# band pass filter
lfp_band = ut.band_pass_iir(y=lfp_pre,
fs=fs,
band=beta_bands[subject_idx])
# remove potential ringing artifacts
idx_167ms = int((fs / 1000) * 167)
lfp_band = lfp_band[idx_167ms:-idx_167ms]
lfp_band -= lfp_band.mean()
lfp_pre = lfp_pre[idx_167ms:-idx_167ms]
lfp_pre -= lfp_pre.mean()
# For plotting figure 3
if plot_figure3_poster:
# find lfp band zeros
zeros_rising, zeros_falling, zeros = ut.find_rising_and_falling_zeros(
lfp_band)
# find the peaks in between the zeros, USING THE RAW DATA!
analysis_lfp = lfp_pre
peaks, troughs, extrema, extrema_kind = ut.find_peaks_and_troughs_cole(
analysis_lfp,
zeros=zeros,
rising_zeros=zeros_rising,
falling_zeros=zeros_falling)
rise_steepness, fall_steepness, steepness_indices, steepness_values = ut.calculate_rise_and_fall_steepness(
analysis_lfp, extrema)
plot_ratio_illustration_for_poster(fs,
lfp_pre,
lfp_band,
zeros,
lfp_band = lfp_band[lfp_cutoff * fs:-lfp_cutoff * fs]
lfp_band -= lfp_band.mean()
lfp_pre = lfp_pre[lfp_cutoff * fs:-lfp_cutoff * fs]
# look at the spectogram to select time periods of higher beta
t, burst_mask = ut.select_time_periods_of_high_power(data=lfp_pre,
band=band)
# burst_mask = np.logical_not(np.zeros_like(lfp_pre))
# plt.plot(t, lfp_pre)
# plt.fill_between(t, np.min(lfp_pre), np.max(lfp_pre), where=burst_mask, alpha=.4, color='red')
# plt.show()
# find rising and falling zero crossings using the filtered data
# calculate zero crossings on band data
zeros_rising, zeros_falling, zeros = ut.find_rising_and_falling_zeros(
lfp_band[burst_mask])
# find the peaks in between the zeros: use the RAW DATA for this step.
analysis_lfp = lfp_pre[burst_mask]
peaks, troughs, extrema = ut.find_peaks_and_troughs(
analysis_lfp, zeros)
# calculate peak sharpness:
peak_sharpness = ut.calculate_peak_sharpness(analysis_lfp,
peaks,
fs=fs)
trough_sharpness = ut.calculate_peak_sharpness(analysis_lfp,
troughs,
fs=fs)
mean_peak_sharpness = np.mean(peak_sharpness)
mean_trough_sharpness = np.mean(trough_sharpness)