Module thunderfish.chirp
Detection of chirps in weakly electric fish recordings.
chirp_analysis(): calculates spectrogram, detects fishes and extracts chirp times(combined for all fishes). !!! recommended for short recordings (up to 5 min) where only the chirp times shall be extracted !!!chirp_detection(): extracts chirp times with help of given spectrogram and fishlist.
Functions
def true_chirp_power_drop(chirp_time_idx, power, power_window=100)-
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def true_chirp_power_drop(chirp_time_idx, power, power_window=100): """ Chirp is only accepted as such if the power of the frequency drops down as expected. :param chirp_time_idx: (array) indices of chirps. :param power: (array) power array containing for each timestamp the max value in power of a certain frequency range. :param power_window (int) datapoints arroung a detected chirp used to verify that there is a chirp. :return: chirp_time_idx: (array) indices of chirps that have been confirmed to be chirps. """ true_chirp_time_idx = [] for i in range(len(chirp_time_idx)): idx0 = int(chirp_time_idx[i] - power_window/2) if idx0 < 0: idx0 = 0 idx1 = int(chirp_time_idx[i] + power_window/2) if idx1 > len(power): idx1 = len(power) tmp_median = np.median(power[idx0:idx1]) tmp_std = np.std(power[idx0:idx1], ddof=1) if np.min(power[idx0:idx1]) < tmp_median - 3*tmp_std: true_chirp_time_idx.append(chirp_time_idx[i]) return np.array(true_chirp_time_idx)Chirp is only accepted as such if the power of the frequency drops down as expected.
:param chirp_time_idx: (array) indices of chirps. :param power: (array) power array containing for each timestamp the max value in power of a certain frequency range. :param power_window (int) datapoints arroung a detected chirp used to verify that there is a chirp. :return: chirp_time_idx: (array) indices of chirps that have been confirmed to be chirps.
def true_chirp_power_rise_above(chirp_time_idx, power_above)-
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def true_chirp_power_rise_above(chirp_time_idx, power_above): median_power_above = np.median(power_above) std_power_above = np.std(power_above, ddof=1) if median_power_above > 0.001: print('another fish disturbs the chirp approval! Have to rely on other algorithms.') return chirp_time_idx else: true_chirp_time_idx = [] for i in range(len(chirp_time_idx)): if power_above[int(chirp_time_idx[i])] > median_power_above + 3*std_power_above: true_chirp_time_idx.append(chirp_time_idx[i]) return true_chirp_time_idx def chirp_detection(spectrum,
freqs,
time,
fishlist=None,
fundamentals=None,
min_power=0.005,
freq_tolerance=1.0,
chirp_th=1.0,
plot_data_func=None)-
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def chirp_detection(spectrum, freqs, time, fishlist=None, fundamentals=None, min_power= 0.005, freq_tolerance=1., chirp_th=1., plot_data_func=None): """ Detects chirps on the basis of a spectrogram. :param spectrum: (2d-array) spectrum calulated with the `spectrogram()` function. :param freqs: (array) frequencies of the spectrum. :param time: (array) time of the nffts used in the spectrum. :param fishlist: (array) power und frequncy for each fundamental/harmonic of a detected fish. fishlist[fish][harmonic][frequency, power] :param min_power: (float) minimum power of the fundamental frequency for each fish to participate in chirp detection. :param freq_tolerance: (float) frequency tolerance in the spectrum to detect the power of a certain frequency. :param chirp_th: (float) minimum chirp duration to be accepted as a chirp. :param plot_data: (bool) If True: plots the process of chirp detection. :return:chirp_time: (array) array of times (in sec) where chirps have been detected. """ if not hasattr(fundamentals, '__len__'): if not hasattr(fishlist, '__len__'): print('fishlist or fundamentals missing as argument !!!') quit() else: fundamentals = [] for fish in fishlist: if fish[0][1] > min_power: fundamentals.append(fish[0][0]) chirp_time = np.array([]) chirp_freq = np.array([]) for enu, fundamental in enumerate(fundamentals): # extract power of only the part of the spectrum that has to be analysied for each fundamental and get the peak # power of every piont in time. power = np.max(spectrum[(freqs >= fundamental - freq_tolerance) & (freqs <= fundamental + freq_tolerance)], axis=0) power_above = np.max(spectrum[(freqs >= fundamental + 50.0 -freq_tolerance) & (freqs <= fundamental + 50.0 + freq_tolerance)], axis=0) #power = np.mean(spectrum[(freqs >= fundamental - freq_tolerance) & (freqs <= fundamental + freq_tolerance)], axis=0) # calculate the slope by calculating the difference in the power power_diff = np.diff(power) # peak detection in the power_diff to detect drops in power indicating chrips threshold = std_threshold(power_diff) peaks, troughs = detect_peaks(power_diff, threshold) troughs, peaks = trim_to_peak(troughs, peaks) # reversed troughs and peaks in output and input to get trim_to_troughs # exclude peaks and troughs with to much time diff to be a chirp # ToDO: not nice !!! peaks = peaks[(troughs - peaks) < chirp_th] troughs = troughs[(troughs - peaks) < chirp_th] if len(troughs) > 0: # chirps times defined as the mean time between the troughs and peaks chirp_time_idx = np.mean([troughs, peaks], axis=0) # exclude detected chirps if the powervalue doesn't drop far enought chirp_time_idx = true_chirp_power_drop(chirp_time_idx, power) # chirp_time_idx = true_chirp_power_rise_above(chirp_time_idx, power_above) # add times of detected chirps to the list. chirp_time = np.concatenate((chirp_time, np.array([time[int(i)] for i in chirp_time_idx]))) chirp_freq = np.concatenate((chirp_freq, np.array(fundamental* np.ones(len(chirp_time_idx))))) else: chirp_time = np.array([]) chirp_freq = np.array([]) if plot_data_func: plot_data_func(enu, chirp_time, time, power, power_above, power_diff, fundamental) return chirp_time, chirp_freqDetects chirps on the basis of a spectrogram.
