adding peak lists
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@ -209,12 +209,12 @@ def main(datapath: str) -> None:
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# calucate median of fish frequencies in window
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median_freq = []
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track_ids = []
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for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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window_index = np.arange(len(data.idx))[
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for el, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id) & (data.time[data.idx] >= t0) & (
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data.time[data.idx] <= (t0 + dt))
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]
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median_freq.append(np.median(data.freq[window_index]))
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median_freq.append(np.median(data.freq[window_idx]))
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track_ids.append(track_id)
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# convert to numpy array
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@ -227,14 +227,14 @@ def main(datapath: str) -> None:
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print(f"Track ID: {track_id}")
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# get index of track data in this time window
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window_index = np.arange(len(data.idx))[
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id) & (data.time[data.idx] >= t0) & (
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data.time[data.idx] <= (t0 + dt))
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]
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# get tracked frequencies and their times
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freq_temp = data.freq[window_index]
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powers_temp = data.powers[window_index, :]
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freq_temp = data.freq[window_idx]
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powers_temp = data.powers[window_idx, :]
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# approximate sampling rate to compute expected durations if there
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# is data available for this time window for this fish id
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@ -256,7 +256,7 @@ def main(datapath: str) -> None:
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# get best electrode
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best_electrodes = np.argsort(np.nanmean(
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powers_temp, axis=0))[-config.electrodes:]
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powers_temp, axis=0))[-config.number_electrodes:]
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# frequency where second filter filters
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search_window = np.arange(
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@ -457,78 +457,78 @@ def main(datapath: str) -> None:
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# SAVE DATA ---------------------------------------------------
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baseline_ts[st][tr][el] = baseline_envelope[baseline_peaks]
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search_ts[st][tr][el] = search_envelope[search_peaks]
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freq_ts[st][tr][el] = inst_freq_filtered[inst_freq_peaks]
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baseline_ts[st][tr][el] = time_oi[baseline_peaks]
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search_ts[st][tr][el] = time_oi[search_peaks]
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freq_ts[st][tr][el] = baseline_freq_time[inst_freq_peaks]
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# PLOT --------------------------------------------------------
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# plot spectrogram
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plot_spectrogram(
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axs[0, i], data_oi[:, electrode], data.raw_rate, t0)
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axs[0, el], data_oi[:, electrode], data.raw_rate, t0)
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# plot baseline instantaneos frequency
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axs[1, i].plot(baseline_freq_time, baseline_freq -
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axs[1, el].plot(baseline_freq_time, baseline_freq -
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np.median(baseline_freq))
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# plot waveform of filtered signal
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axs[2, i].plot(time_oi, baseline, c=ps.green)
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axs[2, el].plot(time_oi, baseline, c=ps.green)
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# plot broad filtered baseline
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axs[2, i].plot(
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axs[2, el].plot(
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time_oi,
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broad_baseline,
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)
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# plot narrow filtered baseline envelope
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axs[2, i].plot(
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axs[2, el].plot(
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time_oi,
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baseline_envelope_unfiltered,
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c=ps.red
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)
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# plot waveform of filtered search signal
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axs[3, i].plot(time_oi, search)
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axs[3, el].plot(time_oi, search)
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# plot envelope of search signal
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axs[3, i].plot(
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axs[3, el].plot(
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time_oi,
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search_envelope,
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c=ps.red
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)
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# plot filtered and rectified envelope
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axs[4, i].plot(time_oi, baseline_envelope)
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axs[4, i].scatter(
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axs[4, el].plot(time_oi, baseline_envelope)
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axs[4, el].scatter(
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(time_oi)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c=ps.red,
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)
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# plot envelope of search signal
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axs[5, i].plot(time_oi, search_envelope)
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axs[5, i].scatter(
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axs[5, el].plot(time_oi, search_envelope)
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axs[5, el].scatter(
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(time_oi)[search_peaks],
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search_envelope[search_peaks],
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c=ps.red,
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)
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# plot filtered instantaneous frequency
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axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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axs[6, i].scatter(
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axs[6, el].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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axs[6, el].scatter(
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baseline_freq_time[inst_freq_peaks],
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np.abs(inst_freq_filtered)[inst_freq_peaks],
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c=ps.red,
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)
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axs[6, i].set_xlabel("Time [s]")
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axs[0, i].set_title("Spectrogram")
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axs[1, i].set_title("Fitered baseline instanenous frequency")
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axs[2, i].set_title("Fitered baseline")
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axs[3, i].set_title("Fitered above")
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axs[4, i].set_title("Filtered envelope of baseline envelope")
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axs[5, i].set_title("Search envelope")
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axs[6, i].set_title(
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axs[6, el].set_xlabel("Time [s]")
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axs[0, el].set_title("Spectrogram")
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axs[1, el].set_title("Fitered baseline instanenous frequency")
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axs[2, el].set_title("Fitered baseline")
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axs[3, el].set_title("Fitered above")
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axs[4, el].set_title("Filtered envelope of baseline envelope")
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axs[5, el].set_title("Search envelope")
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axs[6, el].set_title(
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"Filtered absolute instantaneous frequency")
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plt.show()
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