philiprehberger-state-machine 0.3.1

Lightweight finite state machine with guards and callbacks.

philiprehberger-state-machine

Lightweight finite state machine with guards, callbacks, and visualization.

Installation

pip install philiprehberger-state-machine

Usage

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["pending", "confirmed", "shipped", "delivered"],
    initial="pending",
    transitions=[
        ("pending", "confirmed", "confirm"),
        ("confirmed", "shipped", "ship"),
        ("shipped", "delivered", "deliver"),
    ],
)

sm.trigger("confirm")
print(sm.state)  # "confirmed"

Checking Available Transitions

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["pending", "confirmed", "shipped"],
    initial="pending",
    transitions=[
        ("pending", "confirmed", "confirm"),
        ("confirmed", "shipped", "ship"),
    ],
)

sm.can("confirm")  # True
sm.can("ship")     # False

Callbacks

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["pending", "confirmed", "shipped"],
    initial="pending",
    transitions=[
        ("pending", "confirmed", "confirm"),
        ("confirmed", "shipped", "ship"),
    ],
)

sm.on_enter("confirmed", lambda state, event: print(f"Entered {state} via {event}"))
sm.on_exit("pending", lambda state, event: print(f"Left {state} via {event}"))

sm.trigger("confirm")
# Left pending via confirm
# Entered confirmed via confirm

Guard Conditions

Guards are optional callables that receive a context dict and must return True to allow the transition. If a guard returns falsy, InvalidTransitionError is raised.

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["draft", "published"],
    initial="draft",
    transitions=[],
)

sm.add_transition("draft", "published", "publish", guard=lambda ctx: ctx.get("has_title", False))

sm.trigger("publish", context={"has_title": True})   # succeeds
print(sm.state)  # "published"

Transition Context

Pass a context dict to trigger() to share data with guards and callbacks.

sm.trigger("confirm", context={"user": "alice", "approved": True})

If no context is provided, an empty dict is passed to guards.

History and Reset

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["pending", "confirmed", "shipped"],
    initial="pending",
    transitions=[
        ("pending", "confirmed", "confirm"),
        ("confirmed", "shipped", "ship"),
    ],
)

sm.trigger("confirm")
sm.trigger("ship")
print(sm.history)  # ["pending", "confirmed"]

sm.reset()
print(sm.state)    # "pending"
print(sm.history)  # []

Timeout-Based Automatic Transitions

Define transitions that fire automatically after a state has been active for a given number of seconds.

from philiprehberger_state_machine import StateMachine
import time

sm = StateMachine(
    states=["idle", "processing", "timeout_state"],
    initial="idle",
    transitions=[("idle", "processing", "start")],
)

sm.add_timeout("processing", "timeout_state", seconds=5.0)

sm.trigger("start")
print(sm.state)  # "processing"

time.sleep(6)
print(sm.state)  # "timeout_state"

Snapshot and Restore

Capture and restore the machine's state and history for serialization or checkpointing.

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["a", "b", "c"],
    initial="a",
    transitions=[("a", "b", "go"), ("b", "c", "go")],
)

sm.trigger("go")
snap = sm.snapshot()
print(snap)  # {"state": "b", "history": ["a"]}

sm.trigger("go")
print(sm.state)  # "c"

sm.restore(snap)
print(sm.state)  # "b"

Visualization Export

Export the state machine as a DOT (Graphviz) or Mermaid diagram string.

from philiprehberger_state_machine import StateMachine

sm = StateMachine(
    states=["pending", "confirmed", "shipped"],
    initial="pending",
    transitions=[
        ("pending", "confirmed", "confirm"),
        ("confirmed", "shipped", "ship"),
    ],
)

print(sm.to_dot())
# digraph StateMachine {
#     rankdir=LR;
#
#     "pending" [shape=doublecircle];
#     "confirmed" [shape=circle];
#     "shipped" [shape=circle];
#
#     "pending" -> "confirmed" [label="confirm"];
#     "confirmed" -> "shipped" [label="ship"];
# }

print(sm.to_mermaid())
# stateDiagram-v2
#     [*] --> pending
#     pending --> confirmed : confirm
#     confirmed --> shipped : ship

API

Function / Class	Description
StateMachine(states, initial, transitions)	Create a state machine with given states, initial state, and transitions
StateMachine.state	Current state (read-only property)
StateMachine.history	List of past states (read-only property)
StateMachine.trigger(event, context=None)	Execute a transition or raise InvalidTransitionError. Pass optional context dict to guards.
StateMachine.can(event)	Return whether the event is valid from the current state
StateMachine.add_transition(from_state, to_state, event, guard=None)	Add a transition with an optional guard callable
StateMachine.on_enter(state, callback)	Register a callback for entering a state
StateMachine.on_exit(state, callback)	Register a callback for exiting a state
StateMachine.reset()	Reset to initial state and clear history
StateMachine.add_timeout(state, target, seconds)	Define an automatic transition after seconds in state
StateMachine.snapshot()	Return a serializable dict of current state and history
StateMachine.restore(snapshot)	Restore the machine from a snapshot dict
StateMachine.to_dot()	Return a DOT/Graphviz string of the state machine
StateMachine.to_mermaid()	Return a Mermaid state diagram string
InvalidTransitionError	Raised on invalid transitions; has .state and .event attributes

Development

pip install -e .
python -m pytest tests/ -v

Support

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License

MIT