Tsipor (bird) Dynamics (c4dynamics) is the Python framework for state-space modeling and algorithm development.
✅ State objects for easy modeling
✅ Built-in functions for Kalman filters
✅ Integrated sensors and object detection models
✅ Out-of-the-box environments for reinforcement learning
✅ Seamless integration with OpenCV & Open3D
✅ Optimization for Monte Carlo simulations
c4dynamics is designed to
simplify the development of algorithms for dynamic systems,
using state space representations.
It offers engineers and researchers a systematic approach to model,
simulate, and control systems in fields like
robotics, aerospace, and navigation.
The framework introduces state objects, which are foundational
data structures that encapsulate state vectors and provide
the tools for managing data, simulating system behavior,
and analyzing results.
With integrated modules for sensors, detectors, and filters, c4dynamics accelerates algorithm development while maintaining flexibility and scalability.
- 3.8 <= Python < 3.13
- Required packages are listed in requirements.txt
For detailed instructions on installing c4dynamics, including setup for virtual environments, Python version requirements, and troubleshooting, refer to the c4dynamics setup guide.
>>> pip install c4dynamics
To run the latest GitHub version, download the repo and install required packages:
>>> pip install -r requirements.txt
Import c4dynamics:
>>> import c4dynamics as c4d
Define state space object of two variables in the state space (y, vy) with initial conditions (change the state with your variables):
>>> s = c4d.state(y = 1, vy = 0.5)
Multiply the state vector by a matrix and store:
>>> F = [[1, 1],
[0, 1]]
>>> s.X += F @ s.X
>>> s.store(t = 1)
Print the state variables, the state vector, and the stored data:
>>> print(s)
[ y vy ]
>>> s.X
[2.5 1]
>>> s.data('y')
([0, 1], [1, 2.5])
If you encounter problems, have questions, or would like to suggest improvements, please open an Issue in this repository.
Enhancements and modules in latest release:
- Complete state space objects mechanism
- Seeker and radar measurements
- Kalman filter and Extended Kalman filter
- YOLOv3 object detection API
- Datasets fetching to run examples
- Documentation
