diff --git a/covidrisk.py b/covidrisk.py
old mode 100644
new mode 100755
index 0123bf3..27371e7
--- a/covidrisk.py
+++ b/covidrisk.py
@@ -2,6 +2,11 @@
 
 import matplotlib.pyplot as plt
 
+def get_data():
+    n_14_day_tests = 7366.1
+    n_14_day_positive_tests =  (18.2 + 7.1) / 100 * n_14_day_tests
+    return (n_14_day_tests, n_14_day_positive_tests)
+
 def p_meet_positive(groupsize:int):
     '''
         Naive approach, only works if each individual gets tested.
@@ -12,12 +17,12 @@ def p_meet_positive(groupsize:int):
 
     return (1 - ( 1 - p_positive )**groupsize) * 100
 
-def p_meet_positive_bayes(groupsize:int):
+def p_meet_positive_bayes(groupsize:int, n_positive_tests:float, n_tests:float):
     '''
         Bayesian approach
     '''
-    n_positive_tests = 1863.6     # number of positive tests in the last 14 days
-    n_tests = 7366.1                # total number of tests in the last 14 days
+    #n_positive_tests = n_tests_pos  # number of positive tests in the last 14 days
+    #n_tests = n_tests_tot           # total number of tests in the last 14 days
     n_population = 100000           # sample size -> per 100k for switzerland
     p_test_positive =  0.3          # probability of a positive person getting tested (0.5 is optimistic...)
 
@@ -35,9 +40,13 @@ def plot_data(data):
     plt.grid(linestyle='--', linewidth='0.1')
     plt.show()
 
-data = []
-for i in range(1, 70):
-    data.append(p_meet_positive_bayes(i))
-    print(f'The chance of meeting a positive in a group of {i} is: {p_meet_positive_bayes(i)}%')
+if __name__ == "__main__":
 
-plot_data(data)
+    tests, pos = get_data()
+
+    data = []
+    for i in range(1, 70):
+        data.append(p_meet_positive_bayes(i, tests, pos))
+        print(f'The chance of meeting a positive in a group of {i} is: {p_meet_positive_bayes(i, tests, pos)}%')
+
+    plot_data(data)