removing the inst freq as a feature, and fintuning
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chirpdetection
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chirp_instantaneous_freq/chirp_exploration.py
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chirp_instantaneous_freq/chirp_exploration.py
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#!/usr/bin/env python
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# coding: utf-8
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# # Why can the instantaneous frequency of a band-pass filtered chirp recording go down ...
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# ... if a chirp is an up-modulation of the frequency?
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#
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# This is the question we try to answer in this notebook
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# In[14]:
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import matplotlib.pyplot as plt
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import numpy as np
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import thunderfish.fakefish as ff
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from filters import instantaneous_frequency, bandpass_filter
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#get_ipython().run_line_magic('matplotlib', 'qt')
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# parameters that stay the same
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samplerate = 20000
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duration = 0.2
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chirp_freq = 5
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smooth = 3
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# In[7]:
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def make_chirp(eodf, size, width, kurtosis, contrast, phase0):
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chirp_trace, amp = ff.chirps(
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eodf = eodf,
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samplerate = samplerate,
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duration = duration,
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chirp_freq = chirp_freq,
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chirp_size = size,
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chirp_width = width,
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chirp_kurtosis = kurtosis,
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chirp_contrast = contrast,
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)
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chirp = ff.wavefish_eods(
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fish = 'Alepto',
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frequency = chirp_trace,
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samplerate = samplerate,
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duration = duration,
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phase0 = phase0,
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noise_std = 0,
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)
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chirp *= amp
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return chirp_trace, chirp
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def filtered_chirp(eodf, size, width, kurtosis, contrast, phase0):
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time = np.arange(0, duration, 1/samplerate)
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chirp_trace, chirp = make_chirp(
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eodf = eodf,
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size = size,
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width = width,
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kurtosis = kurtosis,
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contrast = contrast,
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phase0 = phase0,
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)
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# apply filters
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narrow_filtered = bandpass_filter(chirp, samplerate, eodf-10, eodf+10)
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narrow_freqtime, narrow_freq = instantaneous_frequency(narrow_filtered, samplerate, smooth)
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broad_filtered = bandpass_filter(chirp, samplerate, eodf-300, eodf+300)
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broad_freqtime, broad_freq = instantaneous_frequency(broad_filtered, samplerate, smooth)
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original = (time, chirp_trace, chirp)
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broad = (broad_freqtime, broad_freq, broad_filtered)
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narrow = (narrow_freqtime, narrow_freq, narrow_filtered)
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return original, broad, narrow
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def plot(original, broad, narrow, axs):
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axs[0].plot(original[0], original[1], label='chirp trace')
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axs[0].plot(broad[0], broad[1], label='broad filtered')
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axs[0].plot(narrow[0], narrow[1], label='narrow filtered')
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axs[1].plot(original[0], original[2], label='unfiltered')
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axs[1].plot(original[0], broad[2], label='broad filtered')
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axs[1].plot(original[0], narrow[2], label='narrow filtered')
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original, broad, narrow = filtered_chirp(600, 100, 0.02, 1, 0.1, 0)
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fig, axs = plt.subplots(2, 1, figsize=(10, 5), sharex=True)
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plot(original, broad, narrow, axs)
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fig.align_labels()
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plt.show()
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# ## Chirp size
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# now that we have established an easy way to simulate and plot the chirps, lets change the chirp size and see how the narrow-filtered instantaneous frequency changes.
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# In[17]:
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sizes = np.arange(10, 600, 20)[:10]
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fig, axs = plt.subplots(2, len(sizes), figsize=(20, 5), sharex=True, sharey='row')
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integrals = []
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for i, size in enumerate(sizes):
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original, broad, narrow = filtered_chirp(600, size, 0.02, 1, 0.1, 0)
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integral = np.sum(original[1]-600)/(20000)
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integrals.append(integral)
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plot(original, broad, narrow, axs[:, i])
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axs[:, i][0].set_xlim(0.06, 0.14)
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axs[0, i].set_title(np.round(integral, 3))
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print(f'size {size} Hz; Integral {np.round(integral,3)}')
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fig.legend(handles=axs[0,0].get_lines(), loc='upper center', ncol=3)
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axs[0,0].set_ylabel('frequency [Hz]')
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axs[1,0].set_ylabel('amplitude [a.u.]')
