return neural_data[neurons_idx, :]
if pca_comp_num is not None:
pca = PCA(pca_comp_num)
pca.fit(neural_data.T)
return pca.transform(neural_data.T).T
# set seed
seed = np.random.seed(2020)
# #IMPORT DATA
dat = np.load('data/stringer_spontaneous.npy', allow_pickle=True).item()
neural_data = dat['sresp']
run_data = dat['run']
run_onset, run_speed = movement.detect_movement_onset(run_data)
# #%% LOGISTIC REGRESSION ON NEURAL DATA - train model
# # SET PARAMETERS
# C = np.logspace(-4, 0, 20)
# det_window = 13
# neuron_num = 4000
# pca_com = 1
# %% CORR of PCA in windows
# window_corr = np.zeros([(neural_data.shape[1]-(det_window+1)),1])
# window_corr = []
# for idx in range(det_window+1, len(neural_data[0,:])):
# neural_data_window = neural_data[:,(idx-det_window):idx]
# feat_w = extract_features(neural_data_window, pca_comp_num =pca_com)
def load_data(path='data/stringer_spontaneous.npy'):
dat = np.load(path, allow_pickle=True).item()
neural_data = dat['sresp']
run_data = dat['run']
run_onset, run_speed = movement.detect_movement_onset(run_data)
return neural_data, run_onset, run_speed