adding thunderfish chirp simulation

code/chirp_sim.py

@@ -1,24 +1,44 @@
-from modules.filters import create_chirp, bandpass_filter
-import matplotlib.pyplot as plt 
-from chirpdetection import instantaneos_frequency
+import numpy as np
 from IPython import embed
-# create chirp
+import matplotlib.pyplot as plt
+from thunderfish import fakefish
 
-time, signal, ampl, freq = create_chirp(chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
+from modules.filters import bandpass_filter
+from chirpdetection import instantaneos_frequency
+from modules.simulations import create_chirp
 
-# filter signal with bandpass_filter 
+# 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
 signal = bandpass_filter(signal, 1/0.00001, 495, 505)
-embed()
-exit()
 fig, axs = plt.subplots(2, 1, figsize=(10, 10))
-axs[0].plot(time, signal)
+axs[0].plot(np.arange(len(ampl)), ampl)
 
 # plot instatneous frequency
 
 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)
+data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, fphase0=3)
+
+# filter signal with bandpass_filter
+data_filterd = bandpass_filter(data*ampl, samplerate, 495, 505)
+data_freq_time, data_freq = instantaneos_frequency(data_filterd, 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)
+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:])
 
 
 plt.show()