text: #!/usr/local/bin/python
  
  #-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
  #
  # Name:     automate_goodness_of_fit.py 
  #
  # Author:   Constantin Weisser (weisser@mit.edu)
  #
  # Purpose:  This is a python script to automate analysing data files
  #
  #-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# 
  
  from __future__ import print_function
  import os
  import math
  
  number_of_files=100
  
  name           = "dalitz"
  sample1_name   = "seed_000_0"
  # You might have to change this to "seed_100_0"
  sample2_name   = "seed_200_1"
  shuffling_seed = 100
  # You can chose from dectree, adaboost svm neuralnet softmax keras miranda etest or any combination of these by connecting them with underscores
  classifier_name= "keras"
  
  
  #with open("test_statistic_distributions/test_statistics."+name+"_"+sample1_name+"_"+sample2_name, "w") as test_statistics_file:
  	#test_statistics_file.write("CvM U \tCvM T \tKS D \tKS p\n")
  
  for i in range(1,number_of_files+1):
  	# python -O sets __debug__ to 0
  	#os.system("touch test_statistic_distributions/test_statistics.{0}_{1}_{2}".format(name,sample1_name,sample2_name))
  	#os.system("python -O goodness_of_fit.py gaussian_samples/gauss_data/data.2Dgauss_1000_0.5_0.1_0.5_0.1_{0}.0.txt gaussian_samples/gauss_data/data.2Dgauss_1000_0.48_0.1_0.48_0.1_{0}.0.txt {1} {2} {3} {4}".format(i,name,sample1_name,sample2_name,shuffling_seed))
  	#You might want to change data.2{1}.1.txt to data.1{1}.0.txt
  	os.system("python -O $MLToolsDir/Dalitz/goodness_of_fit.py ../../dpmodel/data/data.{0}.0.txt ../../dpmodel/data/data.2{1}.1.txt {2} {3} {4} {5} {6}".format(i-1,str(i-1).zfill(2),name,sample1_name,sample2_name,shuffling_seed,classifier_name))
  
  
  
  
  