def tfce_process(conf):
print('\trun tfce...')
path_home = conf.path_home
grand_average = conf.grand_average
kind = conf.kind
train = conf.train
baseline = conf.baseline
legend = conf.legend
verbose = conf.verbose
# perfom statistical check by means of Threshold Free Cluster Enhancement
options = {
'page-size': 'A3',
'orientation': 'Landscape',
'zoom': 0.57,
'no-outline': None,
'quiet': ''
}
planars = ['planar1', 'planar2', 'combine_planar']
cur_dir = conf.path_tfce
os.chdir(cur_dir) #папка где будут сохраняться картинки
if conf.grand_average == True:
frequency = None
else:
frequency = conf.frequency
if conf.plot_spectrogram:
print('Spectrograms are ready, cannot continue')
return 0
comp1_mean, comp2_mean, contr, temp1, temp2, p_val, binary, subjects1 = compute_p_val(
conf, conf.subjects, kind, train, frequency, conf.check_num_sens)
df1 = contr[:, 0, 204:, :]
df2 = contr[:, 1, 204:, :]
if verbose:
print('df1 shape', df1.shape)
print('df2 shape', df2.shape)
df1 = df1.transpose(2, 0, 1)
df2 = df2.transpose(2, 0, 1)
df1_mean = df1.mean(axis=1)
df2_mean = df2.mean(axis=1)
df1_stderr = np.zeros((df1.shape[0], df1.shape[2]))
df2_stderr = np.zeros((df1.shape[0], df1.shape[2]))
t_stat = np.zeros((df1.shape[0], df1.shape[2]))
p_val = np.zeros((df1.shape[0], df1.shape[2]))
res_tfce = np.zeros((df1.shape[0], df1.shape[2]))
if verbose:
print(res_tfce.shape)
pos = io.loadmat(f'{path_home}pos_store.mat')['pos']
chan_labels = to_str_ar(
io.loadmat(f'{path_home}channel_labels.mat')['chanlabels'])
dict_col = {
'risk': 'salmon',
'norisk': 'olivedrab',
'prerisk': 'mediumpurple',
'postrisk': 'darkturquoise',
'prerisk_fb_negative': 'crimson',
'prerisk_fb_positive': 'cyan',
'risk_fb_negative': 'crimson',
'risk_fb_positive': 'cyan',
'postrisk_fb_negative': 'crimson',
'postrisk_fb_positive': 'cyan',
'norisk_fb_negative': 'red',
'norisk_fb_positive': 'blue',
'fb_positive_risk': 'cyan',
'fb_negative_risk': 'red',
'fb_positive_norisk': 'salmon',
'fb_negative_norisk': 'crimson'
}
#working with combined planars
for i in range(102):
df1_stderr[:, i] = scipy.stats.sem(df1[:, :, i], axis=1)
df2_stderr[:, i] = scipy.stats.sem(df2[:, :, i], axis=1)
t_stat[:, i], p_val[:, i] = stats.ttest_rel(df1[:, :, i],
df2[:, :, i],
axis=1)
res_tfce[:, i] = np.array(
tfce(conf, df1[:, :, i], df2[:, :, i], title=chan_labels[i + 204]))
res = plot_stat_comparison_tfce(conf,
df1_mean[:, i],
df2_mean[:, i],
df1_stderr[:, i],
df2_stderr[:, i],
p_val[:, i],
res_tfce[:, i],
conf.time,
title=chan_labels[i + 204],
folder=f'{legend[0]}_vs_{legend[1]}',
comp1_label=kind[0],
comp2_label=kind[1],
comp1_color=dict_col[f'{kind[0]}'],
comp2_color=dict_col[f'{kind[1]}'])
if verbose:
print('\tPictures generated')
for ind, planar in enumerate(planars):
#place the channel time courses in html file
html_name = conf.path_tfce + 'output_tfce/pic_compose_%s_%s_vs_%s_%s.html' % (
planar, f'{legend[0]}', f'{legend[1]}', 'all')
clear_html(html_name)
add_str_html(html_name, '<!DOCTYPE html>')
