text: import os
  import csv
  import matplotlib.pyplot as plt
  import numpy as np
  import opendp.smartnoise.core as sn
  import pandas as pd
  epsilon = pd.read_pickle('epsilon.pkl')
  
  #data_path = os.path.join("adult.csv")
  
  NUM_TRIALS = 5
  attribute = 'age'
  
  
  
  if attribute == 'age':
      #data_path = os.path.join("E:\\MS_Thesis\\publication_stuff\\real_datasets\\adult.csv")
      data_path = "E:\\MS_Thesis\\publication_stuff\\real_datasets\\adult.csv"
      df = pd.read_csv(data_path)
      age = df.iloc[:, 0]
      maximum = 100.0
      minimum = 0.0
      dataset_rows = len(age)
      actual_count = len(age)
      actual_sum = np.sum(age)
      actual_mean = np.mean(age)
      actual_var = np.var(age)
      i=1
  
  if attribute == 'hrs':
      data_path = os.path.join("E:\\MS_Thesis\\publication_stuff\\real_datasets\\adult.csv")
      df = pd.read_csv(data_path)
      df_hrs = df.iloc[:, 12]
      hrs = np.clip(df_hrs, a_max=80, a_min=None)
      maximum = 80.0
      minimum = 0.0
      dataset_rows = len(hrs)
      actual_count = len(hrs)
      actual_sum = np.sum(hrs)
      actual_mean = np.mean(hrs)
      actual_var = np.var(hrs)
      i=2
  
  if attribute == 'absences':
      data_path = os.path.join("E:\\MS_Thesis\\publication_stuff\\real_datasets\\absences_education.csv")
      df = pd.read_csv(data_path)
      absences = df.iloc[:, 0]
      maximum = 93.0
      minimum = 0.0
      dataset_rows = len(absences)
      actual_count = len(absences)
      actual_sum = np.sum(absences)
      actual_mean = np.mean(absences)
      actual_var = np.var(absences)
      i=3
  
  if attribute == 'grade':
      data_path = os.path.join("E:\\MS_Thesis\\publication_stuff\\real_datasets\\grade_education.csv")
      df = pd.read_csv(data_path)
      grade = df.iloc[:, 0]
      maximum = 20.0
      minimum = 0.0
      dataset_rows = len(grade)
      actual_count = len(grade)
      actual_sum = np.sum(grade)
      actual_mean = np.mean(grade)
      actual_var = np.var(grade)
      i=4
  
  
  ###############
  ### SUM #######
  ###############
  scaled_error_list_sum = []
  relative_error_list_sum = []
  for eps in epsilon:
      print('calculating results for epsilon = {}'.format(eps))
      with sn.Analysis(neighboring='substitute') as analysis:
          var_name = [attribute]
          data = sn.Dataset(path=data_path, column_names=var_name)
          d = sn.resize(sn.to_float(data[attribute]), data_lower=minimum, data_upper=maximum, number_rows=dataset_rows)
          samples = []
          for itr in range(NUM_TRIALS):
              samples.append(sn.dp_sum(data=d, privacy_usage={'epsilon': eps}).value)
          analysis.release()
          print(np.mean(samples))
          print(actual_sum)
  
          scaled_errors_per_itr = []
          relative_errors_per_itr = []
          for sam in samples:
              scaled_errors_per_itr.append(abs((actual_sum - sam) / dataset_rows))
              relative_errors_per_itr.append(abs((actual_sum - sam) / actual_sum))
          scaled_error_list_sum.append(np.std(scaled_errors_per_itr))
          relative_error_list_sum.append(np.mean(relative_errors_per_itr))
          print(relative_error_list_sum)
          print(scaled_error_list_sum)
  
  #sum
  guestFile1 = pd.DataFrame(scaled_error_list_sum)
  guestFile2 = pd.DataFrame(relative_error_list_sum)
  
  guestFile1.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\sum\\results_dataset_{i}\\std_scaled_error\\DP_std_scaled_error.csv".format(i=i), index=False)
  guestFile2.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\sum\\results_dataset_{i}\\mean_relative_error\\DP_mean_relative_error.csv".format(i=i), index=False)
  
