diff --git a/code/chirpdetection.py b/code/chirpdetection.py index 3aa9d2d..ab33d49 100644 --- a/code/chirpdetection.py +++ b/code/chirpdetection.py @@ -1,4 +1,5 @@ from itertools import combinations, compress +from dataclasses import dataclass import numpy as np from IPython import embed @@ -11,13 +12,116 @@ from sklearn.preprocessing import normalize from modules.filters import bandpass_filter, envelope, highpass_filter from modules.filehandling import ConfLoader, LoadData -from modules.datahandling import flatten, purge_duplicates +from modules.datahandling import flatten, purge_duplicates, group_timestamps from modules.plotstyle import PlotStyle +from modules.logger import makeLogger - +logger = makeLogger(__name__) ps = PlotStyle() +@dataclass +class PlotBuffer: + t0: float + dt: float + track_id: float + electrode: int + data: LoadData + + time: np.ndarray + baseline: np.ndarray + baseline_envelope: np.ndarray + baseline_peaks: np.ndarray + search: np.ndarray + search_envelope: np.ndarray + search_peaks: np.ndarray + + frequency_time: np.ndarray + frequency: np.ndarray + frequency_filtered: np.ndarray + frequency_peaks: np.ndarray + + def plot_buffer(self, chirps) -> None: + + logger.debug("Starting plotting") + + # make data for plotting + + # get index of track data in this time window + window_idx = np.arange(len(self.data.idx))[ + (self.data.ident == self.track_id) & (self.data.time[self.data.idx] >= self.t0) & ( + self.data.time[self.data.idx] <= (self.t0 + self.dt)) + ] + + # get tracked frequencies and their times + freq_temp = self.data.freq[window_idx] + # time_temp = self.data.times[window_idx] + + # get indices on raw data + start_idx = self.t0 * self.data.raw_rate + window_duration = self.dt * self.data.raw_rate + stop_idx = start_idx + window_duration + + # get raw data + data_oi = self.data.raw[start_idx:stop_idx, self.electrode] + + fig, axs = plt.subplots( + 7, + 1, + figsize=(20 / 2.54, 12 / 2.54), + constrained_layout=True, + sharex=True, + sharey='row', + ) + + # plot spectrogram + plot_spectrogram(axs[0], data_oi, self.data.raw_rate, self.t0) + + # plot baseline instantaneos frequency + axs[1].plot(self.frequency_time, self.frequency) + + # plot waveform of filtered signal + axs[2].plot(self.time, self.baseline, c=ps.green) + + # plot waveform of filtered search signal + axs[3].plot(self.time, self.search) + + # plot filtered and rectified envelope + axs[4].plot(self.time, self.baseline_envelope) + axs[4].scatter( + (self.time)[self.baseline_peaks], + self.baseline_envelope[self.baseline_peaks], + c=ps.red, + ) + + # plot envelope of search signal + axs[5].plot(self.time, self.search_envelope) + axs[5].scatter( + (self.time)[self.search_peaks], + self.search_envelope[self.search_peaks], + c=ps.red, + ) + + # plot filtered instantaneous frequency + axs[6].plot(self.frequency_time, self.frequency_filtered) + axs[6].scatter( + self.frequency_time[self.frequency_peaks], + self.frequency_filtered[self.frequency_peaks], + c=ps.red, + ) + + axs[6].set_xlabel("Time [s]") + axs[0].set_title("Spectrogram") + axs[1].set_title("Fitered baseline instanenous frequency") + axs[2].set_title("Fitered baseline") + axs[3].set_title("Fitered above") + axs[4].set_title("Filtered envelope of baseline envelope") + axs[5].set_title("Search envelope") + axs[6].set_title( + "Filtered absolute instantaneous frequency") + plt.show() + + def instantaneos_frequency( signal: np.ndarray, samplerate: int ) -> tuple[np.ndarray, np.ndarray]: @@ -78,6 +182,9 @@ def plot_spectrogram(axis, signal: np.ndarray, samplerate: float, t0: float) -> t0 : float Start time of the signal. """ + + logger.debug("Plotting spectrogram") + # compute spectrogram spec_power, spec_freqs, spec_times = spectrogram( signal, @@ -137,7 +244,9 @@ def double_bandpass( return (filtered_baseline, filtered_search_freq) -def main(datapath: str) -> None: +def main(datapath: str, plot: str) -> None: + + assert plot in ["save", "show", "false"] # load raw file data = LoadData(datapath) @@ -165,9 +274,12 @@ def main(datapath: str) -> None: # make time array for raw data raw_time = np.arange(data.raw.shape[0]) / data.raw_rate - # good chirp times for data: 2022-06-02-10_00 - t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate - dt = 60 * data.raw_rate + # # good chirp times for data: 2022-06-02-10_00 + # t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate + # dt = 60 * data.raw_rate + + t0 = 0 + dt = data.raw.shape[0] # generate starting points of rolling window window_starts = np.arange( @@ -177,15 +289,17 @@ def main(datapath: str) -> None: dtype=int ) - # ask how many windows should be calulated - nwindows = int( - input("How many windows should be calculated (integer number)? ")) + # # ask how many windows should be calulated + # nwindows = int( + # input("How many windows should be calculated (integer number)? ")) # ititialize lists to store data chirps = [] fish_ids = [] - for st, start_index in enumerate(window_starts[: nwindows]): + for st, start_index in enumerate(window_starts): + + logger.debug(f"Processing window {st} of {len(window_starts)}") # make t0 and dt t0 = start_index / data.raw_rate @@ -212,6 +326,8 @@ def main(datapath: str) -> None: # iterate through all fish for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): + logger.debug(f"Processing track {tr} of {len(track_ids)}") + print(f"Track ID: {track_id}") # get index of track data in this time window @@ -233,15 +349,6 @@ def main(datapath: str) -> None: if len(freq_temp) < expected_duration * 0.9: continue - fig, axs = plt.subplots( - 7, - config.number_electrodes, - figsize=(20 / 2.54, 12 / 2.54), - constrained_layout=True, - sharex=True, - sharey='row', - ) - # get best electrode best_electrodes = np.argsort(np.nanmean( powers_temp, axis=0))[-config.number_electrodes:] @@ -258,7 +365,7 @@ def main(datapath: str) -> None: check_track_ids = track_ids[(median_freq > search_window[0]) & ( median_freq < search_window[-1])] - # iterate through theses tracks + # iterate through theses tracks if check_track_ids.size != 0: for j, check_track_id in enumerate(check_track_ids): @@ -325,7 +432,10 @@ def main(datapath: str) -> None: # iterate through electrodes for el, electrode in enumerate(best_electrodes): - print(el) + + logger.debug( + f"Processing electrode {el} of {len(best_electrodes)}") + # load region of interest of raw data file data_oi = data.raw[start_index:stop_index, :] time_oi = raw_time[start_index:stop_index] @@ -420,7 +530,7 @@ def main(datapath: str) -> None: baseline_envelope = normalize([baseline_envelope])[0] search_envelope = normalize([search_envelope])[0] - inst_freq_filtered = normalize([inst_freq_filtered])[0] + inst_freq_filtered = normalize([np.abs(inst_freq_filtered)])[0] # PEAK DETECTION ---------------------------------------------- @@ -428,7 +538,7 @@ def main(datapath: str) -> None: prominence = np.percentile( baseline_envelope, config.baseline_prominence_percentile) baseline_peaks, _ = find_peaks( - np.abs(baseline_envelope), prominence=prominence) + baseline_envelope, prominence=prominence) # detect peaks search_envelope prominence = np.percentile( @@ -442,81 +552,80 @@ def main(datapath: str) -> None: config.instantaneous_prominence_percentile ) inst_freq_peaks, _ = find_peaks( - np.abs(inst_freq_filtered), + inst_freq_filtered, prominence=prominence ) - # # SAVE DATA --------------------------------------------------- - - # PLOT -------------------------------------------------------- - - # plot spectrogram - plot_spectrogram( - axs[0, el], data_oi[:, electrode], data.raw_rate, t0) - - # plot baseline instantaneos frequency - axs[1, el].plot(baseline_freq_time, baseline_freq - - np.median(baseline_freq)) - - # plot waveform of filtered signal - axs[2, el].plot(time_oi, baseline, c=ps.green) - - # plot broad filtered baseline - axs[2, el].plot( - time_oi, - broad_baseline, - ) - - # plot narrow filtered baseline envelope - axs[2, el].plot( - time_oi, - baseline_envelope_unfiltered, - c=ps.red - ) - - # plot waveform of filtered search signal - axs[3, el].plot(time_oi, search) - - # plot envelope of search signal - axs[3, el].plot( - time_oi, - search_envelope, - c=ps.red - ) - - # plot filtered and rectified envelope - axs[4, el].plot(time_oi, baseline_envelope) - axs[4, el].scatter( - (time_oi)[baseline_peaks], - baseline_envelope[baseline_peaks], - c=ps.red, - ) - - # plot envelope of search signal - axs[5, el].plot(time_oi, search_envelope) - axs[5, el].scatter( - (time_oi)[search_peaks], - search_envelope[search_peaks], - c=ps.red, - ) - - # plot filtered instantaneous frequency - axs[6, el].plot(baseline_freq_time, np.abs(inst_freq_filtered)) - axs[6, el].scatter( - baseline_freq_time[inst_freq_peaks], - np.abs(inst_freq_filtered)[inst_freq_peaks], - c=ps.red, - ) - - axs[6, el].set_xlabel("Time [s]") - axs[0, el].set_title("Spectrogram") - axs[1, el].set_title("Fitered baseline instanenous frequency") - axs[2, el].set_title("Fitered baseline") - axs[3, el].set_title("Fitered above") - axs[4, el].set_title("Filtered envelope of baseline envelope") - axs[5, el].set_title("Search envelope") - axs[6, el].set_title( - "Filtered absolute instantaneous frequency") + # # PLOT -------------------------------------------------------- + + # # plot spectrogram + # plot_spectrogram( + # axs[0, el], data_oi[:, electrode], data.raw_rate, t0) + + # # plot baseline instantaneos frequency + + # axs[1, el].plot(baseline_freq_time, baseline_freq - + # np.median(baseline_freq)) + + # # plot waveform of filtered signal + # axs[2, el].plot(time_oi, baseline, c=ps.green) + + # # plot broad filtered baseline + # axs[2, el].plot( + # time_oi, + # broad_baseline, + # ) + + # # plot narrow filtered baseline envelope + # axs[2, el].plot( + # time_oi, + # baseline_envelope_unfiltered, + # c=ps.red + # ) + + # # plot waveform of filtered search signal + # axs[3, el].plot(time_oi, search) + + # # plot envelope of search signal + # axs[3, el].plot( + # time_oi, + # search_envelope, + # c=ps.red + # ) + + # # plot filtered and rectified envelope + # axs[4, el].plot(time_oi, baseline_envelope) + # axs[4, el].scatter( + # (time_oi)[baseline_peaks], + # baseline_envelope[baseline_peaks], + # c=ps.red, + # ) + + # # plot envelope of search signal + # axs[5, el].plot(time_oi, search_envelope) + # axs[5, el].scatter( + # (time_oi)[search_peaks], + # search_envelope[search_peaks], + # c=ps.red, + # ) + + # # plot filtered instantaneous frequency + # axs[6, el].plot(baseline_freq_time, np.abs(inst_freq_filtered)) + # axs[6, el].scatter( + # baseline_freq_time[inst_freq_peaks], + # np.abs(inst_freq_filtered)[inst_freq_peaks], + # c=ps.red, + # ) + + # axs[6, el].set_xlabel("Time [s]") + # axs[0, el].set_title("Spectrogram") + # axs[1, el].set_title("Fitered baseline instanenous frequency") + # axs[2, el].set_title("Fitered baseline") + # axs[3, el].set_title("Fitered above") + # axs[4, el].set_title("Filtered envelope of baseline envelope") + # axs[5, el].set_title("Search envelope") + # axs[6, el].set_title( + # "Filtered absolute instantaneous frequency") # DETECT CHIRPS IN SEARCH WINDOW ------------------------------- @@ -556,7 +665,7 @@ def main(datapath: str) -> None: current_chirps = [] bool_timestamps = np.ones_like(timestamps, dtype=bool) for