Metadata-Version: 2.1
Name: probnum
Version: 0.1.24
Summary: Probabilistic Numerics in Python.
License: The MIT License (MIT)
        
        Copyright (c) 2020 ProbNum Development Team
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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Project-URL: homepage, https://probnum.readthedocs.io/en/stable/
Project-URL: github, https://github.com/probabilistic-numerics/probnum
Project-URL: documentation, https://probnum.readthedocs.io/en/stable/
Project-URL: benchmarks, https://probabilistic-numerics.github.io/probnum-benchmarks/benchmarks/
Keywords: probabilistic-numerics,machine-learning,numerical-methods
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: <3.11,>=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt
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<div align="center">
    <a href="https://probnum.readthedocs.io"><img align="center" src="https://raw.githubusercontent.com/probabilistic-numerics/probnum/main/docs/source/assets/img/logo/probnum_logo_dark_txtbelow.svg" alt="probabilistic numerics" width="400" style="padding-right: 10px; padding left: 10px;" title="Probabilistic Numerics in Python"/>
    </a>
    <h3>Learn to Approximate. Approximate to Learn.</h3>
    <p>Probabilistic Numerics in Python.</p>
</div>

---

<div align="center">

<h4 align="center">
  <a href="https://probnum.readthedocs.io">Home</a> |
  <a href="https://probnum.readthedocs.io/en/latest/tutorials.html">Tutorials</a> |  
  <a href="https://probnum.readthedocs.io/en/latest/api.html">API Reference</a> |
  <a href="https://probnum.readthedocs.io/en/latest/development.html">Contributing</a>
</h4>

[![CI build](https://img.shields.io/github/actions/workflow/status/probabilistic-numerics/probnum/CI-build.yml?branch=main&style=flat-square&logo=github&logoColor=white&label=CI-build)](https://github.com/probabilistic-numerics/probnum/actions?query=workflow%3ACI-build)
[![Coverage Status](https://img.shields.io/codecov/c/gh/probabilistic-numerics/probnum/main?style=flat-square&label=Coverage&logo=codecov&logoColor=white)](https://codecov.io/gh/probabilistic-numerics/probnum/branch/main)
[![Benchmarks](http://img.shields.io/badge/Benchmarks-asv-blueviolet.svg?style=flat-square&logo=swift&logoColor=white)](https://probabilistic-numerics.github.io/probnum-benchmarks/benchmarks/)
[![PyPI](https://img.shields.io/pypi/v/probnum?style=flat-square&label=PyPI&logo=pypi&logoColor=white)](https://pypi.org/project/probnum/)

</div>

---

**ProbNum** is a Python toolkit for solving numerical problems in linear algebra, optimization, quadrature and
differential equations. ProbNum solvers not only estimate the solution of the numerical problem, but also its uncertainty (numerical error) which arises from finite computational resources, discretization and stochastic input. This numerical uncertainty can be used in downstream decisions.

Currently, available solvers are:

- **Linear solvers:** Solve $A x = b$ for $x$.
- **ODE solvers:** Solve $\dot{y}(t) = f(y(t), t)$ for $y$.
- **Integral solvers (quadrature):** Solve $F = \int_D f(x) \mathrm{d}p(x)$ for $F$.

Lower level structure includes:

- **Random variables and random processes**, as well as arithmetic operations thereof.
- Memory-efficient and lazy implementation of **linear operators**.
- **Filtering and smoothing** for (probabilistic) state-space models, mostly variants of Kalman filters.

ProbNum is underpinned by the research field probabilistic numerics (PN), which lies at the intersection of machine learning and numerics.
PN aims to quantify uncertainty arising from intractable or incomplete numerical computation and from stochastic input 
using the tools of probability theory. The general vision of probabilistic numerics is to provide well-calibrated 
probability measures over the output of a numerical routine, which then can be propagated along the chain of 
computation.


## Installation
To get started install ProbNum using `pip`.
```bash
pip install probnum
```
Alternatively, you can install the latest version from source.
```bash
pip install git+https://github.com/probabilistic-numerics/probnum.git
```

> Note: This package is currently work in progress, therefore interfaces are subject to change.

## Documentation and Examples
For tips on getting started and how to use this package please refer to the
[**documentation**](https://probnum.readthedocs.io). It contains a 
[quickstart guide](https://probnum.readthedocs.io/en/latest/tutorials/quickstart.html) 
and Jupyter notebooks illustrating the basic usage of the ProbNum solvers.

## Package Development
This repository is currently under development and benefits from contribution to the code, examples or documentation.
Please refer to the [contribution guidelines](https://probnum.readthedocs.io/en/latest/development.html) before
making a pull request.

A list of core contributors to ProbNum can be found
[here](https://probnum.readthedocs.io/en/latest/development.html#probnum-team).

## Citing ProbNum
If you are using ProbNum in your research, please cite as provided. 
The "Cite this repository" button on the sidebar generates a BibTeX entry or an APA entry. 

## License and Contact
This work is released under the [MIT License](https://github.com/probabilistic-numerics/probnum/blob/main/LICENSE.txt).

Please submit an [issue on GitHub](https://github.com/probabilistic-numerics/probnum/issues/new) to report bugs or
request changes.
