Note
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Compute mixed source space connectivity and visualize it using a circular graph#
This example computes the all-to-all connectivity between 75 regions in a mixed source space based on dSPM inverse solutions and a FreeSurfer cortical parcellation. The connectivity is visualized using a circular graph which is ordered based on the locations of the regions in the axial plane.
# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD (3-clause)
import os.path as op
import matplotlib.pyplot as plt
import mne
import numpy as np
from mne import make_forward_solution, setup_source_space, setup_volume_source_space
from mne.datasets import sample
from mne.io import read_raw_fif
from mne.minimum_norm import apply_inverse_epochs, make_inverse_operator
from mne.viz import circular_layout
from mne_connectivity import spectral_connectivity_epochs
from mne_connectivity.viz import plot_connectivity_circle
# Set directories
data_path = sample.data_path()
subject = "sample"
data_dir = op.join(data_path, "MEG", subject)
subjects_dir = op.join(data_path, "subjects")
bem_dir = op.join(subjects_dir, subject, "bem")
# Set file names
fname_aseg = op.join(subjects_dir, subject, "mri", "aseg.mgz")
fname_model = op.join(bem_dir, "%s-5120-bem.fif" % subject)
fname_bem = op.join(bem_dir, "%s-5120-bem-sol.fif" % subject)
fname_raw = op.join(data_dir, "sample_audvis_filt-0-40_raw.fif")
fname_trans = op.join(data_dir, "sample_audvis_raw-trans.fif")
fname_cov = op.join(data_dir, "ernoise-cov.fif")
fname_event = op.join(data_dir, "sample_audvis_filt-0-40_raw-eve.fif")
# List of sub structures we are interested in. We select only the
# sub structures we want to include in the source space
labels_vol = [
"Left-Amygdala",
"Left-Thalamus-Proper",
"Left-Cerebellum-Cortex",
"Brain-Stem",
"Right-Amygdala",
"Right-Thalamus-Proper",
"Right-Cerebellum-Cortex",
]
# Setup a surface-based source space, oct5 is not very dense (just used
# to speed up this example; we recommend oct6 in actual analyses)
src = setup_source_space(
subject, subjects_dir=subjects_dir, spacing="oct5", add_dist=False
)
# Setup a volume source space
# set pos=10.0 for speed, not very accurate; we recommend something smaller
# like 5.0 in actual analyses:
vol_src = setup_volume_source_space(
subject,
mri=fname_aseg,
pos=10.0,
bem=fname_model,
add_interpolator=False, # just for speed, usually use True
volume_label=labels_vol,
subjects_dir=subjects_dir,
)
# Generate the mixed source space
src += vol_src
# Load data
raw = read_raw_fif(fname_raw)
raw.pick_types(meg=True, eeg=False, eog=True, stim=True).load_data()
events = mne.find_events(raw)
noise_cov = mne.read_cov(fname_cov)
# compute the fwd matrix
fwd = make_forward_solution(
raw.info, fname_trans, src, fname_bem, mindist=5.0
) # ignore sources<=5mm from innerskull
del src
# Define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
reject = dict(mag=4e-12, grad=4000e-13, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject=reject, preload=False)
del raw
# Compute inverse solution and for each epoch
snr = 1.0 # use smaller SNR for raw data
inv_method = "dSPM"
parc = "aparc" # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
lambda2 = 1.0 / snr**2
# Compute inverse operator
inverse_operator = make_inverse_operator(
epochs.info, fwd, noise_cov, depth=None, fixed=False
)
del fwd
stcs = apply_inverse_epochs(
epochs, inverse_operator, lambda2, inv_method, pick_ori=None, return_generator=True
)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels_parc = mne.read_labels_from_annot(subject, parc=parc, subjects_dir=subjects_dir)
