Make the number of samples an input parameter

jackknife.py

 import numpy as np
 
 
-def do_jackknife_estimation(input_sample_generator, transformation_function):
+def do_jackknife_estimation(input_sample_generator, n_samples,
+                            transformation_function):
     """Do a jackknife estimation for the transformed sample.
 
     Estimate and return the sample mean, variance and standard deviation
@@ -13,9 +14,7 @@ def do_jackknife_estimation(input_sample_generator, transformation_function):
     """
     gen = input_sample_generator
     f = transformation_function
-
-    samples = gen()
-    n_samples = sum(1 for i in samples)
+    
     samples = gen()
     input_mean = sum(i for i in samples) / n_samples
     transformed_mean = f(input_mean)

main.py

@@ -27,7 +27,7 @@ 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)
+output = jackknife.do_jackknife_estimation(x_generator, n, f)
 x_mean = sum(i for i in x_list) / n
 print("{}{}".format("f(mean(x))             = ", f(x_mean)))
 print("{}{}".format("mean(y')               = ", output[0]))
@@ -38,7 +38,7 @@ 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)
+output = jackknife.do_jackknife_estimation(y_generator, n, lambda x: x)
 print("{}{}".format("mean(y)               = ", output[0]))
 print("{}{}".format("variance(y)           = ", output[1]))
 print("{}{}".format("standard deviation(y) = ", output[2]))
@@ -79,7 +79,7 @@ y_generator = jackknife.get_sample_generator(y_list)
 
 
 #%%
-output = jackknife.do_jackknife_estimation(x_generator, f2)
+output = jackknife.do_jackknife_estimation(x_generator, n2, f2)
 x_mean = sum(i for i in m_2_and_4) / n2
 print("{}{}".format("f2(mean(x))            = ", f2(x_mean)))
 print("{}{}".format("mean(y')               = ", output[0]))