bo, tt in enumerate(timestamps): - if bool_timestamps[bo] == False: + if bool_timestamps[bo] is False: continue cm = timestamps_idx[(timestamps >= tt) & ( timestamps <= tt + config.chirp_window_threshold)] @@ -566,87 +675,141 @@ def main(datapath: str) -> None: bool_timestamps[cm] = False # for checking if there are chirps on multiple electrodes + if len(current_chirps) == 0: + continue chirps_electrodes.append(current_chirps) - for ct in current_chirps: - axs[0, el].axvline(ct, color='r', lw=1) + # for ct in current_chirps: + # axs[0, el].axvline(ct, color='r', lw=1) + + # axs[0, el].scatter( + # baseline_freq_time[inst_freq_peaks], + # np.ones_like(baseline_freq_time[inst_freq_peaks]) * 600, + # c=ps.red, + # ) + # axs[0, el].scatter( + # (time_oi)[search_peaks], + # np.ones_like((time_oi)[search_peaks]) * 600, + # c=ps.red, + # ) + + # axs[0, el].scatter( + # (time_oi)[baseline_peaks], + # np.ones_like((time_oi)[baseline_peaks]) * 600, + # c=ps.red, + # ) + + if (el == config.number_electrodes - 1) & \ + (len(current_chirps) > 0) & \ + (plot in ["show", "save"]): + + logger.debug("Detected chirp, ititialize buffer ...") + + # save data to Buffer + buffer = PlotBuffer( + t0=t0, + dt=dt, + electrode=electrode, + track_id=track_id, + data=data, + time=time_oi, + baseline=baseline, + baseline_envelope=baseline_envelope, + baseline_peaks=baseline_peaks, + search=search, + search_envelope=search_envelope, + search_peaks=search_peaks, + frequency_time=baseline_freq_time, + frequency=baseline_freq, + frequency_filtered=inst_freq_filtered, + frequency_peaks=inst_freq_peaks, + ) - axs[0, el].scatter( - baseline_freq_time[inst_freq_peaks], - np.ones_like(baseline_freq_time[inst_freq_peaks]) * 600, - c=ps.red, - ) - axs[0, el].scatter( - (time_oi)[search_peaks], - np.ones_like((time_oi)[search_peaks]) * 600, - c=ps.red, - ) + logger.debug("Buffer initialized!") - axs[0, el].scatter( - (time_oi)[baseline_peaks], - np.ones_like((time_oi)[baseline_peaks]) * 600, - c=ps.red, - ) + logger.debug( + f"Processed all electrodes for fish {track_id} for this window, sorting chirps ...") + + # continue if no chirps for current fish # make one array - chirps_electrodes = np.concatenate(chirps_electrodes) + # chirps_electrodes = np.concatenate(chirps_electrodes) # make shure they are numpy arrays - chirps_electrodes = np.asarray(chirps_electrodes) - electrodes_of_chirps = np.asarray(electrodes_of_chirps) - # sort them - sort_chirps_electrodes = chirps_electrodes[np.argsort( - chirps_electrodes)] - sort_electrodes = electrodes_of_chirps[np.argsort( - chirps_electrodes)] - bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool) - # make index vector - index_vector = np.arange(len(sort_chirps_electrodes)) - # make it more than only two electrodes for the search after chirps - combinations_best_elctrodes = list( - combinations(range(3), 2)) - - the_real_chirps = [] - for chirp_index, seoc in enumerate(sort_chirps_electrodes): - if bool_vector[chirp_index] == False: - continue - cm = index_vector[(sort_chirps_electrodes >= seoc) & ( - sort_chirps_electrodes <= seoc + config.chirp_window_threshold)] - - chirps_unique = [] - for combination in combinations_best_elctrodes: - if set(combination).issubset(sort_electrodes[cm]): - chirps_unique.append( - np.mean(sort_chirps_electrodes[cm])) - - the_real_chirps.append(np.mean(chirps_unique)) - - """ - if set([0,1]).issubset(sort_electrodes[cm]): - the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) - elif set([1,0]).issubset(sort_electrodes[cm]): - the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) - elif set([0,2]).issubset(sort_electrodes[cm]): - the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) - elif