add_str_html(html_name, '<html>')
add_str_html(html_name, '<body>')
if grand_average == True:
add_str_html(
html_name,
'<p style="font-size:32px;"><b> %s, %s, trained, TFCE corrected, %s, %d subjects <span style="color:cyan;"> (p_val < 0.05)*(res_tfce==0)) </span> <span style="color:crimson;"> res_tfce == 1 </span> </b></p>'
% (planar, conf.ERF, baseline, len(subjects1)))
else:
assert (grand_average == False)
if conf.response:
add_str_html(
html_name,
'<p style="font-size:32px;"><b> %s, averaged %s, trained, TFCE corrected, %s, %s, %d subjects <span style="color:cyan;"> (p_val < 0.05)*(res_tfce==0)) </span> <span style="color:crimson;"> res_tfce == 1 </span> </b></p>'
% (planar, frequency, baseline, conf.zero_point,
len(subjects1)))
if conf.stim:
add_str_html(
html_name,
'<p style="font-size:32px;"><b> %s, averaged %s, trained, TFCE corrected, %s, zero point at stimulus, %d subjects <span style="color:cyan;"> (p_val < 0.05)*(res_tfce==0)) </span> <span style="color:crimson;"> res_tfce == 1 </span> </b></p>'
% (planar, frequency, baseline, len(subjects1)))
add_str_html(
html_name,
'<p style="font-size:32px;"><b> <span style="color: blue;"> %s </span> vs <span style="color: red;"> %s </span> </b></p>'
% (legend[0], legend[1]))
#placing the channel time courses and save the html
if ind == 2:
for ch_num in range(204, len(chan_labels)):
pic = chan_labels[ch_num] + '.svg'
add_pic_time_course_html(html_name, pic,
f'{legend[0]}_vs_{legend[1]}',
pos[ch_num], [200, 150])
else:
for ch_num in range(ind, 204, 2):
pic = chan_labels[ch_num] + '.svg'
add_pic_time_course_html(html_name, pic,
f'{legend[0]}_vs_{legend[1]}',
pos[ch_num], [200, 150])
add_str_html(html_name, '</body>')
add_str_html(html_name, '</html>')
pdf_file = html_name.split("/")[1].split('.')[0]
#print('/%s' % html_name)
path = f'{cur_dir}/output_tfce/{legend[0]}_vs_{legend[1]}/all_pdf/'
if verbose:
print(path)
os.makedirs(path, exist_ok=True)
if verbose:
print('\tAll printed')
print('\ttfce completed')
ch_type='planar1',
time_unit='s',
show=False,
title='%s - %s (cut) with fdr' %
(legend[1], legend[0]),
colorbar=True,
vmax=p_mul_topo_fdr_contrast,
vmin=-p_mul_topo_fdr_contrast,
extrapolate="local")
fig.savefig(os.path.join(output, legend[0] + '_vs_' + legend[1],
'p_value_cut_with_fdr.png'),
dpi=300)
plt.close()
html_name = os.path.join(output, legend[0] + '_vs_' + legend[1] + '.html')
clear_html(html_name)
add_str_html(html_name, '<!DOCTYPE html>')
add_str_html(html_name, '<html>')
add_str_html(html_name, '<body>')
add_str_html(
html_name,
'<p style="font-size:20px;"><b> %s, average %s, %s, trained, %d subjects </b></p>'
% (legend[0] + '_vs_' + legend[1], frequency, stimulus, len(subjects1)))
add_str_html(
html_name,
'<p style="font-size:20px;"><b> P_val < 0.05 marked (or saved from cutting) </b></p>'
)
add_str_html(html_name, '<table>')
for topo in topomaps:
add_str_html(html_name, "<tr>")
add_pic_topo_html(
def topo_stat(conf):
print('\trun fdr for topomaps...')
