blablub

code/spikes_analysis.py

@@ -8,13 +8,22 @@ from IPython import embed
 # define sampling rate and data path
 sampling_rate = 40 #kHz
 data_dir = "../data"
-dataset = "2018-11-09-ad-invivo-1"
+dataset = "2018-11-14-aa-invivo-1"
+#data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",\
+#  "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", \
+# "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", \
+# "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", \
+# "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",\
+# "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", \
+# "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
+
 # parameters for binning, smoothing and plotting
-cut_window = 60
+#cut_window = 60
+cut_window = 20
 chirp_size = 14 #ms
 neuronal_delay = 5 #ms
 chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
-chirp_end = int((chirp_size/2+neuronal_delay+cut_window+1)*sampling_rate)
+chirp_end = int((chirp_size/2+neuronal_delay+cut_window)*sampling_rate)
 num_bin = 12
 window = 1 #ms
 time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
@@ -35,6 +44,8 @@ cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1)
 # make dictionaries for spiketimes
 df_phase_time = {}
 df_phase_binary = {}
+#embed()
+#exit()
 
 # iterate over delta f, repetition, phases and a single chirp
 for deltaf in df_map.keys():
@@ -46,9 +57,9 @@ for deltaf in df_map.keys():
                 # check the phase
                 if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
 
-                    # get spikes between 50 ms befor and after the chirp
+                    # get spikes between 50 ms before and after the chirp
                     spikes_to_cut = np.asarray(spikes[rep][phase])
-                    spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
+                    spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
                     spikes_idx = np.round(spikes_cut*sampling_rate)
                     # also save as binary, 0 no spike, 1 spike
                     binary_spikes = np.isin(cut_range, spikes_idx)*1
@@ -95,7 +106,7 @@ for df in df_phase_time.keys():
         rbs = []
         for i, train in enumerate(train_chirp):
             for j, train2 in enumerate(train_chirp):
-                if np.array_equal(train, train2):
+                if i >= j:
                     continue
                 else:
                     rc, _ = ss.pearsonr(train, train2)
@@ -105,7 +116,8 @@ for df in df_phase_time.keys():
 
         r_train_chirp = np.mean(rcs)
         r_train_beat = np.mean(rbs)
-
+        embed()
+        exit()
         csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
         csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
 
@@ -116,6 +128,9 @@ for df in df_phase_time.keys():
         csi_trains[df].append(csi_train)
         csi_rates[df].append(csi_rate)
 
+        #csi_trains[df].append(abs(csi_train))
+        #csi_rates[df].append(abs(csi_rate))
+
         '''
         # plot
         plot_trials = df_phase_time[df][phase]
@@ -138,9 +153,23 @@ for df in df_phase_time.keys():
         ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
         plt.show()
         '''
-embed()
-exit()
-for i, k in enumerate(sorted(csi_rates.keys())):
-    print(csi_rates[k])
-
 
+fig, ax = plt.subplots()
+for i, k in enumerate(sorted(csi_rates.keys())):
+    ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
+    #ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
+ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
+#ax.set_xticklabels(sorted(csi_rates.keys()))
+fig.tight_layout()
+plt.show()
+
+fig, ax = plt.subplots()
+for i, k in enumerate(sorted(csi_trains.keys())):
+    ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
+    #ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
+ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
+#ax.set_xticklabels(sorted(csi_trains.keys()))
+fig.tight_layout()
+plt.show()