raab/GP2023_chirp_detection
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sorted plots

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This commit is contained in:
weygoldt 2023-01-12 17:52:19 +01:00
parent 41ffe7ecce
commit 96933c0532
1 changed files with 101 additions and 54 deletions
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155
code/chirpdetection.py
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@ -180,8 +180,21 @@ def main(datapath: str) -> None:
# set time window # <------------------------ Iterate through windows here
window_duration = 5 * data.samplerate # 5 seconds window
window_overlap = 0.5 * data.samplerate # 30 seconds overlap
raw_time = np.arange(data.shape[0])
window_overlap = 0.5 * data.samplerate # 0.5 seconds overlap
# check if window duration is even
if window_duration % 2 == 0:
window_duration = int(window_duration)
else:
raise ValueError("Window duration must be even.")
# check if window ovelap is even
if window_overlap % 2 == 0:
window_overlap = int(window_overlap)
else:
raise ValueError("Window overlap must be even.")
raw_time = np.arange(data.shape[0]) / data.samplerate
t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.samplerate
dt = 60 * data.samplerate
@ -202,9 +215,6 @@ def main(datapath: str) -> None:
# start_index = t0 * data.samplerate
# stop_index = (t0 + dt) * data.samplerate
# load region of interest of raw data file
data_oi = data[start_index:stop_index, :]
fig, axs = plt.subplots(
7,
2,
@ -217,6 +227,10 @@ def main(datapath: str) -> None:
# iterate through all fish
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
window_index = np.arange(len(idx))[
(ident == track_id) & (time[idx] >= t0) & (
@ -226,7 +240,7 @@ def main(datapath: str) -> None:
# get tracked frequencies and their times
freq_temp = freq[window_index]
powers_temp = powers[window_index, :]
# time_temp = time[idx[window_index]]
time_temp = time[idx[window_index]]
track_samplerate = np.mean(1 / np.diff(time))
expected_duration = ((t0 + dt) - t0) * track_samplerate
@ -236,13 +250,9 @@ def main(datapath: str) -> None:
# get best electrode
electrode = np.argsort(np.nanmean(powers_temp, axis=0))[-1]
# electrode = best_electrodes[0]
# <------------------------------------------ Iterate through electrodes
# plot spectrogram
plot_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate)
# plot wavetracker tracks to spectrogram
# for track_id in np.unique(ident): # <---------- Find freq gaps later
# here
@ -263,62 +273,43 @@ def main(datapath: str) -> None:
# track_id = ids
# frequency where second filter filters
search_freq = -50
search_freq = 50
# filter baseline and above
baseline, search = double_bandpass(
data_oi[:, electrode], data.samplerate, freq_temp, search_freq
)
# plot waveform of filtered signal
axs[2, i].plot(np.arange(len(baseline)) /
data.samplerate, baseline, c="k")
# plot instatneous frequency
broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
freq_temp)-5, highf=np.mean(freq_temp)+100)
# compute instantaneous frequency on broad signal
broad_baseline = bandpass_filter(
data_oi[:, electrode],
data.samplerate,
lowf=np.mean(freq_temp)-5,
highf=np.mean(freq_temp)+100
)
# compute instantaneous frequency on narrow signal
baseline_freq_time, baseline_freq = instantaneos_frequency(
baseline, data.samplerate
)
axs[1, i].plot(baseline_freq_time, baseline_freq -
np.median(baseline_freq), marker=".")