set([1,2]).issubset(sort_electrodes[cm]): - the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) - """ - bool_vector[cm] = False + # electrodes_of_chirps = np.asarray(electrodes_of_chirps) + + # # sort them + # sort_chirps_electrodes = chirps_electrodes[np.argsort( + # chirps_electrodes)] + # sort_electrodes = electrodes_of_chirps[np.argsort( + # chirps_electrodes)] + # bool_vector = np.ones(len(sort_chirps_electrodes), dtype=bool) + + # # make index vector + # index_vector = np.arange(len(sort_chirps_electrodes)) + + # # make it more than only two electrodes for the search after chirps + # combinations_best_elctrodes = list( + # combinations(range(3), 2)) + + if len(chirps_electrodes) == 0: + continue + + the_real_chirps = group_timestamps(chirps_electrodes, 2, 0.05) + + # for chirp_index, seoc in enumerate(sort_chirps_electrodes): + # if bool_vector[chirp_index] is False: + # continue + # cm = index_vector[(sort_chirps_electrodes >= seoc) & ( + # sort_chirps_electrodes <= seoc + config.chirp_window_threshold)] + + # chirps_unique = [] + # for combination in combinations_best_elctrodes: + # if set(combination).issubset(sort_electrodes[cm]): + # chirps_unique.append( + # np.mean(sort_chirps_electrodes[cm])) + + # the_real_chirps.append(np.mean(chirps_unique)) + + # """ + # if set([0,1]).issubset(sort_electrodes[cm]): + # the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) + # elif set([1,0]).issubset(sort_electrodes[cm]): + # the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) + # elif set([0,2]).issubset(sort_electrodes[cm]): + # the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) + # elif set([1,2]).issubset(sort_electrodes[cm]): + # the_real_chirps.append(np.mean(sort_chirps_electrodes[cm])) + # """ + # bool_vector[cm] = False + chirps.append(the_real_chirps) fish_ids.append(track_id) - for ct in the_real_chirps: - axs[0, el].axvline(ct, color='b', lw=1) +# for ct in the_real_chirps: +# axs[0, el].axvline(ct, color='b', lw=1) - plt.close() - fig, ax = plt.subplots() - t0 = (3 * 60 * 60 + 6 * 60 + 43.5) - data_oi = data.raw[window_starts[0]:window_starts[-1] + int(dt*data.raw_rate), 10] - plot_spectrogram(ax, data_oi, data.raw_rate, t0) - chirps_concat = np.concatenate(chirps) - for ch in chirps_concat: - ax. axvline(ch, color='b', lw=1) + logger.debug('Found %d chirps, starting plotting ... ' % + len(the_real_chirps)) + if len(the_real_chirps) > 0: + try: + buffer.plot_buffer(the_real_chirps) + except NameError: + pass + else: + try: + del buffer + except NameError: + pass + + # fig, ax = plt.subplots() + # t0 = (3 * 60 * 60 + 6 * 60 + 43.5) + # data_oi = data.raw[window_starts[0]:window_starts[-1] + int(dt*data.raw_rate), 10] + # plot_spectrogram(ax, data_oi, data.raw_rate, t0) + # chirps_concat = np.concatenate(chirps) + # for ch in chirps_concat: + # ax. axvline(ch, color='b', lw=1) chirps_new = [] chirps_ids = [] @@ -667,9 +830,7 @@ def main(datapath: str) -> None: purged_chirps.extend(list(tr_chirps_purged)) purged_chirps_ids.extend(list(np.ones_like(tr_chirps_purged)*tr)) - embed() - if __name__ == "__main__": datapath = "../data/2022-06-02-10_00/" - main(datapath) + main(datapath, plot="show") diff --git a/code/modules/logger.py b/code/modules/logger.py index 5dabf80..2c81447 100644 --- a/code/modules/logger.py +++ b/code/modules/logger.py @@ -17,13 +17,13 @@ def makeLogger(name: str): # create stream handler for terminal output console_handler = logging.StreamHandler() console_handler.setFormatter(console_formatter) - console_handler.setLevel(logging.INFO) + console_handler.setLevel(logging.DEBUG) # create script specific logger logger = logging.getLogger(name) logger.addHandler(file_handler) logger.addHandler(console_handler) - logger.setLevel(logging.INFO) + logger.setLevel(logging.DEBUG) return logger