# Continuous Automata Network Automata needn't consist of discrete activities. The units in an automaton can also take on continuous-valued activities (i.e. states). The example below implements a continuous-valued Cellular Automaton from Wolfram's NKS book, found on page 157: ```python import math import netomaton as ntm network = ntm.topology.cellular_automaton(n=200) initial_conditions = [0.0]*100 + [1.0] + [0.0]*99 # NKS page 157 def activity_rule(ctx): activities = ctx.neighbourhood_activities result = (sum(activities) / len(activities)) * (3 / 2) frac, whole = math.modf(result) return frac trajectory = ntm.evolve(initial_conditions=initial_conditions, network=network, activity_rule=activity_rule, timesteps=150) ntm.plot_activities(trajectory) ``` <img src="../../resources/continuous_ca.png" width="40%"/> The full source code for this example can be found [here](continuous_automata_demo.py).