state-rules 2026.3.35

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Why?

My (author) motivation is to be able to generally describe systems that respond to change.

Related (but not the same):

• reactive programming libraries: Doesn't focus on a 'state'
• dynamical systems pathsim: This library doesn't, at the face of it, look like it can do what pathsim does, but I think sim descriptions could be mapped somehow.

How?

Rules/functions are repeatedly applied to a 'state' (dict) until there are no more changes.

import state_rules.main as rm
r = rm.Rules({'x':1})
#
@r.register({'return': 'x', 'x': 'x' })
def f1(x):
    return x+x

r.run(5)
r.log

[
Iteration(i=0, state={'x': 1})
Iteration(i=1, state={'x': 2})
Iteration(i=2, state={'x': 4})
Iteration(i=3, state={'x': 8})
Iteration(i=4, state={'x': 16})
Iteration(i=5, state={'x': 32})
]

Tips

• The state is a (flat) dictionary but you can use a fancy dotted dict if you want more structure. Then, use use a function to get at a key. In the example above: x -> lambda k: k=='x' or k.endswith('.x')
• Cache function calls (yourself)
• Define your notion of dictionary equality