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@ -25,9 +25,9 @@ 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], ampl)
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plt.plot(time[10000:20000][p], ampl[p], lw=2, color='k')
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plt.plot(time[10000:20000][t], ampl[t], lw=2, color='k')
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plt.plot(time[10000:20000]*1000, ampl, color='royalblue')
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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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@ -36,8 +36,8 @@ 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.png')
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#plt.show()
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plt.savefig('beat.png')
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@ -8,7 +8,7 @@ from IPython import embed
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# define sampling rate and data path
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sampling_rate = 40 #kHz
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data_dir = "../data"
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dataset = "2018-11-14-al-invivo-1"
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dataset = "2018-11-09-ad-invivo-1"
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inch_factor = 2.54
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# parameters for binning, smoothing and plotting
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cut_window = 60
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@ -87,7 +87,7 @@ for df in df_phase_time.keys():
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ax[1].plot(time_axis[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='royalblue', lw = 2)
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ax[0].set_title('df = %s Hz' %(df), fontsize = 18)
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ax[0].set_title('$\Delta$f = %s Hz' %(df), fontsize = 18)
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ax[0].set_ylabel('repetition', fontsize=22)
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ax[0].yaxis.set_label_coords(-0.1, 0.5)
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ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2))
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