stateforward.hsm 0.3.0

Hierarchical State Machine implementation for Python with asyncio support

stateforward.hsm

A high-performance, hierarchical state machine (HSM) implementation for Python with asyncio support. This library provides a declarative, async-first approach to building complex state machines with efficient O(1) runtime performance.

Features

• Hierarchical States: Support for nested state machines with proper scoping
• Async/Await: Full asyncio support for actions, guards, effects, and activities
• Precomputed Lookups: O(1) performance through precomputed transition and element maps
• Event-Driven: Deterministic event processing with guards and effects
• Timers: Built-in support for one-shot and recurring timers
• Activities: Long-running async operations that auto-cancel on state exit
• Error Handling: Automatic error event dispatching for robust error recovery
• Type Safety: Strong typing with comprehensive type hints

Installation

pip install stateforward.hsm

Quick Start

import asyncio
import hsm

class Counter(hsm.Instance):
    def __init__(self):
        super().__init__()
        self.value = 0

    @staticmethod
    async def increment(ctx, self, event):
        self.value += 1
        print(f"Count: {self.value}")

    model = hsm.define('Counter',
        hsm.initial(hsm.target('counting')),
        hsm.state('counting',
            hsm.transition(
                hsm.on('inc'),
                hsm.target('.'),  # Self-transition
                hsm.effect(increment)
            )
        )
    )

async def main():
    instance = Counter()
    ctx = hsm.Context()

    # Start the state machine
    sm = await hsm.start(ctx, instance, Counter.model)
    print(f"Initial state: {sm.state()}")

    # Dispatch events
    await sm.dispatch(hsm.Event('inc'))
    await sm.dispatch(hsm.Event('inc'))

    print(f"Final count: {instance.value}")

    # Clean shutdown
    await hsm.stop(sm)

if __name__ == "__main__":
    asyncio.run(main())

Core Concepts

Declarative Definition

State machines are defined using a declarative API that precomputes all lookups for optimal runtime performance:

model = hsm.define(
    "MyMachine",
    hsm.initial(hsm.target("ready")),
    hsm.state("ready",
        hsm.transition(hsm.on("start"), hsm.target("running"))
    ),
    hsm.state("running",
        hsm.transition(hsm.on("stop"), hsm.target("ready"))
    )
)

States and Hierarchy

• hsm.state(name, ...children): Regular states that can contain nested states
• hsm.initial(hsm.target(path)): Defines the default entry point
• hsm.final(name): Terminal states for completion handling
• hsm.choice(name, ...transitions): Conditional branching logic

Transitions

Transitions define how the machine responds to events:

hsm.transition(
    hsm.on("event_name"),           # Event trigger
    hsm.source("current_state"),    # Optional explicit source
    hsm.target("next_state"),       # Destination state
    hsm.guard(async_guard_func),    # Optional condition
    hsm.effect(async_effect_func)   # Optional side effect
)

Actions and Behaviors

All behavioral functions are async and receive (ctx, instance, event):

• hsm.entry(action): Executed when entering a state
• hsm.exit(action): Executed when exiting a state
• hsm.effect(action): Executed during transitions
• hsm.guard(guard_func): Returns boolean to allow/block transitions
• hsm.activity(activity_func): Long-running operations that auto-cancel

Timers

Built-in timer support for time-based transitions:

from datetime import timedelta

@staticmethod
async def one_second(ctx, self, event):
    return timedelta(seconds=1)

hsm.transition(
    hsm.after(one_second),  # One-shot timer
    hsm.target("next_state")
)

hsm.transition(
    hsm.every(one_second),  # Recurring timer
    hsm.target(".")
)

Timer scheduling is controlled by the runtime clock. Use hsm.Config(Clock=...) when tests or embedded runtimes need deterministic time:

from datetime import timedelta

pending = []

async def manual_sleep(duration: timedelta):
    future = asyncio.get_running_loop().create_future()
    pending.append((duration, future))
    await future

clock = hsm.Clock(sleep=manual_sleep)
sm = await hsm.started(ctx, instance, model, hsm.Config(Clock=clock))

# Later in a test, release the timer manually.
pending[0][1].set_result(None)

Error Handling

Exceptions in actions automatically dispatch hsm_error events:

class RobustMachine(hsm.Instance):
    @staticmethod
    async def risky_action(ctx, self, event):
        if random.random() < 0.1:
            raise ValueError("Something went wrong!")

