import importlib
import os

import matplotlib.pyplot as plt
import numpy as np
import modules.classifier as classifier
import aux
from helpers import data_management as dm
from analytics import analytics1

importlib.reload(classifier)
importlib.reload(dm)
importlib.reload(analytics1)

aux.config_logging()
params = aux.load_config()
features_fname = params.file_handling.datafile_path + '/train_trials.pkl'    # name of features array

# fids = np.arange(51,63)					# 22.5.19-24.5.19 ids of sessions, NOTE: this will change if sessions from the past are added
# fids = np.arange(51,56)					# 22.5.19
fids = np.array([60, 61, 62, 64, 65])	# 24.5.19
# fids = np.append(fids,[64,65])
psth_tot1 = np.empty((0,140, 128))		
psth_tot2 = np.empty((0,140, 128))

for ii in range(fids.size):		# stack psth from different sessions
	data_tot, tt, triggers, ch_rec_list, file_names = dm.get_raw(n_triggers=params.classifier.n_classes, fids=[fids[ii]])
	clf = classifier.Classifier(params)
	clf.get_trials_kiap(data_tot[:, :], triggers, features_fname=features_fname)
	psth_tot1 = np.vstack((psth_tot1, clf.psth[0]))
	psth_tot2 = np.vstack((psth_tot2, clf.psth[1]))

clf.psth[0] = psth_tot1
clf.psth[1] = psth_tot2

fig_name0 = os.path.basename(os.path.splitext(file_names[0])[0])
fig_name = params.file_handling.results+fig_name0 + '/'
if not os.path.exists(fig_name):
	os.mkdir(fig_name)
fig_name = fig_name + fig_name0

plt.close('all')
plt.figure(1, figsize=[10 ,  7])

for ii in range(128):
	analytics1.plot_psth2(clf,[ii],fig_name)