Merge branch 'master' into alex
This commit is contained in:
commit
41b75f1a68
@ -187,22 +187,9 @@ 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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(ident == track_id) & (time[idx] >= t0) & (
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@ -220,213 +207,229 @@ 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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fig, axs = plt.subplots(
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7,
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config.electrodes,
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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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# get best electrode
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electrode = np.argsort(np.nanmean(powers_temp, axis=0))[-1]
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best_electrodes = np.argsort(np.nanmean(
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powers_temp, axis=0))[-config.electrodes:]
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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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)
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# compute envelopes
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baseline_envelope = envelope(
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baseline, data.samplerate, config.envelope_cutoff)
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search_envelope = envelope(
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search, data.samplerate, config.envelope_cutoff)
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# highpass filter envelopes
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baseline_envelope = highpass_filter(
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baseline_envelope,
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data.samplerate,
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config.envelope_highpass_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,
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# data.samplerate,
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# config.envelope_highpass_cutoff
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# )
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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),
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data.samplerate,
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config.envelope_envelope_cutoff
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)
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for i, 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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baseline_envelope = envelope(
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baseline, data.samplerate, config.envelope_cutoff)
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search_envelope = envelope(
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search, data.samplerate, config.envelope_cutoff)
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# highpass filter envelopes
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baseline_envelope = highpass_filter(
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baseline_envelope,
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data.samplerate,
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config.envelope_highpass_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,
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# data.samplerate,
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# config.envelope_highpass_cutoff
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# )
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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),
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data.samplerate,
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config.envelope_envelope_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,
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data.samplerate,
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lowf=config.instantaneous_lowf,
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highf=config.instantaneous_highf
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)
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# test taking the log of the envelopes
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# baseline_envelope = np.log(baseline_envelope)
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# search_envelope = np.log(search_envelope)
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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(window_overlap / 2), len(baseline_envelope) -
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int(window_overlap / 2)
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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(window_overlap / 2) / data.samplerate
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valid_t1 = baseline_freq_time[-1] - \
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(int(window_overlap / 2) / 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(
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baseline_envelope, config.baseline_prominence_percentile)
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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(
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search_envelope, config.search_prominence_percentile)
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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 = np.percentile(
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inst_freq_filtered, config.instantaneous_prominence_percentile)
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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(
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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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plt.show()
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq,
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data.samplerate,
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lowf=config.instantaneous_lowf,
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highf=config.instantaneous_highf
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)
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# test taking the log of the envelopes
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# baseline_envelope = np.log(baseline_envelope)
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# search_envelope = np.log(search_envelope)
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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(window_overlap / 2), len(baseline_envelope) -
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int(window_overlap / 2)
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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(window_overlap / 2) / data.samplerate
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valid_t1 = baseline_freq_time[-1] - \
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(int(window_overlap / 2) / 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(
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baseline_envelope, config.baseline_prominence_percentile)
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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(
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search_envelope, config.search_prominence_percentile)
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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 = np.percentile(
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inst_freq_filtered, config.instantaneous_prominence_percentile)
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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(
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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(
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"Filtered absolute instantaneous frequency")
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plt.show()
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if __name__ == "__main__":
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@ -1,6 +1,7 @@
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# Duration and overlap of the analysis window in seconds
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window: 5
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overlap: 0.5
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overlap: 1
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edges: 0.25
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# Number of electrodes to go over
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electrodes: 3
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Block a user