new_contents: #!/usr/bin/python
  
  ###############################################################
  # Data preprocessing and similarity computation
  # Please see README.md for more information
  ###############################################################
  
  import sys
  import numpy as np
  
  
  def DataPreprocessingAndSimilarityMatrixComputation(raw_data_matrix, simType = "Pearson"):
      # raw_data_matrix is a numpy array with dimension N*d,
      # where N is the number of assays and d is the dimension for each assay.
      
      raw_data_matrix = np.arcsinh(raw_data_matrix) # preprocess the data by asinh function
      if simType == "Pearson":
          corr_mat = np.corrcoef(raw_data_matrix) # compute the pearson correlation among assays
      
      return corr_mat
  
  
  def main(argv):
      
      ########################################################
      # Load in your own genomics data.
      # Here, we use synthetic data for illustration purpose.
      ########################################################
      
      N = 100
      d = 1000
      rawData = np.random.rand(N,d)
      outputCorr = DataPreprocessingAndSimilarityMatrixComputation(rawData, simType="Pearson")
      #print (outputCorr)
  
  
  
  
  if __name__ == "__main__":
      main(sys.argv[1:])
  
subject: Create data_preprocessing_and_similarity_compute.py