cleanup
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weygoldt
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@ -87,12 +87,18 @@ class ChirpPlotBuffer:
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print(search_upper, search_lower)
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# get indices on raw data
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start_idx = (self.t0 - 5) * self.data.raw_rate
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start_idx = int((self.t0 - 5) * self.data.raw_rate)
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window_duration = (self.dt + 10) * self.data.raw_rate
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stop_idx = start_idx + window_duration
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stop_idx = int(start_idx + window_duration)
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if start_idx < 0:
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start_idx = 0
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# get raw data
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data_oi = self.data.raw[start_idx:stop_idx, self.electrode]
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try:
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data_oi = self.data.raw[start_idx:stop_idx, self.electrode]
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except:
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embed()
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self.time = self.time - self.t0
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self.frequency_time = self.frequency_time - self.t0
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@ -281,9 +287,7 @@ class ChirpPlotBuffer:
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)
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# plot filtered instantaneous frequency
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ax6.plot(
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self.frequency_time, self.frequency_filtered, c=ps.gblue3, lw=lw
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)
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ax6.plot(self.frequency_time, self.frequency_filtered, c=ps.gblue3, lw=lw)
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ax6.scatter(
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self.frequency_time[self.frequency_peaks],
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self.frequency_filtered[self.frequency_peaks],
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@ -317,9 +321,7 @@ class ChirpPlotBuffer:
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# ax7.spines.bottom.set_bounds((0, 5))
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ax0.set_xlim(0, self.config.window)
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plt.subplots_adjust(
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left=0.165, right=0.975, top=0.98, bottom=0.074, hspace=0.2
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)
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plt.subplots_adjust(left=0.165, right=0.975, top=0.98, bottom=0.074, hspace=0.2)
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fig.align_labels()
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if plot == "show":
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@ -330,8 +332,9 @@ class ChirpPlotBuffer:
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self.config.outputdir + self.data.datapath.split("/")[-2] + "/"
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)
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.pdf")
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.svg")
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# plt.savefig(f"{out}{self.track_id}_{self.t0_old}.pdf")
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# plt.savefig(f"{out}{self.track_id}_{self.t0_old}.svg")
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.png")
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plt.close()
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@ -424,9 +427,7 @@ def extract_frequency_bands(
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q25, q75 = q50 - minimal_bandwidth / 2, q50 + minimal_bandwidth / 2
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# filter baseline
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filtered_baseline = bandpass_filter(
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raw_data, samplerate, lowf=q25, highf=q75
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)
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filtered_baseline = bandpass_filter(raw_data, samplerate, lowf=q25, highf=q75)
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# filter search area
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filtered_search_freq = bandpass_filter(
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@ -475,10 +476,7 @@ def window_median_all_track_ids(
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id)
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& (data.time[data.idx] >= window_start_seconds)
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& (
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data.time[data.idx]
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<= (window_start_seconds + window_duration_seconds)
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)
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& (data.time[data.idx] <= (window_start_seconds + window_duration_seconds))
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]
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if len(data.freq[window_idx]) > 0:
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@ -609,9 +607,7 @@ def find_searchband(
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q75 = np.asarray([i[2] for i in frequency_percentiles])
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# get tracks that fall into search window
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check_track_ids = percentiles_ids[
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(q25 > current_median) & (q75 < search_window[-1])
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]
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check_track_ids = percentiles_ids[(q25 > current_median) & (q75 < search_window[-1])]
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# iterate through theses tracks
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if check_track_ids.size != 0:
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@ -621,9 +617,7 @@ def find_searchband(
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bool_lower[search_window > q25_temp - config.search_res] = False
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bool_upper[search_window < q75_temp + config.search_res] = False
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search_window_bool[
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(bool_lower == False) & (bool_upper == False)
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] = False
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search_window_bool[(bool_lower == False) & (bool_upper == False)] = False
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# find gaps in search window
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search_window_indices = np.arange(len(search_window))
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@ -642,9 +636,7 @@ def find_searchband(
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# if the first value is -1, the array starst with true, so a gap
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if nonzeros[0] == -1:
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stops = search_window_indices[search_window_gaps == -1]
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starts = np.append(
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0, search_window_indices[search_window_gaps == 1]
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)
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starts = np.append(0, search_window_indices[search_window_gaps == 1])
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# if the last value is -1, the array ends with true, so a gap
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if nonzeros[-1] == 1:
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@ -749,8 +741,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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multiwindow_chirps = []
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multiwindow_ids = []
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for st, window_start_index in enumerate(window_start_indices[1853:]):
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for st, window_start_index in enumerate(window_start_indices):
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logger.info(f"Processing window {st} of {len(window_start_indices)}")
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window_start_seconds = window_start_index / data.raw_rate
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@ -765,9 +756,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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)
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# iterate through all fish
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for tr, track_id in enumerate(
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np.unique(data.ident[~np.isnan(data.ident)])
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):
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for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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logger.debug(f"Processing track {tr} of {len(data.ids)}")
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# get index of track data in this time window
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@ -786,26 +775,23 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# check if tracked data available in this window
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if len(current_frequencies) < 3:
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logger.warning(
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f"Track {track_id} has no data in window {st}, skipping."
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)
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logger.warning(f"Track {track_id} has no data in window {st}, skipping.")
