electrode loop and adjusted plot
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6fb5dd560a
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49b77ddb77
@ -217,21 +217,10 @@ def main(datapath: str) -> None:
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# start_index = t0 * data.samplerate
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# stop_index = (t0 + dt) * data.samplerate
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fig, axs = plt.subplots(
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7,
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2,
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figsize=(20 / 2.54, 12 / 2.54),
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constrained_layout=True,
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sharex=True,
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sharey='row',
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)
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# iterate through all fish
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for i, track_id in enumerate(np.unique(ident[~np.isnan(ident)])[:2]):
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# load region of interest of raw data file
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data_oi = data[start_index:stop_index, :]
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time_oi = raw_time[start_index:stop_index]
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# get indices for time array in time window
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window_index = np.arange(len(idx))[
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@ -250,194 +239,208 @@ def main(datapath: str) -> None:
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if len(freq_temp) < expected_duration * 0.9:
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continue
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# get best electrode
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electrode = np.argsort(np.nanmean(powers_temp, axis=0))[-1]
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# <------------------------------------------ Iterate through electrodes
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# plot wavetracker tracks to spectrogram
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# for track_id in np.unique(ident): # <---------- Find freq gaps later
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# here
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# # get indices for time array in time window
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# window_index = np.arange(len(idx))[
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# (ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0 + dt))
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# ]
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# freq_temp = freq[window_index]
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# time_temp = time[idx[window_index]]
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# axs[0].plot(time_temp-t0, freq_temp, lw=2)
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# axs[0].set_ylim(500, 1000)
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# track_id = ids
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# frequency where second filter filters
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search_freq = 50
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# filter baseline and above
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.samplerate, freq_temp, search_freq
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)
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# compute instantaneous frequency on broad signal
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broad_baseline = bandpass_filter(
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data_oi[:, electrode],
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data.samplerate,
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lowf=np.mean(freq_temp)-5,
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highf=np.mean(freq_temp)+100
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)
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# compute instantaneous frequency on narrow signal
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.samplerate
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# Create plot (three electrodes per fish)
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fig, axs = plt.subplots(
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7,
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3,
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figsize=(20 / 2.54, 12 / 2.54),
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constrained_layout=True,
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sharex=True,
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sharey='row',
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)
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# compute envelopes
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cutoff = 25
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baseline_envelope = envelope(baseline, data.samplerate, cutoff)
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search_envelope = envelope(search, data.samplerate, cutoff)
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# highpass filter envelopes
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cutoff = 5
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baseline_envelope = highpass_filter(
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baseline_envelope, data.samplerate, cutoff=cutoff
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)
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baseline_envelope = np.abs(baseline_envelope)
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# search_envelope = highpass_filter(
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# search_envelope, data.samplerate, cutoff=cutoff)
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# envelopes of filtered envelope of filtered baseline
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# baseline_envelope = envelope(
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# np.abs(baseline_envelope), data.samplerate, cutoff
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# )
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# search_envelope = bandpass_filter(
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# search_envelope, data.samplerate, lowf=lowf, highf=highf)
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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# CUT OFF OVERLAP -------------------------------------------------
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# cut off first and last 0.5 * overlap at start and end
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valid = np.arange(
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int(0.5 * window_overlap), len(baseline_envelope) -
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int(0.5 * window_overlap)
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)
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baseline_envelope = baseline_envelope[valid]
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search_envelope = search_envelope[valid]
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# get inst freq valid snippet
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valid_t0 = int(0.5 * window_overlap) / data.samplerate
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valid_t1 = baseline_freq_time[-1] - \
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(int(0.5 * window_overlap) / data.samplerate)
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inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq = baseline_freq[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)] + t0
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# overwrite raw time to valid region
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time_oi = time_oi[valid]
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baseline = baseline[valid]
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broad_baseline = broad_baseline[valid]
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search = search[valid]
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# PEAK DETECTION --------------------------------------------------
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# detect peaks baseline_enelope
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prominence = np.percentile(baseline_envelope, 90)
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baseline_peaks, _ = find_peaks(
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np.abs(baseline_envelope), prominence=prominence)
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# detect peaks search_envelope
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prominence = np.percentile(search_envelope, 75)
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search_peaks, _ = find_peaks(
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search_envelope, prominence=prominence)
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# detect peaks inst_freq_filtered
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prominence = 2
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inst_freq_peaks, _ = find_peaks(
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np.abs(inst_freq_filtered), prominence=prominence)
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# PLOT ------------------------------------------------------------
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# plot spectrogram
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plot_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate, 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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np.median(baseline_freq), marker=".")
