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GA_unfoldeffects_sensors.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 8 14:25:12 2023
@author: fm02
"""
import pandas as pd
import numpy as np
import mne
import seaborn as sns
import matplotlib.pyplot as plt
import sys
import os
from os import path
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import mne
os.chdir("/home/fm02/MEG_NEOS/NEOS")
import NEOS_config as config
from my_eyeCA import apply_ica
from sklearn.preprocessing import OneHotEncoder
import h5py
mne.viz.set_browser_backend("matplotlib")
sbj_ids = [1,2,3,5,6,8,9,10,11,12,13,14,15,16,17,18,19,
21,22,23,24,25,26,27,28,29,30]
ave_path = path.join(config.data_path, "AVE")
predictables = list()
unpredictables = list()
concretes = list()
abstracts = list()
for sbj_id in sbj_ids:
meta = pd.read_csv('/imaging/hauk/users/fm02/MEG_NEOS/stim/meg_metadata.csv', header=0)
def ovr_sub(ovr):
if ovr in ['nover', 'novr', 'novrw']:
ovr = ''
elif ovr in ['ovrw', 'ovr', 'over', 'overw']:
ovr = '_ovrw'
elif ovr in ['ovrwonset', 'ovrons', 'overonset']:
ovr = '_ovrwonset'
return ovr
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
bad_eeg = config.bad_channels_all[sbj_id]['eeg']
ovr = config.ovr_procedure[sbj_id]
ovr = ovr_sub(ovr)
raw = mne.io.read_raw(path.join(sbj_path, f"block1_sss_f_ica{ovr}_both_raw.fif"))
raw.resample(250)
raw.pick_types(meg=True, eeg=True)
info = raw.info
eeg_file = pd.read_csv(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/eeg_effect_{sbj_id}_pred.csv")
meg_file = pd.read_csv(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/meg_effect_{sbj_id}_pred.csv")
target_eeg = eeg_file[['channel', 'Predictability','yhat', 'time']][eeg_file['basisname']=='targ']
target_meg = meg_file[['channel', 'Predictability','yhat', 'time']][meg_file['basisname']=='targ']
eeg_P = [target_eeg['yhat'][(target_eeg['Predictability']=='Predictable') & (target_eeg['channel']==ch)] \
for ch in target_eeg['channel'].unique()]
meg_P = [target_meg['yhat'][(target_meg['Predictability']=='Predictable') & (target_meg['channel']==ch)] \
for ch in target_meg['channel'].unique()]
eeg_P = np.array(eeg_P)
meg_P = np.array(meg_P)
data_P = np.concatenate(([eeg_P, meg_P]))
evoked_P = mne.EvokedArray(data_P, info, tmin=-0.15)
fig = evoked_P.plot_joint(times=[0, 0.11, 0.167, 0.21, 0.266, 0.33, 0.43])
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/{sbj_id}_{ch}_Predictable.png",
dpi=300)
predictables.append(evoked_P)
eeg_U = [target_eeg['yhat'][(target_eeg['Predictability']=='Unpredictable') & (target_eeg['channel']==ch)] \
for ch in target_eeg['channel'].unique()]
meg_U = [target_meg['yhat'][(target_meg['Predictability']=='Unpredictable') & (target_meg['channel']==ch)] \
for ch in target_meg['channel'].unique()]
eeg_U = np.array(eeg_U)
meg_U = np.array(meg_U)
data_U = np.concatenate(([eeg_U, meg_U]))
evoked_U = mne.EvokedArray(data_U, info, tmin=-0.15)
fig = evoked_U.plot_joint(times=[0, 0.11, 0.167, 0.21, 0.266, 0.33, 0.43])
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/{sbj_id}_{ch}_Unpredictable.png",
dpi=300)
unpredictables.append(evoked_U)
eeg_file = pd.read_csv(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/eeg_effect_{sbj_id}_conc.csv")
meg_file = pd.read_csv(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/meg_effect_{sbj_id}_conc.csv")
target_eeg = eeg_file[['channel', 'Concreteness','yhat', 'time']][eeg_file['basisname']=='targ']
target_meg = meg_file[['channel', 'Concreteness','yhat', 'time']][meg_file['basisname']=='targ']
