# 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)