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@ -47,7 +47,7 @@ def plot_chirp(eodf, eodf1, phase, axis):
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y = chirp_eod * 0.4 + eod
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p, t = pd.detect_peaks(y, 0.1)
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axis.plot(time*1000, y, color = 'royalblue')
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axis.plot(time*1000, y, color = 'darkblue')
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axis.plot(time[p]*1000, (y)[p], lw=2, color='k')
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axis.plot(time[t]*1000, (y)[t], lw=2, color='k')
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axis.spines["top"].set_visible(False)
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@ -93,4 +93,4 @@ ax4.set_yticklabels([])
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fig.tight_layout()
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#plt.show()
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plt.savefig('chirps_while_beat.png')
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plt.savefig('chirps_while_beat.pdf')
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@ -25,7 +25,7 @@ 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='royalblue')
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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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@ -37,7 +37,7 @@ 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.savefig('beat.pdf')
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@ -35,7 +35,7 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
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interspikeintervals = np.diff(spikes)/eod_duration
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='royalblue')
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='darkblue')
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plt.xlabel("EOD cycles", fontsize = 22)
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plt.xticks(fontsize = 18)
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plt.ylabel("Number of \n interspikeintervals", fontsize = 22)
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@ -44,8 +44,8 @@ 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('isis.pdf')
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plt.savefig('isis.png')
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plt.savefig('isis.pdf')
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#plt.savefig('isis.png')
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# calculate coefficient of variation
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@ -98,13 +98,13 @@ 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='royalblue', alpha=0.5)
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='darkblue', 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='royalblue')
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ax2.tick_params(axis='y', labelcolor='darkblue')
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ax2.spines['top'].set_visible(False)
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plt.yticks(fontsize=18)
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fig.tight_layout()
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#plt.show()
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plt.savefig('eodform_spikehist.png')
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plt.savefig('eodform_spikehist.pdf')
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@ -8,7 +8,8 @@ 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 = "2018-11-14-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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@ -85,7 +86,7 @@ for df in df_phase_time.keys():
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for i, trial in enumerate(plot_trials):
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ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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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[1].plot(time_axis[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='darkblue', lw = 2)
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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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@ -101,5 +102,5 @@ for df in df_phase_time.keys():
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fig.tight_layout()
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#plt.show()
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#exit()
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namefigure = '../figures/%s_%i_%i_firingrate.png' %(dataset, df, index_phase)
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namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
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plt.savefig(namefigure)
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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 = 'royalblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
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ax1.plot(time*1000, signal, color = 'darkblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'darkblue', lw = 3)
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ax1.set_ylabel("Field [mV]", fontsize = 22)
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@ -58,4 +58,4 @@ ax2.spines["top"].set_visible(False)
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ax2.spines["right"].set_visible(False)
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fig.tight_layout()
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#plt.show()
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plt.savefig('stimulus_chirp.png')
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plt.savefig('stimulus_chirp.pdf')
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