original_docstring: 
      Returns the data vector `D` from a blurred mapping matrix `f` and the 1D image `d` and 1D noise-map $\sigma$`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      blurred_mapping_matrix
          The matrix representing the blurred 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
  
  blurred_mapping_matrix
  The matrix representing the blurred 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 blurred_mapping_matrix The matrix representing the blurred 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_blurred_mapping_matrix_from(
      blurred_mapping_matrix: np.ndarray, image: np.ndarray, noise_map: np.ndarray
  ) -> np.ndarray:
      """
      Returns the data vector `D` from a blurred mapping matrix `f` and the 1D image `d` and 1D noise-map $\sigma$`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      blurred_mapping_matrix
          The matrix representing the blurred 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_shape = blurred_mapping_matrix.shape
  
      data_vector = np.zeros(data_shape[1])
  
      for data_index in range(data_shape[0]):
          for pix_index in range(data_shape[1]):
              data_vector[pix_index] += (
                  image[data_index]
                  * blurred_mapping_matrix[data_index, pix_index]
                  / (noise_map[data_index] ** 2.0)
              )
  
      return data_vector
code_tokens: def data_vector_via_blurred_mapping_matrix_from ( blurred_mapping_matrix : np . ndarray , image : np . ndarray , noise_map : np . ndarray ) -> np . ndarray : data_shape = blurred_mapping_matrix . shape data_vector = np . zeros ( data_shape [ 1 ] ) for data_index in range ( data_shape [ 0 ] ) : for pix_index in range ( data_shape [ 1 ] ) : data_vector [ pix_index ] += ( image [ data_index ] * blurred_mapping_matrix [ data_index , pix_index ] / ( noise_map [ data_index ] ** 2.0 ) ) return data_vector
short_docstring: Returns the data vector `D` from a blurred 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 blurred 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 blurred mapping matrix `f` and the 1D image `d` and 1D noise-map $\sigma$`
      (see Warren & Dye 2003).
  
      Parameters
      -----------
      blurred_mapping_matrix
          The matrix representing the blurred 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.
      """