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huhu

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efish 2018-11-27 11:25:28 +01:00
parent 6fdf60f69e
commit 275bd7a9b8
2 changed files with 39 additions and 20 deletions
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41
code/plot_eodform_spikehist.py
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@ -20,9 +20,18 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
# calculate interpike intervals and plot them # calculate interpike intervals and plot them
interspikeintervals = np.diff(spikes) interspikeintervals = np.diff(spikes)
fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor)) fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001)) plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
plt.show() plt.xlabel("time [ms]", fontsize = 22)
plt.xticks(fontsize = 18)
plt.ylabel("number of \n interspikeintervals", fontsize = 22)
plt.yticks(fontsize = 18)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
fig.tight_layout()
#plt.show()
plt.savefig('isis.pdf')
# calculate coefficient of variation # calculate coefficient of variation
mu = np.mean(interspikeintervals) mu = np.mean(interspikeintervals)
@ -64,21 +73,23 @@ std_eod = np.nanstd(eod_cuts, axis=0)*3
time_axis = np.arange(max_cut)/sampling_rate*1000 time_axis = np.arange(max_cut)/sampling_rate*1000
# plot eod form and spike histogram # plot eod form and spike histogram
fig, ax1 = plt.subplots(figsize=(12/inch_factor, 8/inch_factor)) fig, ax1 = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
ax1.hist(spike_times, color='crimson') ax1.hist(spike_times, color='firebrick')
ax1.set_xlabel('time [ms]', fontsize=12) ax1.set_xlabel('time [ms]', fontsize=22)
ax1.set_ylabel('number', fontsize=12) ax1.set_ylabel('number', fontsize=22)
ax1.tick_params(axis='y', labelcolor='crimson') ax1.tick_params(axis='y', labelcolor='firebrick')
plt.yticks(fontsize=8) plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
ax1.spines['top'].set_visible(False) ax1.spines['top'].set_visible(False)
ax2 = ax1.twinx() ax2 = ax1.twinx()
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='dodgerblue', alpha=0.5) ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
ax2.plot(time_axis, mu_eod, color='black', lw=2) ax2.plot(time_axis, mu_eod, color='black', lw=2)
ax2.set_ylabel('voltage [mV]', fontsize=12) ax2.set_ylabel('voltage [mV]', fontsize=22)
ax2.tick_params(axis='y', labelcolor='dodgerblue') ax2.tick_params(axis='y', labelcolor='darkblue')
ax2.spines['top'].set_visible(False)
plt.xticks(fontsize=8) plt.yticks(fontsize=18)
plt.yticks(fontsize=8)
fig.tight_layout() fig.tight_layout()
plt.show() #plt.show()
plt.savefig('eodform_spikehist.pdf')

18
code/plot_spikesduringbaselineactivity.py
View File

@ -15,11 +15,19 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
# calculate interpike intervals and plot them # calculate interpike intervals and plot them
interspikeintervals = np.diff(spikes)*1000 interspikeintervals = np.diff(spikes)*1000
fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor)) fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1)) plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
ax.set_xlabel('time [ms]', fontsize=14) plt.xlabel("time [ms]", fontsize = 22)
ax.set_ylabel('number of interspikeintervals', fontsize=14) plt.xticks(fontsize = 18)
plt.ylabel("Number of \n Interspikeinterval", fontsize = 22)
plt.yticks(fontsize = 18)
ax.spines["top"].set_visible(False) ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False) ax.spines["right"].set_visible(False)
fig.tight_layout() fig.tight_layout()
plt.show() plt.show()
#plt.savefig('isis.pdf')