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fig.supxlabel('time [s]')
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fig.align_labels()
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plt.show()
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# ## Chirp width
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# In[9]:
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widths = np.arange(0.02, 0.08, 0.005)
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fig, axs = plt.subplots(2, len(widths), figsize=(10, 5), sharex=True, sharey='row')
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integrals = []
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for i, width in enumerate(widths):
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if i > 9:
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break
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original, broad, narrow = filtered_chirp(600, 100, width, 1, 0.1, 0)
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integral = np.sum(original[1]-600)/(20000)
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plot(original, broad, narrow, axs[:, i])
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axs[:, i][0].set_xlim(0.06, 0.14)
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axs[0, i].set_title(f'width {np.round(width, 2)} s')
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print(f'width {width} s; Integral {np.round(integral, 3)}')
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fig.legend(handles=axs[0,0].get_lines(), loc='upper center', ncol=3)
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axs[0,0].set_ylabel('frequency [Hz]')
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axs[1,0].set_ylabel('amplitude [a.u.]')
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fig.supxlabel('time [s]')
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fig.align_labels()
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plt.show()
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# ## Chirp kurtosis
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# In[10]:
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kurtosiss = np.arange(0, 20, 1.6)
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fig, axs = plt.subplots(2, len(kurtosiss), figsize=(10, 5), sharex=True, sharey='row')
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integrals = []
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for i, kurtosis in enumerate(kurtosiss):
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original, broad, narrow = filtered_chirp(600, 100, 0.02, kurtosis, 0.1, 0)
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integral = np.sum(original[1]-600)/(20000)
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plot(original, broad, narrow, axs[:, i])
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axs[:, i][0].set_xlim(0.06, 0.14)
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axs[0, i].set_title(f'kurt {np.round(kurtosis, 2)}')
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print(f'kurt {kurtosis}; Integral {np.round(integral, 3)}')
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fig.legend(handles=axs[0,0].get_lines(), loc='upper center', ncol=3)
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axs[0,0].set_ylabel('frequency [Hz]')
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axs[1,0].set_ylabel('amplitude [a.u.]')
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fig.supxlabel('time [s]')
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fig.align_labels()
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plt.show()
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# ## Chirp contrast
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# In[11]:
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contrasts = np.arange(0.0, 1.1, 0.1)
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fig, axs = plt.subplots(2, len(sizes), figsize=(10, 5), sharex=True, sharey='row')
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integrals = []
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for i, contrast in enumerate(contrasts):
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if i > 9:
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break
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original, broad, narrow = filtered_chirp(600, 100, 0.02, 1, contrast, 0)
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integral = np.trapz(original[2], original[0])
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integrals.append(integral)
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plot(original, broad, narrow, axs[:, i])
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axs[:, i][0].set_xlim(0.06, 0.14)
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axs[0, i].set_title(f'contr {np.round(contrast, 2)}')
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fig.legend(handles=axs[0,0].get_lines(), loc='upper center', ncol=3)
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axs[0,0].set_ylabel('frequency [Hz]')
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axs[1,0].set_ylabel('amplitude [a.u.]')
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fig.supxlabel('time [s]')
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fig.align_labels()
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plt.show()
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# ## Chirp phase
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# In[12]:
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phases = np.arange(0.0, 2 * np.pi, 0.2)
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fig, axs = plt.subplots(2, len(sizes), figsize=(10, 5), sharex=True, sharey='row')
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integrals = []
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for i, phase in enumerate(phases):
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if i > 9:
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break
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original, broad, narrow = filtered_chirp(600, 100, 0.02, 1, 0.1, phase)
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integral = np.trapz(original[2], original[0])
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integrals.append(integral)
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plot(original, broad, narrow, axs[:, i])
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axs[:, i][0].set_xlim(0.06, 0.14)
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axs[0, i].set_title(f'phase {np.round(phase, 2)}')
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fig.legend(handles=axs[0,0].get_lines(), loc='upper center', ncol=3)
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axs[0,0].set_ylabel('frequency [Hz]')
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axs[1,0].set_ylabel('amplitude [a.u.]')
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fig.supxlabel('time [s]')
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fig.align_labels()
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plt.show()
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# These experiments show, that the narrow filtered instantaneous freuqency only switches its sign, when the integral of the instantaneous frequency (that was used to make the signal)
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# changes. Specifically, when the instantaneous frequency is 0.57, 1.57, 2.57 etc., the sign swithes.
