pysampling 0.2.0

Sampling methods for design of experiments in Python (random, LHS, Halton, Sobol).

pysampling

Sampling methods for design of experiments in Python. pysampling generates well-spread point sets in the unit hypercube [0, 1]^d and provides a suite of measures to assess their uniformity.

Detailed documentation: https://anyoptimization.com/projects/pysampling

Algorithms

Key	Method
random	Uniform random sampling
lhs	Latin Hypercube Sampling
halton	Halton sequence
sobol	Sobol sequence
riesz	Riesz s-energy (maximin)

Installation

pip install -U pysampling

Usage

Import the sample function and pick an algorithm. Here we draw 50 points in 2 dimensions with Latin Hypercube Sampling:

from pysampling.sample import sample

X = sample("lhs", 50, 2)

To visualize the result (requires the optional plot extra, pip install pysampling[plot]):

import matplotlib.pyplot as plt

plt.scatter(X[:, 0], X[:, 1], s=30, facecolors="none", edgecolors="r")
plt.show()

See examples/plot_sampling.py for a runnable demo.

Development

This project uses pyclawd as its dev toolkit.

pip install -e . --group dev
pyclawd check        # format-check -> lint -> typecheck -> test
pyclawd test fast    # quick test run

Documentation

The docs are built by a cached pipeline (see docs/):

pip install -e . --group docs   # one-time: install the docs toolchain
pyclawd docs build              # compile -> execute (cached) -> render HTML
pyclawd docs serve              # serve docs/build/html locally

Contact

Questions, bugs, or feature requests? Please open an issue on GitHub.

Maintained by Julian Blank.

License

Apache License 2.0 — see LICENSE.