Metadata-Version: 2.1
Name: tfilterpy
Version: 0.0.3
Summary: This package is for Bayesian filtering models.
Home-page: https://github.com/leparalamapara/tfilterpy
Author: Thabang L. Mashinini- Sekgoto, Lebogang M. Mashinini-Sekgoto, Palesa D. Mashinini-Sekgoto
Author-email: thabangline@gmail.com
Project-URL: Logo, https://raw.githubusercontent.com/LeparaLaMapara/tfilterpy/main/branding/logo/tfilters-logo.jpeg
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy

<p align="center">
  <img src="branding/logo/tfilters-logo.jpeg?" alt="tfilterspy logo"/>
</p>

The **tfilterspy** package is a Python library for implementing Bayesian filter algorithms, widely used mathematical tools in estimation theory and control engineering.

## Installation

You can install the **tfilterspy** package via pip, the Python package installer. Open a terminal and type the following command:



### Supported Methods
Currently, the following Bayesian filter algorithms are implemented in **tfilterspy**:

- **Kalman Filters**: A class of linear estimators used in filtering and smoothing applications.
- **Particle Filters**: A family of sequential Monte Carlo methods used for sampling from posterior distributions.

More methods will be added in the future.

## Usage
Here's are examples of how to use **tfilterspy** to estimate the state of a noisy linear system using a Kalman filter in the example folder.

# Contribution
If you find a bug or have a feature request, please open an issue on the GitHub repository. Pull requests are welcome, but please open an issue first to discuss your changes.

# License

This package is licensed under the MIT License.
