mambo nr 5

code/analysis_rs.py

@@ -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()
+mu_eod = np.nanmean(eod_cuts, axis=0)
->>>>>>> 477fa15dc430b3d9c42ac3e40c59d67b3075c007
+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()