# and labeled as the peaks and troughs, respectively.
from bycycle.filt import bandpass_filter
from bycycle.cyclepoints import _fzerorise, _fzerofall, find_extrema
# Narrowband filter signal
N_seconds_theta = .75
signal_narrow = bandpass_filter(signal,
Fs,
f_theta,
remove_edge_artifacts=False,
N_seconds=N_seconds_theta)
# Find rising and falling zerocrossings (narrowband)
zeroriseN = _fzerorise(signal_narrow)
zerofallN = _fzerofall(signal_narrow)
####################################################################################################
# Find peaks and troughs (this function also does the above)
Ps, Ts = find_extrema(signal_low,
Fs,
f_theta,
filter_kwargs={'N_seconds': N_seconds_theta})
tlim = (12, 15)
tidx = np.logical_and(t >= tlim[0], t < tlim[1])
tidxPs = Ps[np.logical_and(Ps > tlim[0] * Fs, Ps < tlim[1] * Fs)]
tidxTs = Ts[np.logical_and(Ts > tlim[0] * Fs, Ts < tlim[1] * Fs)]
plt.figure(figsize=(12, 2))
# individual cycles. To do this, the signal is first narrow-bandpass filtered in order to estimate
# "zero-crossings." Then, in between these zerocrossings, the absolute maxima and minima are found
# and labeled as the peaks and troughs, respectively.
# Narrowband filter signal
n_seconds_theta = .75
sig_narrow = filter_signal(sig,
fs,
'bandpass',
f_theta,
n_seconds=n_seconds_theta,
remove_edges=False)
# Find rising and falling zerocrossings (narrowband)
zerorise_narrow = _fzerorise(sig_narrow)
zerofall_narrow = _fzerofall(sig_narrow)
####################################################################################################
# Find peaks and troughs (this function also does the above)
peaks, troughs = find_extrema(sig_low,
fs,
f_theta,
filter_kwargs={'n_seconds': n_seconds_theta})
plot_cyclepoints_array(sig_low,
fs,
peaks=peaks,
troughs=troughs,
xlim=(12, 15))