old_contents: from __future__ import division
  
  import math
  
  import numpy as np
  
  
  # Non-monotonic Ishigami Function (3 parameters)
  # Using Saltelli sampling with a sample size of ~1000
  # the expected first-order indices would be:
  # x1: 0.3139
  # x2: 0.4424
  # x3: 0.0
  def evaluate(values):
      Y = np.zeros([values.shape[0]])
      A = 7
      B = 0.1
  
      for i, X in enumerate(values):
          Y[i] = math.sin(X[0]) + A * math.pow(math.sin(X[1]), 2) + \
              B * math.pow(X[2], 4) * math.sin(X[0])
  
      return Y
  
new_contents: from __future__ import division
  
  import numpy as np
  
  
  # Non-monotonic Ishigami Function (3 parameters)
  # Using Saltelli sampling with a sample size of ~1000
  # the expected first-order indices would be:
  # x1: 0.3139
  # x2: 0.4424
  # x3: 0.0
  def evaluate(values):
      Y = np.zeros(values.shape[0])
      A = 7
      B = 0.1
  
      # X = values
      # Y = np.sin(X[:, 0]) + A * np.power(np.sin(X[:, 1]), 2) + \
      #         B * np.power(X[:, 2], 4) * np.sin(X[:, 0])
      for i, X in enumerate(values):
          Y[i] = np.sin(X[0]) + A * np.power(np.sin(X[1]), 2) + \
              B * np.power(X[2], 4) * np.sin(X[0])
  
      return Y
  
subject: Use numpy functions to allow future vectorization