# Average the source estimates within each label of the cortical parcellation
# and each sub-structure contained in the source space.
# When mode = 'mean_flip', this option is used only for the cortical labels.
src = inverse_operator["src"]
label_ts = mne.extract_label_time_course(
stcs, labels_parc, src, mode="mean_flip", allow_empty=True, return_generator=True
)
# We compute the connectivity in the alpha band and plot it using a circular
# graph layout
fmin = 8.0
fmax = 13.0
sfreq = epochs.info["sfreq"] # the sampling frequency
con = spectral_connectivity_epochs(
label_ts,
method="pli",
mode="multitaper",
sfreq=sfreq,
fmin=fmin,
fmax=fmax,
faverage=True,
mt_adaptive=True,
n_jobs=1,
)
# We create a list of Label containing also the sub structures
labels_aseg = mne.get_volume_labels_from_src(src, subject, subjects_dir)
labels = labels_parc + labels_aseg
# read colors
node_colors = [label.color for label in labels]
# 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")]
rh_labels = [name for name in label_names if name.endswith("rh")]
# Get the y-location of the label
label_ypos_lh = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(labels[idx].pos[:, 1])
label_ypos_lh.append(ypos)
try:
idx = label_names.index("Brain-Stem")
except ValueError:
pass
else:
ypos = np.mean(labels[idx].pos[:, 1])
lh_labels.append("Brain-Stem")
label_ypos_lh.append(ypos)
# 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 = 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.get_data(output="dense")[:, :, 0]
fig, ax = plt.subplots(figsize=(8, 8), facecolor="black", subplot_kw=dict(polar=True))
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)",
ax=ax,
)
fig.tight_layout()
Setting up the source space with the following parameters:
SUBJECTS_DIR = /home/circleci/mne_data/MNE-sample-data/subjects
Subject = sample
Surface = white
Octahedron subdivision grade 5
>>> 1. Creating the source space...
Doing the octahedral vertex picking...
Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.white...
Mapping lh sample -> oct (5) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/lh.sphere...
Setting up the triangulation for the decimated surface...
loaded lh.white 1026/155407 selected to source space (oct = 5)
Loading /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.white...
Mapping rh sample -> oct (5) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/circleci/mne_data/MNE-sample-data/subjects/sample/surf/rh.sphere...
Setting up the triangulation for the decimated surface...
loaded rh.white 1026/156866 selected to source space (oct = 5)
You are now one step closer to computing the gain matrix
BEM : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem.fif
grid : 10.0 mm
mindist : 5.0 mm
MRI volume : /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz
Reading /home/circleci/mne_data/MNE-sample-data/subjects/sample/mri/aseg.mgz...
Loaded inner skull from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem.fif (2562 nodes)
Surface CM = ( 0.7 -10.0 44.3) mm
Surface fits inside a sphere with radius 91.8 mm
Surface extent:
x = -66.7 ... 68.8 mm
y = -88.0 ... 79.0 mm
z = -44.5 ... 105.8 mm
Grid extent:
x = -70.0 ... 70.0 mm
y = -90.0 ... 80.0 mm
z = -50.0 ... 110.0 mm
4590 sources before omitting any.
2961 sources after omitting infeasible sources not within 0.0 - 91.8 mm.
Source spaces are in MRI coordinates.
Checking that the sources are inside the surface and at least 5.0 mm away (will take a few...)
Checking surface interior status for 2961 points...
Found 346/2961 points inside an interior sphere of radius 43.6 mm
Found 0/2961 points outside an exterior sphere of radius 91.8 mm
Found 1275/2615 points outside using surface Qhull
Found 97/1340 points outside using solid angles
Total 1589/2961 points inside the surface
Interior check completed in 705.4 ms
1372 source space points omitted because they are outside the inner skull surface.
283 source space points omitted because of the 5.0-mm distance limit.
1306 sources remaining after excluding the sources outside the surface and less than 5.0 mm inside.
Selected 2 voxels from Left-Amygdala
Selected 9 voxels from Left-Thalamus-Proper
Selected 33 voxels from Left-Cerebellum-Cortex
Selected 21 voxels from Brain-Stem
Selected 1 voxel from Right-Amygdala
Selected 7 voxels from Right-Thalamus-Proper
Selected 44 voxels from Right-Cerebellum-Cortex
Adjusting the neighborhood info.
Source space : MRI voxel -> MRI (surface RAS)
0.010000 0.000000 0.000000 -70.00 mm
0.000000 0.010000 0.000000 -90.00 mm
0.000000 0.000000 0.010000 -50.00 mm
0.000000 0.000000 0.000000 1.00
MRI volume : MRI voxel -> MRI (surface RAS)
-0.001000 0.000000 0.000000 128.00 mm
0.000000 0.000000 0.001000 -128.00 mm
0.000000 -0.001000 0.000000 128.00 mm
0.000000 0.000000 0.000000 1.00
MRI volume : MRI (surface RAS) -> RAS (non-zero origin)
1.000000 -0.000000 -0.000000 -5.27 mm
-0.000000 1.000000 -0.000000 9.04 mm
-0.000000 0.000000 1.000000 -27.29 mm
0.000000 0.000000 0.000000 1.00
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
319 events found on stim channel STI 014
Event IDs: [ 1 2 3 4 5 32]
306 x 306 full covariance (kind = 1) found.