  
  ###############
  ### MEAN ######
  ###############
  scaled_error_list_mean = []
  relative_error_list_mean = []
  for eps in epsilon:
      print('calculating results for epsilon = {}'.format(eps))
      with sn.Analysis(neighboring='substitute') as analysis:
          var_name = [attribute]
          data = sn.Dataset(path=data_path, column_names=var_name)
          d = sn.resize(sn.to_float(data[attribute]), data_lower=minimum, data_upper=maximum, number_rows=dataset_rows)
          samples = []
          for itr in range(NUM_TRIALS):
              samples.append(sn.dp_mean(data=d, privacy_usage={'epsilon': eps}).value)
          analysis.release()
          print(np.mean(samples))
          print(actual_mean)
  
          scaled_errors_per_itr = []
          relative_errors_per_itr = []
          for sam in samples:
              scaled_errors_per_itr.append(abs((actual_mean - sam) / dataset_rows))
              relative_errors_per_itr.append(abs((actual_mean - sam) / actual_mean))
          scaled_error_list_mean.append(np.std(scaled_errors_per_itr))
          relative_error_list_mean.append(np.mean(relative_errors_per_itr))
          print(relative_error_list_mean)
          print(scaled_error_list_mean)
  
  #mean
  guestFile3 = pd.DataFrame(scaled_error_list_mean)
  guestFile4 = pd.DataFrame(relative_error_list_mean)
  
  guestFile3.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\mean\\results_dataset_{i}\\std_scaled_error\\DP_std_scaled_error.csv".format(i=i), index=False)
  guestFile4.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\mean\\results_dataset_{i}\\mean_relative_error\\DP_mean_relative_error.csv".format(i=i), index=False)
  
  
  ###############
  ### Var #######
  ###############
  scaled_error_list_var = []
  relative_error_list_var = []
  for eps in epsilon:
      print('calculating results for epsilon = {}'.format(eps))
      with sn.Analysis(neighboring='substitute') as analysis:
          var_name = [attribute]
          data = sn.Dataset(path=data_path, column_names=var_name)
          d = sn.resize(sn.to_float(data[attribute]), data_lower=minimum, data_upper=maximum, number_rows=dataset_rows)
          samples = []
          for itr in range(NUM_TRIALS):
              samples.append(sn.dp_variance(data=d, privacy_usage={'epsilon': eps}).value)
          analysis.release()
          print(np.mean(samples))
          print(actual_var)
  
          scaled_errors_per_itr = []
          relative_errors_per_itr = []
          for sam in samples:
              scaled_errors_per_itr.append(abs((actual_var - sam) / dataset_rows))
              relative_errors_per_itr.append(abs((actual_var - sam) / actual_var))
          scaled_error_list_var.append(np.std(scaled_errors_per_itr))
          relative_error_list_var.append(np.mean(relative_errors_per_itr))
          print(relative_error_list_var)
          print(scaled_error_list_var)
  
  #var
  guestFile5 = pd.DataFrame(scaled_error_list_var)
  guestFile6 = pd.DataFrame(relative_error_list_var)
  
  guestFile5.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\var\\results_dataset_{i}\\std_scaled_error\\DP_std_scaled_error.csv".format(i=i), index=False)
  guestFile6.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\var\\results_dataset_{i}\\mean_relative_error\\DP_mean_relative_error.csv".format(i=i), index=False)
  
  ###############
  ### Count #####
  ###############
  scaled_error_list_count = []
  relative_error_list_count = []
  for eps in epsilon:
      print('calculating results for epsilon = {}'.format(eps))
      with sn.Analysis(neighboring='substitute') as analysis:
          var_name = [attribute]
          data = sn.Dataset(path=data_path, column_names=var_name)
          d = sn.to_float(data[attribute])
          samples = []
          for itr in range(NUM_TRIALS):
              samples.append(sn.dp_count(data=d, privacy_usage={'epsilon': eps}).value)
          analysis.release()
          print(np.mean(samples))
          print(actual_count)
  
          scaled_errors_per_itr = []
          relative_errors_per_itr = []
          for sam in samples:
              scaled_errors_per_itr.append(abs((actual_count - sam) / dataset_rows))
              relative_errors_per_itr.append(abs((actual_count - sam) / actual_count))
          scaled_error_list_count.append(np.std(scaled_errors_per_itr))
          relative_error_list_count.append(np.mean(relative_errors_per_itr))
          print(relative_error_list_count)
          print(scaled_error_list_count)
  
  #count
  guestFile7 = pd.DataFrame(scaled_error_list_count)
  guestFile8 = pd.DataFrame(relative_error_list_count)
  
  guestFile7.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\var\\results_dataset_{i}\\std_scaled_error\\DP_std_scaled_error.csv".format(i=i), index=False)
  guestFile8.to_csv("E:\\MS_Thesis\\publication_stuff\\refactored_code\\micro\\smartnoise\\var\\results_dataset_{i}\\mean_relative_error\\DP_mean_relative_error.csv".format(i=i), index=False)
  