39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
from thunderfish.dataloader import DataLoader as open_data
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from thunderfish.powerspectrum import spectrogram, decibel
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from IPython import embed
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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from scipy.ndimage import gaussian_filter1d
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from modules.filters import bandpass_filter
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from modules.filehandling import LoadData
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def main(folder):
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data = LoadData(folder)
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t0 = 3 * 60 * 60 + 6 * 60 + 43.5
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dt = 60
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data_oi = data.raw[t0 * data.raw_rate : (t0 + dt) * data.raw_rate, :]
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# good electrode
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electrode = 10
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data_oi = data_oi[:, electrode]
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fig, axs = plt.subplots(2, 1)
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axs[0].plot(np.arange(data_oi.shape[0]) / data.raw_rate, data_oi)
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for tr, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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rack_window_index = np.arange(len(data.idx))[
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(data.ident == track_id)
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& (data.time[data.idx] >= t0)
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& (data.time[data.idx] <= (t0 + dt))
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]
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freq_fish = data.freq[rack_window_index]
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axs[1].plot(np.arange(freq_fish.shape[0]) / data.raw_rate, freq_fish)
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plt.show()
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if __name__ == "__main__":
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main(
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"/Users/acfw/Documents/uni_tuebingen/chirpdetection/GP2023_chirp_detection/data/2022-06-02-10_00/"
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)
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