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read_data.py
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import numpy as np
import os
import pandas as pd
csvdir = 'data/train'
n_subs = 12
n_series = 8
n_channels = 32
data = []
label = []
for sub in np.arange(n_subs):
sub_data = []
sub_label = []
for series in np.arange(n_series):
csv = 'subj' + str(sub + 1) + '_series' + str(series + 1) + '_data.csv'
series_data = pd.read_csv(os.path.join(csvdir, csv))
ch_names = list(series_data.columns[1:])
series_data = np.array(series_data[ch_names], 'float32')
sub_data.append(series_data)
csv = 'subj' + str(sub + 1) + '_series' + str(series + 1) + '_events.csv'
series_label = pd.read_csv(os.path.join(csvdir, csv))
ch_names = list(series_label.columns[1:])
series_label = np.array(series_label[ch_names], 'float32')
sub_label.append(series_label)
data.append(sub_data)
label.append(sub_label)
np.save('eeg_train.npy', [data, label])
csvdir = 'data/test'
n_subs = 12
n_series = 2
n_channels = 32
data = []
label = []
for sub in np.arange(n_subs):
sub_data = []
sub_label = []
for series in np.arange(9, 9 + n_series):
csv = 'subj' + str(sub + 1) + '_series' + str(series) + '_data.csv'
series_data = pd.read_csv(os.path.join(csvdir, csv))
ch_names = list(series_data.columns[1:])
series_data = np.array(series_data[ch_names], 'float32')
sub_data.append(series_data)
series_label = np.zeros([series_data.shape[0], 6])
sub_label.append(series_label)
data.append(sub_data)
label.append(sub_label)
np.save('eeg_test.npy', [data, label])