still fix smoothing

code/spikes_analysis.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from read_chirp_data import *
+from utility import *
 from IPython import embed
 
 data_dir = "../data"
@@ -18,6 +19,7 @@ for k in spikes.keys():
     else:
         df_map[df] = [k]
 
+# make phases together, 12 phases
 spikes_mat = {}
 for deltaf in df_map.keys():
     for rep in df_map[deltaf]:
@@ -27,15 +29,23 @@ for deltaf in df_map.keys():
             if deltaf == '-50Hz' and phase == (9, 0.54):
                 spikes_mat[deltaf, rep, phase] = spikes_one_chirp
 
-#embed()
-#exit()
-
 plot_spikes = spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)]
-#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
+
+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)
+
 fig, ax = plt.subplots()
 ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k')
-#ax.plot(hist_data[1][:-1], hist_data[0])
+ax.plot(time_vec, smoothed_spikes)
 plt.show()
 
 #embed()
 #exit()
+#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
+#ax.plot(hist_data[1][:-1], hist_data[0])