diff --git a/Baseline.py b/Baseline.py
index 53f5455..b13a089 100644
--- a/Baseline.py
+++ b/Baseline.py
@@ -27,18 +27,18 @@ class Baseline:
     def get_coefficient_of_variation(self):
         raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
 
-    def get_burstiness(self):
+    def get_burstiness(self, test_plot=False):
         isis = np.array(self.get_interspike_intervals()) * 1000  # change unit to ms
 
         if len(isis) <= 10:
             return 0
 
         step = 0.1
-        bins = np.arange(0, min(isis) * 3, step)
+        bins = np.arange(0, max(isis), step)
         num_spikes_per_bin = np.zeros(bins.shape)
 
         for i, bin in enumerate(bins):
-            num_of_spikes = np.sum(isis[(isis >= bin) & (isis < bin + step)])
+            num_of_spikes = len((isis[(isis >= bin) & (isis < bin + step)]))
             num_spikes_per_bin[i] = num_of_spikes
 
         max_found = -1
@@ -58,19 +58,25 @@ class Baseline:
             else:
 
                 if num_spikes_per_bin[i + 1] > num:
-                    end_of_peak = i +1
+                    end_of_peak = i + 1
                     break
 
         burstiness = sum(num_spikes_per_bin[:end_of_peak]) / len(isis)
 
-        # bins = np.arange(0, max(isis) * 1.01, 0.1)
-        #
-        # plt.title('Baseline ISIs - burstiness {:.2f}'.format(burstiness))
-        # plt.xlabel('ISI in ms')
-        # plt.ylabel('Count')
-        # plt.hist(isis, bins=bins)
-        # plt.plot((0.5*step, bins[end_of_peak-1] + 0.5*step,), (0, 0), 'o')
-        # plt.show()
+        if test_plot:
+            print("burst peak:", sum(num_spikes_per_bin[:end_of_peak]))
+            print("sum num per bin:", sum(num_spikes_per_bin))
+            print("len isis:", len(isis))
+            bins = np.arange(0, max(isis) * 1.01, 0.1)
+
+            plt.title('Baseline ISIs - burstiness {:.2f}'.format(burstiness))
+            plt.xlabel('ISI in ms')
+            plt.ylabel('Count')
+            plt.hist(isis, bins=bins)
+
+            plt.plot([step*(i+0.5) for i in range(len(num_spikes_per_bin))], num_spikes_per_bin, 'o', alpha=0.5)
+            plt.plot((0.5 * step, bins[end_of_peak - 1] + 0.5 * step,), (0, 0), 'o')
+            plt.show()
 
         return burstiness