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A package for complexity science research

Project description

Complexity Science Package

This package is created mainly for convenience in doing complex systems research.

The design implementation and structure of the package is motivated for scalability and ease of use. Optimization maybe limited by the design, or the language itself.


pip install complexity-science

Cellular Automata

Basic usage:

import as cs

1-D CA

ca = cs.wolfram(N, 20) #creates a 1-Dimensional CA of N cells with wolfram rule number 20

ca.initialize([50]) #initializes the 50th cell of the CA #returns the resulting state of the CA for 100 iterations following the rule and plots the result with a default colormap

2-D CA

model = cs.brians_brain([128,128], toroidal=False)

Initializes a CA based on brians brain with toroidal boundary conditions

Available models[dim], toroidal=True)

cs.applause([dim], alpha=1)

cs.mpa([dim], percent_mpa=0)

The dim parameter is the only required parameter for all models, others are optional. Parameters are set to default value if not specified.

alpha and percent_mpa are examples of model specific parameters.

See model documentation for more information.

Modifying Parameters

Models can be initialized randomly, binary, by index, using different functions e.g.




Models with specific parameters can be modified by this function.

model.modify_rule(parameter = new_value)

See model documentation for more information on available parameters

Adding rules and models

Please contact the author for more information.



If iteration is not set, animation will continue infinitely.

Animation in jupyter notebook

import as cs
import matplotlib.pyplot as plt

from matplotlib import animation, rc
from IPython.display import HTML

An example Game Of Life animation will be as follows

game =[50,50])
anim = game.jyp_anim()


Network Fragmentation



git clone

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