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buh

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efish 2018-11-30 17:33:18 +01:00
parent ca4185a64a
commit 90388959c4
5 changed files with 17 additions and 16 deletions
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4
code/eod_chirp_beat.py
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@ -47,7 +47,7 @@ def plot_chirp(eodf, eodf1, phase, axis):
y = chirp_eod * 0.4 + eod y = chirp_eod * 0.4 + eod
p, t = pd.detect_peaks(y, 0.1) p, t = pd.detect_peaks(y, 0.1)
axis.plot(time*1000, y, color = 'royalblue') axis.plot(time*1000, y, color = 'darkblue')
axis.plot(time[p]*1000, (y)[p], lw=2, color='k') axis.plot(time[p]*1000, (y)[p], lw=2, color='k')
axis.plot(time[t]*1000, (y)[t], lw=2, color='k') axis.plot(time[t]*1000, (y)[t], lw=2, color='k')
axis.spines["top"].set_visible(False) axis.spines["top"].set_visible(False)
@ -93,4 +93,4 @@ ax4.set_yticklabels([])
fig.tight_layout() fig.tight_layout()
#plt.show() #plt.show()
plt.savefig('chirps_while_beat.png') plt.savefig('chirps_while_beat.pdf')

4
code/eod_freq_beat.py
View File

@ -25,7 +25,7 @@ time_axis = np.arange(len(ampl))
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor)) fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
plt.plot(time[10000:20000]*1000, ampl, color='royalblue') plt.plot(time[10000:20000]*1000, ampl, color='darkblue')
plt.plot(time[10000:20000][p]*1000, ampl[p], lw=2, color='k') plt.plot(time[10000:20000][p]*1000, ampl[p], lw=2, color='k')
plt.plot(time[10000:20000][t]*1000, ampl[t], lw=2, color='k') plt.plot(time[10000:20000][t]*1000, ampl[t], lw=2, color='k')
ax.set_xlabel("Time [ms]", fontsize = 22) ax.set_xlabel("Time [ms]", fontsize = 22)
@ -37,7 +37,7 @@ ax.spines["right"].set_visible(False)
fig.tight_layout() fig.tight_layout()
#plt.show() #plt.show()
plt.savefig('beat.png') plt.savefig('beat.pdf')

12
code/plot_eodform_spikehist.py
View File

@ -35,7 +35,7 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
interspikeintervals = np.diff(spikes)/eod_duration interspikeintervals = np.diff(spikes)/eod_duration
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/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), color='royalblue') plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='darkblue')
plt.xlabel("EOD cycles", fontsize = 22) plt.xlabel("EOD cycles", fontsize = 22)
plt.xticks(fontsize = 18) plt.xticks(fontsize = 18)
plt.ylabel("Number of \n interspikeintervals", fontsize = 22) plt.ylabel("Number of \n interspikeintervals", fontsize = 22)
@ -44,8 +44,8 @@ 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') plt.savefig('isis.pdf')
plt.savefig('isis.png') #plt.savefig('isis.png')
# calculate coefficient of variation # calculate coefficient of variation
@ -98,13 +98,13 @@ 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='royalblue', alpha=0.5) ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='darkblue', 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=22) ax2.set_ylabel('Voltage [mV]', fontsize=22)
ax2.tick_params(axis='y', labelcolor='royalblue') ax2.tick_params(axis='y', labelcolor='darkblue')
ax2.spines['top'].set_visible(False) ax2.spines['top'].set_visible(False)
plt.yticks(fontsize=18) plt.yticks(fontsize=18)
fig.tight_layout() fig.tight_layout()
#plt.show() #plt.show()
plt.savefig('eodform_spikehist.png') plt.savefig('eodform_spikehist.pdf')

7
code/repetition_firingrate.py
View File

@ -8,7 +8,8 @@ from IPython import embed
# define sampling rate and data path # define sampling rate and data path
sampling_rate = 40 #kHz sampling_rate = 40 #kHz
data_dir = "../data" data_dir = "../data"
dataset = "2018-11-14-al-invivo-1" #dataset = "2018-11-14-al-invivo-1"
dataset = "2018-11-09-ad-invivo-1"
inch_factor = 2.54 inch_factor = 2.54
# parameters for binning, smoothing and plotting # parameters for binning, smoothing and plotting
cut_window = 60 cut_window = 60
@ -85,7 +86,7 @@ for df in df_phase_time.keys():
for i, trial in enumerate(plot_trials): for i, trial in enumerate(plot_trials):
ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k') ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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) 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)
ax[0].set_title('$\Delta$f = %s Hz' %(df), fontsize = 18) ax[0].set_title('$\Delta$f = %s Hz' %(df), fontsize = 18)
ax[0].set_ylabel('Repetition', fontsize=22) ax[0].set_ylabel('Repetition', fontsize=22)
@ -101,5 +102,5 @@ for df in df_phase_time.keys():
fig.tight_layout() fig.tight_layout()
#plt.show() #plt.show()
#exit() #exit()
namefigure = '../figures/%s_%i_%i_firingrate.png' %(dataset, df, index_phase) namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
plt.savefig(namefigure) plt.savefig(namefigure)

6
code/stimulus_chirp.py
View File

@ -40,8 +40,8 @@ ax1 = fig.add_subplot(211)
plt.yticks(fontsize=18) plt.yticks(fontsize=18)
ax2 = fig.add_subplot(212, sharex=ax1) ax2 = fig.add_subplot(212, sharex=ax1)
plt.setp(ax1.get_xticklabels(), visible=False) plt.setp(ax1.get_xticklabels(), visible=False)
ax1.plot(time*1000, signal, color = 'royalblue', lw = 1) ax1.plot(time*1000, signal, color = 'darkblue', lw = 1)
ax2.plot(time*1000, freq, color = 'royalblue', lw = 3) ax2.plot(time*1000, freq, color = 'darkblue', lw = 3)
ax1.set_ylabel("Field [mV]", fontsize = 22) ax1.set_ylabel("Field [mV]", fontsize = 22)
@ -58,4 +58,4 @@ ax2.spines["top"].set_visible(False)
ax2.spines["right"].set_visible(False) ax2.spines["right"].set_visible(False)
fig.tight_layout() fig.tight_layout()
#plt.show() #plt.show()
plt.savefig('stimulus_chirp.png') plt.savefig('stimulus_chirp.pdf')