huhu
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@ -14,27 +14,22 @@ 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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<<<<<<< HEAD
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=======
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>>>>>>> d77d377849b65effc29e550f25a9107463c2120b
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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)]*40000
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eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
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eod_duration = eod_times[2]- eod_times[1] #time in s
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<<<<<<< HEAD
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eod_duration = eod_times[2]- eod_times[1]
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=======
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>>>>>>> d77d377849b65effc29e550f25a9107463c2120b
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# read spikes during baseline activity
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
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# calculate interpike intervals and plot them
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@ -49,9 +44,8 @@ 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.show()
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#plt.savefig('isis.pdf')
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exit()
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plt.savefig('isis.png')
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@ -105,10 +99,10 @@ plt.yticks(fontsize=18)
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ax1.spines['top'].set_visible(False)
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ax2 = ax1.twinx()
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
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ax2.plot(time_axis, mu_eod, color='black', lw=2)
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ax2.set_ylabel('voltage [mV]', fontsize=22)
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ax2.tick_params(axis='y', labelcolor='navy')
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ax2.tick_params(axis='y', labelcolor='royalblue')
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ax2.spines['top'].set_visible(False)
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plt.yticks(fontsize=18)
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