else:
band = None
file_list = os.listdir(DATA_PATH)
n_patients = 27
n_electrodes = 153
# subject counters
i_subjects = 0
i_channels = 0
for file in file_list:
if file.startswith('spmeeg_'):
print('Processing file: {}'.format(file))
d = ut.load_data_spm(file)
fs = d['fsample'][0][0]
channels = d['chanlabels'][0]
# make new dict
subject_dict = dict(channels=channels,
phase={},
fs=fs,
sharpness={},
steepness={},
subject_number=int(file[7:-4]))
# for every lfp channel
for i, lfp in enumerate(d['data']):
current_channel = channels[i][0]
import matplotlib.pyplot as plt
import numpy as np
from definitions import SAVE_PATH_FIGURES, DATA_PATH
from utils import load_data_spm, band_pass_filter
file_list = os.listdir(DATA_PATH)
i_subjects = 0
i_channels = 0
band = [13, 30]
for f in file_list:
if f.startswith('spmeeg_'):
d = load_data_spm(f)
fs = d['fsample'][0][0]
dt = 1 / fs
t = np.arange(0, d['data'].shape[1] * dt, dt)
channel_labels = d['chanlabels'][0]
plt.figure(figsize=(10, 5))
for i, lfp in enumerate(d['data']):
# remove 50Hz noise or band pass filter
lfp_clean = band_pass_filter(lfp, band=band, fs=fs)
# lfp_clean = remove_50_noise(lfp, fs)
plt.subplot(3, 2, i + 1)
plt.plot(t[::200], lfp_clean[::200], linewidth=.7)
plt.legend()
plt.title('LFP channel {}'.format(channel_labels[i]))
if not (i == 4 or i == 5):
band = np.array([4., 12.])
elif frequ_range == 'beta':
band = np.array([12., 30.])
else:
band = None
# amount of time to be considered
seconds = 5
seconds_str = '{}'.format(seconds)
# for every subject file
for sub, sub_file in enumerate(file_list):
subject_number = int(sub_file[7:-4])
if subject_number > 0:
# load data
d = ut.load_data_spm(sub_file, data_folder=data_folder)
fs = d['fsample'][0][0]
channels = d['chanlabels'][0]
print('analysing subject file {}'.format(sub_file))
# make new dict
subject_dict = dict(lfp_band={}, channels=channels, phase={}, fs=fs)
plt.figure(figsize=(10, 7))
for chan_idx, chan in enumerate(channels):
# mean center
lfp = d['data'][chan_idx] - d['data'][chan_idx].mean()
# filter around the theta peak
lfp_band = ut.band_pass_filter(lfp,
import matplotlib.pyplot as plt
import numpy as np
import os
from definitions import SAVE_PATH_FIGURES
from utils import load_data_spm, get_array_mask, band_pass_filter
filename = 'spmeeg_19.mat'
channel_name = 'GPiR23'
d = load_data_spm(filename)
fs = d['fsample'][0][0]
lfp = None
for idx, str in enumerate(d['chanlabels'][0]):
if str[0] == channel_name:
lfp = d['data'][idx]
if lfp is None:
raise ValueError(
'Could not find the specified channel in the data file: {}'.format(
channel_name))
# remove mean
lfp = lfp - np.mean(lfp)
# filter in theta range:
lfp_filt = band_pass_filter(y=lfp, fs=fs, band=[4, 12], plot_response=False)
# construct the time vector
dt = 1 / fs
t = np.arange(0, d['data'].shape[1] * dt, dt)
n_electrodes = 153
max_amp_psds_theta = np.zeros((n_patients, n_frequ_samples))
max_amp_peak_frequency_theta = np.zeros(n_patients)
max_amp_psds_beta = np.zeros((n_patients, n_frequ_samples))
max_amp_peak_frequency_beta = np.zeros(n_patients)
theta_peaks = np.zeros(n_electrodes)
beta_peaks = np.zeros(n_electrodes) # there are supposed to be 153 channels
frequs = None
# subject counters
subject_idx = 0
electrode_idx = 0
for f in file_list:
if f.startswith('spmeeg_'):
d = ut.load_data_spm(f)
fs = d['fsample'][0][0]
chanlabels = d['chanlabels'][0]
# prelocate amp arrays for saving the peak amp of every channel
n_channels = d['data'].shape[0]
beta_amp = np.zeros(n_channels)
theta_amp = np.zeros(n_channels)
psds = np.zeros((n_channels, n_frequ_samples))
theta_peak_frequency = np.zeros(n_channels)
beta_peak_frequency = np.zeros(n_channels)
plt.figure(figsize=(10, 5))
for channel_idx, lfp in enumerate(d['data']):
# remove 50Hz noise
lfp_clean = lfp
import matplotlib.pyplot as plt
import numpy as np
import os
from definitions import SAVE_PATH_FIGURES
from utils import load_data_spm, get_array_mask, band_pass_filter
subject_number = 3
filename = 'spmeeg_{}.mat'.format(subject_number)
channel_name = 'GPiL23'
d = load_data_spm(filename, data_folder='/Users/Jan/Dropbox/Master/LR_Kuehn/data/dystonia_rest/for_python')
fs = d['fsample'][0][0]
lfp = None
for idx, str in enumerate(d['chanlabels'][0]):
if str[0] == channel_name:
lfp = d['data'][idx]
if lfp is None:
raise ValueError('Could not find the specified channel in the data file: {}'.format(channel_name))
# remove mean
lfp = lfp - np.mean(lfp)
# filter in theta range:
band = [4, 40]
lfp_filt = band_pass_filter(y=lfp, fs=fs, band=band, plot_response=False)
# construct the time vector
dt = 1 / fs
t = np.arange(0, d['data'].shape[1] * dt, dt)
n_subjects = 16
suffix = ''
for sub in np.arange(1, n_subjects + 1):
# save all data of current subject in dict
subject_dict = dict(filenames={}, lfp={}, fs={}, conditions={}, chanlabels={}, time={}, id={})
plt.figure(figsize=(7, 5))
plot_idx = 1
for f in file_list:
# process only the current subject
if f.startswith('spmeeg_{}_'.format(sub)) and not f.startswith('spmeeg_{}_d'.format(sub)):
current_file_name = f[:-4]
print('plotting from {}'.format(f))
d = ut.load_data_spm(f, data_folder=DATA_PATH_BAROW)
fs = d['fsample'][0][0]
chanlabels = d['chanlabels'][0][0]
condition = d['condition_str'][0]
# save data
subject_dict['lfp'][condition] = d['data'][0, :]
subject_dict['filenames'][condition] = current_file_name
subject_dict['fs'][condition] = fs
subject_dict['conditions'][condition] = condition
subject_dict['id'][condition] = d['subject_id'][0]
subject_dict['chanlabels'][condition] = chanlabels[0]
subject_dict['number'] = sub
# downsample to make plotting faster
down_factor = 300