Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio

code/spikes_analysis.py

@@ -42,33 +42,28 @@ for deltaf in df_map.keys():
                     binary_spikes = np.isin(cut_range, spikes_idx)*1
 
                     if phase_vec[idx] in df_phase_time[deltaf].keys():
-                        df_phase_time[deltaf][phase_vec[idx]].append(spikes[rep][phase])
+                        df_phase_time[deltaf][phase_vec[idx]].append(spikes_cut)
                         df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes))
                     else:
-                        df_phase_time[deltaf][phase_vec[idx]] = [spikes[rep][phase]]
+                        df_phase_time[deltaf][phase_vec[idx]] = [spikes_cut]
                         df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes
 
 
-plot_trials = df_phase_binary['-50Hz'][0.0]
-#hist_data = plt.hist(plot_trials)
-#ax.plot(hist_data[1][:-1], hist_data[0])
+
+plot_trials = df_phase_time['-50Hz'][0.0]
+plot_trials_binary = np.mean(df_phase_binary['-50Hz'][0.0], axis=0)
+
+window = 100
+smoothed_spikes = smooth(plot_trials_binary, window)
+time_axis = np.arange(-50, 50, 1/sampling_rate)
 
 fig, ax = plt.subplots()
 for i, trial in enumerate(plot_trials):
-    embed()
-    exit()
-    trial[trial == 0] = np.nan
-    ax.scatter(np.ones(len(trial)), trial, marker='|', color='k', size=12)
+    ax.scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
+ax.plot(time_axis, smoothed_spikes)
 plt.show()
 
 
-#mu = 1
-#sigma = 1
-#time_gauss = np.arange(-4, 4, 1)
-#gauss = gaussian(time_gauss, mu, sigma)
-# spikes during time vec (00010000001)?
-
-#smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
 #window = np.mean(np.diff(plot_spikes))
 #time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)
 

code/utility.py

@@ -23,6 +23,16 @@ def gaussian(x, mu, sig):
 	y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
 	return y
 
+
+def smooth(data, window):
+	mu = 1
+	sigma = window
+	time_gauss = np.arange(-4 * sigma, 4 * sigma, 1)
+	gauss = gaussian(time_gauss, mu, sigma)
+	smoothed_data = np.convolve(data, gauss, 'same')
+	return smoothed_data
+
+
 def map_keys(input):
 	df_map = {} 
 	for k in input.keys():