diff --git a/planning.py b/planning.py
index 4c02c3d72..e31c8b3a3 100644
--- a/planning.py
+++ b/planning.py
@@ -524,7 +524,7 @@ def goal_test(kb, goals):
             if solution:
                 return solution
         graphplan.graph.expand_graph()
-        if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff():
+        if len(graphplan.graph.levels) >=2 and graphplan.check_leveloff():
             return None
 
 
diff --git a/probability.py b/probability.py
index a9f65fbb0..9b732edd7 100644
--- a/probability.py
+++ b/probability.py
@@ -653,6 +653,7 @@ def particle_filtering(e, N, HMM):
 # _________________________________________________________________________
 ## TODO: Implement continuous map for MonteCarlo similar to Fig25.10 from the book
 
+
 class MCLmap:
     """Map which provides probability distributions and sensor readings.
     Consists of discrete cells which are either an obstacle or empty"""
@@ -679,7 +680,7 @@ def ray_cast(self, sensor_num, kin_state):
         #  0
         # 3R1
         #  2
-        delta = ((sensor_num%2 == 0)*(sensor_num - 1), (sensor_num%2 == 1)*(2 - sensor_num))
+        delta = ((sensor_num % 2 == 0)*(sensor_num - 1), (sensor_num % 2 == 1)*(2 - sensor_num))
         # sensor direction changes based on orientation
         for _ in range(orient):
             delta = (delta[1], -delta[0])