def plot_epoch(start=0.0, duration=10.0):
"plot all the signals one above another in a 10 second epoch"
from stacklineplot import stackplot
x0 = start * fs
x1 = (start + duration) * fs
stackplot(allsig[:, x0:x1],
seconds=duration,
start_time=start,
ylabels=signal_labels)
def test_edflibpyx():
bfile_name = FILE_NAME.encode(TEXT_ENCODING)
with _edflib.CyEdfReader(bfile_name, TEXT_ENCODING) as efc:
signal_labels, signal_nsamples = fileinfo(efc)
# convert byte strings to regular string
signal_labels = [
label.decode(TEXT_ENCODING) for label in signal_labels
]
sig1 = np.zeros(2000, dtype='float64')
efc.read_phys_signal(1, 0, 2000, sig1)
nsigs = efc.signals_in_file
sigbufs = np.zeros((nsigs, 2000), dtype='float64')
# read the first 10 sec
readpt = 0
for ii in range(nsigs):
efc.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
if __name__ == '__main__':
import matplotlib.pyplot as plt
from stacklineplot import stackplot, figure
fig1 = plt.figure(1, figsize=(8, 6))
stackplot(sigbufs,
seconds=10.0,
start_time=0.0,
ylabels=signal_labels)
fig1.savefig('test_edflibpyx_fig1.png', bbox_inches='tight')
# now read the next 10 seconds
readpt = 2000
for ii in range(nsigs):
efc.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
if __name__ == '__main__':
fig2 = plt.figure(2, figsize=(8, 6))
stackplot(sigbufs,
seconds=10.0,
start_time=10.0,
ylabels=signal_labels)
fig2.savefig('test_edflibpyx_fig2.png', bbox_inches='tight')
# now read the overlap 5-15 seconds
readpt = 1000
for ii in range(nsigs):
efc.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
Nibuf = 1000
ibuf = np.zeros(Nibuf, dtype='int32')
# try pickout signal 5
_edflib.rewind(efc.handle, 5)
_edflib.read_int_samples(efc.handle, 5, Nibuf, ibuf)
def plotEDFdata(eeg_path):
f = pyedflib.EdfReader(eeg_path)
n_channels = f.signals_in_file # number of EEG channels
signal_labels = f.getSignalLabels() # names of EEG channels
n_min = f.getNSamples()[0]
sigbufs = [np.zeros(f.getNSamples()[i]) for i in np.arange(n_channels)]
for i in np.arange(n_channels):
sigbufs[i] = f.readSignal(i)
if n_min < len(sigbufs[i]):
n_min = len(sigbufs[i])
f._close()
del f
n_plot = np.min((n_min, 2800))
sigbufs_plot = np.zeros((n_channels, n_plot))
for i in np.arange(n_channels):
sigbufs_plot[i, :] = sigbufs[i][:n_plot]
stackplot(sigbufs_plot[:, :n_plot], ylabels=signal_labels)
def readsignals(edf, start_time, end_time, buf=None):
"""times in seconds"""
# assume same sampling rate for all channels for a moment and use signal#0
assert end_time <= edf.file_duration
readpt = int(edf.samplefrequency(0)*(start_time))
print( "readpt:", readpt)
readlen = int( edf.samplefrequency(0)*(end_time-start_time))
assert readlen <= MAXSIGLEN
print( "readlen:", readlen)
for ii in range(nsigs):
e.readsignal(ii, readpt, readlen, sigbufs[ii])
return readpt, readlen
L = 8.0
s,l = readsignals(e, 0, L)
stackplot(sigbufs[:, s:s+l], seconds = L, ylabels=signal_labels)
# findx2 = [ (ii, signal_labels[ii]) for ii in range(len(signal_labels))]
# X2 is signal 27
# A2 is signal 23
ekg = sigbufs[27][0:l] - sigbufs[23][0:l]
from scipy.signal import kaiserord, lfilter, firwin, freqz
# lowpass filter
sample_rate = e.samplefrequency(0)
nyq_rate = sample_rate/2.0
width = 5.0/nyq_rate
ripple_db = 60.0
N, beta = kaiserord(ripple_db, width)
#!/usr/bin/env python
from __future__ import division, print_function, absolute_import
import numpy as np
import pyedflib
from stacklineplot import stackplot
if __name__ == '__main__':
f = pyedflib.data.test_generator()
n = f.signals_in_file
signal_labels = f.getSignalLabels()
sigbufs = np.zeros((n, f.getNSamples()[0]))
for i in np.arange(n):
sigbufs[i, :] = f.readSignal(i)
f._close()
del f
# stackplot(sigbufs,seconds=10.0, start_time=0.0, ylabels=signal_labels)
stackplot(sigbufs[:, :2000], ylabels=signal_labels)
