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gnu: Add python-nautilus-sampler.

* gnu/packages/statistics.scm (python-nautilus-sampler): New variable.

Change-Id: Ic2881d9d26b02d7dd8ea03523a6a20195dd94727
This commit is contained in:
Sharlatan Hellseher
2025-07-03 17:14:40 +01:00
parent e8b6982425
commit 120edb6b36

View File

@@ -916,6 +916,51 @@ point (up to 50% contamination) and have a number of nice applications in
machine learning, computer vision, and high-dimensional statistics.")
(license license:asl2.0)))
(define-public python-nautilus-sampler
(package
(name "python-nautilus-sampler")
(version "1.0.5")
(source
(origin
(method url-fetch)
(uri (pypi-uri "nautilus_sampler" version))
(sha256
(base32 "1b73rxg7b5fzpw4ss4py98xdxddkl1dh2dszp2pxv3y179iyniqj"))))
(build-system pyproject-build-system)
(arguments
(list
#:test-flags
#~(list "--durations=0"
;; One Dask test hangs.
"-k" "not test_pool[dask]")
#:phases
#~(modify-phases %standard-phases
(add-before 'check 'pre-check
(lambda _ (setenv "OMP_NUM_THREADS" "1"))))))
(native-inputs
(list python-dask
python-distributed
python-flit-core
python-h5py
python-pytest
python-pytest-xdist))
(propagated-inputs
(list python-numpy
python-scikit-learn
python-scipy
python-threadpoolctl))
(home-page "https://github.com/johannesulf/nautilus")
(synopsis "Neural Network-Boosted Importance Sampling for Bayesian Statistics")
(description
"Nautilus is an pure-Python package for Bayesian posterior and evidence
estimation. It utilizes importance sampling and efficient space exploration
using neural networks. Compared to traditional @acronym{MCMC, Markov chain
Monte Carlo} and Nested Sampling codes, it often needs fewer likelihood calls
and produces much larger posterior samples. Additionally, nautilus is highly
accurate and produces Bayesian evidence estimates with percent precision. It
is widely used in many areas of astrophysical research.")
(license license:expat)))
(define-public python-nestle
(package
(name "python-nestle")