# 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)
# extrema sharpness ratio, from the paper
esr = np.max([
mean_peak_sharpness / mean_trough_sharpness,
mean_trough_sharpness / mean_peak_sharpness
])
esr_matrix[sub, i] = esr
# calculate the steepness
rise_steepness, fall_steepness = ut.calculate_rise_and_fall_steepness(
lfp_band = ut.band_pass_filter(data,
fs,
band=band,
plot_response=False)
# find rising and falling zero crossings
zeros_rising, zeros_falling, zeros = ut.find_rising_and_falling_zeros(
lfp_band)
# find the peaks in between the zeros
peaks, troughs, extrema = ut.find_peaks_and_troughs(
lfp_band, zeros)
# calculate peak sharpness:
peak_sharpness = ut.calculate_peak_sharpness(lfp_band,
peaks,
fs=fs)
trough_sharpness = ut.calculate_peak_sharpness(lfp_band,
troughs,
fs=fs)
mean_peak_sharpness = np.mean(peak_sharpness)
mean_trough_sharpness = np.mean(trough_sharpness)
# extrema sharpness ratio, from the paper
esr = np.max([
mean_peak_sharpness / mean_trough_sharpness,
mean_trough_sharpness / mean_peak_sharpness
])
# calculate the steepness
rise_steepness, fall_steepness, steepness_indices = ut.calculate_rise_and_fall_steepness(
lfp_band, extrema)