# plot waveform of filtered search signal
axs[3, i].plot(np.arange(len(baseline)) / data.samplerate, search)
# compute envelopes
cutoff = 25
baseline_envelope = envelope(baseline, data.samplerate, cutoff)
axs[2, i].plot(
np.arange(len(baseline)) / data.samplerate,
baseline_envelope,
c="orange",
)
axs[2, i].plot(
np.arange(len(baseline)) / data.samplerate,
broad_baseline,
c="green",
)
search_envelope = envelope(search, data.samplerate, cutoff)
axs[3, i].plot(
np.arange(len(baseline)) / data.samplerate,
search_envelope,
c="orange",
)
# highpass filter envelopes
cutoff = 5
baseline_envelope = highpass_filter(
baseline_envelope, data.samplerate, cutoff=cutoff
)
baseline_envelope = np.abs(baseline_envelope)
# search_envelope = highpass_filter(
# search_envelope, data.samplerate, cutoff=cutoff)
# envelopes of filtered envelope of filtered baseline
baseline_envelope = envelope(
np.abs(baseline_envelope), data.samplerate, cutoff
)
# baseline_envelope = envelope(
# np.abs(baseline_envelope), data.samplerate, cutoff
# )
# search_envelope = bandpass_filter(
# search_envelope, data.samplerate, lowf=lowf, highf=highf)
@ -328,43 +319,99 @@ def main(datapath: str) -> None:
baseline_freq, data.samplerate, lowf=15, highf=8000
)
# plot filtered and rectified envelope
axs[4, i].plot(
np.arange(len(baseline)) / data.samplerate, baseline_envelope
# cut off first and last 0.5 * overlap at start and end
valid = np.arange(
int(0.5 * window_overlap), len(baseline_envelope) -
int(0.5 * window_overlap)
)
baseline_envelope = baseline_envelope[valid]
search_envelope = search_envelope[valid]
axs[5, i].plot(np.arange(len(baseline)) /
data.samplerate, search_envelope)
# get inst freq valid snippet
valid_t0 = int(0.5 * window_overlap)
valid_t1 = len(baseline_envelope) - int(0.5 * window_overlap)
inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & (
baseline_freq_time <= valid_t1)]
baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
baseline_freq_time <= valid_t1)]
axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered))
# overwrite raw time to valid region
time_oi = time_oi[valid]
# detect peaks baseline_enelope
prominence = iqr(baseline_envelope)
prominence = np.percentile(baseline_envelope, 90)
baseline_peaks, _ = find_peaks(
baseline_envelope, prominence=prominence)
np.abs(baseline_envelope), prominence=prominence)
axs[4, i].scatter(
(np.arange(len(baseline)) / data.samplerate)[baseline_peaks],
(time_oi)[baseline_peaks],
baseline_envelope[baseline_peaks],
c="red",
)
# detect peaks search_envelope
search_peaks, _ = find_peaks(search_envelope, height=0.0001)
prominence = np.percentile(search_envelope, 75)
search_peaks, _ = find_peaks(
search_envelope, prominence=prominence)
axs[5, i].scatter(
(np.arange(len(baseline)) / data.samplerate)[search_peaks],
(time_oi)[search_peaks],
search_envelope[search_peaks],
c="red",
)
# detect peaks inst_freq_filtered
prominence = 2
inst_freq_peaks, _ = find_peaks(
np.abs(inst_freq_filtered), height=2)
np.abs(inst_freq_filtered), prominence=prominence)
axs[6, i].scatter(
baseline_freq_time[inst_freq_peaks],
np.abs(inst_freq_filtered)[inst_freq_peaks],
c="red",
)
# plot spectrogram
plot_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate)
# plot baseline instantaneos frequency
axs[1, i].plot(baseline_freq_time, baseline_freq -
np.median(baseline_freq), marker=".")
# plot waveform of filtered signal
axs[2, i].plot(time_oi, baseline, c="k")
# plot waveform of filtered search signal
axs[3, i].plot(time_oi, search)
# plot narrow filtered baseline
axs[2, i].plot(
time_oi,
baseline_envelope,
c="orange",
)
# plot broad filtered baseline
axs[2, i].plot(
time_oi,
broad_baseline,
c="green",
)
# plot envelope of search signal
axs[3, i].plot(
time_oi,
search_envelope,
c="orange",
)
# plot filtered and rectified envelope
axs[4, i].plot(
time_oi, baseline_envelope
)
# plot envelope of search signal
axs[5, i].plot(time_oi, search_envelope)
# plot filtered instantaneous frequency
axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered))
axs[0, i].set_title("Spectrogram")
axs[1, i].set_title("Fitered baseline instanenous frequency")
axs[2, i].set_title("Fitered baseline")