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# plot waveform of filtered signal
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axs[2, i].plot(time_oi, baseline, c="k")
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# plot narrow filtered baseline
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axs[2, i].plot(
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time_oi,
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baseline_envelope,
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c="orange",
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)
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# plot broad filtered baseline
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axs[2, i].plot(
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time_oi,
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broad_baseline,
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c="green",
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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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# plot envelope of search signal
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axs[3, i].plot(
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time_oi,
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search_envelope,
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c="orange",
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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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(time_oi)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c="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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(time_oi)[search_peaks],
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search_envelope[search_peaks],
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c="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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baseline_freq_time[inst_freq_peaks],
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np.abs(inst_freq_filtered)[inst_freq_peaks],
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c="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("Filtered absolute instantaneous frequency")
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# get best electrode
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best_electrodes = np.argsort(np.nanmean(powers_temp, axis=0))[-3:]
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plt.show()
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# <------------------------------------------ Iterate through electrodes
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for e, electrode in enumerate(best_electrodes):
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# load region of interest of raw data file
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data_oi = data[start_index:stop_index, :]
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time_oi = raw_time[start_index:stop_index]
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# plot wavetracker tracks to spectrogram
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# for track_id in np.unique(ident): # <---------- Find freq gaps later
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# here
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# # get indices for time array in time window
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# window_index = np.arange(len(idx))[
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# (ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0 + dt))
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# ]
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# freq_temp = freq[window_index]
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# time_temp = time[idx[window_index]]
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# axs[0].plot(time_temp-t0, freq_temp, lw=2)
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# axs[0].set_ylim(500, 1000)
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# track_id = ids
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# frequency where second filter filters
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search_freq = 50
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# filter baseline and above
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.samplerate, freq_temp, search_freq
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)
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# compute instantaneous frequency on broad signal
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broad_baseline = bandpass_filter(
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data_oi[:, electrode],
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data.samplerate,
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lowf=np.mean(freq_temp)-5,
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highf=np.mean(freq_temp)+100
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)
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# compute instantaneous frequency on narrow signal
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.samplerate
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)
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# compute envelopes
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cutoff = 25
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baseline_envelope = envelope(baseline, data.samplerate, cutoff)
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search_envelope = envelope(search, data.samplerate, cutoff)
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# highpass filter envelopes
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cutoff = 5
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baseline_envelope = highpass_filter(
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baseline_envelope, data.samplerate, cutoff=cutoff
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)
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baseline_envelope = np.abs(baseline_envelope)
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# search_envelope = highpass_filter(
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# search_envelope, data.samplerate, cutoff=cutoff)
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# envelopes of filtered envelope of filtered baseline
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# baseline_envelope = envelope(
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# np.abs(baseline_envelope), data.samplerate, cutoff
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# )
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# search_envelope = bandpass_filter(
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# search_envelope, data.samplerate, lowf=lowf, highf=highf)
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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# CUT OFF OVERLAP -------------------------------------------------
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# cut off first and last 0.5 * overlap at start and end
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valid = np.arange(
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int(0.5 * window_overlap), len(baseline_envelope) -
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int(0.5 * window_overlap)
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)
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baseline_envelope = baseline_envelope[valid]
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search_envelope = search_envelope[valid]
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# get inst freq valid snippet
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valid_t0 = int(0.5 * window_overlap) / data.samplerate
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valid_t1 = baseline_freq_time[-1] - \
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(int(0.5 * window_overlap) / data.samplerate)
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inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq = baseline_freq[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)] + t0
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# overwrite raw time to valid region
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time_oi = time_oi[valid]
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baseline = baseline[valid]
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broad_baseline = broad_baseline[valid]
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search = search[valid]
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# PEAK DETECTION --------------------------------------------------
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# detect peaks baseline_enelope
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prominence = np.percentile(baseline_envelope, 90)
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baseline_peaks, _ = find_peaks(
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np.abs(baseline_envelope), prominence=prominence)
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# detect peaks search_envelope
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prominence = np.percentile(search_envelope, 75)
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search_peaks, _ = find_peaks(
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search_envelope, prominence=prominence)
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# detect peaks inst_freq_filtered
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prominence = 2
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inst_freq_peaks, _ = find_peaks(
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np.abs(inst_freq_filtered), prominence=prominence)
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# PLOT ------------------------------------------------------------
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# plot spectrogram
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plot_spectrogram(axs[0, e], data_oi[:, electrode], data.samplerate, t0)
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# plot baseline instantaneos frequency
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axs[1, e].plot(baseline_freq_time, baseline_freq -
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np.median(baseline_freq), marker=".")
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# plot waveform of filtered signal
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axs[2, e].plot(time_oi, baseline, c="k")
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# plot narrow filtered baseline
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axs[2, e].plot(
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time_oi,
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baseline_envelope,
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c="orange",
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)
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# plot broad filtered baseline
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axs[2, e].plot(
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time_oi,
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broad_baseline,
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c="green",
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)
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# plot waveform of filtered search signal
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axs[3, e].plot(time_oi, search)
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# plot envelope of search signal
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axs[3, e].plot(
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time_oi,
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search_envelope,
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c="orange",
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)
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# plot filtered and rectified envelope
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axs[4, e].plot(time_oi, baseline_envelope)
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axs[4, e].scatter(
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(time_oi)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c="red",
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)
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# plot envelope of search signal
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axs[5, e].plot(time_oi, search_envelope)
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axs[5, e].scatter(
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(time_oi)[search_peaks],
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search_envelope[search_peaks],
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c="red",
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)
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# plot filtered instantaneous frequency
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axs[6, e].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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axs[6, e].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="red",
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)
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axs[6, e].set_xlabel("Time [s]")
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axs[0, e].set_title("Spectrogram")
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axs[1, e].set_title("Fitered baseline instanenous frequency")
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axs[2, e].set_title("Fitered baseline")
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axs[3, e].set_title("Fitered above")
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axs[4, e].set_title("Filtered envelope of baseline envelope")
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axs[5, e].set_title("Search envelope")
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axs[6, e].set_title("Filtered absolute instantaneous frequency")
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fig.suptitle('Fish ID %i' %track_id)
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plt.show()
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if __name__ == "__main__":
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