134 lines
3.7 KiB
Python
134 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 + 6.20
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window_start_index = window_start_seconds * data.raw_rate
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window_duration_seconds = 0.4
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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[
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window_start_index : window_start_index + window_duration_index, 10
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]
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fig, (ax1, ax2) = plt.subplots(
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1, 2, figsize=(21 * ps.cm, 8 * ps.cm), sharex=True, sharey=True
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)
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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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)
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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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)
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freqtime1, freq1 = instantaneous_frequency(
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filtered1, data.raw_rate, smoothing_window=3
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)
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freqtime2, freq2 = instantaneous_frequency(
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filtered2, data.raw_rate, smoothing_window=3
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)
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ax1.plot(freqtime1 * timescaler, freq1, color=ps.g, lw=2, label="Fish 1")
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ax1.plot(freqtime2 * timescaler, freq2, color=ps.gray, lw=2, label="Fish 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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ax1.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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# # 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=15,
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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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ax2.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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ax2.plot(freqtime1 * timescaler, freq1, color=ps.g, lw=2, label="_")
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ax2.plot(freqtime2 * timescaler, freq2, color=ps.gray, lw=2, label="_")
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ax2.set_xlim(75, 200)
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ax1.set_ylim(400, 1200)
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fig.supxlabel("Time [ms]", fontsize=14)
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fig.supylabel("Frequency [Hz]", fontsize=14)
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handles, labels = ax1.get_legend_handles_labels()
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ax2.legend(
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handles,
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labels,
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bbox_to_anchor=(1.04, 1),
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loc="upper left",
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ncol=1,
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)
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ps.letter_subplots(xoffset=[-0.27, -0.1], yoffset=1.05)
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plt.subplots_adjust(
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left=0.12, right=0.85, top=0.89, bottom=0.18, hspace=0.35
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)
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plt.savefig("../poster/figs/introplot.pdf")
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if __name__ == "__main__":
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main()
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