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- from sklearn.preprocessing import normalize
- from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler
- ch_ids = range(32)
- i_stop = -1
- i_start = 0
- fs = 50
- color = 'C0'
- array_msk = []
- step = 1
- data = data_tot[0, 0]
- if ch_ids == []:
- ch_ids = range(data.shape[1])
- if i_stop == -1:
- i_stop = data.shape[0]
- # data2 = normalize(data)
- # data2 = standardize(data)
- scaler = StandardScaler() #MinMaxScaler() #RobustScaler()
- # scaler =
- scaler.fit(data)
- data2 = scaler.transform(data)
- data2 = np.copy(data)
- # offset = np.cumsum(4 * np.var(data2[:, ch_ids], axis=0)[:, np.newaxis]).T
- offset = np.cumsum(4 * np.var(data2[:, ch_ids], axis=0)[:, np.newaxis]).T
- plt.figure(1)
- plt.clf()
- plt.plot(np.arange(i_start, i_stop) / fs, data2[i_start:i_stop, ch_ids] + offset, color=color, lw=1)
- if array_msk != []: # highlight excluded channels
- plt.plot(np.arange(i_start, i_stop) / fs, data2[i_start:i_stop, array_msk] + offset[array_msk], color='C3', lw=1)
- plt.xlabel('Time (sec)')
- plt.yticks(offset[::step], range(0, len(ch_ids), step))
- plt.ylim(0, offset[-1] + 4)
- # plt.title(f'raw data2 from array {arr_id}')
- plt.tight_layout()
- plt.draw()
- plt.show()
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