snowline


Namesnowline JSON
Version 0.6.3 PyPI version JSON
download
home_pagehttps://github.com/JohannesBuchner/snowline
SummaryFit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.
upload_time2023-07-14 08:31:32
maintainer
docs_urlNone
authorJohannes Buchner
requires_python>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
licenseGNU General Public License v3
keywords snowline
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI
coveralls test coverage No coveralls.
            =========
snowline
=========

Fit and compare models very quickly. MCMC-free.

.. image:: https://img.shields.io/pypi/v/snowline.svg
        :target: https://pypi.python.org/pypi/snowline

.. image:: https://github.com/JohannesBuchner/snowline/actions/workflows/tests.yml/badge.svg
        :target: https://github.com/JohannesBuchner/snowline/actions/workflows/tests.yml

.. image:: https://img.shields.io/badge/docs-published-ok.svg
        :target: https://johannesbuchner.github.io/snowline/
        :alt: Documentation Status

About
-----

Posterior distributions and corner plots without MCMC?
No dealing with convergence criteria?

Yes!

Tailored for low-dimensional (d<10) problems with a single mode,
this package automatically finds the best fit and uses the local covariance matrix
as a Laplace Approximation. Then Importance Sampling and Variational Bayes come 
in to improve from a single-gaussian approximation to more complex shapes.
This allows sampling efficiently in some problems, and provides a estimate
for the marginal likelihood.

This package is built on top the excellent (i)minuit and pypmc packages.

You can help by testing snowline and reporting issues. Code contributions are welcome.
See the `Contributing page <https://johannesbuchner.github.io/snowline/contributing.html>`_.

Features
---------

* Pythonic. pip installable.
* Easy to program for: Sanity checks with meaningful errors
* Fast
* MPI support

Usage
^^^^^

Read the full documentation at:

https://johannesbuchner.github.io/snowline/


Licence
^^^^^^^

GPLv3 (see LICENCE file). If you require another license, please contact me.

Icon made by `Vecteezy <https://www.vecteezy.com/free-vector/hill>`_.


Other projects
^^^^^^^^^^^^^^

See also:

 * UltraNest: https://johannesbuchner.github.io/UltraNest/
 * autoemcee: https://johannesbuchner.github.io/autoemcee/


==============
Release Notes
==============

0.4.0 (2020-03-07)
------------------

* Improve robustness to poor Laplace approximations


0.3.0 (2020-05-07)
------------------

* Numerical robustness


0.2.0 (2020-04-21)
------------------

* Robustness improvements
* Packaging and testing improvements


0.1.0 (2020-03-07)
------------------

* First version

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/JohannesBuchner/snowline",
    "name": "snowline",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*",
    "maintainer_email": "",
    "keywords": "snowline",
    "author": "Johannes Buchner",
    "author_email": "johannes.buchner.acad@gmx.com",
    "download_url": "https://files.pythonhosted.org/packages/1e/7d/545a1bad0362af5199517bdf00f6144edea6086beed8a59b351c44233c3f/snowline-0.6.3.tar.gz",
    "platform": null,
    "description": "=========\nsnowline\n=========\n\nFit and compare models very quickly. MCMC-free.\n\n.. image:: https://img.shields.io/pypi/v/snowline.svg\n        :target: https://pypi.python.org/pypi/snowline\n\n.. image:: https://github.com/JohannesBuchner/snowline/actions/workflows/tests.yml/badge.svg\n        :target: https://github.com/JohannesBuchner/snowline/actions/workflows/tests.yml\n\n.. image:: https://img.shields.io/badge/docs-published-ok.svg\n        :target: https://johannesbuchner.github.io/snowline/\n        :alt: Documentation Status\n\nAbout\n-----\n\nPosterior distributions and corner plots without MCMC?\nNo dealing with convergence criteria?\n\nYes!\n\nTailored for low-dimensional (d<10) problems with a single mode,\nthis package automatically finds the best fit and uses the local covariance matrix\nas a Laplace Approximation. Then Importance Sampling and Variational Bayes come \nin to improve from a single-gaussian approximation to more complex shapes.\nThis allows sampling efficiently in some problems, and provides a estimate\nfor the marginal likelihood.\n\nThis package is built on top the excellent (i)minuit and pypmc packages.\n\nYou can help by testing snowline and reporting issues. Code contributions are welcome.\nSee the `Contributing page <https://johannesbuchner.github.io/snowline/contributing.html>`_.\n\nFeatures\n---------\n\n* Pythonic. pip installable.\n* Easy to program for: Sanity checks with meaningful errors\n* Fast\n* MPI support\n\nUsage\n^^^^^\n\nRead the full documentation at:\n\nhttps://johannesbuchner.github.io/snowline/\n\n\nLicence\n^^^^^^^\n\nGPLv3 (see LICENCE file). If you require another license, please contact me.\n\nIcon made by `Vecteezy <https://www.vecteezy.com/free-vector/hill>`_.\n\n\nOther projects\n^^^^^^^^^^^^^^\n\nSee also:\n\n * UltraNest: https://johannesbuchner.github.io/UltraNest/\n * autoemcee: https://johannesbuchner.github.io/autoemcee/\n\n\n==============\nRelease Notes\n==============\n\n0.4.0 (2020-03-07)\n------------------\n\n* Improve robustness to poor Laplace approximations\n\n\n0.3.0 (2020-05-07)\n------------------\n\n* Numerical robustness\n\n\n0.2.0 (2020-04-21)\n------------------\n\n* Robustness improvements\n* Packaging and testing improvements\n\n\n0.1.0 (2020-03-07)\n------------------\n\n* First version\n",
    "bugtrack_url": null,
    "license": "GNU General Public License v3",
    "summary": "Fit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.",
    "version": "0.6.3",
    "project_urls": {
        "Homepage": "https://github.com/JohannesBuchner/snowline"
    },
    "split_keywords": [
        "snowline"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1e7d545a1bad0362af5199517bdf00f6144edea6086beed8a59b351c44233c3f",
                "md5": "4e7c73ef3b11af2b8408d65a85689661",
                "sha256": "dd1eeb23300b68a9c9171851e4daef98b676d02f90f5d304c06d1ccc53f5471a"
            },
            "downloads": -1,
            "filename": "snowline-0.6.3.tar.gz",
            "has_sig": false,
            "md5_digest": "4e7c73ef3b11af2b8408d65a85689661",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*",
            "size": 20478,
            "upload_time": "2023-07-14T08:31:32",
            "upload_time_iso_8601": "2023-07-14T08:31:32.045598Z",
            "url": "https://files.pythonhosted.org/packages/1e/7d/545a1bad0362af5199517bdf00f6144edea6086beed8a59b351c44233c3f/snowline-0.6.3.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-07-14 08:31:32",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "JohannesBuchner",
    "github_project": "snowline",
    "travis_ci": true,
    "coveralls": false,
    "github_actions": true,
    "tox": true,
    "lcname": "snowline"
}
        
Elapsed time: 0.37818s