Read a total of 3 projection items:
PCA-v1 (1 x 102) active
PCA-v2 (1 x 102) active
PCA-v3 (1 x 102) active
Source space : <SourceSpaces: [<surface (lh), n_vertices=155407, n_used=1026>, <surface (rh), n_vertices=156866, n_used=1026>, <volume (Left-Amygdala), n_used=2>, <volume (Left-Thalamus-Proper), n_used=9>, <volume (Left-Cerebellum-Cortex), n_used=33>, <volume (Brain-Stem), n_used=21>, <volume (Right-Amygdala), n_used=1>, <volume (Right-Thalamus-Proper), n_used=7>, <volume (Right-Cerebellum-Cortex), n_used=44>] MRI (surface RAS) coords, subject 'sample', ~30.5 MB>
MRI -> head transform : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw-trans.fif
Measurement data : instance of Info
Conductor model : /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif
Accurate field computations
Do computations in head coordinates
Free source orientations
Read 9 source spaces a total of 2169 active source locations
Coordinate transformation: MRI (surface RAS) -> head
0.999310 0.009985 -0.035787 -3.17 mm
0.012759 0.812405 0.582954 6.86 mm
0.034894 -0.583008 0.811716 28.88 mm
0.000000 0.000000 0.000000 1.00
Read 305 MEG channels from info
105 coil definitions read
Coordinate transformation: MEG device -> head
0.991420 -0.039936 -0.124467 -6.13 mm
0.060661 0.984012 0.167456 0.06 mm
0.115790 -0.173570 0.977991 64.74 mm
0.000000 0.000000 0.000000 1.00
MEG coil definitions created in head coordinates.
Source spaces are now in head coordinates.
Setting up the BEM model using /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif...
Loading surfaces...
Loading the solution matrix...
Homogeneous model surface loaded.
Loaded linear collocation BEM solution from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-5120-bem-sol.fif
Employing the head->MRI coordinate transform with the BEM model.
BEM model sample-5120-bem-sol.fif is now set up
Source spaces are in head coordinates.
Checking that the sources are inside the surface and at least 5.0 mm away (will take a few...)
Checking surface interior status for 1026 points...
Found 211/1026 points inside an interior sphere of radius 43.6 mm
Found 0/1026 points outside an exterior sphere of radius 91.8 mm
Found 0/ 815 points outside using surface Qhull
Found 0/ 815 points outside using solid angles
Total 1026/1026 points inside the surface
Interior check completed in 466.6 ms
84 source space point omitted because of the 5.0-mm distance limit.
Checking surface interior status for 1026 points...
Found 219/1026 points inside an interior sphere of radius 43.6 mm
Found 0/1026 points outside an exterior sphere of radius 91.8 mm
Found 0/ 807 points outside using surface Qhull
Found 0/ 807 points outside using solid angles
Total 1026/1026 points inside the surface
Interior check completed in 465.2 ms
84 source space point omitted because of the 5.0-mm distance limit.
Checking surface interior status for 2 points...
Found 2/2 points inside an interior sphere of radius 43.6 mm
Found 0/2 points outside an exterior sphere of radius 91.8 mm
Found 0/0 points outside using surface Qhull
Found 0/0 points outside using solid angles
Total 2/2 points inside the surface
Interior check completed in 21.2 ms
Checking surface interior status for 9 points...
Found 9/9 points inside an interior sphere of radius 43.6 mm
Found 0/9 points outside an exterior sphere of radius 91.8 mm
Found 0/0 points outside using surface Qhull
Found 0/0 points outside using solid angles
Total 9/9 points inside the surface
Interior check completed in 20.8 ms
Checking surface interior status for 33 points...
Found 1/33 point inside an interior sphere of radius 43.6 mm
Found 0/33 points outside an exterior sphere of radius 91.8 mm
Found 0/32 points outside using surface Qhull
Found 0/32 points outside using solid angles
Total 33/33 points inside the surface
Interior check completed in 34.9 ms
Checking surface interior status for 21 points...
Found 8/21 points inside an interior sphere of radius 43.6 mm
Found 0/21 points outside an exterior sphere of radius 91.8 mm
Found 0/13 points outside using surface Qhull
Found 0/13 points outside using solid angles
Total 21/21 points inside the surface
Interior check completed in 29.0 ms
Checking surface interior status for 1 point ...
Found 1/1 point inside an interior sphere of radius 43.6 mm
Found 0/1 points outside an exterior sphere of radius 91.8 mm
Found 0/0 points outside using surface Qhull
Found 0/0 points outside using solid angles
Total 1/1 point inside the surface
Interior check completed in 23.3 ms
Checking surface interior status for 7 points...
