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Welcome to TFilterPy's Documentation!
=====================================

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   <p style="color: crimson; font-size: 0.9em; margin-top: 0.5em;">
     Where we say: <strong>Sawubona, Molo, Hallo, Dumela, Ndaa, Xewani</strong>,
     and a heartfelt <strong>Dinstang</strong> in pure Sipitori style 🇿🇦✨.
   </p>

**TFiltersPy** is your go-to open-source Python library for applying state-of-the-art Bayesian filtering — built with the power of Dask for scale,  
the elegance of Kalman and Particle Filters for precision, and a sprinkle of local flavor to make it proudly African.

✨ This library is part of the **Ubunye Artificial Intelligent Ecosystems (UIAE)** —  
a collaborative initiative to build powerful, locally rooted, and globally relevant AI tools.  
Explore more projects at 👉 https://github.com/ubunye-ai-ecosystems

🚀 Whether you're building AI for space tech, smart grids, autonomous cars, or township telemetry —  
if it’s noisy, dynamic, and uncertain, we’ve got you covered.

Features
--------
- **Bayesian Filtering:** Supports both linear (Kalman) and nonlinear/non-Gaussian (Particle) filtering for robust state estimation in dynamic systems.
- **Distributed Computation:** Built on Dask, enabling parallel and out-of-core filtering for large-scale or streaming data.
- **Uncertainty Quantification:** Includes tools to quantify estimation confidence through residual analysis, covariance estimation, and adaptive strategies.
- **Parameter Estimation:** Advanced methods for estimating system parameters using Bayesian techniques, including maximum likelihood and cross-validation.
- **User-Friendly API:** Clean, modular, and scikit-learn like API — with examples, documentation, and sensible defaults so you can go from idea 💡 to insight 📈 fast.

.. toctree::
   :maxdepth: 1
   :caption: Contents:

   installation
   examples
   literature
   api_cheatsheet
   modules
   CONTRIBUTING
   MAINTAINERS