✨ TFilterPy 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!
🎉 TFilterPy 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.
Getting started is as easy as pie (or Pi)! 🍰
pip install tfilterpyWant 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]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())- Dask Support for large-scale filtering with parallelism 🏎️
- Modular structure for extensibility 🛠️
- Lightweight and easy to use 👌
- Designed for both linear and nonlinear systems 🔄
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. 🎉
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.
- Clone the repo:
git clone https://github.com/LeparaLaMapara/tfilterpy.git- Install dependencies:
pip install .[dev]- Run tests:
pytest tests/- Adding Unscented Kalman Filters (UKF) 🦄
- Implementing Gaussian Process Filters 📈
- Enhancing scalability with advanced parallelism ⚡
Detailed documentation is available at: https://leparalamapara.github.io/tfilterpy (Yes, we made it look fancy. You're welcome. ✨)
If this library made your life easier, consider:
Giving it a ⭐ on GitHub.
Telling your friends, colleagues, and cats about TFilterPy.
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! 🎉🚀