40 lines
924 B
Python
40 lines
924 B
Python
from read_chirp_data import *
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import os
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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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data_dir = "../data"
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dataset = "2018-11-14-ak-invivo-1"
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stim = read_chirp_stimulus(os.path.join(data_dir, dataset))
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s = stim[(1, 675.0, 100.0)][0]
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eod = s[1]
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time = s[0]
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eod_norm = eod - np.mean(eod)
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# calculate chirp times and indices by zero crossings
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threshold = 0
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shift_eod = np.roll(eod_norm, 1)
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eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
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eod_freq_chirp = 1/np.diff(eod_times)
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kernel = np.ones(11)/11
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smooth_eod_freq_chirp = np.convolve(eod_freq_chirp, kernel, mode = 'valid')
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time_axis = np.arange(len(smooth_eod_freq_chirp))
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fig = plt.plot(time_axis, smooth_eod_freq_chirp)
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plt.xlabel("time [ms]", fontsize = 14)
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plt.xticks(fontsize = 12)
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plt.ylabel("eod frequency [mV]", fontsize = 14)
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plt.yticks(fontsize = 12)
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plt.axis([0, len(time_axis), 0, 2000])
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plt.show()
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