mv-laplace 0.2.1

Sampler-focused multivariate Laplace implementation for simulation workflows.

mv-laplace

mv-laplace is a sampler-focused implementation of the Multivariate Laplace distribution.

Overview

The package exposes a MvLaplaceSampler class for generating multivariate Laplace-like samples from a location vector and covariance matrix. It is intentionally focused on sampling and practical workflow support rather than a full SciPy-style distribution API.

Installation

pip install mv-laplace

Python Version

• Python >= 3.11

Support

• Supported Python: 3.11+
• Primary package index: PyPI (mv-laplace)

Quick Start

import numpy as np
from mv_laplace import MvLaplaceSampler

loc = np.array([0.0, 1.0, -2.0])
cov = np.array([
    [1.0, 0.2, 0.1],
    [0.2, 2.0, -0.3],
    [0.1, -0.3, 1.5],
])

sampler = MvLaplaceSampler(loc=loc, cov=cov)
samples = sampler.sample(sample_size=1000)

Implemented API

Constructor

• MvLaplaceSampler(loc, cov)

Methods

• sample(sample_size=None)

Input Expectations

• loc should be a length-M vector.
• cov should be an M x M covariance matrix.
• Input validation and most constructor error behavior are delegated to underlying SciPy distributions.
• sample(sample_size=None) returns:
  • shape (M,) when sample_size is None
  • shape (N, M) when sample_size is an integer N

Testing

The project includes a pytest suite under tests/, including coverage for:

• output shape behavior for single and batched sampling
• finite-value checks for generated samples
• empirical mean/variance checks for diagonal-covariance cases

Run tests with:

uv run pytest -q

Development Setup

Install development dependencies:

uv sync --all-extras --dev

Run lint and tests:

uv run ruff check .
uv run pytest -q

Roadmap

• Add richer input validation and explicit error messages.
• Add summary-statistic utilities and SciPy-style API extensions.
• Explore a Multivariate Asymmetric Laplace distribution class.

Version History

See VersionHistory.md.

Contributing

• Community and core-developer contribution workflow is documented in CONTRIBUTING.md.
• Repository guardrails and policy details are defined in AGENTS.md.

Release Process (High-Level)

• Pull requests from release/* and hotfix/* into main run publish dry-run checks.
• Merged release/* / hotfix/* PRs to main trigger publish, tagging, release metadata, and post-release verification workflows.
• Recovery actions (including yank/unyank verification with runbook guidance) are run manually when needed.

Reporting Issues

• Bug reports and feature requests: GitHub Issues
• Security-sensitive concerns can be reported privately using GitHub repository security reporting.

Repository Policy

High-level development policy summary (full details in AGENTS.md):

• GitFlow is used: feature/* -> dev, release/*|hotfix/* -> main, with PR-based merges.
• Release and hotfix branches must use SemVer suffixes: release/<MAJOR.MINOR.PATCH>, hotfix/<MAJOR.MINOR.PATCH>.
• Community contributions are welcome through feature/* -> dev pull requests; release/* and hotfix/* flows are core-developer managed.
• CI runs on PRs to dev and main; release dry-runs run on release/*/hotfix/* PRs to main; release publish runs after merge to main.
• Semantic Versioning is required (MAJOR.MINOR.PATCH) and versioning must be intentional.
• Some defaults are guidance (for example draft PR by default) and developer discretion is explicitly supported.
• uv.lock is developer-local and is not tracked in this repository.
• Functional library code is authored manually.
• AI tooling may assist with test authoring, documentation drafting/editing, GitHub Actions/workflow authoring and maintenance, and development guidance for planning/decision support.

For release notes and historical change context, see VersionHistory.md.

License

This project is licensed under the GNU GPL. See LICENSE.