code: def parseNoise(rawArray): 
      '''
      Function returns indices that contain non-noisy genes as an integer array
      :param rawArray: numpy ndarray of data set
      :return nnGenes : numpy ndarray of non-noise gene indices
      '''
      nnGenes=[] 
      for i in range(0,(rawArray.shape[1])): #Checks all genes
          count0=np.asarray(np.where(rawArray[:,i]==0))
          count1=np.asarray(np.where(rawArray[:,i]==1))
          if ((count1.shape[1]+count0.shape[1])<rawArray.shape[0]):
              nnGenes=np.append(nnGenes,i)
      return nnGenes.astype(intf parseNoise(rawArray): 
      '''
      Function returns indices that contain non-noisy genes as an integer array
      :param rawArray: numpy ndarray of data set
      :return nnGenes : numpy ndarray of non-noise gene indices
      '''
      nnGenes=[] 
      for i in range(0,(rawArray.shape[1])): #Checks all genes
          count0=np.asarray(np.where(rawArray[:,i]==0))
          count1=np.asarray(np.where(rawArray[:,i]==1))
          if ((count1.shape[1]+count0.shape[1])<rawArray.shape[0]):
              nnGenes=np.append(nnGenes,i)
      return nnGenes.astype(int)
docs: Function returns indices that contain non-noisy genes as an integer array
      :param rawArray: numpy ndarray of data set
      :return nnGenes : numpy ndarray of non-noise gene indices