eeg_C = [target_eeg['yhat'][(target_eeg['Concreteness']=='Concrete') & (target_eeg['channel']==ch)] \
for ch in target_eeg['channel'].unique()]
meg_C = [target_meg['yhat'][(target_meg['Concreteness']=='Concrete') & (target_meg['channel']==ch)] \
for ch in target_meg['channel'].unique()]
eeg_C = np.array(eeg_C)
meg_C = np.array(meg_C)
data_C = np.concatenate(([eeg_C, meg_C]))
evoked_C = mne.EvokedArray(data_C, info, tmin=-0.15)
fig = evoked_C.plot_joint(times=[0, 0.11, 0.167, 0.21, 0.266, 0.33, 0.43])
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/{sbj_id}_{ch}_Concrete.png",
dpi=300)
concretes.append(evoked_C)
eeg_A = [target_eeg['yhat'][(target_eeg['Concreteness']=='Abstract') & (target_eeg['channel']==ch)] \
for ch in target_eeg['channel'].unique()]
meg_A = [target_meg['yhat'][(target_meg['Concreteness']=='Abstract') & (target_meg['channel']==ch)] \
for ch in target_meg['channel'].unique()]
eeg_A = np.array(eeg_A)
meg_A = np.array(meg_A)
data_A = np.concatenate(([eeg_A, meg_A]))
evoked_A = mne.EvokedArray(data_A, info, tmin=-0.15)
fig = evoked_A.plot_joint(times=[0, 0.11, 0.167, 0.21, 0.266, 0.33, 0.43])
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/{sbj_id}_{ch}_Abstract.png",
dpi=300)
abstracts.append(evoked_A)
[mne.write_evokeds(path.join(ave_path, f"{sbj_id}_Predictable_unfold_evoked-ave.fif"),
predictable) for sbj_id, predictable in zip(sbj_ids, predictables)]
[mne.write_evokeds(path.join(ave_path, f"{sbj_id}_Unpredictable_unfold_evoked-ave.fif"),
unpredictable) for sbj_id, unpredictable in zip(sbj_ids, unpredictables)]
[mne.write_evokeds(path.join(ave_path, f"{sbj_id}_Concrete_unfold_evoked-ave.fif"),
concrete) for sbj_id, concrete in zip(sbj_ids, concretes)]
[mne.write_evokeds(path.join(ave_path, f"{sbj_id}_Abstract_unfold_evoked-ave.fif"),
abstract) for sbj_id, abstract in zip(sbj_ids, abstracts)]
grand_average_P = mne.grand_average(predictables)
mne.write_evokeds(path.join(ave_path, "GA_predictable-ave.fif"),
grand_average_P)
grand_average_U = mne.grand_average(unpredictables)
mne.write_evokeds(path.join(ave_path, "GA_unpredictable-ave.fif"),
grand_average_U)
contrast = mne.combine_evoked([grand_average_U, grand_average_P],
weights=[1, -1])
mne.write_evokeds(path.join(ave_path, "GA_predictability-contrast-ave.fif"),
contrast)
fig = grand_average_U.plot_joint()
for f, ch in zip(fig, ['EEG', 'GRAD', 'MAG']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_unpredictable.png",
dpi=300)
fig = grand_average_P.plot_joint()
for f, ch in zip(fig, ['EEG', 'GRAD', 'MAG']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_predictable.png",
dpi=300)
fig = contrast.plot_joint()
for f, ch in zip(fig, ['EEG', 'GRAD', 'MAG']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_predictability.png",
dpi=300)
grand_average_C = mne.grand_average(concretes)
mne.write_evokeds(path.join(ave_path, "GA_concrete-ave.fif"),
grand_average_C)
grand_average_A = mne.grand_average(abstracts)
mne.write_evokeds(path.join(ave_path, "GA_abstract-ave.fif"),
grand_average_A)
contrast = mne.combine_evoked([grand_average_A, grand_average_C],
weights=[1, -1])
mne.write_evokeds(path.join(ave_path, "GA_concreteness-contrast-ave.fif"),
contrast)
fig = grand_average_A.plot_joint()
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_abstract.png",
dpi=300)
fig = grand_average_C.plot_joint()
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_concrete.png",
dpi=300)
fig = contrast.plot_joint()
for f, ch in zip(fig, ['EEG', 'MAG', 'GRAD']):
f.savefig(f"/imaging/hauk/users/fm02/MEG_NEOS/jl_evts/Figures/GA_{ch}_concreteness.png",
dpi=300)