diff --git a/py/brain2_playground.py b/py/brain2_playground.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/py/brain2_playground.py

diff --git a/py/brain2_playground.py b/py/brain2_playground.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/py/brain2_playground.py
diff --git a/py/poisson_generator.py b/py/poisson_generator.py
index 48db27d..cbba848 100644
--- a/py/poisson_generator.py
+++ b/py/poisson_generator.py
@@ -2,6 +2,7 @@
 import random
 from math import log
 import matplotlib.pyplot as plt
+import numpy as np
 
 class PoissonProcess():
   def __init__(self, **kwargs):
@@ -25,13 +26,33 @@
 print(poi)
 
 #matplotlib: x=index, y=poi[index]
-plt.plot(range(100),poi)
+plt.eventplot(poi)
 
 
 #2 (points)
-import numpy as np
-poi = np.random.poisson(5,100)
-plt.plot(poi)
+poi = np.random.poisson(5,size=100)
+print(f"avergae is {np.average(poi)}")
+
+#poi = [i-j for i,j in zip(poi[:-1],poi[1:])]
+
+poi2 = [np.sum(poi[1:i]) for i in range(len(poi))]
+print(poi2)
+#poi = np.diff(poi) # alternative numpy solution for inter-interval durations
+#plt.plot(poi2)
 
 plt.show()
 #%%
+
+import numpy as np
+from scipy.special import factorial
+
+def events_prob(events_per_unit_time, total_time, num_of_events):
+  # poisson dist
+  lam = events_per_unit_time * total_time
+  return np.exp(-lam) * np.power(lam, num_of_events) * factorial(num_of_events)
+
+
+rate = 5
+ks = np.arange(20)
+ps = events_prob(5,20,ks)
+print(np.argmax(ps), np.max(ps))
\ No newline at end of file