FiniteFree
Python tools for finite free probability, random matrix sampling and spectral computation. FiniteFree focuses on efficient repeated calculations, numerical stability and reproducible workflows, with explicit precision and storage contracts.
Install
python -m pip install finitefree
Requires Python 3.10 or later. Release wheels cover CPython 3.10–3.13 on Linux x86-64, macOS ARM64 and Windows x86-64. A compatible wheel avoids compiling FiniteFree locally; source builds require a C compiler. On musl Linux, python-flint may also require FLINT/GMP/MPFR source-build prerequisites.
Start with the tutorial or the API reference.
Main workflows
- Polynomial calculations: finite free convolutions, cumulants, transforms and classical orthogonal families, with integer/rational coefficient arithmetic where supported and numerical root evaluation.
- Reproducible sampling: Gaussian, Wishart and Haar random matrices, plus
discrete DPP sampling. Pass an optional NumPy
Generatorthroughrng=; omitted arguments preserve the existing global RNG behavior. - Reusable numerical work: prepared orthogonal kernels, batched basis/kernel evaluation and reusable interval quadrature; prepared matrix pencils reuse LU factorizations for log-determinants, gradients and Hessian-vector products.
- Count statistics: finite-rank DPP count PMFs, CDFs, directly summed upper tails, means and variances.
- Bounded-storage summaries: streaming empirical coefficient moments and configured interval-count histograms. Retained-sample comparisons remain available when later plots or individual observations are needed.
See the GUE spectral workflow for a worked example connecting sampling, expected polynomials and kernels. Source-checkout examples and plotting dependencies are documented separately.
Precision and performance
Exactness applies to supported integer/rational algebraic operations. Roots,
sampled matrices, prepared numerical kernels and continuous probabilities use
floating-point methods with precision and conditioning limits. An exact=True
root request still returns float64 values, rather than exact roots or certified
error bounds.
Continuous gap and count probabilities are quadrature estimates. Supply the kernel's reference measure density explicitly for weighted measures, including Hermite kernels. Refinement and spectrum diagnostics do not certify rare tails.
Performance depends on the input, chosen API and environment. Prepared objects expose reuse; streaming summaries avoid retaining every sample. GPU correctness checks do not establish a speed or memory advantage. See the numerical contracts and benchmark instructions.
Release and project links
Version 0.4.1 updates package descriptions, project links and documentation. Library calculations, defaults and runtime dependencies are unchanged from 0.4.0.
MIT licensed.
1 maintainer
Metadata
1 maintainer
Release files for finitefree 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| finitefree-0.4.1.tar.gz | 12.9 MB | Details |
Built distributions (wheels)
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Release files / finitefree-0.4.1.tar.gz
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