grand_average = conf.grand_average
time = conf.time
times_to_plot = conf.times_to_plot
p_mul_topo = conf.p_mul_topo
p_mul_topo_contrast = conf.p_mul_topo_contrast
p_mul_topo_fdr_contrast = conf.p_mul_topo_fdr_contrast
baseline = conf.baseline
legend = conf.legend
cur_dir = conf.path_fdr
verbose = conf.verbose
os.chdir(cur_dir)
topomaps = [
f'{legend[0]}', f'{legend[1]}', 'difference_fdr', 'difference_deep_fdr'
]
options = {
'page-size': 'A3',
'orientation': 'Landscape',
'zoom': 1.0,
'no-outline': None,
'quiet': ''
}
os.makedirs(os.path.join(conf.path_fdr, f'{legend[0]}_vs_{legend[1]}'),
exist_ok=True)
if grand_average == True:
frequency = None
else:
frequency = conf.frequency
#donor data file
temp = mne.Evoked(f'{conf.path_home}donor-ave.fif', verbose='ERROR')
temp.times = conf.time
subjects = conf.subjects
comp1_mean, comp2_mean, contr, temp1, temp2, p_val, binary, subjects1 = compute_p_val(
conf, subjects, conf.kind, conf.train, frequency, conf.check_num_sens)
df1 = contr[:, 0, 204:, :] #per channel
df2 = contr[:, 1, 204:, :]
if verbose:
print('df1 shape', df1.shape)
print('df2 shape', df2.shape)
#res_tfce = np.zeros((876, 102))
pos = io.loadmat(f'{conf.path_home}pos_store.mat')['pos']
chan_labels = to_str_ar(
io.loadmat(f'{conf.path_home}channel_labels.mat')['chanlabels'])
dict_col = {
'risk': 'salmon',
'norisk': 'olivedrab',
'prerisk': 'mediumpurple',
'postrisk': 'darkturquoise'
}
p_val_fdr = space_fdr(p_val)
##### CONDITION1 and Stat ######
# average = 0.1 means averaging of the power data over 100 ms
if verbose:
print('comp1_mean', comp1_mean.shape)
print('df1', df1.shape)
print('time', len(time))
print('times to plot', len(times_to_plot))
t_stat_con1, p_val_con1 = stats.ttest_1samp(df1, 0, axis=0)
if verbose:
print('p val con1 shape', p_val_con1.shape)
width, height = p_val_con1.shape
p_val_con1 = p_val_con1.reshape(width * height)
_, p_val_con1 = mul.fdrcorrection(p_val_con1)
p_val_con1 = p_val_con1.reshape((width, height))
binary = p_val_binary(p_val_con1, treshold=0.05)
temp.data = comp1_mean[204:, :]
fig = temp.plot_topomap(times=times_to_plot,
average=0.1,
units="dB",
scalings=dict(eeg=1e6, grad=1, mag=1e15),
ch_type='planar1',
time_unit='s',
show=False,
title=legend[0] + ' stat fdr against zero',
colorbar=True,
vmax=p_mul_topo,
vmin=-p_mul_topo,
extrapolate="local",
mask=np.bool_(binary),
mask_params=dict(marker='o',
markerfacecolor='w',
markeredgecolor='k',
linewidth=0,
markersize=7,
markeredgewidth=2))
fig.savefig(os.path.join(conf.path_fdr, legend[0] + '_vs_' + legend[1],
legend[0] + '.png'),
dpi=300)
plt.close()
##### CONDITION2 and Stat ######
#temp.data = comp2_mean[204:,:]
t_stat_con2, p_val_con2 = stats.ttest_1samp(df2, 0, axis=0)
width, height = p_val_con2.shape
p_val_con2 = p_val_con2.reshape(width * height)
_, p_val_con2 = mul.fdrcorrection(p_val_con2)
p_val_con2 = p_val_con2.reshape((width, height))
binary = p_val_binary(p_val_con2, treshold=0.05)
temp.data = comp2_mean[204:, :]
fig = temp.plot_topomap(times=times_to_plot,
average=0.1,
units='dB',
scalings=dict(eeg=1e6, grad=1, mag=1e15),
ch_type='planar1',
time_unit='s',
show=False,
title=legend[1] + ' stat fdr against zero',
colorbar=True,
vmax=p_mul_topo,
vmin=-p_mul_topo,
