57 lines
1.3 KiB
Python
57 lines
1.3 KiB
Python
from read_baseline_data import *
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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 = '2018-11-09-ad-invivo-1'
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# read eod and time of baseline
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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eod_norm = eod - np.mean(eod)
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# calculate eod 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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## normal
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eod_freq_normal = 1/np.diff(eod_times)
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kernel = np.ones(7)/7
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smooth_eod_freq_normal = np.convolve(eod_freq_normal, kernel, mode = 'valid')
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time_axis = np.arange(len(smooth_eod_freq_normal))
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#print(eod_freq)
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#print(smooth_eod_freq)
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fig = plt.plot(time_axis,smooth_eod_freq_normal, linewidth = 2.0)
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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, 1000])
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plt.show()
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## beat
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#x = np.arange(0, len(time_axis, 0.000001) # Start, Stop, Step
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#y = np.sin(x * 2* np.pi * 600)
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#plt.plot(x, y)
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#plt.show()
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#eod_freq_beat = eod_freq_normal + np.sin(x * 2* np.pi * 600)
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#smooth_eod_freq_beat = np.convolve(eod_freq_beat, kernel, mode = 'valid')
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#fig = plt.plot(time_axis,smooth_eod_freq_beat)
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#plt.xlabel("time [ms]")
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#plt.ylabel("eod frequency [mV]")
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#plt.show()
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