huhu
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				@ -14,21 +14,19 @@ dataset = '2018-11-14-al-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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<<<<<<< HEAD
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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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eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]*40000
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eod_duration = eod_times[2]- eod_times[1]
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=======
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>>>>>>> 5cd62554fa5af12a6a50661f0a60cd2b0457e702
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# read spikes during baseline activity
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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# calculate interpike intervals and plot them
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@ -40,8 +40,8 @@ ax1 = fig.add_subplot(211)
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plt.yticks(fontsize=18)
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ax2 = fig.add_subplot(212, sharex=ax1)
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plt.setp(ax1.get_xticklabels(), visible=False)
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ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
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ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
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ax1.set_ylabel("field [mV]", fontsize = 22)
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