:param spectrum: (2d-array) spectrum calulated with the
spectrogram()function. :param freqs: (array) frequencies of the spectrum. :param time: (array) time of the nffts used in the spectrum. :param fishlist: (array) power und frequncy for each fundamental/harmonic of a detected fish. fishlist[fish][harmonic][frequency, power] :param min_power: (float) minimum power of the fundamental frequency for each fish to participate in chirp detection. :param freq_tolerance: (float) frequency tolerance in the spectrum to detect the power of a certain frequency. :param chirp_th: (float) minimum chirp duration to be accepted as a chirp. :param plot_data: (bool) If True: plots the process of chirp detection. :return:chirp_time: (array) array of times (in sec) where chirps have been detected. def chirp_detection_plot(enu, chirp_time, time, power, power2, power_diff, fundamental)-
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def chirp_detection_plot(enu, chirp_time, time, power, power2, power_diff, fundamental): """ plots the process of chirp detection. :param enu: (int) indication which fish in list is processed. :param chirp_time: (array) timestamps when chirps have been detected. :param time: (array) time array. :param power: (array) power of a certain frequency band. :param power_diff: (array) slope of the power array. :param fundamental: (float) fundamental frequency around which the algorithm looked for chirps. """ try: ax except NameError: fig, ax = plt.subplots() colors = ['r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue'] # if enu == 0: # fig, ax = plt.subplots() # colors = ['r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue', 'r', 'g', 'k', 'blue'] ax.plot(chirp_time, np.zeros(len(chirp_time)), 'o', markersize=10, color=colors[enu], alpha=0.8, label='chirps') ax.set_xlabel('time in sec') ax.set_ylabel('power') ax.plot(time, power, colors[enu], marker='.', label='%.1f Hz' % fundamental) ax.plot(time, power2, colors[enu+1], label='%.1f Hz' % (fundamental+50.0)) ax.plot(time[:len(power_diff)], power_diff, colors[enu], label='slope') ax.legend(loc='upper right', bbox_to_anchor=(1, 1), frameon=False)plots the process of chirp detection.
:param enu: (int) indication which fish in list is processed. :param chirp_time: (array) timestamps when chirps have been detected. :param time: (array) time array. :param power: (array) power of a certain frequency band. :param power_diff: (array) slope of the power array. :param fundamental: (float) fundamental frequency around which the algorithm looked for chirps.
def chirp_analysis(data, rate)-
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def chirp_analysis(data, rate): """ Performs all steps to detect chirps in a given dataset. This includes spectrogram calculation, fish detection and analysing of specific frequency bands. For further documentation see functions chirp_spectrogram() and chirp_detection(). !!! recommended for short recordings (up to 5 min) where only the chirp times shall be extracted !!! :param data: (array) data. :param rate: (float) smaplerate of the data. :param min_power: (float) minimal power of the fish fundamental to include this fish in chirp detection. """ freqs, time, spectrum = spectrogram(data, rate, freq_resolution=2., overlap_frac=0.95) power = np.mean(spectrum, axis=1) # spectrum[:, t0:t1] to only let spectrum of certain time.... fishlist = harmonic_groups(freqs, power)[0] chirp_time, chirp_freq = chirp_detection(spectrum, freqs, time, fishlist, plot_data_func=chirp_detection_plot) plt.show() return chirp_time, chirp_freqPerforms all steps to detect chirps in a given dataset. This includes spectrogram calculation, fish detection and analysing of specific frequency bands. For further documentation see functions chirp_spectrogram() and chirp_detection(). !!! recommended for short recordings (up to 5 min) where only the chirp times shall be extracted !!!
:param data: (array) data. :param rate: (float) smaplerate of the data. :param min_power: (float) minimal power of the fish fundamental to include this fish in chirp detection.