#p4f, p4ax = plt.subplots(4, 4, sharex=True, sharey=True)
for m, metric_type in enumerate(
['magnitude', 'n_spindles', 'amplitude', 'duration']):
for k, fb_type in enumerate(['FB0', 'FB250', 'FB500', 'FBMock']):
results = smf.ols(
'np.log(env) ~ k:fb_type + C(channel) + C(subj_id) - 1',
data=y_df.query(
'fb_type == "{}" & subj_id!="s11" & metric_type=="{}"'.format(
fb_type, metric_type))).fit()
ax[m, k].set_title('{}\n{}'.format(fb_type, metric_type))
print(results.summary())
indx = [
CHANNELS.index(chn.split('[')[1].split(']')[0])
for c, chn in enumerate(results.params.index) if 'channel' in chn
]
im = plot_topomap(
np.exp(results.params[:32] - max(results.params[:32])) *
(np.exp(results.params[-1])),
MONTAGE.get_pos()[indx],
vmin=0,
vmax=1.2,
axes=ax[m, k],
show=False,
cmap='inferno',
contours=[1.25])
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.8])
plt.colorbar(im[0], cax=cax)
data = y_df.query('metric_type == "{}"'.format(metric_type))
md = sm.MixedLM.from_formula("np.log(env) ~ k:fb_type:channel",
data,
groups=data["subj_id"],
re_formula='1+k:channel:fb_type')
results = md.fit()
print(results.summary())
#topo0 = np.exp([results.params['channel[T.{}]'.format(ch)] if ch !='C3' else 0 for ch in CHANNELS])
topo_fb0 = np.exp([
results.params['k:fb_type[FBMock]:channel[{}]'.format(ch)]
for ch in CHANNELS
])
plot_topomap(topo_fb0, MONTAGE.get_pos(), vmin=0, vmax=1)
md = sm.MixedLM.from_formula("np.log(env) ~ channel + k:fb_type:channel",
data,
groups=data["subj_id"],
re_formula='1')
results = md.fit()
print(results.summary())
m = sm.OLS.from_formula(
'env ~ fb_type*channel',
data=y_df.query('metric_type == "n_spindles" & k==0')).fit()
#m1 = sm.OLS.from_formula('env ~ fb_type:k', data=y_df.query('metric_type == "n_spindles" & channel=="P4"')).fit()
print(m.summary())
from statsmodels.stats.anova import anova_lm
anova_lm(m)
axis_spinecolor='white'):
curves = {}
for f, fb_type in enumerate(['FB0', 'FB250', 'FB500', 'FBMock']):
curves[fb_type] = y_df.query(
'metric_type=="{}" & fb_type=="{}" & channel=="{}"'.format(
metric_type, fb_type,
CHANNELS[idx])).groupby('k').median()['env'].values
curves_array[f, idx, :] = curves[fb_type]
ax.plot(curves['FB0'] - curves['FBMock'] * 0)
ax.plot(curves['FB250'] - curves['FBMock'] * 0)
ax.plot(curves['FB500'] - curves['FBMock'] * 0)
ax.plot(curves['FBMock'])
ax.set_xlabel(CHANNELS[idx])
ax.axhline(0, color='k', alpha=0.3)
if idx > 0:
ax.get_shared_x_axes().join(prev_ax, ax)
ax.get_shared_y_axes().join(prev_ax, ax)
prev_ax = ax
#ax.set_ylim(0, y_df.query('metric_type=="{}"'.format(metric_type))['env'].max())
plt.gcf().suptitle(metric_type)
plt.show()
fig, axes = plt.subplots(4)
for f, fb_type in enumerate(['FB0', 'FB250', 'FB500', 'FBMock']):
plot_topomap(curves_array[f].mean(1),
MONTAGE.get_pos(),
0,
curves_array.mean(2).max(),
axes=axes[f],
show=False,
cmap='inferno')
for c, comp in enumerate(all_stats_df['Contrast'].unique()):
for m, metric_type in enumerate(['magnitude', 'n_spindles', 'duration', 'amplitude']):
comp_df = all_stats_df.query('metric_type=="{}" & Contrast == "{}"'.format(metric_type, comp))
data_t[b, c, m] = np.array([comp_df.query('channel == "{}" & band=="{}"'.format(ch, band))['T-stat'].values[0] for ch in CHANNELS])
data[b, c, m] = np.array([stats.t.sf(comp_df.query('channel == "{}" & band=="{}"'.format(ch, band))['T-stat'].values[0], 9) for ch in CHANNELS])
data = fdr_correction(data, 0.05)[1]
fig, axes = plt.subplots(3*4, 6)
col_order = {'FB0 - FBMock': 0, 'FB250 - FBMock': 1, 'FB500 - FBMock': 2, 'FB0 - FB250': 3, 'FB0 - FB500': 4, 'FB250 - FB500': 5}
for b, band in enumerate(['alpha', 'beta', 'theta']):
for c, comp in enumerate(all_stats_df['Contrast'].unique()):
