cratermaker.utils.montecarlo_utils.bounded_norm

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cratermaker.utils.montecarlo_utils.bounded_norm#

cratermaker.utils.montecarlo_utils.bounded_norm(loc, scale, size=1, lower_bound=None, upper_bound=None, rng=None, rng_seed=None, rng_state=None, **kwargs)[source]#

Sample from a truncated normal distribution that is bounded by either an upper and lower bound, or if None is provided, by the 1-sigma stdev.

Parameters:

  • loc (FloatLike) – mean of the distribution

  • scale (FloatLike) – standard deviation and bounds of the distribution

  • size (int or tuple of ints, optional) – The number of samples to generate. If the shape is (m, n, k), then m * n * k samples are drawn. If size is None (the default), a single value is returned if diameters is a scalar, otherwise an array of samples is returned with the same size as diameters.

  • lower_bound (FloatLike | None, optional) – The lower bound of the distribution. If None, the lower bound is set to mean - scale.

  • upper_bound (FloatLike | None, optional) – The upper bound of the distribution. If None, the upper bound is set to mean + scale.

  • rng (numpy.random.Generator | None) – A numpy random number generator. If None, a new generator is created using the rng_seed if it is provided.

  • rng_seed (Any type allowed by the rng_seed argument of numpy.random.Generator, optional) – The rng_seed for the RNG. If None, a new RNG is created.

  • rng_state (dict, optional) – The state of the random number generator. If None, a new state is created.

  • **kwargs (Any)

Returns:

float – Truncated norm bounded by loc-scale, loc+scale

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