synthpop.modules.metallicity.gaussian ===================================== .. py:module:: synthpop.modules.metallicity.gaussian .. autoapi-nested-parse:: Metallicity class for a Gaussian distribution, given a mean, standard deviation, and upper and lower limits. Classes ------- .. autoapisummary:: synthpop.modules.metallicity.gaussian.Gaussian Module Contents --------------- .. py:class:: Gaussian(mean: float, std: float, low_bound: float = -4, high_bound: float = 0.5, gradient=0.0, **kwargs) Bases: :py:obj:`synthpop.modules.metallicity._metallicity.Metallicity` Gaussian metallicity distribution Attributes ---------- mean : float [[Fe/H]] the mean metallicity in [Fe/H] for the Gaussian distribution std : float [[Fe/H]] the standard deviation of metallicity in [Fe/H] for the Gaussian distribution lower_bound : float [[FE/H]] lower limit for truncation of the distribution upper_bound : float [[FE/H]] upper limit for truncation of the distribution Methods ------- __init__(self,Population) : None initialize the metallicity class, and set the control parameters. draw_random_metallicity(self, N: int or None = None) : np.ndarray, float [[Fe/H]] return a random metallicity drawn from a Gaussian distribution average_metallicity(self) : float [[Fe/H]] return the average metallicity .. py:attribute:: metallicity_func_name :value: 'gaussian' .. py:attribute:: mean .. py:attribute:: std .. py:attribute:: lower :value: -4 .. py:attribute:: upper :value: 0.5 .. py:attribute:: gradient :value: 0.0 .. py:method:: draw_random_metallicity(N: int or None = None, x=None, y=None, z=None, **kwargs) -> np.ndarray or float Returns one or more metallicities in [Fe/H] from a Gaussian distribution. Parameters ---------- N : int, None, optional if N is set to an integer, an array with N random ages is returned Returns ------- val : ndarray, float [Gyr] single metallicities or ndarray of N metallicities in [Fe/H] .. py:method:: average_metallicity() -> float Determine the average metallicity of the population .. py:method:: likelyhood_distribution(met: numpy.ndarray) -> numpy.ndarray analytic version of likelyhood_distribution. only used for the validating