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berlin_pipeline.py
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berlin_pipeline.py
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from typing import Union
import runpy
from turtle import done
import pandas as pd
from bids import BIDSLayout
import mne_bids
import matplotlib.pyplot as plt
import seaborn as sns
from src import pipeline, plot_utils, preprocessing, postprocessing
import seaborn as sns
import numpy as np
from scipy.stats import wilcoxon, permutation_test, spearmanr
# SCRIPT START #
def main():
"""Run this script."""
# Load project constants
project_constants = runpy.run_path("berlin_constants.py")
data_path = project_constants["data_path"]
path_bids = project_constants["PATH_BIDS"]
m1_ids = project_constants["M1_IDS"]
new_ch_names_map = project_constants["NEW_CH_NAMES_MAP"]
files = project_constants["files"]
remove_subjects: Union[str, None] = ["001", "002" "010"]
if remove_subjects:
for remove_subject in remove_subjects:
files = [file for file in files if remove_subject not in file]
files = preprocessing.pick_runs(files)
files_x = [f for f in files if "EL016" in f]
# Define variables
burst_char_pd_all = []
M1_burst_dynamics_all = []
npow_list_all = []
# Process runs #
for path_run in files:
entities = mne_bids.get_entities_from_fname(path_run)
sub = entities["subject"]
session = entities["session"]
task = entities["task"]
acquisition = entities["acquisition"]
run = entities["run"]
med = "On" if "MedOn" in entities["session"] else "Off"
(burst_char_pd, M1_burst_dynamics, npow,) = pipeline.bursts_single_run(
path_run=path_run,
path_bids=path_bids,
sub=sub,
m1=m1_ids[sub],
new_ch_names=new_ch_names_map[sub],
med=med,
session=session,
task=task,
acquisition=acquisition,
run=run,
)
burst_char_pd["Subject"] = sub
burst_char_pd["Medication"] = med
burst_char_pd["Run"] = run
M1_burst_dynamics["Subject"] = sub
M1_burst_dynamics["Medication"] = med
M1_burst_dynamics["Run"] = run
npow["Subject"] = sub
npow["Medication"] = med
npow["Run"] = run
burst_char_pd_all.append(burst_char_pd)
M1_burst_dynamics_all.append(M1_burst_dynamics)
npow_list_all.append(npow)
return (
burst_char_pd_all,
M1_burst_dynamics_all,
npow_list_all,
)
if __name__ == "__main__":
(burst_char_pd_all, M1_burst_dynamics_all, npow_list_all,) = main()
# Structure Results in DataFrame
features = pd.concat(burst_char_pd_all)
dist = pd.concat(M1_burst_dynamics_all)
psd = pd.concat(npow_list_all)
# print("done")
# Average Runs (multiple subs)
avg_features = postprocessing.avg_features_sub(burst_char_pd_all)
df_gavg_dist, df_sub_dist = postprocessing.avg_distribution(M1_burst_dynamics_all)
plot_utils.plot_avgm1_burst_features(avg_features)
plot_utils.plot_m1_burst_features(features)
plot_utils.plot_distribution(df_gavg_dist, df_sub_dist)
print("done")
# Statistic
on = features[features["Medication"] == "On"]
off = features[features["Medication"] == "Off"]
duration_on = on["Duration (s)"]
