original_docstring: 
              Generate a synthetic spectrum with Gaussians with the
              specified parameters, on a given x axis.
  
              GaussianModel is used here to remain internally consistent
              with the rest of the code.
  
               x: array of x values to evaluate Gaussians on
               centers: array of Gaussian centers
               widths: array of Gaussian widths
               amplitudes: array of Gaussian amplitudes
               fake: bool indicating whether false intensities are used for the simulation
              :return y: array of y values
          
docstring: Generate a synthetic spectrum with Gaussians with the
  specified parameters, on a given x axis.
  
  GaussianModel is used here to remain internally consistent
  with the rest of the code.
  
  array of x values to evaluate Gaussians on
  centers: array of Gaussian centers
  widths: array of Gaussian widths
  amplitudes: array of Gaussian amplitudes
  fake: bool indicating whether false intensities are used for the simulation
docstring_tokens: Generate a synthetic spectrum with Gaussians with the specified parameters on a given x axis . GaussianModel is used here to remain internally consistent with the rest of the code . array of x values to evaluate Gaussians on centers : array of Gaussian centers widths : array of Gaussian widths amplitudes : array of Gaussian amplitudes fake : bool indicating whether false intensities are used for the simulation
code: def simulate_spectrum(
          self,
          x: np.ndarray,
          centers: List[float],
          widths: List[float],
          amplitudes: List[float],
          fake=False,
      ):
          """
              Generate a synthetic spectrum with Gaussians with the
              specified parameters, on a given x axis.
  
              GaussianModel is used here to remain internally consistent
              with the rest of the code.
  
               x: array of x values to evaluate Gaussians on
               centers: array of Gaussian centers
               widths: array of Gaussian widths
               amplitudes: array of Gaussian amplitudes
               fake: bool indicating whether false intensities are used for the simulation
              :return y: array of y values
          """
          y = np.zeros(len(x))
          model = GaussianModel()
          for c, w, a in zip(centers, widths, amplitudes):
              if fake is True:
                  scaling = a
              else:
                  scaling = 1.0
              y += scaling * model.eval(x=x, center=c, sigma=w, amplitude=a)
          return y
code_tokens: def simulate_spectrum ( self , x : np . ndarray , centers : List [ float ] , widths : List [ float ] , amplitudes : List [ float ] , fake = False , ) : y = np . zeros ( len ( x ) ) model = GaussianModel ( ) for c , w , a in zip ( centers , widths , amplitudes ) : if fake is True : scaling = a else : scaling = 1.0 y += scaling * model . eval ( x = x , center = c , sigma = w , amplitude = a ) return y
short_docstring: Generate a synthetic spectrum with Gaussians with the
  specified parameters, on a given x axis.
short_docstring_tokens: Generate a synthetic spectrum with Gaussians with the specified parameters on a given x axis .
comment: """
              Generate a synthetic spectrum with Gaussians with the
              specified parameters, on a given x axis.
  
              GaussianModel is used here to remain internally consistent
              with the rest of the code.
  
               x: array of x values to evaluate Gaussians on
               centers: array of Gaussian centers
               widths: array of Gaussian widths
               amplitudes: array of Gaussian amplitudes
               fake: bool indicating whether false intensities are used for the simulation
              :return y: array of y values
          """