parameters: (self, xvals)
return_statement: return self.getParameterValue('A') * eisf
docstring: r"""Calculate the intensities
  
          Parameters
          ----------
          xvals : sequence of floats
            The domain where to evaluate the function
          jacobian: 2D array
            partial derivative of the function with respect to the fitting
            parameters evaluated at the domain.
  
          Returns
          -------
          numpy.ndarray
              Function values
docstring_summary: r"""Calculate the intensities
docstring_tokens: r Calculate the intensities
function: def function1D(self, xvals):
          r"""Calculate the intensities
  
          Parameters
          ----------
          xvals : sequence of floats
            The domain where to evaluate the function
          jacobian: 2D array
            partial derivative of the function with respect to the fitting
            parameters evaluated at the domain.
  
          Returns
          -------
          numpy.ndarray
              Function values
          """
          r = self.getParameterValue('Rmin')
          R = self.getParameterValue('Rmax')
          zs = np.outer(np.linspace(r, R, self._M), np.asarray(xvals))
          eisf = np.mean(np.square(3 * self.vecbessel(zs)), axis=0)
          return self.getParameterValue('A') * eisf
function_tokens: def function1D ( self , xvals ) : r = self . getParameterValue ( 'Rmin' ) R = self . getParameterValue ( 'Rmax' ) zs = np . outer ( np . linspace ( r , R , self . _M ) , np . asarray ( xvals ) ) eisf = np . mean ( np . square ( 3 * self . vecbessel ( zs ) ) , axis = 0 ) return self . getParameterValue ( 'A' ) * eisf