How to Use lintsampler

lintsampler has one public sampling class: LintSampler.

At the simplest, the constructor takes a probability density function (pdf) input, along with a set of cells in which to sample. Once the instance has been built, the .sample() function can be used to draw a number of samples from the pdf.

For example, to draw six samples within a single 3D cell, with first and last corners at \((x, y, z) = (10, 100, 1000)\) and \((20, 200, 2000)\) respectively:

>>> import numpy as np
>>> from lintsampler import LintSampler
>>> x = np.linspace(10,20,2)
>>> y = np.linspace(100,200,2)
>>> z = np.linspace(1000,2000,2)
>>> def rndmpdf(X): return np.random.uniform()
>>> LintSampler((x,y,z),pdf=rndmpdf).sample(N=6)
array([[  12.63103673,  186.7514952 , 1716.6187807 ],
       [  14.67375968,  116.20984414, 1557.59629547],
       [  11.47055697,  178.41650558, 1592.18260186],
       [  12.41780309,  105.28009531, 1436.39525998],
       [  13.44764381,  152.57623376, 1880.55963378],
       [  18.5522151 ,  133.87092063, 1558.85620176]])

See the function docstring or the example notebooks for further details and examples.

As well as LintSampler, the package contains a few other public classes which all represent pdf evaluation domains the user can provide as the first parameter in the LintSampler constructor in more advanced use cases. DensityGrid and DensityTree are respectively grid-like and tree-like structures, while DensityStructure is the abstract base class which can be easily extended to create custom structures as desired. To see these classes in action, see the example notebooks.