Found 7/7 points inside an interior sphere of radius 43.6 mm
Found 0/7 points outside an exterior sphere of radius 91.8 mm
Found 0/0 points outside using surface Qhull
Found 0/0 points outside using solid angles
Total 7/7 points inside the surface
Interior check completed in 23.9 ms
Checking surface interior status for 44 points...
Found 1/44 point inside an interior sphere of radius 43.6 mm
Found 0/44 points outside an exterior sphere of radius 91.8 mm
Found 0/43 points outside using surface Qhull
Found 0/43 points outside using solid angles
Total 44/44 points inside the surface
Interior check completed in 50.6 ms
Checking surface interior status for 305 points...
Found 0/305 points inside an interior sphere of radius 43.6 mm
Found 305/305 points outside an exterior sphere of radius 91.8 mm
Found 0/ 0 points outside using surface Qhull
Found 0/ 0 points outside using solid angles
Total 0/305 points inside the surface
Interior check completed in 22.3 ms
Composing the field computation matrix...
Computing MEG at 2001 source locations (free orientations)...
Finished.
Not setting metadata
72 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
info["bads"] and noise_cov["bads"] do not match, excluding bad channels from both
Computing inverse operator with 305 channels.
305 out of 305 channels remain after picking
Selected 305 channels
Whitening the forward solution.
Created an SSP operator (subspace dimension = 3)
Computing rank from covariance with rank=None
Using tolerance 1.3e-13 (2.2e-16 eps * 305 dim * 2 max singular value)
Estimated rank (mag + grad): 302
MEG: rank 302 computed from 305 data channels with 3 projectors
Setting small MEG eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 6.38137
scaling factor to adjust the trace = 1.17172e+21 (nchan = 305 nzero = 3)
Reading labels from parcellation...
read 34 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot
read 34 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot
Connectivity computation...
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 1
Created the regularized inverter
Created an SSP operator (subspace dimension = 3)
Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
Computing noise-normalization factors (dSPM)...
[done]
Picked 305 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
Processing epoch : 1 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
using t=0.000s..0.699s for estimation (106 points)
frequencies: 8.5Hz..12.7Hz (4 points)
connectivity scores will be averaged for each band
only using indices for lower-triangular matrix
computing connectivity for 2775 connections
Using multitaper spectrum estimation with 7 DPSS windows
the following metrics will be computed: PLI
computing cross-spectral density for epoch 1
Processing epoch : 2 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 2
Processing epoch : 3 / 72 (at most)
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computing cross-spectral density for epoch 3
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computing cross-spectral density for epoch 4
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computing cross-spectral density for epoch 5
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computing cross-spectral density for epoch 8
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computing cross-spectral density for epoch 9
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Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 10
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 11 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 11
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computing cross-spectral density for epoch 13
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
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combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 14
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computing cross-spectral density for epoch 17
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 18 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 18
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computing cross-spectral density for epoch 20
Rejecting epoch based on EOG : ['EOG 061']
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computing cross-spectral density for epoch 21
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Rejecting epoch based on MAG : ['MEG 1711']
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computing cross-spectral density for epoch 28
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 29 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 29
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computing cross-spectral density for epoch 31
Rejecting epoch based on EOG : ['EOG 061']
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Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 32
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Rejecting epoch based on EOG : ['EOG 061']
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combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 34
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Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 35
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 36 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 36
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 37 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 37
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computing cross-spectral density for epoch 40
Processing epoch : 41 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 41
Processing epoch : 42 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 42
Processing epoch : 43 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 43
Processing epoch : 44 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 44
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 45 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 45
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 46 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 46
Processing epoch : 47 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 47
Processing epoch : 48 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 48
Rejecting epoch based on EOG : ['EOG 061']
Rejecting epoch based on EOG : ['EOG 061']
Processing epoch : 49 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 49
Processing epoch : 50 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 50
Processing epoch : 51 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 51
Processing epoch : 52 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 52
Processing epoch : 53 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 53
Processing epoch : 54 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 54
Processing epoch : 55 / 72 (at most)
combining the current components...
Extracting time courses for 75 labels (mode: mean_flip)
computing cross-spectral density for epoch 55
[done]
assembling connectivity matrix
[Connectivity computation done]
Save the figure (optional)#
By default matplotlib does not save using the facecolor, even though this was set when the figure was generated. If not set via savefig, the labels, title, and legend will be cut off from the output png file.
Total running time of the script: (0 minutes 12.355 seconds)