45 lines
1.1 KiB
Python
45 lines
1.1 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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import thunderfish.peakdetection as pd
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from IPython import embed
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## beat
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data_dir = '../data'
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dataset = '2018-11-09-ad-invivo-1'
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inch_factor = 2.54
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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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eod_norm = eod_norm[10000:20000]
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x = np.arange(0., len(eod_norm))
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y = np.sin(time[10000:20000]*2*np.pi*600)*0.5
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ampl = eod_norm + y
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p, t = pd.detect_peaks(ampl, 0.1)
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time_axis = np.arange(len(ampl))
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.plot(time[10000:20000]*1000, ampl, color='darkblue')
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plt.plot(time[10000:20000][p]*1000, ampl[p], lw=2, color='k')
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plt.plot(time[10000:20000][t]*1000, ampl[t], lw=2, color='k')
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ax.set_xlabel("Time [ms]", fontsize = 22)
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plt.xticks(fontsize = 18)
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ax.set_ylabel("EOD amplitude [mV]", fontsize = 22)
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plt.yticks(fontsize = 18)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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fig.tight_layout()
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#plt.show()
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plt.savefig('beat.pdf')
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