parameters: (x, p)
return_statement: return p[0] * np.exp(-(x - p[1])**2 / (2 * p[2]**2)) + p[3]
docstring: Generates a 1D Gaussian peak.
      See http://gpufit.readthedocs.io/en/latest/fit_model_functions.html#d-gaussian-function
  
      :param x: x grid position values
      :param p: parameters (amplitude, center position, width, offset)
      :return: Gaussian peak
docstring_summary: Generates a 1D Gaussian peak.
      See http://gpufit.readthedocs.io/en/latest/fit_model_functions.html#d-gaussian-function
docstring_tokens: Generates a 1D Gaussian peak . See http : // gpufit . readthedocs . io / en / latest / fit_model_functions . html#d - gaussian - function
function: def gaussian_peak_1d(x, p):
      """
      Generates a 1D Gaussian peak.
      See http://gpufit.readthedocs.io/en/latest/fit_model_functions.html#d-gaussian-function
  
      :param x: x grid position values
      :param p: parameters (amplitude, center position, width, offset)
      :return: Gaussian peak
      """
      p = p.flatten()
      return p[0] * np.exp(-(x - p[1])**2 / (2 * p[2]**2)) + p[3]
function_tokens: def gaussian_peak_1d ( x , p ) : p = p . flatten ( ) return p [ 0 ] * np . exp ( - ( x - p [ 1 ] ) ** 2 / ( 2 * p [ 2 ] ** 2 ) ) + p [ 3 ]