pyfracval.particle_generation
=============================

.. py:module:: pyfracval.particle_generation

.. autoapi-nested-parse::

   Functions for generating primary particle radii.





Module Contents
---------------

.. py:function:: random_normal_custom()

   Generates a normally distributed random number using the Ziggurat method
   as implemented in numpy, which is generally preferred over custom
   acceptance-rejection methods unless specific properties are needed.


.. py:function:: lognormal_pp_radii(rp_gstd, rp_g, n, seed = None, truncate = True)

   Generate N random radii from a lognormal distribution.

   :param rp_gstd: Geometric standard deviation of the distribution (must be >= 1.0).
                   If 1.0, generates monodisperse particles.
   :type rp_gstd: float
   :param rp_g: Geometric mean radius of the distribution (must be > 0).
   :type rp_g: float
   :param n: Number of radii to generate.
   :type n: int
   :param seed: Random seed for reproducibility, by default None.
   :type seed: int | None, optional
   :param truncate: Use the FracVAL 2*sigma truncate version
   :type truncate: bool, optional

   :returns: A 1D NumPy array of N generated radii.
   :rtype: np.ndarray

   :raises ValueError: If `rp_g` is not positive.

   .. rubric:: Notes

   Uses `numpy.random.lognormal`. The underlying normal distribution's
   parameters are mu=log(rp_g) and sigma=log(rp_gstd).
   The original Fortran code included optional truncation at approximately
   +/- 2 geometric standard deviations; this is not enabled by default here.