raab/GP2023_chirp_detection
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electrode loop and adjusted plot

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sprause 2023-01-13 13:51:21 +01:00
parent 6fb5dd560a
commit 49b77ddb77
1 changed files with 199 additions and 196 deletions
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71
code/chirpdetection.py
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@ -217,21 +217,10 @@ def main(datapath: str) -> None:
# start_index = t0 * data.samplerate # start_index = t0 * data.samplerate
# stop_index = (t0 + dt) * data.samplerate # stop_index = (t0 + dt) * data.samplerate
fig, axs = plt.subplots(
7,
2,
figsize=(20 / 2.54, 12 / 2.54),
constrained_layout=True,
sharex=True,
sharey='row',
)
# iterate through all fish # iterate through all fish
for i, track_id in enumerate(np.unique(ident[~np.isnan(ident)])[:2]): for i, track_id in enumerate(np.unique(ident[~np.isnan(ident)])[:2]):
# load region of interest of raw data file
data_oi = data[start_index:stop_index, :]
time_oi = raw_time[start_index:stop_index]
# get indices for time array in time window # get indices for time array in time window
window_index = np.arange(len(idx))[ window_index = np.arange(len(idx))[
@ -250,11 +239,25 @@ def main(datapath: str) -> None:
if len(freq_temp) < expected_duration * 0.9: if len(freq_temp) < expected_duration * 0.9:
continue continue
# Create plot (three electrodes per fish)
fig, axs = plt.subplots(
7,
3,
figsize=(20 / 2.54, 12 / 2.54),
constrained_layout=True,
sharex=True,
sharey='row',
)
# get best electrode # get best electrode
electrode = np.argsort(np.nanmean(powers_temp, axis=0))[-1] best_electrodes = np.argsort(np.nanmean(powers_temp, axis=0))[-3:]
# <------------------------------------------ Iterate through electrodes # <------------------------------------------ Iterate through electrodes
for e, electrode in enumerate(best_electrodes):
# load region of interest of raw data file
data_oi = data[start_index:stop_index, :]
time_oi = raw_time[start_index:stop_index]
# plot wavetracker tracks to spectrogram # plot wavetracker tracks to spectrogram
# for track_id in np.unique(ident): # <---------- Find freq gaps later # for track_id in np.unique(ident): # <---------- Find freq gaps later
# here # here
@ -371,72 +374,72 @@ def main(datapath: str) -> None:
# PLOT ------------------------------------------------------------ # PLOT ------------------------------------------------------------
# plot spectrogram # plot spectrogram
plot_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate, t0) plot_spectrogram(axs[0, e], data_oi[:, electrode], data.samplerate, t0)
# plot baseline instantaneos frequency # plot baseline instantaneos frequency
axs[1, i].plot(baseline_freq_time, baseline_freq - axs[1, e].plot(baseline_freq_time, baseline_freq -
np.median(baseline_freq), marker=".") np.median(baseline_freq), marker=".")
# plot waveform of filtered signal # plot waveform of filtered signal
axs[2, i].plot(time_oi, baseline, c="k") axs[2, e].plot(time_oi, baseline, c="k")
# plot narrow filtered baseline # plot narrow filtered baseline
axs[2, i].plot( axs[2, e].plot(
time_oi, time_oi,
baseline_envelope, baseline_envelope,
c="orange", c="orange",
) )
# plot broad filtered baseline # plot broad filtered baseline
axs[2, i].plot( axs[2, e].plot(
time_oi, time_oi,
broad_baseline, broad_baseline,
c="green", c="green",
) )
# plot waveform of filtered search signal # plot waveform of filtered search signal
axs[3, i].plot(time_oi, search) axs[3, e].plot(time_oi, search)
# plot envelope of search signal # plot envelope of search signal
axs[3, i].plot( axs[3, e].plot(
time_oi, time_oi,
search_envelope, search_envelope,
c="orange", c="orange",
) )
# plot filtered and rectified envelope # plot filtered and rectified envelope
axs[4, i].plot(time_oi, baseline_envelope) axs[4, e].plot(time_oi, baseline_envelope)
axs[4, i].scatter( axs[4, e].scatter(
(time_oi)[baseline_peaks], (time_oi)[baseline_peaks],
baseline_envelope[baseline_peaks], baseline_envelope[baseline_peaks],
c="red", c="red",
) )
# plot envelope of search signal # plot envelope of search signal
axs[5, i].plot(time_oi, search_envelope) axs[5, e].plot(time_oi, search_envelope)
axs[5, i].scatter( axs[5, e].scatter(
(time_oi)[search_peaks], (time_oi)[search_peaks],
search_envelope[search_peaks], search_envelope[search_peaks],
c="red", c="red",
) )
# plot filtered instantaneous frequency # plot filtered instantaneous frequency
axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered)) axs[6, e].plot(baseline_freq_time, np.abs(inst_freq_filtered))
axs[6, i].scatter( axs[6, e].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="red", c="red",
) )
axs[6, i].set_xlabel("Time [s]") axs[6, e].set_xlabel("Time [s]")
axs[0, i].set_title("Spectrogram") axs[0, e].set_title("Spectrogram")
axs[1, i].set_title("Fitered baseline instanenous frequency") axs[1, e].set_title("Fitered baseline instanenous frequency")
axs[2, i].set_title("Fitered baseline") axs[2, e].set_title("Fitered baseline")
axs[3, i].set_title("Fitered above") axs[3, e].set_title("Fitered above")
axs[4, i].set_title("Filtered envelope of baseline envelope") axs[4, e].set_title("Filtered envelope of baseline envelope")
axs[5, i].set_title("Search envelope") axs[5, e].set_title("Search envelope")
axs[6, i].set_title("Filtered absolute instantaneous frequency") axs[6, e].set_title("Filtered absolute instantaneous frequency")
fig.suptitle('Fish ID %i' %track_id)
plt.show() plt.show()