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plot_timecourse_source.py
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plot_timecourse_source.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 21 18:35:29 2023
@author: fm02
"""
from scipy import stats as stats
import pandas as pd
import numpy as np
import mne
import seaborn as sns
import matplotlib.pyplot as plt
import pickle
import sys
import os
from os import path
from mne.stats import spatio_temporal_cluster_1samp_test, summarize_clusters_stc
from mpl_toolkits.axes_grid1 import make_axes_locatable
os.chdir("/home/fm02/MEG_NEOS/NEOS")
import NEOS_config as config
stcs_path = path.join(config.data_path, "stcs")
sbj_ids = [
1,
2,
3,
# 4, #fell asleep
5,
6,
# 7, #no MRI
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
# 20, #too magnetic to test
21,
22,
23,
24,
25,
26,
27,
28,
29,
30
]
src_fname = path.join(config.subjects_dir, "fsaverage", "bem", "fsaverage-ico-5-src.fif")
src = mne.read_source_spaces(src_fname)
vertices = [src[0]['vertno'], src[1]['vertno']]
predictables = [mne.read_source_estimate(os.path.join(stcs_path, f"{sbj_id}_unfold_stc_Predictable_eLORETA_MEGonly_auto_dropbads_fsaverage")) \
if sbj_id==12 else \
mne.read_source_estimate(os.path.join(stcs_path, f"{sbj_id}_unfold_stc_Predictable_eLORETA_EEGMEGauto_dropbads_fsaverage")) \
for sbj_id in sbj_ids ]
p = [np.array(stc.data) for stc in predictables]
avg_p = np.stack(p).mean(axis=0)
GA_unfold = mne.SourceEstimate(avg_p, vertices=vertices, tmin=-0.152, tstep=0.004)
cropped_screenshot = list()
for i in np.arange(-1, 6, 0.5):
clim = dict(kind="value", lims=[1.2e-11, 1.248e-11, 1.5e-11])
brain = GA_unfold.plot(
views="lat",
hemi="split",
surface="pial_semi_inflated",
size=(800, 400),
subject="fsaverage",
subjects_dir=config.subjects_dir,
initial_time=0.1*i,
background="w",
time_viewer=False,
show_traces=False,
clim=clim,
colorbar=None,
cortex='low_contrast',
)
screenshot = brain.screenshot()
brain.close()
nonwhite_pix = (screenshot != 255).any(-1)
nonwhite_row = nonwhite_pix.any(1)
nonwhite_col = nonwhite_pix.any(0)
cropped_screenshot.append(screenshot[nonwhite_row][:, nonwhite_col])
fig, axs = plt.subplots(nrows=len(cropped_screenshot)+1, ncols=1,
sharex=True, sharey=True, figsize=(8,20))
for f, ax in zip(cropped_screenshot, axs.ravel()):
ax.imshow(f)
ax.axis('off')
divider = make_axes_locatable(axs[-1])
cax = divider.append_axes("right", size="5%")
cbar = mne.viz.plot_brain_colorbar(cax, clim, colormap='hot', label="Activation (F)")
#fig.colorbar(cbar)
plt.savefig('GA_unfold_predictable.png', bbox_inches='tight')
unpredictables = [mne.read_source_estimate(os.path.join(stcs_path, f"{sbj_id}_unfold_stc_Unpredictable_eLORETA_MEGonly_auto_dropbads_fsaverage")) \
if sbj_id==12 else \
mne.read_source_estimate(os.path.join(stcs_path, f"{sbj_id}_unfold_stc_Unpredictable_eLORETA_EEGMEGauto_dropbads_fsaverage")) \
for sbj_id in sbj_ids ]
unp = [np.array(stc.data) for stc in unpredictables]
avg_unp = np.stack(unp).mean(axis=0)
GA_unpunfold = mne.SourceEstimate(avg_unp, vertices=vertices, tmin=-0.152, tstep=0.004)
cropped_screenshot = list()
for i in np.arange(-1, 6, 0.5):
clim = dict(kind="value", lims=[1.2e-11, 1.248e-11, 1.5e-11])
brain = GA_unpunfold.plot(
views="lat",
hemi="split",
surface="pial_semi_inflated",
size=(800, 400),
subject="fsaverage",
subjects_dir=config.subjects_dir,
initial_time=0.1*i,
background="w",
time_viewer=False,
show_traces=False,
clim=clim,
colorbar=None,
cortex='low_contrast',
)
screenshot = brain.screenshot()
brain.close()
nonwhite_pix = (screenshot != 255).any(-1)
nonwhite_row = nonwhite_pix.any(1)
nonwhite_col = nonwhite_pix.any(0)
cropped_screenshot.append(screenshot[nonwhite_row][:, nonwhite_col])
fig, axs = plt.subplots(nrows=len(cropped_screenshot)+1, ncols=1,
sharex=True, sharey=True, figsize=(8,20))
for f, ax in zip(cropped_screenshot, axs.ravel()):
ax.imshow(f)
ax.axis('off')
divider = make_axes_locatable(axs[-1])
cax = divider.append_axes("right", size="5%")
cbar = mne.viz.plot_brain_colorbar(cax, clim, colormap='hot', label="Activation (F)")
#fig.colorbar(cbar)
plt.savefig('GA_unfold_unpredictable.png', bbox_inches='tight')
X = np.stack(p) - np.stack(unp)
out = stats.ttest_1samp(X, 0, axis=0)
t_values = out[0]
for i in range(len(t_values)):
t_values[i][abs(t_values[i])<2] = 0
t_maps = mne.SourceEstimate(t_values, vertices=vertices, tmin=-0.152, tstep=0.004)
cropped_screenshot = list()
for i in np.arange(-1, 6, 0.5):
clim = dict(kind="value", lims=[-3, 0, 3])
brain = t_maps.plot(
views="lat",
hemi="split",
surface="pial_semi_inflated",
size=(800, 400),
subject="fsaverage",
subjects_dir=config.subjects_dir,
initial_time=0.1*i,
background="w",
time_viewer=False,
show_traces=False,
clim=clim,
colorbar=None,
colormap='mne',
cortex='low_contrast',
)
screenshot = brain.screenshot()
brain.close()
nonwhite_pix = (screenshot != 255).any(-1)
nonwhite_row = nonwhite_pix.any(1)
nonwhite_col = nonwhite_pix.any(0)
cropped_screenshot.append(screenshot[nonwhite_row][:, nonwhite_col])
fig, axs = plt.subplots(nrows=len(cropped_screenshot)+1, ncols=1,
sharex=True, sharey=True, figsize=(8,20))
for f, ax in zip(cropped_screenshot, axs.ravel()):
ax.imshow(f)
ax.axis('off')
divider = make_axes_locatable(axs[-1])
cax = divider.append_axes("right", size="5%")
cbar = mne.viz.plot_brain_colorbar(cax, clim, colormap='mne', label="T-values uncorrected")
#fig.colorbar(cbar)
plt.savefig('GA_unfold_tmap.png', bbox_inches='tight')