125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from thunderfish.powerspectrum import spectrogram, decibel
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from modules.filehandling import LoadData
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from modules.datahandling import instantaneous_frequency
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from modules.filters import bandpass_filter
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from modules.plotstyle import PlotStyle
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ps = PlotStyle()
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def main():
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# Load data
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datapath = "../data/2022-06-02-10_00/"
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data = LoadData(datapath)
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# good chirp times for data: 2022-06-02-10_00
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window_start_seconds = 3 * 60 * 60 + 6 * 60 + 43.5 + 9 + 6.24
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window_start_index = window_start_seconds * data.raw_rate
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window_duration_seconds = 0.2
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window_duration_index = window_duration_seconds * data.raw_rate
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timescaler = 1000
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raw = data.raw[window_start_index:window_start_index +
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window_duration_index, 10]
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fig, ax = plt.subplots(
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1, 1, figsize=(14 * ps.cm, 6*ps.cm), sharex=True, sharey=True)
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# plot instantaneous frequency
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filtered1 = bandpass_filter(
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signal=raw, lowf=750, highf=1200, samplerate=data.raw_rate)
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filtered2 = bandpass_filter(
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signal=raw, lowf=550, highf=700, samplerate=data.raw_rate)
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freqtime1, freq1 = instantaneous_frequency(
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filtered1, data.raw_rate, smoothing_window=3)
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freqtime2, freq2 = instantaneous_frequency(
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filtered2, data.raw_rate, smoothing_window=3)
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ax.plot(freqtime1*timescaler, freq1, color=ps.g,
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lw=2, label="Fish 1")
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ax.plot(freqtime2*timescaler, freq2, color=ps.gray,
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lw=2, label="Fish 2")
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ax.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center",
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mode="normal", borderaxespad=0, ncol=2)
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# ax.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
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# # ps.hide_xax(ax1)
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# plot fine spectrogram
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spec_power, spec_freqs, spec_times = spectrogram(
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raw,
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ratetime=data.raw_rate,
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freq_resolution=150,
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overlap_frac=0.2,
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)
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ylims = [300, 1300]
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fmask = np.zeros(spec_freqs.shape, dtype=bool)
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fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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ax.imshow(
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decibel(spec_power[fmask, :]),
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extent=[
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spec_times[0]*timescaler,
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spec_times[-1]*timescaler,
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spec_freqs[fmask][0],
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spec_freqs[fmask][-1],
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],
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aspect="auto",
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origin="lower",
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interpolation="gaussian",
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alpha=1,
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# vmin=-100,
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# vmax=-80,
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)
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# ps.hide_xax(ax2)
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# # plot coarse spectrogram
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# spec_power, spec_freqs, spec_times = spectrogram(
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# raw,
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# ratetime=data.raw_rate,
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# freq_resolution=10,
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# overlap_frac=0.3,
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# )
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# fmask = np.zeros(spec_freqs.shape, dtype=bool)
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# fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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# ax3.imshow(
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# decibel(spec_power[fmask, :]),
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# extent=[
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# spec_times[0]*timescaler,
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# spec_times[-1]*timescaler,
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# spec_freqs[fmask][0],
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# spec_freqs[fmask][-1],
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# ],
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# aspect="auto",
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# origin="lower",
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# interpolation="gaussian",
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# alpha=1,
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# )
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# # ps.hide_xax(ax3)
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ax.set_xlabel("Time [ms]")
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ax.set_ylabel("Frequency [Hz]")
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# ax.set_yticks(np.arange(400, 1201, 400))
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# ax.spines.left.set_bounds((400, 1200))
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# ax2.set_yticks(np.arange(400, 1201, 400))
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# ax2.spines.left.set_bounds((400, 1200))
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# ax3.set_yticks(np.arange(400, 1201, 400))
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# ax3.spines.left.set_bounds((400, 1200))
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plt.subplots_adjust(left=0.17, right=0.98, top=0.87,
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bottom=0.24, hspace=0.35)
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plt.savefig('../poster/figs/introplot.pdf')
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plt.show()
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if __name__ == '__main__':
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main()
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