c4dynamics 2.0.1

The framework for algorithms engineering with Python.

Tsipor Dynamics

Algorithms Engineering and Development

Tsipor (bird) Dynamics (c4dynamics) is the open-source framework of algorithms development for objects in space and time.

Complete Documentation: https://c4dynamics.github.io/C4dynamics/

Motivation

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.

Installation

• PIP

>>> pip install c4dynamics

• GitHub

To run the latest GitHub version, download c4dynamics:

https://github.com/C4dynamics/C4dynamics

      

Install the required packages:

>>> pip install -r requirements.txt

Quickstart

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])

Load an object detection module (YOLO):

>>> yolodet = c4d.detectors.yolo(height = height, width = width)

Define errors to a general-purpose seeker with C4dynamics:

>>> rdr = c4d.seekers.radar(sf = 0.9, bias = 0, noisestd = 1)

Define a linear Kalman Filter, perform a prediction and an update:

>>> pt.filter = c4d.filters.kalman(np.hstack((z, np.zeros(2))), P, A, H, Q, R)

>>> pt.filter.predict()

>>> pt.filter.correct(measure)

Define a point in space (pre-defined state) with some initial conditions:

>>> pt = c4d.datapoint(x = 1000, vx = 100)

Define a body in space (pre-defined state) with some initial conditions:

>>> body = c4d.rigidbody(theta = 15 * 3.14 / 180)

Architecture

For Architecture & Roadmap, see the Wiki page.