KIAPBCI/kiap_bci/analytics/test2.py

import matplotlib.pyplot as plt
import numpy as np
import os
import importlib

from helpers import data_management as dm
from analytics import analytics1
from analytics import fr_analytics as fran
importlib.reload(fran)

import aux
from scipy import signal

params = aux.load_config()


data_tot, tt, triggers_tot, ch_rec_list, file_names = dm.get_raw(n_triggers=2, exploration=False)


ch_id = 20
data2 = data_tot[0, 0]


ids = np.argwhere(data2.mean(axis=0) == 0)
data3 = np.delete(data2, ids, axis=1)

cc = np.corrcoef(data3.T)

# y = signal.savgol_filter(data2[:, ch_id], 41, 2)


# ch_ids = list(range(32)) + list(range(96, 128))
# args = {'v_thr': 10, 'ymax': 7, 'arr_id': 1}

# an.plot_pdf(f_id=0, arr_id=args['arr_id'])
# an.plot_pdf(f_id=1, arr_id=args['arr_id'])
for fid in range(len(file_names)):
    an = fran.fr_analytics(data_tot, params, file_names[fid])
    args = {'v_thr': 10, 'ymax': 7, 'arr_id': 1, 'save_fig': True}
    an.plot_spectra(f_id=fid, recompute=True, **args)

args = {'v_thr': 10, 'ymax': 7, 'arr_id': 2}
an.plot_spectra(f_id=0, recompute=True, **args)
# an.plot_spectra(f_id=1, recompute=True, **args)
xx

# plt.tight_layout()

tt2 = np.arange(data2.shape[0]) * 0.050
# nperseg = 100
# fmax = 5
# ff, tt1, Sxx1 = signal.spectrogram(y, fs=20, axis=0, nperseg=nperseg, noverlap=int(nperseg * 0.9)) 
# fidx = (ff >= 0) & (ff < fmax)

plt.figure()
plt.clf()
plt.subplot(211)
# plt.plot(tt2, data2[:, ch_id])
plt.plot(tt2, y, 'C2')
# plt.plot(data2[:, ch_id])
plt.xlim(0, tt2.max())

for tr_id in range(3, 4):
    plt.vlines(triggers_tot[0, 0][0][tr_id] * 0.05, 0, 50, color='r', alpha=0.8)
plt.vlines(triggers_tot[0, 0][1][0] * 0.05, 0, 50, color='k', alpha=0.8)
# # plt.vlines(triggers_tot[0, 0][0][3], 0, 50, color='r', alpha=0.8)


# plt.subplot(212)
# plt.pcolormesh(tt1, ff[fidx], Sxx1[fidx, :])  
# plt.vlines(triggers_tot[0, 0][0][3] * 0.05, 0, fmax, color='r', alpha=0.8)

# plt.xlim(0, tt2.max())
# # plt.clim(0, 1)
plt.draw()
plt.show()