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- 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)
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