Add comments to main.py

main.py

@@ -13,6 +13,8 @@ def f(x):
     return 1 / x
 
 #%%
+# Generate a list of n normal distributed x-values and apply f() on each
+# x-value to obtain the list of y-values.
 n = 100
 mu = 2
 sigma = 1
@@ -22,6 +24,8 @@ y_list = [f(x) for x in x_list]
 y_generator = jackknife.get_sample_generator(y_list)
 
 #%%
+# Do a Jackknife estimation with the x-values and f() and print the
+# results
 output = jackknife.do_jackknife_estimation(x_generator, f)
 x_mean = sum(i for i in x_list) / n
 print("{}{}".format("f(mean(x))             = ", f(x_mean)))
@@ -30,6 +34,9 @@ print("{}{}".format("variance(y')           = ", output[1]))
 print("{}{}".format("standard deviation(y') = ", output[2]))
 
 #%%
+# Do a Jackknife estimation with the y-values and the identity function
+# and print the results. This means that statistics are done on a
+# transformed sample set y, that is not bias corrected.
 output = jackknife.do_jackknife_estimation(y_generator, lambda x: x)
 print("{}{}".format("mean(y)               = ", output[0]))
 print("{}{}".format("variance(y)           = ", output[1]))