raab/GP2023_chirp_detection
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adding peak lists

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This commit is contained in:
wendtalexander 2023-01-17 09:54:40 +01:00
parent fded125f5d
commit d2eb169490
1 changed files with 31 additions and 31 deletions
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code/chirpdetection.py
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@ -209,12 +209,12 @@ def main(datapath: str) -> None:
# calucate median of fish frequencies in window # calucate median of fish frequencies in window
median_freq = [] median_freq = []
track_ids = [] track_ids = []
for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])): for el, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
window_index = np.arange(len(data.idx))[ window_idx = np.arange(len(data.idx))[
(data.ident == track_id) & (data.time[data.idx] >= t0) & ( (data.ident == track_id) & (data.time[data.idx] >= t0) & (
data.time[data.idx] <= (t0 + dt)) data.time[data.idx] <= (t0 + dt))
] ]
median_freq.append(np.median(data.freq[window_index])) median_freq.append(np.median(data.freq[window_idx]))
track_ids.append(track_id) track_ids.append(track_id)
# convert to numpy array # convert to numpy array
@ -227,14 +227,14 @@ def main(datapath: str) -> None:
print(f"Track ID: {track_id}") print(f"Track ID: {track_id}")
# get index of track data in this time window # get index of track data in this time window
window_index = np.arange(len(data.idx))[ window_idx = np.arange(len(data.idx))[
(data.ident == track_id) & (data.time[data.idx] >= t0) & ( (data.ident == track_id) & (data.time[data.idx] >= t0) & (
data.time[data.idx] <= (t0 + dt)) data.time[data.idx] <= (t0 + dt))
] ]
# get tracked frequencies and their times # get tracked frequencies and their times
freq_temp = data.freq[window_index] freq_temp = data.freq[window_idx]
powers_temp = data.powers[window_index, :] powers_temp = data.powers[window_idx, :]
# approximate sampling rate to compute expected durations if there # approximate sampling rate to compute expected durations if there
# is data available for this time window for this fish id # is data available for this time window for this fish id
@ -256,7 +256,7 @@ def main(datapath: str) -> None:
# get best electrode # get best electrode
best_electrodes = np.argsort(np.nanmean( best_electrodes = np.argsort(np.nanmean(
powers_temp, axis=0))[-config.electrodes:] powers_temp, axis=0))[-config.number_electrodes:]
# frequency where second filter filters # frequency where second filter filters
search_window = np.arange( search_window = np.arange(
@ -457,78 +457,78 @@ def main(datapath: str) -> None:
# SAVE DATA --------------------------------------------------- # SAVE DATA ---------------------------------------------------
baseline_ts[st][tr][el] = baseline_envelope[baseline_peaks] baseline_ts[st][tr][el] = time_oi[baseline_peaks]
search_ts[st][tr][el] = search_envelope[search_peaks] search_ts[st][tr][el] = time_oi[search_peaks]
freq_ts[st][tr][el] = inst_freq_filtered[inst_freq_peaks] freq_ts[st][tr][el] = baseline_freq_time[inst_freq_peaks]
# PLOT -------------------------------------------------------- # PLOT --------------------------------------------------------
# plot spectrogram # plot spectrogram
plot_spectrogram( plot_spectrogram(
axs[0, i], data_oi[:, electrode], data.raw_rate, t0) axs[0, el], data_oi[:, electrode], data.raw_rate, t0)
# plot baseline instantaneos frequency # plot baseline instantaneos frequency
axs[1, i].plot(baseline_freq_time, baseline_freq - axs[1, el].plot(baseline_freq_time, baseline_freq -
np.median(baseline_freq)) np.median(baseline_freq))
# plot waveform of filtered signal # plot waveform of filtered signal
axs[2, i].plot(time_oi, baseline, c=ps.green) axs[2, el].plot(time_oi, baseline, c=ps.green)
# plot broad filtered baseline # plot broad filtered baseline
axs[2, i].plot( axs[2, el].plot(
time_oi, time_oi,
broad_baseline, broad_baseline,
) )
# plot narrow filtered baseline envelope # plot narrow filtered baseline envelope
axs[2, i].plot( axs[2, el].plot(
time_oi, time_oi,
baseline_envelope_unfiltered, baseline_envelope_unfiltered,
c=ps.red c=ps.red
) )
# plot waveform of filtered search signal # plot waveform of filtered search signal
axs[3, i].plot(time_oi, search) axs[3, el].plot(time_oi, search)
# plot envelope of search signal # plot envelope of search signal
axs[3, i].plot( axs[3, el].plot(
time_oi, time_oi,
search_envelope, search_envelope,
c=ps.red c=ps.red
) )
# plot filtered and rectified envelope # plot filtered and rectified envelope
axs[4, i].plot(time_oi, baseline_envelope) axs[4, el].plot(time_oi, baseline_envelope)
axs[4, i].scatter( axs[4, el].scatter(
(time_oi)[baseline_peaks], (time_oi)[baseline_peaks],
baseline_envelope[baseline_peaks], baseline_envelope[baseline_peaks],
c=ps.red, c=ps.red,
) )
# plot envelope of search signal # plot envelope of search signal
axs[5, i].plot(time_oi, search_envelope) axs[5, el].plot(time_oi, search_envelope)
axs[5, i].scatter( axs[5, el].scatter(
(time_oi)[search_peaks], (time_oi)[search_peaks],
search_envelope[search_peaks], search_envelope[search_peaks],
c=ps.red, c=ps.red,
) )
# plot filtered instantaneous frequency # plot filtered instantaneous frequency
axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered)) axs[6, el].plot(baseline_freq_time, np.abs(inst_freq_filtered))
axs[6, i].scatter( axs[6, el].scatter(
baseline_freq_time[inst_freq_peaks], baseline_freq_time[inst_freq_peaks],
np.abs(inst_freq_filtered)[inst_freq_peaks], np.abs(inst_freq_filtered)[inst_freq_peaks],
c=ps.red, c=ps.red,
) )
axs[6, i].set_xlabel("Time [s]") axs[6, el].set_xlabel("Time [s]")
axs[0, i].set_title("Spectrogram") axs[0, el].set_title("Spectrogram")
axs[1, i].set_title("Fitered baseline instanenous frequency") axs[1, el].set_title("Fitered baseline instanenous frequency")
axs[2, i].set_title("Fitered baseline") axs[2, el].set_title("Fitered baseline")
axs[3, i].set_title("Fitered above") axs[3, el].set_title("Fitered above")
axs[4, i].set_title("Filtered envelope of baseline envelope") axs[4, el].set_title("Filtered envelope of baseline envelope")
axs[5, i].set_title("Search envelope") axs[5, el].set_title("Search envelope")
axs[6, i].set_title( axs[6, el].set_title(
"Filtered absolute instantaneous frequency") "Filtered absolute instantaneous frequency")
plt.show() plt.show()