# make events from block names le = preprocessing.LabelEncoder() le.fit(df["block_name"].unique()) triggers = pd.Series(le.transform(df["block_name"])).diff(1) events = np.array(triggers.where(abs(triggers) > 0).dropna().index) events_ids = df["block_name"].iloc[events] events_channels = np.zeros([events.shape[0]]) block_events = np.stack([events, events_channels, events_ids], axis=1) # block_events = np.array([[i, 0, df["block_name"][i]] for i in range(df["block_name"].shape[0] - 1) # if df["block_name"][i] != df["block_name"][i + 1]]) # concatenate events of different types events_list = np.concatenate([photo_events, block_events]) # save events data as readable for Brainstorm .fif mne.write_events("{}-eve.fif".format(name), events_list) if __name__ == "__main__": import os from data.load_results import load_data load_path = "/Users/basilminkov/Neuroscience/Data/discrete_feedback" subject = 'df_0_05-09_12-01-47' data_name = "experiment_data.h5" df, fs, channels = load_data(os.path.join(load_path, subject, data_name)) save_data_and_events_as_fif(df, "df0", fs, channels)
'df_11_05-13_17-08-06', 'df_12_05-15_12-43-13', ] load_path = "/Users/basilminkov/Neuroscience/Data/discrete_feedback/{}/experiment_data.h5" channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat" considered_protocols = ['Closed', 'Baseline'] real_list = [] mock_list = [] # for i in range(len(sub)): for i in range(1): df, fs, channels = load_data(load_path.format(sub[i])) try: channels.remove("T7") except ValueError: pass ch3, channels_in_list, ind_in_list = parse_channels_locations_from_mat( channels_path, channels) ch2 = np.delete(ch3, 2, 1) df1 = df[df['block_name'] == considered_protocols[0]][channels_in_list].T.as_matrix() df2 = df[df['block_name'] == considered_protocols[1]][channels_in_list].T.as_matrix() # window = signal.get_window(window="hamming", Nx=fs) F, Pxx_1 = signal.welch(x=df1,
if __name__ == "__main__": import os import pandas as pd import matplotlib.pyplot as plt from data import parse_channels_locations_from_mat import numpy as np print("Last year algorithm") # Prepossessing considered_protocols = ['Closed', 'Baseline'] save_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/results/eye_test/" df, fs, channels = load_data("/Users/basilminkov/Neuroscience/Data/discrete_feedback/{}/experiment_data.h5".format('df_0_05-09_12-01-47')) df = pd.concat([df.loc[df['block_name'] == considered_protocols[0]], df.loc[df['block_name'] == considered_protocols[1]]]) # An estimate of the power spectral density # welsh_comparison(df, considered_protocols, channels, fs) # Dealing with channels colour_map = "magma" channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat" ch3, channels_in_list, ind_in_list = parse_channels_locations_from_mat(channels_path, channels) ch2 = np.delete(ch3, 2, 1)
# load_path = "/Users/basilminkov/Neuroscience/Data/alpha-delay-subj-14_05-16_17-17-22/experiment_data.h5" # load_path = "/Users/basilminkov/Desktop/test/VasyaTest1_04-10_17-50-12/experiment_data.h5" save_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/results/eye_test/{}" colour_map = "magma" channels_path = "/Users/basilminkov/Scripts/python3/Neuroimaging/static/chanlocs_mod.mat" fs = 500 # Load data # clear_data = pd.read_csv("/Users/basilminkov/Scripts/python3/Neuroimaging/results/discrete_feedback/df_1_05-09_15-44-03/clear_eeg") # clear_data = pd.read_csv("/Users/basilminkov/Scripts/python3/Neuroimaging/results/eye_test/clear_eeg") considered_protocols = ['Real', 'Mock'] # considered_protocols = ['FB0', 'FBMock'] df, fs, channels = load_data(load_path) # df = pd.concat([df.loc[df['block_name'] == considered_protocols[0]], # df.loc[df['block_name'] == considered_protocols[1]]]) # Dealing with channels # a = df.loc[df['block_name'] == 'Real'] # b = df.loc[df['block_name'] == 'Mock'] ch3, channels_in_list, ind_in_list = parse_channels_locations_from_mat(channels_path, channels) ch2 = np.delete(ch3, 2, 1) # # sp = abs(np.fft.fft(clear_data['Fp1'])) # freq = np.fft.fftfreq(clear_data['Fp1'].shape[0], 1/500) # plt.plot(freq, sp.real) # plt.show()
p_value[p_value > 0.05] = 1 progressbar.update_progressbar("Test is done!") return p_value, p_value_rightsided, p_value_leftsided, vectors_list, values_list, frequencies if __name__ == "__main__": from data import parse_channels_locations_from_mat, load_signals_data, load_data import numpy as np # Load experimental data data_path = "/Users/basilminkov/Neuroscience/Data/Test/20.02.17/Alpha1_02-20_17-52-50/experiment_data.h5" df, fs, p_names, channels = load_data(data_path) # Load signal data df_full = load_signals_data( "/Users/basilminkov/Neuroscience/Data/Test/20.02.17/Alpha1_02-20_17-52-50/experiment_data.h5" ) # Prepare epochs x_r = df_full.loc[df_full['block_name'] == 'Real', 'P4'].as_matrix() y_r = df_full.loc[df_full['block_name'] == 'Real', 'Composite'].as_matrix() df_r = df.loc[df['block_name'] == 'Real'][channels] x_m = df_full.loc[df_full['block_name'] == 'Mock', 'P4'].as_matrix() y_m = df_full.loc[df_full['block_name'] == 'Mock', 'Composite'].as_matrix()