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@ -749,8 +749,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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multiwindow_chirps = []
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multiwindow_ids = []
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for st, window_start_index in enumerate(window_start_indices):
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logger.info(f"Processing window {st+1} of {len(window_start_indices)}")
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for st, window_start_index in enumerate(window_start_indices[1853:]):
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logger.info(f"Processing window {st} of {len(window_start_indices)}")
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window_start_seconds = window_start_index / data.raw_rate
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window_duration_seconds = window_duration / data.raw_rate
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@ -914,17 +915,19 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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baseline_frequency - np.median(baseline_frequency)
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)
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# check if there is at least one superthreshold peak on the
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# instantaneous and exit the loop if not. This is used to
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# prevent windows that do definetely not include a chirp
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# to enter normalization, where small changes due to noise
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# would be amplified
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# # check if there is at least one superthreshold peak on the
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# # instantaneous and exit the loop if not. This is used to
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# # prevent windows that do definetely not include a chirp
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# # to enter normalization, where small changes due to noise
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# # would be amplified
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if not has_chirp(
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baseline_frequency_filtered[amplitude_mask],
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config.baseline_frequency_peakheight,
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):
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continue
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# if not has_chirp(
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# baseline_frequency_filtered[amplitude_mask],
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# config.baseline_frequency_peakheight,
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# ):
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# logger.warning(
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# f"Amplitude to small for the chirp detection of track {track_id} window {st},")
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# continue
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# CUT OFF OVERLAP ---------------------------------------------
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@ -974,11 +977,11 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# normalize all three feature arrays to the same range to make
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# peak detection simpler
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baseline_envelope = minmaxnorm([baseline_envelope])[0]
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search_envelope = minmaxnorm([search_envelope])[0]
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baseline_frequency_filtered = minmaxnorm(
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[baseline_frequency_filtered]
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)[0]
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# baseline_envelope = minmaxnorm([baseline_envelope])[0]
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# search_envelope = minmaxnorm([search_envelope])[0]
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# baseline_frequency_filtered = minmaxnorm(
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# [baseline_frequency_filtered]
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# )[0]
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# PEAK DETECTION ----------------------------------------------
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@ -995,6 +998,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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baseline_frequency_filtered,
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prominence=config.frequency_prominence,
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)
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# DETECT CHIRPS IN SEARCH WINDOW ------------------------------
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@ -1010,11 +1014,10 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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# check if one list is empty and if so, skip to the next
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# electrode because a chirp cannot be detected if one is empty
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one_feature_empty = (
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len(baseline_peak_timestamps) == 0
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or len(search_peak_timestamps) == 0
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or len(frequency_peak_timestamps) == 0
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#or len(frequency_peak_timestamps) == 0
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)
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if one_feature_empty and (debug == "false"):
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@ -1026,15 +1029,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
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sublists = [
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list(baseline_peak_timestamps),
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list(search_peak_timestamps),
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list(frequency_peak_timestamps),
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#list(frequency_peak_timestamps),
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]
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singleelectrode_chirps = group_timestamps(
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sublists=sublists,
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at_least_in=3,
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at_least_in=2,
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difference_threshold=config.chirp_window_threshold,
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)
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# check it there are chirps detected after grouping, continue
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# with the loop if not
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@ -1185,4 +1189,4 @@ if __name__ == "__main__":
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# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
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datapath = "../data/2022-06-02-10_00/"
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chirpdetection(datapath, plot="save", debug="false")
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chirpdetection(datapath, plot="show", debug="false")
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@ -38,9 +38,9 @@ search_envelope_cutoff: 10 # search envelope estimation cufoff
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# search_prominence: 0.000004 # peak prominence threshold for search envelope
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# frequency_prominence: 2 # peak prominence threshold for baseline freq
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baseline_prominence: 0.3 # peak prominence threshold for baseline envelope
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search_prominence: 0.3 # peak prominence threshold for search envelope
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frequency_prominence: 0.3 # peak prominence threshold for baseline freq
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baseline_prominence: 0.00005 # peak prominence threshold for baseline envelope
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search_prominence: 0.000005 # peak prominence threshold for search envelope
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frequency_prominence: 1 # peak prominence threshold for baseline freq
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# Classify events as chirps if they are less than this time apart
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chirp_window_threshold: 0.02
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