duration_off = off["Duration (s)"]
res_dur = wilcoxon(duration_off, duration_on)
amplitude_on = on["Amplitude (au)"]
amplitude_off = off["Amplitude (au)"]
res_ampl = wilcoxon(amplitude_off, amplitude_on)
rate_on = on["Rate (/s)"]
rate_off = off["Rate (/s)"]
res_rate = wilcoxon(rate_off, rate_on)
on_dist = dist[dist["Medication"] == "On"]
off_dist = dist[dist["Medication"] == "Off"]
dist_on = on_dist.iloc[:,[7]]
dist_off = off_dist.iloc[:,[7]]
res_dist = wilcoxon(dist_off, dist_on)
res_dur.statistic, res_dur.pvalue
res_ampl.statistic, res_ampl.pvalue
res_rate.statistic, res_rate.pvalue
res_dist.statistic[0], res_dist.pvalue[0]
# Permutation test
x_dur = duration_on
y_dur = duration_off
x_ampl = amplitude_on
y_ampl = amplitude_off
x_rate = rate_on
y_rate = rate_off
# def statistic(x, y):
# return pearsonr(x, y)
def statistic(x, y):
return np.mean(x) - np.mean(y)
resi_dur = permutation_test(
(x_dur.values, y_dur.values),
statistic,
vectorized=False,
permutation_type="samples",
alternative="two-sided",
)
resi_ampl = permutation_test(
(x_ampl.values, y_ampl.values),
statistic,
vectorized=False,
permutation_type="samples",
alternative="two-sided",
)
resi_rate = permutation_test(
(x_rate.values, y_rate.values),
statistic,
vectorized=False,
permutation_type="samples",
alternative="two-sided",
)
resi_dist = permutation_test(
(dist_off.values, dist_on.values),
statistic,
vectorized=False,
permutation_type="samples",
alternative="two-sided",
)
r_dur, pvalue_dur, null_dur = resi_dur.statistic, resi_dur.pvalue, resi_dur.null_distribution
r_ampl, pvalue_ampl, null_ampl = resi_ampl.statistic, resi_ampl.pvalue, resi_ampl.null_distribution
r_rate, pvalue_rate, null_rate = resi_rate.statistic, resi_rate.pvalue, resi_rate.null_distribution
r_dist, pvalue_dist, null_dist = resi_dist.statistic, resi_dist.pvalue, resi_dist.null_distribution
r_dist, pvalue_dist = r_dist[0], pvalue_dist[0]
print("done")
# Plot Distribution single subject
dist3 = df_sub_dist[df_sub_dist["Subject"] == 3]
dist4 = df_sub_dist[df_sub_dist["Subject"] == 4]
dist5 = df_sub_dist[df_sub_dist["Subject"] == 5]
dist6 = df_sub_dist[df_sub_dist["Subject"] == 6]
dist7 = df_sub_dist[df_sub_dist["Subject"] == 7]
dist8 = df_sub_dist[df_sub_dist["Subject"] == 8]
dist9 = df_sub_dist[df_sub_dist["Subject"] == 9]
dist11 = df_sub_dist[df_sub_dist["Subject"] == 11]
dist12 = df_sub_dist[df_sub_dist["Subject"] == 12]
dist13 = df_sub_dist[df_sub_dist["Subject"] == 13]
dist14 = df_sub_dist[df_sub_dist["Subject"] == 14]
dist15 = df_sub_dist[df_sub_dist["Subject"] == 15]
dist16 = df_sub_dist[df_sub_dist["Subject"] == 16]
# plot_utils.plot_distribution_sub3(dist3)
# plot_utils.plot_distribution_sub4(dist4)
# plot_utils.plot_distribution_sub5(dist5)
# plot_utils.plot_distribution_sub6(dist6)
# plot_utils.plot_distribution_sub7(dist7)
# plot_utils.plot_distribution_sub8(dist8)
# plot_utils.plot_distribution_sub9(dist9)
# plot_utils.plot_distribution_sub10(dist10)
# plot_utils.plot_distribution_sub11(dist11)
# plot_utils.plot_distribution_sub12(dist12)
# plot_utils.plot_distribution_sub13(dist13)
# plot_utils.plot_distribution_sub14(dist14)
# plot_utils.plot_distribution_sub15(dist15)
# plot_utils.plot_distribution_sub16(dist16)
(
