:py:mod:`ler.image_properties.sample_caustic_points_njit`
=========================================================

.. py:module:: ler.image_properties.sample_caustic_points_njit

.. autoapi-nested-parse::

   Module for sampling source positions from EPL + shear caustic regions.

   Provides numba-accelerated routines for uniform rejection sampling inside
   arbitrary polygons, with convenience wrappers to sample from the double
   caustic of an EPL + external shear lens model.

   Usage:
       Draw a single source from the double-caustic region:

       >>> from ler.image_properties.sample_caustic_points_njit import sample_source_from_double_caustic
       >>> beta, pts = sample_source_from_double_caustic(
       ...     q=0.8, phi=0.0, gamma=2.0, gamma1=0.03, gamma2=-0.01
       ... )

   Copyright (C) 2026 Phurailatpam Hemantakumar. Distributed under MIT License.

   ..
       !! processed by numpydoc !!


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


Functions
~~~~~~~~~

.. autoapisummary::

   ler.image_properties.sample_caustic_points_njit.sample_uniform_in_polygon
   ler.image_properties.sample_caustic_points_njit.sample_many_uniform_in_polygon
   ler.image_properties.sample_caustic_points_njit.sample_source_from_double_caustic
   ler.image_properties.sample_caustic_points_njit.sample_many_sources_from_double_caustic



Attributes
~~~~~~~~~~

.. autoapisummary::

   ler.image_properties.sample_caustic_points_njit.TWO_PI


.. py:data:: TWO_PI

   

.. py:function:: sample_uniform_in_polygon(xv, yv, max_tries=100000)

   
   Draw one uniform random point inside a polygon via rejection sampling.


   :Parameters:

       **xv** : ``numpy.ndarray``
           Polygon vertex x-coordinates.

       **yv** : ``numpy.ndarray``
           Polygon vertex y-coordinates.

       **max_tries** : ``int``
           Maximum number of rejection-sampling attempts.
           default: 100000



   :Returns:

       **x** : ``float``
           Sampled x-coordinate.

       **y** : ``float``
           Sampled y-coordinate.

       **ok** : ``int``
           Sampling flag where 1 indicates success and 0 indicates failure.










   .. rubric:: Examples

   >>> import numpy as np
   >>> xv = np.array([0.0, 1.0, 1.0, 0.0])
   >>> yv = np.array([0.0, 0.0, 1.0, 1.0])
   >>> x, y, ok = sample_uniform_in_polygon(xv, yv)

   ..
       !! processed by numpydoc !!

.. py:function:: sample_many_uniform_in_polygon(xv, yv, n_samples, max_tries_per=100000)

   
   Draw multiple uniform random points inside a polygon.

   Sampling uses batched rejection from the polygon bounding box.

   :Parameters:

       **xv** : ``numpy.ndarray``
           Polygon vertex x-coordinates.

       **yv** : ``numpy.ndarray``
           Polygon vertex y-coordinates.

       **n_samples** : ``int``
           Number of requested samples.

       **max_tries_per** : ``int``
           Maximum attempts per requested sample.
           default: 100000



   :Returns:

       **x_src** : ``numpy.ndarray``
           Sampled x-coordinates with capacity ``n_samples``.

       **y_src** : ``numpy.ndarray``
           Sampled y-coordinates with capacity ``n_samples``.

       **n_ok** : ``int``
           Number of valid samples in the leading entries.










   .. rubric:: Examples

   >>> import numpy as np
   >>> xv = np.array([0.0, 1.0, 1.0, 0.0])
   >>> yv = np.array([0.0, 0.0, 1.0, 1.0])
   >>> x_src, y_src, n_ok = sample_many_uniform_in_polygon(xv, yv, 100)

   ..
       !! processed by numpydoc !!

.. py:function:: sample_source_from_double_caustic(q, phi, gamma, gamma1, gamma2, theta_E=1.0, num_th=500, maginf=-100.0, max_tries=10)

   
   Draw one source position uniformly inside the double caustic.


   :Parameters:

       **q** : ``float``
           Lens axis ratio.

       **phi** : ``float``
           Lens position angle (rad).

       **gamma** : ``float``
           EPL slope parameter.

       **gamma1** : ``float``
           External shear component 1.

       **gamma2** : ``float``
           External shear component 2.

       **theta_E** : ``float``
           Einstein radius used to construct the caustic.
           default: 1.0

       **num_th** : ``int``
           Number of angular samples for the boundary curve.
           default: 500

       **maginf** : ``float``
           Magnification cutoff used in the caustic construction.
           default: -100.0

       **max_tries** : ``int``
           Maximum rejection-sampling attempts.
           default: 100000



   :Returns:

       **beta_x** : ``float``
           Sampled source x-coordinate.

       **beta_y** : ``float``
           Sampled source y-coordinate.

       **ok** : ``int``
           Sampling status flag where 1 indicates success.










   .. rubric:: Examples

   >>> beta_x, beta_y, ok = sample_source_from_double_caustic(
   ...     q=0.8, phi=0.0, gamma=2.0, gamma1=0.03, gamma2=-0.01
   ... )

   ..
       !! processed by numpydoc !!

.. py:function:: sample_many_sources_from_double_caustic(q, phi, gamma, gamma1, gamma2, n_samples, theta_E=1.0, num_th=500, maginf=-100.0, max_tries_per=10)

   
   Draw many source positions uniformly inside the double caustic.


   :Parameters:

       **q** : ``float``
           Lens axis ratio.

       **phi** : ``float``
           Lens position angle (rad).

       **gamma** : ``float``
           EPL slope parameter.

       **gamma1** : ``float``
           External shear component 1.

       **gamma2** : ``float``
           External shear component 2.

       **n_samples** : ``int``
           Number of source samples requested.

       **theta_E** : ``float``
           Einstein radius used to construct the caustic.
           default: 1.0

       **num_th** : ``int``
           Number of angular samples for the boundary curve.
           default: 500

       **maginf** : ``float``
           Magnification cutoff used in the caustic construction.
           default: -100.0

       **max_tries_per** : ``int``
           Maximum rejection attempts per requested sample.
           default: 100000



   :Returns:

       **beta_x** : ``numpy.ndarray``
           Sampled source x-coordinates.

       **beta_y** : ``numpy.ndarray``
           Sampled source y-coordinates.

       **n_ok** : ``int``
           Number of valid samples in the leading entries.










   .. rubric:: Examples

   >>> bx, by, n_ok = sample_many_sources_from_double_caustic(
   ...     q=0.8, phi=0.0, gamma=2.0, gamma1=0.03, gamma2=-0.01, n_samples=1000
   ... )

   ..
       !! processed by numpydoc !!