiz = 10
else:
ftsiz = 5
fig, ax = plot_connectivity_circle(
M,
label_names,