raab/GP2023_chirp_detection
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plotting true zeros

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This commit is contained in:
wendtalexander 2023-01-17 09:07:50 +01:00
parent 3c5ab4381e
commit 0ade3b101a
2 changed files with 15 additions and 8 deletions
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13
code/chirp_sim.py
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@ -8,7 +8,7 @@ from chirpdetection import instantaneos_frequency
from modules.simulations import create_chirp
# create chirp
"""
time, signal, ampl, freq = create_chirp(
chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
# filter signal with bandpass_filter
@ -22,7 +22,7 @@ baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
plt.close()
"""
# trying thunderfish fakefish chirp simulation ---------------------------------
samplerate = 44100
freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
@ -30,12 +30,13 @@ data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3)
# filter signal with bandpass_filter
data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
data_freq_time, data_freq = instantaneos_frequency(data*ampl, samplerate)
data_freq_time, data_freq, true_zero = instantaneos_frequency(data*ampl, samplerate)
fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
ax[0].plot(np.arange(len(data))/samplerate, data*ampl+1)
ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
ax[2].plot(np.arange(len(freq))/samplerate, freq)
ax[3].plot(data_freq_time[1:], data_freq[1:])
@ -43,3 +44,7 @@ ax[3].plot(data_freq_time[1:], data_freq[1:])
plt.show()
embed()

8
code/chirpdetection.py
View File

@ -58,7 +58,7 @@ def instantaneos_frequency(
# compute frequency
inst_freq = gaussian_filter1d(1 / np.diff(true_zero), 5)
return inst_freq_time, inst_freq
return inst_freq_time, inst_freq, true_zero
def plot_spectrogram(axis, signal: np.ndarray, samplerate: float, t0: float) -> None:
@ -353,7 +353,7 @@ def main(datapath: str) -> None:
)
# compute instantaneous frequency on narrow signal
baseline_freq_time, baseline_freq = instantaneos_frequency(
baseline_freq_time, baseline_freq, true_zero = instantaneos_frequency(
baseline, data.raw_rate
)
@ -423,7 +423,7 @@ def main(datapath: str) -> None:
baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
baseline_freq_time <= valid_t1)] + t0
true_zero = true_zero + t0
# overwrite raw time to valid region
time_oi = time_oi[valid]
baseline = baseline[valid]
@ -468,6 +468,8 @@ def main(datapath: str) -> None:
# plot waveform of filtered signal
axs[2, i].plot(time_oi, baseline, c=ps.green)
axs[2, i].scatter(
true_zero, np.zeros_like(true_zero), c=ps.red)
# plot broad filtered baseline
axs[2, i].plot(