def load_cscdata(csc_name, maxlength=None):
data = nlxio.load_ncs(csc_name)
sig_data = data['data']
fs = float(data['sampling_rate'])
times = data['time']
if maxlength is not None:
sig_data = sig_data[:maxlength]
times = times[:maxlength]
times = times/1e6
times = times - times[0]
times = times
return sig_data, times, fs
# coding: utf-8
import neuralynxio
import os
import scipy.io
for filename in os.listdir(os.getcwd()):
if filename.endswith(".ncs") and filename.startswith("CSC"):
channelname = filename.split('.')[0]
rawData = []
csc = neuralynxio.load_ncs(filename)
rawData.append(csc['data'])
header = csc['header']
samplingRate = float(csc['sampling_rate'])
scipy.io.savemat('_' + channelname + '_rawData.mat',
mdict={
'rawData': rawData,
'samplingRate': samplingRate
})
"""
import os, mea
import warnings
import numpy as np
import scipy.signal as spsig
import neuralynxio as nlxio
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from detect_peaks import detect_peaks
warnings.filterwarnings('ignore')
for filename in os.listdir(os.getcwd()):
if 'CSC36' in filename and '.ncs' in filename:
# load the data
lfpdata = nlxio.load_ncs(filename)
eeg_sig = lfpdata['data']
time = lfpdata['time'] / 1e6 # convert to seconds
time = time - time[0]
fs = lfpdata['sampling_rate']
del lfpdata
# load the filtered ripple data
f_ripple = (140, 250)
filt_rip_sig = mea.get_bandpass_filter_signal(eeg_sig, fs, f_ripple)
# mea.plot_lfp(eeg_sig[:fs*1000], filt_rip_sig[:fs*1000], time[:fs*1000])
# calculate the envelope of filtered data
filt_rip_env = abs(spsig.hilbert(filt_rip_sig))
filt_rip_env_zscore = mea.zscore(filt_rip_env)
# Root mean square (RMS) ripple power calculation
# coding: utf-8
import neuralynxio
import os
import scipy.io
for filename in os.listdir(os.getcwd()):
if filename.endswith(".ncs")and filename.startswith("CSC"):
channelname = filename.split('.')[0]
rawData = []
csc = neuralynxio.load_ncs(filename)
rawData.append(csc['data'])
header = csc['header']
samplingRate = float(csc['sampling_rate'])
scipy.io.savemat('_' + channelname + '_rawData.mat', mdict = {'rawData':rawData, 'samplingRate': samplingRate})