for m, metric_type in enumerate(['magnitude', 'n_spindles', 'duration', 'amplitude']):
d = np.log10(data[b, c, m])
d[(data_t[b, c, m] > 0)] *= -1
im=plot_topomap(d, MONTAGE.get_pos(), axes=axes[m+4*b, col_order[comp]], show=False, vmin=-4, vmax=4, cmap='RdBu_r', contours=[np.log10(0.05), -np.log10(0.05)])
axes[m+4*b, col_order[comp]].set_title('{}\n{}\n{}'.format(band, metric_type, comp))
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.8])
b = plt.colorbar(im[0], cax=cax)
b.set_ticks([np.log10(0.05), -np.log10(0.05), 0, np.log10(0.01), -np.log10(0.01), np.log10(0.001), -np.log10(0.001)])
b.set_ticklabels([ '* (<)', '* (>)', 'ns', '** (<)', '** (>)', '*** (<)', '*** (>)'])
cax.set_title('p-value')
col_order = {'FB0 - FBMock': 0, 'FB250 - FBMock': 1, 'FB500 - FBMock': 2, 'FB0 - FB250': 3, 'FB0 - FB500': 4, 'FB250 - FB500': 5}
for b, band in enumerate(['alpha', 'beta', 'theta']):
import pandas as pd
from scipy import stats
import seaborn as sns
import numpy as np
from scipy.stats import rankdata
import pylab as plt
from proc.settings import FB_ALL
from proc.settings import CHANNELS, MONTAGE
from mne.viz import plot_topomap
from tqdm import tqdm
import scipy.signal as sg
from itertools import combinations
poses = MONTAGE.get_pos()
def ranksums(x, y):
x, y = map(np.asarray, (x, y))
n1 = len(x)
n2 = len(y)
alldata = np.concatenate((x, y))
ranked = rankdata(alldata)
x = ranked[:n1]
s = np.sum(x, axis=0)
return s
all_stats_df = pd.read_pickle('data/metrics_chs_ica_all.pkl')
all_stats_df = all_stats_df.loc[all_stats_df['block_number'].isin(FB_ALL)]
all_stats_df = all_stats_df.loc[all_stats_df['channel'].isin(CHANNELS)]
all_stats_df = all_stats_df.loc[all_stats_df['threshold_factor'].isin([2])]
#axes[f, s].plot(np.arange(30)*lin_r.slope + lin_r.intercept)
if s == 9:
axes[0, s+1].plot(np.median(curves[f, :, c], 0))
if f > 0:
axes[f, s + 1].plot(np.median(curves[f, :, c], 0))
vmax = 10
fig, axes = plt.subplots(4, 11)
pers = 0.5/29*10000
for f in range(4):
for s in range(10):
plot_topomap(slopes[f, s], MONTAGE.get_pos(), mask=slopes[f, s]>pers, axes=axes[f, s], cmap='viridis', vmin=0, vmax=np.percentile(slopes, 90), contours=0)
plot_topomap(np.median(slopes[f], 0), MONTAGE.get_pos(), mask=np.median(slopes[f], 0)>pers, axes=axes[f, -1], cmap='viridis', vmin=0, vmax=np.percentile(slopes, 90), contours=0)
vmax = 10
fig, axes = plt.subplots(4, 11)
pers = 0.5/29*10000
for f in range(4):
for s in range(10):
plot_topomap(curves[f, s].mean(1), MONTAGE.get_pos(), axes=axes[f, s], cmap='viridis', vmin=0, vmax=0.5, contours=0)
pvals = np.array([ttest_1samp(np.mean(curves[f, :, c], 1), 0) for c in range(32)])
plot_topomap(np.mean(curves[f], 0).mean(1), MONTAGE.get_pos(), axes=axes[f, -1], mask=pvals<0.01, cmap='viridis', vmin=0, vmax=0.5, contours=0)
res = get_slope(y[:, np.random.randint(0, 10, 10)])
print(res.fun, res.x[-1] * 29 * 100)
topos.append(res.x[10:10 + 32])
funs.append(res.fun)
slopes.append(res.x[-1])
plt.figure()
wean = lambda x, a: np.sum([topo * 1 / fun for topo, fun in zip(x, funs)], 0
) / np.sum(1 / np.array(funs), a)
indx = [
CHANNELS.index(chn)
for c, chn in enumerate(all_stats_df['channel'].unique())
]
plot_topomap(wean(topos, 0), MONTAGE.get_pos()[indx], vmin=0, vmax=1)
plt.title('{}\n{} +- {}'.format(
fb_type, int(wean(slopes, 0) * 29 * 100),
int(wean((np.array(slopes) - wean(slopes, 0))**2, 0)**0.5 * 29 * 100)))
plt.tight_layout()
fig, axes = plt.subplots(11, sharey=True, sharex=True)
for c in np.arange(32):
for s in range(10):
axes[s].plot(
np.arange(30) / 30 + c,
(y[:, s, c] - res.x[s]) / res.x[10 + c] / res.x[10 + 32 + s] - 1,
'b')
axes[s].plot(np.arange(30) / 30 + c, np.arange(30) * res.x[-1], 'r')
axes[0].set_ylim(-1, 1)