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mambo nr 5

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Ramona 2018-11-15 15:05:36 +01:00
parent 67353f5c0b
commit 4bff151b3a
1 changed files with 37 additions and 19 deletions
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56
code/analysis_rs.py
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@ -48,28 +48,46 @@ eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
sampling_rate = 40000.0
eod_idx = eod_times*sampling_rate
<<<<<<< HEAD
#fig = plt.figure()
eod_cuts = [];
#for i, idx in enumerate(eod_idx)-1:
#eod_cuts.append(eod[int(idx):int(eod_idx[i+1])])
#time_cut = time[int(idx):int(eod_idx[i+1])]
#plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
#plt.show()
data = NixToFrame(data_dir)
=======
fig = plt.figure()
for i, idx in enumerate(eod_idx):
#embed()
#exit()
plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
max_cut = int(np.max(np.diff(eod_idx)))
eod_cuts = np.zeros([len(eod_idx)-1, max_cut])
# eods 15 + 16 are to short
relative_times = []
for i, idx in enumerate(eod_idx[:-1]):
eod_cut = eod[int(idx):int(eod_idx[i+1])]
eod_cuts[i, :len(eod_cut)] = eod_cut
eod_cuts[i, len(eod_cut):] = np.nan
time_cut = time[int(idx):int(eod_idx[i+1])]
spike_cut = spikes[(spikes > time_cut[0]) & (spikes < time_cut[-1])]
relative_time = spike_cut - time_cut[0]
if len(relative_time) > 0:
relative_times.append(relative_time[:][0]*1000)
plt.show()
>>>>>>> 477fa15dc430b3d9c42ac3e40c59d67b3075c007
mu_eod = np.nanmean(eod_cuts, axis=0)
std_eod = np.nanstd(eod_cuts, axis=0)*3
time_axis = np.arange(max_cut)/sampling_rate*1000
#fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
fig, ax1 = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
ax1.hist(relative_times, color='crimson')
ax1.set_xlabel('time [ms]', fontsize=12)
ax1.set_ylabel('number', fontsize=12)
ax1.tick_params(axis='y', labelcolor='crimson')
plt.yticks(fontsize = 8)
ax1.spines['top'].set_visible(False)
ax2 = ax1.twinx()
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='dodgerblue', alpha=0.5)
ax2.plot(time_axis, mu_eod, color='black', lw=2)
ax2.set_ylabel('voltage [mV]', fontsize=12)
ax2.tick_params(axis='y', labelcolor='dodgerblue')
plt.xticks(fontsize = 8)
plt.yticks(fontsize = 8)
fig.tight_layout()
plt.show()
embed()
exit()