51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
import numpy as np
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from IPython import embed
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import matplotlib.pyplot as plt
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from thunderfish import fakefish
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from modules.filters import bandpass_filter
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from chirpdetection import instantaneos_frequency
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from modules.simulations import create_chirp
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# create chirp
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"""
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time, signal, ampl, freq = create_chirp(
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chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
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# filter signal with bandpass_filter
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signal = bandpass_filter(signal, 1/0.00001, 495, 505)
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fig, axs = plt.subplots(2, 1, figsize=(10, 10))
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axs[0].plot(np.arange(len(ampl)), ampl)
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# plot instatneous frequency
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baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
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axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
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plt.close()
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"""
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# trying thunderfish fakefish chirp simulation ---------------------------------
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samplerate = 44100
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freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
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data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3)
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# filter signal with bandpass_filter
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data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
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data_freq_time, data_freq, true_zero = instantaneos_frequency(data*ampl, samplerate)
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fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
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ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
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ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
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ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
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ax[2].plot(np.arange(len(freq))/samplerate, freq)
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ax[3].plot(data_freq_time[1:], data_freq[1:])
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plt.show()
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embed()
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