Getting Started

In this guide you will install pyCA and watch your first cellular automaton compute.

Installation

pyCA requires Python 3.8 or newer; pip pulls in numpy and matplotlib. Clone the repository and install it into a virtual environment:

git clone https://github.com/EternalTime/CellularAutomata.git
cd CellularAutomata
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -e .

The -e flag installs in editable mode, so changes to the source are picked up immediately. Check the install:

>>> import pyCA

First automaton

Rule 30 from a single live cell, the canonical demonstration that a trivial rule need not behave trivially:

import numpy as np
import matplotlib.pyplot as plt
from pyCA import ECA

state = np.zeros(301, dtype=int)
state[150] = 1

ca = ECA(30, state)
ca.run(150)

plt.imshow(ca.spacetime(), cmap='binary', interpolation='nearest')
plt.axis('off')
plt.show()

The left edge of the triangle is periodic, the right edge is intricate, and the center column is random enough that it once served as a random number generator. Call ca.play() to watch an automaton evolve live instead; close the window to stop.

Now ask the question the rest of this library is built to answer: how much information is in that picture?

from pyCA import measures

print(measures.block_entropy(ca.spacetime(), k=3))
print(measures.entropy_rate(ca.spacetime(), k=3))

Hold those numbers in hand as you read Information Measures.