psd_s3off,
psd_s3on,
psd_s4off,
psd_s4on,
psd_s5off,
psd_s5on,
psd_s6off,
psd_s6on,
psd_s7off,
psd_s7on,
psd_s8off,
psd_s8on,
psd_s9off,
psd_s9on,
psd_s11off,
psd_s11on,
psd_s12off,
psd_s12on,
psd_s13off,
psd_s13on,
psd_s14off,
psd_s14on,
psd_s15off,
psd_s15on,
psd_s16off,
psd_s16on,
psd_off,
psd_on,
) = postprocessing.arrange_psd(npow_list_all)
# PLOTS #
# Features
#plot_utils.plot_avgm1_burst_features(avg_features)
#plot_utils.plot_m1_burst_features(features)
# Distribution of Duration
#plot_utils.plot_distribution(df_gavg_dist, df_sub_dist)
print("done")
# PSD
plot_utils.plot_gavg_psd(psd_off, psd_on)
plot_utils.plot_psd_s3(psd_s3off, psd_s3on)
plot_utils.plot_psd_s4(psd_s4off, psd_s4on)
plot_utils.plot_psd_s5(psd_s5off, psd_s5on)
plot_utils.plot_psd_s6(psd_s6off, psd_s6on)
plot_utils.plot_psd_s7(psd_s7off, psd_s7on)
plot_utils.plot_psd_s8(psd_s8off, psd_s8on)
plot_utils.plot_psd_s9(psd_s9off, psd_s9on)
plot_utils.plot_psd_s11(psd_s11off, psd_s11on)
plot_utils.plot_psd_s12(psd_s12off, psd_s12on)
plot_utils.plot_psd_s13(psd_s13off, psd_s13on)
plot_utils.plot_psd_s14(psd_s14off, psd_s14on)
plot_utils.plot_psd_s15(psd_s15off, psd_s15on)
plot_utils.plot_psd_s16(psd_s16off, psd_s16on)
print("done")
# peak analyse
from matplotlib.patheffects import PathPatchEffect, SimpleLineShadow, Normal
sns.set()
x_ticks = np.arange(-2,9)
# find peak in plot and write frequency down
peak_3_off = psd_s3off[4:15]
peak_4_off = psd_s4off[3:14]
peak_5_off = psd_s5off[4:15]
peak_6_off = psd_s6off[6:17]
peak_7_off = psd_s7off[3:14]
peak_8_off = psd_s8off[3:14]
peak_9_off = psd_s9off[8:19]
peak_11_off = psd_s11off[6:17]
peak_12_off = psd_s12off[8:19]
peak_13_off = psd_s13off[6:17]
peak_14_off = psd_s14off[3:14]
peak_15_off = psd_s15off[3:14]
peak_16_off = psd_s16off[8:19]
peak_g_off = psd_off[3:14]
peak_3_on = psd_s3on[3:14]
peak_4_on = psd_s4on[3:14]
peak_5_on = psd_s5on[3:14]
peak_6_on = psd_s6on[6:17]
peak_7_on = psd_s7on[4:15]
peak_8_on = psd_s8on[5:16]
peak_9_on = psd_s9on[7:18]
peak_11_on = psd_s11on[6:17]
peak_12_on = psd_s12on[5:16]
peak_13_on = psd_s13on[6:17]
peak_14_on = psd_s14on[7:18]
peak_15_on = psd_s15on[3:14]
peak_16_on = psd_s16on[8:19]
peak_g_on = psd_on[6:17]
plot_utils.plot_theta_peaks(peak_3_off,peak_4_off,peak_5_off,peak_6_off,peak_7_off,peak_8_off,peak_9_off,peak_11_off,peak_12_off,peak_13_off,peak_14_off,peak_15_off,peak_16_off,peak_3_on,peak_4_on,peak_5_on,peak_6_on,peak_7_on,peak_8_on,peak_9_on,peak_11_on,peak_12_on,peak_13_on,peak_14_on,peak_15_on,peak_16_on, peak_g_off, peak_g_on)
# Beta
x_ticks = np.arange(-5,6)
# find peak in plot and write frequency down
peak_3_off = psd_s3off[15:26]
peak_4_off = psd_s4off[9:20]
peak_5_off = psd_s5off[10:21]
peak_6_off = psd_s6off[20:31]
peak_7_off = psd_s7off[16:27]
