Published in the Journal of Open Source Software (JOSS)

Tsipor Dynamics

Algorithms Engineering and Development

Tsipor (bird) Dynamics (c4dynamics) is the Python framework for state-space modeling and algorithm development.

Stop starting from scratch every time you change systems

Same workflow. Different systems.

Most engineers rebuild everything. c4dynamics keeps the structure fixed.

What is c4dynamics?

c4dynamics is a Python framework for building, simulating, estimating, and controlling physical systems — without resetting your workflow every time the system changes.

It gives you one consistent way to:

• define a system
• simulate its evolution
• estimate its state
• design control

Across:

robotics · aerospace · autonomous systems · navigation

🧪 Examples

Real implementations of modeling, estimation, and control

Not isolated demos. They all follow the same structure.

Cascade PID	EKF State Estimation	ArduPilot Simulation
Quadcopter figure-8 tracking	Quadcopter GPS/IMU/MAG sensor fusion	Quadcopter figure-8 under ArduCopter control
6-DOF Simulation	Extended Kalman Filter	Model Predictive Control
Proportional navigation guidance	Ballistic coefficient estimation	Vehicle steering
YOLO3 + Kalman Filter	Neural Network based Controller	YOLO11 + Kalman Filter
Vehicle tracking	Helicopter Learning Controller	Vehicle tracking (newer detector)

The switching problem

Switching systems shouldn’t feel like starting over.

But it does:

• new models
• new simulation structure
• new estimation logic
• new control pipeline

You don’t just learn new physics.

You rebuild everything.

The workflow

Keep the workflow. Change the physics.

c4dynamics enforces a consistent structure:

define → simulate → estimate → control

So when the system changes:

your thinking doesn’t.

Core principle

Physics first. Programming second.

• Code implements
• Models define reality
• Algorithms follow structure

What you get

• state-based modeling primitives
• simulation infrastructure
• Kalman / Extended Kalman filters
• sensor and detection modules
• reinforcement learning environments
• OpenCV / Open3D integration
• Monte Carlo simulation support

Who this is for

• control engineers
• robotics engineers
• aerospace engineers
• autonomy developers

Especially if you’ve felt:

“I know this stuff… but I don’t use it.”

Quickstart

>>> import c4dynamics as c4d

define system

s = c4d.state(y=1, vy=0.5)

simulate

F = [[1, 1],
     [0, 1]]

s.X += F @ s.X
s.store(t=1)

Requirements

• Python >= 3.8
• Required packages are listed in requirements.txt

Installation

For detailed instructions on installing c4dynamics, including setup for virtual environments, Python version requirements, and troubleshooting, refer to the c4dynamics setup guide.

• PIP

>>> pip install c4dynamics

• GitHub

To run the latest GitHub version, download the repo and install required packages:

>>> pip install -r requirements.txt

Documentation

📘 https://c4dynamics.github.io/c4dynamics/

• concepts
• API
• examples
• tutorials

Contributing

This is not just a library.

It’s a shared way of building systems.

• build examples
• improve structure
• explore new systems

Support

If you encounter problems, have questions, or would like to suggest improvements, please open an Issue in this repository.

New system. Same workflow.