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Part_Category_w_time_3.py
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def Cat_all(sub):
suffics = { 1:'no_max',
2:'max_wo_head',
3:'max_w_head'}
sensor = ('meg','grad','mag')
delta_T=50
participant_arr=list(Part_info.keys())
participant=participant_arr[sub-1]
Category = {
"11":"move",
"12":"still",
"21":"big",
"22":"small",
"31":"nat",
"32":"man"}
def run_category(mod, cat, Category, participant, delta_T, sens, xx, method):
cat_1=mod + '/' + Category[str(cat)]
cat_2=mod + '/' + Category[str(cat+1)]
epochs_1 = epochs[cat_1].copy() #choose epochs to classify
epochs_2 = epochs[cat_2].copy()
epochs_rs=mne.concatenate_epochs([epochs_1 ,epochs_2])
#epochs_rs=epochs_rs_raw.copy().filter(1,30)
epochs_rs = epochs_rs.copy().apply_baseline(baseline=(-0.1, 0))
X = epochs_rs.get_data(picks=sens)
X.shape
# Get the dimensions of the original matrix
num_conditions, num_channels, num_timepoints = X.shape
X_lagged = np.zeros((num_conditions, num_channels*num_lags, num_timepoints-num_lags))
# For each channel, concatenate the original data with the lagged time points
for i in range(num_timepoints-num_lags):
lagged_data = np.concatenate([X[:, :, i+j] for j in range(num_lags)], axis=1)
X_lagged[:, :, i] = lagged_data
tr_1=np.unique(epochs_1.events[:,2]) #prepare the labels
tr_2=np.unique(epochs_2.events[:,2])
merged_events = mne.merge_events(epochs_rs.events, tr_1, 1)
merged_events = mne.merge_events(merged_events, tr_2, 2)
epochs_rs.events=merged_events
y = merged_events[:,2] #labels
clf = make_pipeline(Vectorizer(),StandardScaler(),
LinearModel(sklearn.svm.SVC(kernel = 'linear')))
time_decod = SlidingEstimator(clf, n_jobs=-1, scoring='roc_auc', verbose=True)
scores = cross_val_multiscore(time_decod, X_lagged, y, cv=5, n_jobs=-1)
scores = np.mean(scores, axis=0)
row_idx = (mod=='p')*3 + (cat//10-1)
scores_all[row_idx, :] = scores
times_all[:] = epochs_rs.times[0:(num_timepoints-num_lags)]
fig=plt.figure(row_idx)
plt.plot(times_all, scores, label='score')
plt.axhline(.5, color='k', linestyle='--', label='chance')
plt.xlabel('Times')
plt.ylabel('AUC') # Area Under the Curve
plt.legend()
plt.axvline(.0, color='k', linestyle='-')
plt.title(cat_1+' vc '+cat_2+method)
# plt.show()
# filename_fig = op.join(path_to_save, mod+'_'+Category[str(cat)]+'VC'+Category[str(cat+1)]+method+str(delta_T)+'.png')
# fig.savefig(filename_fig, dpi=600)
aaa = 'no_ica'
for xx in [3]:
for sens in ['meg','mag','grad']:
method = '_' + suffics[xx] + '_' + sens + '_'+ aaa
result_all_path='/rds/projects/k/kowalcau-opm-recordings/cross_modal_project/Across_participants/Category_w_time/'+ suffics[xx]+'/' + aaa + '/'
data_path ='/rds/projects/k/kowalcau-opm-recordings/cross_modal_project/'+participant+'/'
result_path=data_path+'proccessed/' + suffics[xx]
path_to_save=result_path + '/category_w_time/' + aaa + '/'
data_name='full'
if not os.path.exists(result_all_path):
os.makedirs(result_all_path)
if not os.path.exists(result_path):
os.makedirs(result_path)
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
path_file = os.path.join(result_path, data_name + '_' + suffics[xx] + '_supertrials-right'+aaa+'.fif')
epochs_raw = mne.read_epochs(path_file, preload=True, verbose=True)
epochs_raw.event_id=events_id
epochs=epochs_raw.copy().filter(1,30).crop(tmin=-0.1, tmax=0.7)
T_full = epochs.resample(500).times
num_lags = round(delta_T/(T_full[2]-T_full[1])/1000)
scores_all = np.zeros((len(['w', 'p']) * len([11, 21, 31]), len(T_full)-num_lags))
times_all = np.zeros(len(T_full)-num_lags)
results = Parallel(n_jobs=-1)(delayed(run_category)(mod, cat, Category, participant, delta_T, sens, xx, method) for mod in ['w', 'p'] for cat in [11, 21, 31])
np.save(result_all_path + 'scores_move_big_nat_W_P_' + str(Part_info[participant]) + method+'_'+str(delta_T), scores_all)
np.save(result_all_path + 'times' + method +'_'+ str(delta_T), times_all)
np.save(path_to_save + 'scores_move_big_nat_W_P_' + str(Part_info[participant]) + method+'_'+str(delta_T), scores_all)
np.save(path_to_save + 'times' + method +'_'+ str(delta_T), times_all)
#np.save(result_path+'scores_'+mod+'_'+Category[str(cat)]+'VC'+Category[str(cat+1)]+method[aa], scores)
#np.save(result_all_path+'scores_'+mod+'_'+Category[str(cat)]+'VC'+Category[str(cat+1)]+str(Part_info[participant])+method[aa], scores)
#np.save(result_all_path+'time_'+mod+'_'+Category[str(cat)]+'VC'+Category[str(cat+1)]+str(Part_info[participant])+method[aa], epochs_rs.times)
if __name__ == "__main__":
import sys
import os
import numpy as np
import mne
import matplotlib
import sys
#matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from mne.preprocessing import annotate_muscle_zscore
import os.path as op
from mne.preprocessing import ICA
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Scaler,
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
import sklearn.svm
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from init_y import *
from joblib import delayed, Parallel
Cat_all(int(sys.argv[1]))
#if Part_info[participant]<109:
# old=1
#else:
# old=0
# data_name = 'full'
# path_data = os.path.join(result_path,data_name +'_ann-1.fif')
# data_raw = mne.io.read_raw_fif(path_data, allow_maxshield=True,preload=True,verbose=True)
#if old==1:
# filename_events = op.join(result_path,data_name + '_eve-all-new' +'.fif')
#else:
#filename_events = op.join(result_path,data_name + '_eve-right' +'.fif')
#events = mne.read_events(filename_events, verbose=True)