61 lines
1.4 KiB
Python
61 lines
1.4 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from read_baseline_data import *
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from IPython import embed
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inch_factor = 2.54
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data_dir = '../data'
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dataset = '2018-11-09-ad-invivo-1'
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
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ax = fig.add_subplot(111)
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ax.plot(time[:1000], eod[:1000])
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ax.set_xlabel('time [ms]', fontsize=12)
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ax.set_ylabel('voltage [mV]', fontsize=12)
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plt.xticks(fontsize = 8)
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plt.yticks(fontsize = 8)
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fig.tight_layout()
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plt.savefig('eod.pdf')
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#interspikeintervalhistogram, windowsize = 1 ms
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#plt.hist
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#coefficient of variation
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#embed()
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#exit()
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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interspikeintervals = np.diff(spikes)
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fig = plt.figure()
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
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plt.show()
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mu = np.mean(interspikeintervals)
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sigma = np.std(interspikeintervals)
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cv = sigma/mu
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print(cv)
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# calculate zero crossings of the eod
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# plot mean of eod circles
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# plot std of eod circles
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# plot psth into the same plot
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# calculate vector strength
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threshold = 0;
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shift_eod = np.roll(eod, 1)
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eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
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sampling_rate = 40000.0
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eod_idx = eod_times*sampling_rate
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fig = plt.figure()
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for i, idx in enumerate(eod_idx):
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#embed()
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#exit()
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plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
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plt.show()
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