peak_8_off = psd_s8off[11:22]
peak_9_off = psd_s9off[14:25]
peak_11_off = psd_s11off[12:23]
peak_12_off = psd_s12off[18:29]
peak_13_off = psd_s13off[18:29]
peak_14_off = psd_s14off[15:26]
peak_15_off = psd_s15off[12:23]
peak_16_off = psd_s16off[15:26]
peak_g_off = psd_off[17:28]
peak_3_on = psd_s3on[7:18]
peak_4_on = psd_s4on[21:32]
peak_5_on = psd_s5on[10:21]
peak_6_on = psd_s6on[20:31]
peak_7_on = psd_s7on[16:27]
peak_8_on = psd_s8on[8:19]
peak_9_on = psd_s9on[12:23]
peak_11_on = psd_s11on[13:24]
peak_12_on = psd_s12on[19:30]
peak_13_on = psd_s13on[19:30]
peak_14_on = psd_s14on[15:26]
peak_15_on = psd_s15on[16:27]
peak_16_on = psd_s16on[10:21]
peak_g_on = psd_on[17:28]
plot_utils.plot_beta_peaks(peak_3_off,peak_4_off,peak_5_off,peak_6_off,peak_7_off,peak_8_off,peak_9_off,peak_11_off,peak_12_off,peak_13_off,peak_14_off,peak_15_off,peak_16_off,peak_3_on,peak_4_on,peak_5_on,peak_6_on,peak_7_on,peak_8_on,peak_9_on,peak_11_on,peak_12_on,peak_13_on,peak_14_on,peak_15_on,peak_16_on, peak_g_off, peak_g_on)
#Correlation Burst Features and Score
scores_off = np.array([20, 36, 18, 27, 32, 26, 15, 43, 28, 27, 35, 47, 23])
scores_on = np.array([15, 19, 12, 15, 13, 18, 7, 35, 9, 9, 13, 28, 14])
scores_diff = scores_off - scores_on
duration_off
amplitude_off
rate_off
duration_diff = duration_off - duration_on
amplitude_diff = amplitude_off - amplitude_on
rate_diff = rate_off - rate_on
dist_diff = dist_off - dist_on
plot_utils.plot_duration_scores_off(duration_off, scores_off)
plot_utils.plot_duration_scores_on(duration_on, scores_on)
plot_utils.plot_duration_diff(duration_diff, scores_diff)
plot_utils.plot_amplitude_scores_off(amplitude_off, scores_off)
plot_utils.plot_amplitude_scores_on(amplitude_on, scores_off)
plot_utils.plot_amplitude_diff(rate_diff, scores_diff)
plot_utils.plot_rate_scores_off(amplitude_off, scores_off)
plot_utils.plot_rate_scores_on(amplitude_on, scores_off)
plot_utils.plot_rate_diff(rate_diff, scores_diff)
plot_utils.plot_prol_off(dist_off, scores_off)
plot_utils.plot_prol_on(dist_on, scores_on)
plot_utils.plot_prol_diff(dist_diff, scores_diff)
plot_utils.plot_dur_ampl(duration_off, amplitude_off)
plot_utils.plot_dur_ampl(duration_on, amplitude_on)
rduroff_stat, rduroff_pval = spearmanr(duration_off, scores_off)
rduron_stat, rduron_pval = spearmanr(duration_on, scores_on)
rdurdiff_stat, rdurdiff_pval = spearmanr(duration_diff, scores_diff)
ramploff_stat, ramploff_pval = spearmanr(amplitude_off, scores_off)
ramplon_stat, ramplon_pval = spearmanr(amplitude_on, scores_on)
rampldiff_stat, rampldiff_pval = spearmanr(amplitude_diff, scores_diff)
rrateoff_stat, rrateoff_pval = spearmanr(rate_off, scores_off)
rrateon_stat, rrateon_pval = spearmanr(rate_on, scores_on)
rratediff_stat, rratediff_pval = spearmanr(rate_diff, scores_diff)
rdistoff_stat, rdistoff_pval = spearmanr(dist_off, scores_off)
rdiston_stat, rdiston_pval = spearmanr(dist_on, scores_on)
rdistdiff_stat, rdistdiff_pval = spearmanr(dist_diff, scores_diff)