# 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()