tfilterspy 1.0.3

A Python package for state estimation using Kalman and Particle Filters

TFiltersPy 🌀

✨ TFiltersPy is your new favorite Python library for implementing state-of-the-art Bayesian filtering techniques like Kalman Filters and Particle Filters. Whether you're working on noisy linear systems, nonlinear dynamics, or want to sound cool at a party when you say "I coded my own Kalman Filter," this is the library for you!

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What’s Inside? 📦

🎉 TFiltersPy offers:

• Kalman Filters 🧮 – A classic but still iconic tool for linear filtering and smoothing.
• Particle Filters 🎲 – Sampling-based estimators for nonlinear systems.
• Nonlinear Filters 🔀 – For when your system decides to be complicated.
• Extensible design for implementing more advanced filtering algorithms like Unscented Kalman Filters (UKF) and beyond.

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Installation 🚀

Getting started is as easy as pie (or Pi)! 🍰

pip install tfilterpy

Want to contribute or tinker with the code? Clone the repo and install the development dependencies:

git clone https://github.com/LeparaLaMapara/tfilterpy.git
cd tfilterpy
pip install .[dev]

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Usage 🛠️

Example 1: Using a Kalman Filter to tame noisy data 🤖

import numpy as np
from TFilterPy.state_estimation.linear_filters import DaskKalmanFilter

# Define your system
F = np.eye(2)
H = np.eye(2)
Q = np.eye(2) * 0.01
R = np.eye(2) * 0.1
x0 = np.zeros(2)
P0 = np.eye(2)

# Create a Kalman Filter
kf = DaskKalmanFilter(F, H, Q, R, x0, P0)

# Simulate some noisy measurements
measurements = np.random.randn(100, 2)

# Run the filter
filtered_states = kf.run_filter(measurements)
print(filtered_states.compute())

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Features 🌟

• Dask Support for large-scale filtering with parallelism 🏎️
• Modular structure for extensibility 🛠️
• Lightweight and easy to use 👌
• Designed for both linear and nonlinear systems 🔄

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Why TFilterPy? 💡

Because Kalman deserves better branding! Instead of grappling with matrices and equations from scratch, use TFilterPy and focus on the fun part: tweaking models until they (hopefully) work. 🎉

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Contributing 🤝

We welcome contributions of all types:

• 🐛 Found a bug? Let us know in the Issues.
• 🌟 Want to add a feature? Fork the repo, make your changes, and create a pull request.
• 🧪 Testers needed! Write more test cases for improved coverage.

Development Setup

1. Clone the repo:

  git clone https://github.com/LeparaLaMapara/tfilterpy.git

2. Install dependencies:

  pip install .[dev]

3. Run tests:

  pytest tests/

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Future Plans 🔮

• Adding Unscented Kalman Filters (UKF) 🦄
• Implementing Gaussian Process Filters 📈
• Enhancing scalability with advanced parallelism ⚡

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Documentation 📚

Detailed documentation is available at: https://leparalamapara.github.io/tfilterpy (Yes, we made it look fancy. You're welcome. ✨)

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Support ❤️

If this library made your life easier, consider:

Giving it a ⭐ on GitHub.
Telling your friends, colleagues, and cats about TFilterPy.

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License 📜

This project is licensed under the MIT License. Feel free to use it, modify it, or use it as a coaster.

Enjoy your filtering adventures with TFilterPy! 🎉🚀