extrapolate="local",
mask=np.bool_(binary),
mask_params=dict(marker='o',
markerfacecolor='w',
markeredgecolor='k',
linewidth=0,
markersize=7,
markeredgewidth=2))
fig.savefig(os.path.join(conf.path_fdr, legend[0] + '_vs_' + legend[1],
legend[1] + '.png'),
dpi=300)
plt.close()
##### CONDITION2 - CONDITION1 with marks no time (space FDR) ######
p_val_fdr = space_fdr(p_val)
binary_fdr = p_val_binary(p_val_fdr, treshold=0.05)
temp.data = comp2_mean[204:, :] - comp1_mean[204:, :]
fig = temp.plot_topomap(
times=times_to_plot,
average=0.1,
units='dB',
scalings=dict(eeg=1e6, grad=1, mag=1e15),
ch_type='planar1',
time_unit='s',
show=False,
title='%s - %s space fdr: marks for each head separately' %
(legend[1], legend[0]),
colorbar=True,
vmax=p_mul_topo_contrast,
vmin=-p_mul_topo_contrast,
extrapolate="local",
mask=np.bool_(binary_fdr[204:, :]),
mask_params=dict(marker='o',
markerfacecolor='w',
markeredgecolor='k',
linewidth=0,
markersize=7,
markeredgewidth=2))
fig.savefig(os.path.join(conf.path_fdr, legend[0] + '_vs_' + legend[1],
'difference_fdr.png'),
dpi=300)
plt.close()
#### CONDITION2 - CONDITION1 with marks (WITH FDR) deep FDR with time ####
t_stat, p_val_deep = stats.ttest_rel(df1, df2, axis=0)
width, height = p_val_deep.shape
p_val_resh = p_val_deep.reshape(width * height)
_, p_val_deep_fdr = mul.fdrcorrection(p_val_resh)
p_val_deep_fdr = p_val_deep_fdr.reshape((width, height))
binary_deep_fdr = p_val_binary(p_val_deep_fdr, treshold=0.05)
temp.data = comp2_mean[204:, :] - comp1_mean[204:, :]
fig = temp.plot_topomap(times=times_to_plot,
average=0.1,
units='dB',
scalings=dict(eeg=1e6, grad=1, mag=1e15),
ch_type='planar1',
time_unit='s',
show=False,
title='%s - %s deep fdr over all heads' %
(legend[1], legend[0]),
colorbar=True,
vmax=p_mul_topo_fdr_contrast,
vmin=-p_mul_topo_fdr_contrast,
extrapolate="local",
mask=np.bool_(binary_deep_fdr),
mask_params=dict(marker='o',
markerfacecolor='w',
markeredgecolor='k',
linewidth=0,
markersize=7,
markeredgewidth=2))
fig.savefig(os.path.join(conf.path_fdr, legend[0] + '_vs_' + legend[1],
'difference_deep_fdr.png'),
dpi=300)
plt.close()
html_name = os.path.join(conf.path_fdr,
legend[0] + '_vs_' + legend[1] + '.html')
clear_html(html_name)
add_str_html(html_name, '<!DOCTYPE html>')
add_str_html(html_name, '<html>')
add_str_html(html_name, '<body>')
if grand_average:
add_str_html(
html_name,
'<p style="font-size:20px;"><b> %s, %s, trained, %s, %s, %d subjects </b></p>'
% (legend[0] + '_vs_' + legend[1], conf.ERF, baseline,
conf.zero_point, len(subjects1)))
else:
add_str_html(
html_name,
'<p style="font-size:20px;"><b> %s, %s, trained, %s, %s, %d subjects </b></p>'
% (legend[0] + '_vs_' + legend[1], frequency, baseline,
conf.zero_point, len(subjects1)))
add_str_html(
html_name,
'<p style="font-size:20px;"><b> boolean fdr = 1 marked </b></p>')
add_str_html(html_name, '<table>')
for topo in topomaps:
add_str_html(html_name, "<tr>")
add_pic_topo_html(
html_name,
os.path.join(legend[0] + '_vs_' + legend[1], topo + '.png'))
add_str_html(html_name, "</tr>")
add_str_html(html_name, '</body>')
add_str_html(html_name, '</html>')
pdf_file = html_name.replace("html", "pdf")
if verbose:
print('All done!')
print('\tfdr for topomaps completed')