original_docstring: 
      Returns the data vector `D` from a transformed mapping matrix `f` and the 1D image `d` and 1D noise-map `sigma`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      transformed_mapping_matrix
          The matrix representing the transformed mappings between sub-grid pixels and pixelization pixels.
      image
          Flattened 1D array of the observed image the inversion is fitting.
      noise_map
          Flattened 1D array of the noise-map used by the inversion during the fit.
      
docstring: 
  
  Parameters
  
  transformed_mapping_matrix
  The matrix representing the transformed mappings between sub-grid pixels and pixelization pixels.
  image
  Flattened 1D array of the observed image the inversion is fitting.
  noise_map
  Flattened 1D array of the noise-map used by the inversion during the fit.
docstring_tokens: Parameters transformed_mapping_matrix The matrix representing the transformed mappings between sub - grid pixels and pixelization pixels . image Flattened 1D array of the observed image the inversion is fitting . noise_map Flattened 1D array of the noise - map used by the inversion during the fit .
code: def data_vector_via_transformed_mapping_matrix_from(
      transformed_mapping_matrix: np.ndarray,
      visibilities: np.ndarray,
      noise_map: np.ndarray,
  ) -> np.ndarray:
      """
      Returns the data vector `D` from a transformed mapping matrix `f` and the 1D image `d` and 1D noise-map `sigma`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      transformed_mapping_matrix
          The matrix representing the transformed mappings between sub-grid pixels and pixelization pixels.
      image
          Flattened 1D array of the observed image the inversion is fitting.
      noise_map
          Flattened 1D array of the noise-map used by the inversion during the fit.
      """
  
      data_vector = np.zeros(transformed_mapping_matrix.shape[1])
  
      visibilities_real = visibilities.real
      visibilities_imag = visibilities.imag
      transformed_mapping_matrix_real = transformed_mapping_matrix.real
      transformed_mapping_matrix_imag = transformed_mapping_matrix.imag
      noise_map_real = noise_map.real
      noise_map_imag = noise_map.imag
  
      for vis_1d_index in range(transformed_mapping_matrix.shape[0]):
          for pix_1d_index in range(transformed_mapping_matrix.shape[1]):
              real_value = (
                  visibilities_real[vis_1d_index]
                  * transformed_mapping_matrix_real[vis_1d_index, pix_1d_index]
                  / (noise_map_real[vis_1d_index] ** 2.0)
              )
              imag_value = (
                  visibilities_imag[vis_1d_index]
                  * transformed_mapping_matrix_imag[vis_1d_index, pix_1d_index]
                  / (noise_map_imag[vis_1d_index] ** 2.0)
              )
              data_vector[pix_1d_index] += real_value + imag_value
  
      return data_vector
code_tokens: def data_vector_via_transformed_mapping_matrix_from ( transformed_mapping_matrix : np . ndarray , visibilities : np . ndarray , noise_map : np . ndarray , ) -> np . ndarray : data_vector = np . zeros ( transformed_mapping_matrix . shape [ 1 ] ) visibilities_real = visibilities . real visibilities_imag = visibilities . imag transformed_mapping_matrix_real = transformed_mapping_matrix . real transformed_mapping_matrix_imag = transformed_mapping_matrix . imag noise_map_real = noise_map . real noise_map_imag = noise_map . imag for vis_1d_index in range ( transformed_mapping_matrix . shape [ 0 ] ) : for pix_1d_index in range ( transformed_mapping_matrix . shape [ 1 ] ) : real_value = ( visibilities_real [ vis_1d_index ] * transformed_mapping_matrix_real [ vis_1d_index , pix_1d_index ] / ( noise_map_real [ vis_1d_index ] ** 2.0 ) ) imag_value = ( visibilities_imag [ vis_1d_index ] * transformed_mapping_matrix_imag [ vis_1d_index , pix_1d_index ] / ( noise_map_imag [ vis_1d_index ] ** 2.0 ) ) data_vector [ pix_1d_index ] += real_value + imag_value return data_vector
short_docstring: Returns the data vector `D` from a transformed mapping matrix `f` and the 1D image `d` and 1D noise-map `sigma`
  (see Warren & Dye 2003).
short_docstring_tokens: Returns the data vector ` D ` from a transformed mapping matrix ` f ` and the 1D image ` d ` and 1D noise - map ` sigma ` ( see Warren & Dye 2003 ) .
comment: """
      Returns the data vector `D` from a transformed mapping matrix `f` and the 1D image `d` and 1D noise-map `sigma`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      transformed_mapping_matrix
          The matrix representing the transformed mappings between sub-grid pixels and pixelization pixels.
      image
          Flattened 1D array of the observed image the inversion is fitting.
      noise_map
          Flattened 1D array of the noise-map used by the inversion during the fit.
      """