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continue
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# check if there are powers available in this window
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nanchecker = np.unique(np.isnan(current_powers))
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if (len(nanchecker) == 1) and nanchecker[0] is True:
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logger.warning(
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f"No powers available for track {track_id} window {st},"
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"skipping."
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f"No powers available for track {track_id} window {st}," "skipping."
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)
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continue
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# find the strongest electrodes for the current fish in the current
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# window
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best_electrode_index = np.argsort(
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np.nanmean(current_powers, axis=0)
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)[-config.number_electrodes :]
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best_electrode_index = np.argsort(np.nanmean(current_powers, axis=0))[
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-config.number_electrodes :
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]
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# find a frequency above the baseline of the current fish in which
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# no other fish is active to search for chirps there
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@ -826,8 +812,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# iterate through electrodes
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for el, electrode_index in enumerate(best_electrode_index):
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logger.debug(
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f"Processing electrode {el+1} of "
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f"{len(best_electrode_index)}"
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f"Processing electrode {el+1} of " f"{len(best_electrode_index)}"
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)
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# LOAD DATA FOR CURRENT ELECTRODE AND CURRENT FISH ------------
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@ -836,9 +821,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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current_raw_data = data.raw[
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window_start_index:window_stop_index, electrode_index
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]
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current_raw_time = raw_time[
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window_start_index:window_stop_index
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]
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current_raw_time = raw_time[window_start_index:window_stop_index]
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# EXTRACT FEATURES --------------------------------------------
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@ -924,7 +907,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# if not has_chirp(
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# baseline_frequency_filtered[amplitude_mask],
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# config.baseline_frequency_peakheight,
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# ):
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# ):
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# logger.warning(
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# f"Amplitude to small for the chirp detection of track {track_id} window {st},")
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# continue
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@ -941,20 +924,14 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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current_raw_time = current_raw_time[no_edges]
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baselineband = baselineband[no_edges]
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baseline_envelope_unfiltered = baseline_envelope_unfiltered[
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no_edges
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]
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baseline_envelope_unfiltered = baseline_envelope_unfiltered[no_edges]
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searchband = searchband[no_edges]
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baseline_envelope = baseline_envelope[no_edges]
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search_envelope_unfiltered = search_envelope_unfiltered[
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no_edges
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]
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search_envelope_unfiltered = search_envelope_unfiltered[no_edges]
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search_envelope = search_envelope[no_edges]
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baseline_frequency = baseline_frequency[no_edges]
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baseline_frequency_filtered = baseline_frequency_filtered[
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no_edges
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]
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baseline_frequency_filtered = baseline_frequency_filtered[no_edges]
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baseline_frequency_time = current_raw_time
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# # get instantaneous frequency withoup edges
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@ -998,14 +975,11 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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baseline_frequency_filtered,
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prominence=config.frequency_prominence,
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)
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# DETECT CHIRPS IN SEARCH WINDOW ------------------------------
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# get the peak timestamps from the peak indices
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baseline_peak_timestamps = current_raw_time[
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baseline_peak_indices
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]
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baseline_peak_timestamps = current_raw_time[baseline_peak_indices]
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search_peak_timestamps = current_raw_time[search_peak_indices]
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frequency_peak_timestamps = baseline_frequency_time[
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@ -1017,7 +991,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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one_feature_empty = (
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len(baseline_peak_timestamps) == 0
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or len(search_peak_timestamps) == 0
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#or len(frequency_peak_timestamps) == 0
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# or len(frequency_peak_timestamps) == 0
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)
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if one_feature_empty and (debug == "false"):
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@ -1029,7 +1003,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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sublists = [
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list(baseline_peak_timestamps),
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list(search_peak_timestamps),
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#list(frequency_peak_timestamps),
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# list(frequency_peak_timestamps),
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]
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singleelectrode_chirps = group_timestamps(
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@ -1038,7 +1012,6 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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difference_threshold=config.chirp_window_threshold,
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)
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# check it there are chirps detected after grouping, continue
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# with the loop if not
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@ -1153,9 +1126,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# add flattened chirps to the list
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multiwindow_chirps_flat.extend(current_track_chirps)
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multiwindow_ids_flat.extend(
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list(np.ones_like(current_track_chirps) * track_id)
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)
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multiwindow_ids_flat.extend(list(np.ones_like(current_track_chirps) * track_id))
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# purge duplicates, i.e. chirps that are very close to each other
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# duplites arise due to overlapping windows
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@ -1167,9 +1138,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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np.asarray(multiwindow_ids_flat) == track_id
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]
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if len(tr_chirps) > 0:
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tr_chirps_purged = purge_duplicates(
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tr_chirps, config.chirp_window_threshold
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)
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tr_chirps_purged = purge_duplicates(tr_chirps, config.chirp_window_threshold)
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purged_chirps.extend(list(tr_chirps_purged))
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purged_ids.extend(list(np.ones_like(tr_chirps_purged) * track_id))
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@ -1189,4 +1158,5 @@ if __name__ == "__main__":
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# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
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datapath = "../data/2022-06-02-10_00/"
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chirpdetection(datapath, plot="show", debug="false")
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datapath = "../../../local_data/randgrid/"
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chirpdetection(datapath, plot="save", debug="false")
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