    @staticmethod
    async def handle_error(ctx, self, event):
        print(f"Error occurred: {event.data}")
        # Recovery logic here

    model = hsm.define('RobustMachine',
        hsm.state('working',
            hsm.effect(risky_action),
            hsm.transition(hsm.on('hsm_error'), hsm.target('error_state'))
        ),
        hsm.state('error_state',
            hsm.entry(handle_error)
        )
    )

Advanced Usage

Class-Based Pattern

The recommended pattern is to define your state machine as a class:

class TrafficLight(hsm.Instance):
    def __init__(self):
        super().__init__()
        self.cycles = 0

    @staticmethod
    async def change_to_green(ctx, self, event):
        print("🟢 Green light")
        self.cycles += 1

    model = hsm.define('TrafficLight',
        hsm.initial(hsm.target('red')),
        hsm.state('red',
            hsm.transition(hsm.on('timer'), hsm.target('green'))
        ),
        hsm.state('green',
            hsm.entry(change_to_green),
            hsm.transition(hsm.on('timer'), hsm.target('yellow'))
        ),
        hsm.state('yellow',
            hsm.transition(hsm.on('timer'), hsm.target('red'))
        )
    )

Path Resolution

State paths are resolved relative to the transition's source state:

• Child: 'child_state'
• Parent: '..'
• Sibling: '../sibling_state'
• Absolute: '/MachineName/state'
• Self: '.'

Runtime Lifecycle

# Create instance and context
instance = MyMachine()
ctx = hsm.Context()

# Start the machine
sm = await hsm.start(ctx, instance, MyMachine.model)

# Or configure runtime identity, initial data, and timer scheduling
sm = await hsm.started(ctx, instance, MyMachine.model, hsm.Config(
    ID="machine-1",
    Name="/RuntimeName",
    Data={"boot": True},
    Clock=hsm.DefaultClock,
))

# Check current state
current_state = sm.state()

# Dispatch events
await sm.dispatch(hsm.Event('my_event', data={'key': 'value'}))

# Stop the machine
await hsm.stop(sm)

API Reference

Core Functions

• hsm.define(name, ...elements): Create a state machine model
• hsm.start(ctx, instance, model): Start a state machine instance
• hsm.stop(instance): Stop and cleanup a state machine

State Elements

• hsm.state(name, ...children): Define a state
• hsm.initial(target, ...effects): Define initial state
• hsm.final(name): Define final state
• hsm.choice(name, ...transitions): Define choice pseudostate

Transitions

• hsm.transition(...conditions): Define a transition
• hsm.on(event_name): Event condition
• hsm.after(duration_func): Timer condition (one-shot)
• hsm.every(duration_func): Timer condition (recurring)
• hsm.target(path): Target state
• hsm.guard(func): Guard condition
• hsm.effect(func): Transition effect

Actions

• hsm.entry(func): Entry action
• hsm.exit(func): Exit action
• hsm.activity(func): Long-running activity

Runtime

• hsm.Instance: Base class for state machine instances
• hsm.Context: Execution context with cancellation support
• hsm.Config: Runtime configuration for identity, initial data, and clock injection
• hsm.Clock: Runtime scheduling hook used by after and every timers
• hsm.DefaultClock: Default asyncio-backed runtime clock
• hsm.Event(name, data=None): Event object

Performance

This implementation uses precomputed lookup tables for O(1) transition resolution, making it suitable for high-performance applications. All behavioral functions are async-first, enabling efficient concurrent execution.

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please see the main HSM repository for contribution guidelines.