import matplotlib.pyplot as plt
"""
for every subject make a plot of histograms of sharpness of peaks and troughs
"""
data_folder = os.path.join(SAVE_PATH_DATA_BAROW, 'analysis')
# read all files in the data folder
file_list = [
f for f in os.listdir(data_folder)
if f.endswith('beta.p') and f.startswith('subject')
]
# for every subject file
for sub, sub_file in enumerate(file_list):
# load data
d = ut.load_data_analysis(sub_file, data_folder=data_folder)
frequ_range = d['frequ_range']
print('analysing subject file {}'.format(sub_file))
plt.figure(figsize=(10, 5))
plt.subplot(1, 3, 1)
plt.ylabel('cycle count')
condition_order = ['rest', 'stim', 'poststim']
conditions = d['conditions']
# for all three conditions
for i in range(len(conditions)):
condition = condition_order[i]
print('Condition {}'.format(condition))
n_bins = 30
import os
import matplotlib.pyplot as plt
import numpy as np
import utils as ut
from definitions import SAVE_PATH_FIGURES
"""
Plot figure figure 1A from the Neumann et al 2017 paper manuscript: the average psd over subjects
as well as the histogram over psd peaks in beta and in theta range
"""
# load data
suffix = '_linear_search_and_amp'
data = ut.load_data_analysis('psd_maxamp_theta_beta{}.p'.format(suffix))
# now make a plot of all selected channels and the histogram over peaks
frequs = data['frequs']
mask = ut.get_array_mask(frequs > 4, frequs < 40)
plt.figure(figsize=(7, 5))
# plot individual stectra
# plt.plot(frequs[mask], data['psd_beta'][:, mask].T, linewidth=.7, color='C1', alpha=.5)
# plt.plot(frequs[mask], data['psd_theta'][:, mask].T, linewidth=.7, color='C4', alpha=.5)
# plot std envelope
joined_psd_mean = np.mean(np.vstack((data['psd_theta'], data['psd_beta'])),
axis=0)
se = ut.standard_error(np.vstack((data['psd_theta'], data['psd_beta'])),
axis=0)
plt.fill_between(frequs[mask],
joined_psd_mean[mask] + se[mask],
joined_psd_mean[mask] - se[mask],
alpha=.2,
import os
import matplotlib.pyplot as plt
import numpy as np
import utils as ut
from definitions import SAVE_PATH_FIGURES
"""
Plot the mean coherence over subjects. select for every subject the channel with the largest amplitude in coherence.
"""
# load data
suffix = ''
window_length = 1024
data = ut.load_data_analysis('coh_icoh_allsubjects_w{}_{}.p'.format(
window_length, suffix))
# now make a plot of all selected channels and the histogram over peaks
f = data['frequs']
mask = ut.get_array_mask(f > 1, f < 30)
coh_mat = data['coh_mat'][:, :, mask]
icoh_mat = data['icoh_mat'][:, :, mask]
n_subjects, n_channels, n_frequ_samples = coh_mat.shape
max_coh_channels = np.zeros((n_subjects, n_frequ_samples))
max_icoh_channels = np.zeros((n_subjects, n_frequ_samples))
for subject_idx in range(n_subjects):
# select the channel with the largest coherence
channel_idx = np.argmax(np.max(coh_mat[subject_idx, ], axis=1))
max_coh_channels[subject_idx, ] = coh_mat[subject_idx, channel_idx, :]
max_icoh_channels[subject_idx, ] = icoh_mat[subject_idx, channel_idx, :]
import os import matplotlib.pyplot as plt import numpy as np import utils as ut from definitions import SAVE_PATH_FIGURES, SAVE_PATH_DATA, DATA_PATH data_folder = os.path.join(SAVE_PATH_DATA, 'stn') save_folder = os.path.join(SAVE_PATH_FIGURES, 'stn') filename = 'stn_data_sharpness_over_epochs.p' data_dict = ut.load_data_analysis(filename, data_folder) n_subject = len(data_dict.keys()) n_channels = 6 # make one big figure with channels subplot showing the esr and rdsr over condition for all subjects plt.figure(figsize=(15, 8)) # for every subject, select the channel with the largest difference in esr esr_over_conditions = np.zeros((n_subject, 2)) rdsr_over_conditions = np.zeros((n_subject, 2)) esr_over_channels = np.zeros((n_subject, 6, 2)) rdsr_over_channels = np.zeros((n_subject, 6, 2)) esr_over_channels_std = np.zeros((n_subject, 6, 2)) rdsr_over_channels_std = np.zeros((n_subject, 6, 2)) band_idx = 0 bands = [[11, 22], [18, 32]] band_strings = ['low_beta', 'high_beta'] for subject_idx, subject_dict in enumerate(data_dict.values()):