sig1 = np.zeros(2000.0, dtype='float64')
e.readsignal(1,0, 2000, sig1)
nsigs = e.signals_in_file
MAXSIGLEN = 20000
sigbufs = np.zeros((nsigs,MAXSIGLEN), dtype='float64')
# read the first 10 sec
readpt = 0
for ii in range(nsigs):
e.readsignal(ii, readpt, 2000, sigbufs[ii])
# stackplot(sigbufs,seconds=10.0, start_time=0.0, ylabels=signal_labels)
L = 8.0
s,l = readsignals(e, 0, L)
stackplot(sigbufs[:, s:s+l], seconds = L, ylabels=signal_labels)
# findx2 = [ (ii, signal_labels[ii]) for ii in range(len(signal_labels))]
# X2 is signal 27
# A2 is signal 23
ekg = sigbufs[27][0:l] - sigbufs[23][0:l]
from scipy.signal import kaiserord, lfilter, firwin, freqz
sample_rate = e.samplefrequency(0)
nyq_rate = sample_rate/2.0
width = 5.0/nyq_rate
ripple_db = 60.0
N, beta = kaiserord(ripple_db, width)
cutoff_hz = 10.0
taps = firwin(N, cutoff_hz/nyq_rate, window=('kaiser', beta))
print(edf.samplefrequency(ii))
fileinfo(e)
sig1 = np.zeros(2000, dtype='float64')
e.read_phys_signal(1, 0, 2000, sig1)
nsigs = e.signals_in_file
sigbufs = np.zeros((nsigs, 2000), dtype='float64')
# read the first 10 sec
readpt = 0
for ii in range(nsigs):
e.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
from stacklineplot import stackplot, figure
stackplot(sigbufs, seconds=10.0, start_time=0.0, ylabels=signal_labels)
# now read the next 10 seconds
readpt = 2000
for ii in range(nsigs):
e.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
figure()
stackplot(sigbufs, seconds=10.0, start_time=10.0, ylabels=signal_labels)
# now read the overlap 5-15 seconds
readpt = 1000
for ii in range(nsigs):
e.read_phys_signal(ii, readpt, 2000, sigbufs[ii])
Nibuf = 1000
ibuf = np.zeros(Nibuf, dtype='int32')
print edf.samplefrequency(ii)
fileinfo(e)
sig1 = np.zeros(2000.0, dtype='float64')
e.readsignal(1,0, 2000, sig1)
nsigs = e.signals_in_file
sigbufs = np.zeros((nsigs,2000), dtype='float64')
# read the first 10 sec
readpt = 0
for ii in range(nsigs):
e.readsignal(ii, readpt, 2000, sigbufs[ii])
from stacklineplot import stackplot, figure
stackplot(sigbufs,seconds=10.0, start_time=0.0, ylabels=signal_labels)
# now read the next 10 seconds
readpt = 2000
for ii in range(nsigs):
e.readsignal(ii, readpt, 2000, sigbufs[ii])
figure()
stackplot(sigbufs,seconds=10.0, start_time=10.0, ylabels=signal_labels)
# now read the overlap 5-15 seconds
readpt = 1000
for ii in range(nsigs):
e.readsignal(ii, readpt, 2000, sigbufs[ii])
Nibuf=1000
def showEEG(start, end):
stackplot(sigbufs[:, (start * sr):(end * sr)], ylabels=signal_labels)
#!/usr/bin/env python
from __future__ import division, print_function, absolute_import
import numpy as np
import pyedflib
from stacklineplot import stackplot
if __name__ == '__main__':
f = pyedflib.data.test_generator()
n = f.signals_in_file
signal_labels = f.getSignalLabels()
n_min = f.getNSamples()[0]
sigbufs = [np.zeros(f.getNSamples()[i]) for i in np.arange(n)]
for i in np.arange(n):
sigbufs[i] = f.readSignal(i)
if n_min < len(sigbufs[i]):
n_min = len(sigbufs[i])
f._close()
del f
n_plot = np.min((n_min, 2000))
sigbufs_plot = np.zeros((n, n_plot))
for i in np.arange(n):
sigbufs_plot[i, :] = sigbufs[i][:n_plot]
stackplot(sigbufs_plot[:, :n_plot], ylabels=signal_labels)
#!/usr/bin/env python
from __future__ import division, print_function, absolute_import
import os
import numpy as np
import pyedflib
from stacklineplot import stackplot
if __name__ == '__main__':
f = pyedflib.EdfReader("data/test_generator.edf")
n = f.signals_in_file
signal_labels = f.getSignalLabels()
sigbufs = np.zeros((n, f.getNSamples()[0]))
for i in np.arange(n):
sigbufs[i, :] = f.readSignal(i)
f._close()
del f
# stackplot(sigbufs,seconds=10.0, start_time=0.0, ylabels=signal_labels)
stackplot(sigbufs[:, :2000], ylabels=signal_labels)