stackbased-fsm 0.1

Stack-Based Finite State Machine

Welcome to Stack-Based Finite State Machine

This project provides a stack-based finite state machine.

A state machine has a stack. States are pushed onto and popped from stack. The state machine will call 'enter' and 'exit' methods on the states when they are 'pushed' and 'popped'. The states will then push and pop other states, according to their individual implementation. States have access to the stack, and also to a context object containing "global" variables.

A few basic state classes are also provided, with which "programs" can be defined. Programs define a nested set of state types, from which states will be constructed.

Installation

$ pip install stackbased-fsm

Getting started

We can use the Context class to define an example context object.

from stackbased_fsm import Context

class ExampleContext(Context):
    def __init__(self):
        self.a = 1
        self.b = "2"
        self.c = []


context = ExampleContext()

We can use the StateMachine class to construct state machine object that uses the context object.

from stackbased_fsm import StateMachine

sm = StateMachine(context=context)

The state machine has a stack of states. All states on the state machine's stack will have access to the context object. The attributes of the context object are like the "global" variables for the states of the state machine.

We can use the State class to define types of state for our state machine. It is a generic class, which has one type variable that is expected to be a context class.

from stackbased_fsm import State

class ExampleState(State[ExampleContext]):
    pass

We can use the ExampleState as a base class to define IncrementA, AssignB, and AppendC which will increment a, assign to b, and append to c respectively.

class IncrementA(ExampleState):
    def enter(self) -> None:
        self.context.a += 1
        self.pop()


class AssignB(ExampleState):
    def enter(self) -> None:
        self.context.b = "def"
        self.pop()


class AppendC(ExampleState):
    def enter(self) -> None:
        self.context.c.append("xyz")
        self.pop()

The state machine has a run() method which can be used by a client to pass a "program" to the state machine. A program is a type construct, in the simplest case a single state class, and more usually a nested set of subscripted generic state classes (see below).

sm.run(IncrementA)

assert context.a == 2

We can use the SequenceOfStates class to define a sequence of states. It is a variadic generic state class, and so can take any number of state classes as its type arguments.

from stackbased_fsm import SequenceOfStates

ExampleSequence = SequenceOfStates[IncrementA, AssignB, AppendC]

We can run the sequence and check the context has been updated.

sm.run(ExampleSequence)

assert context.a == 3
assert context.b == "def"
assert context.c == ["xyz"]

The state machine object has methods to push(), pop(), and poppush() states on the stack. State objects have four methods, enter(), exit(), suspend() and resume().

When a program is passed to the state machine using the run() method, a state object is constructed from the root type of the program (a type of state). The state object is then pushed onto the stack, and its enter() method is called. The state machine then iterates over the stack, detecting when states have been pushed and popped, calling methods on the stacked states accordingly, until the stack is empty.

After a state has been pushed onto the stack, the state's enter() method will be called. After a state is popped off the stack, the state's exit() method will be called. A state's suspend() method will be called when another state is pushed on top of it, and its resume() method will be called after that state is popped off.

For example, the SequenceOfStates works in the following way. When it is pushed onto the stack, its enter() method is called. Its enter() method will push the first item in the sequence onto the stack. Its suspend() method is then called, and then the enter() method of the first item is called. If the pushed state neither pushes or pops another state, it will be automatically popped and its exit() method will be called. When that state is popped, the resume() method of the sequence will be called, which will push the next item in the sequence onto the stack. After all items have been pushed onto the stack, the sequence's resume() method will call pop(), which will result in itself being popped off the stack. This may result in an empty stack, and the end of a program.

Conditions and conditioned states

The "condition" state class ConditionState can be used to define conditions. When the enter() method of a condition state is called, its condition() method will be called. The condition() method is abstract on the ConditionState class and is expected to be implemented on subclasses. This method is expected to return a Boolean value (true or false). This value will be used by the condition state's enter() method to call the set_condition_result() method of state below it on the stack, which is expected to be a "conditioned" state, and therefore have such a method.

In the example below, the class AIsLessThan5 has a condition() method that returns True if a is less than 5.

from stackbased_fsm import ConditionState


class AIsLessThan5(ConditionState):
    def condition(self):
        return self.context.a < 5

Conditions are used by conditioned states. For example, the classes RepeatUntil, RepeatWhile are conditioned states. These conditioned states take two type variables. The first type variable is expected to be a type of condition. The second type variable is expected to be a type of state. These conditioned states alternate between pushing the condition state and then pushing the other state. RepeatUntil continues in this way until the condition is true. RepeatWhile continues in this way until the condition is false.

In the example below, ExampleLoop will push IncrementA again and again so long as a is less than 5.

from stackbased_fsm import RepeatWhile

ExampleLoop = RepeatWhile[AIsLessThan5, IncrementA]

sm.run(ExampleLoop)

The result is the value of a is 5.

assert context.a == 5, context.a

Conditions can be grouped with AnyCondition and AllConditions (aliased as Or and And respectively).

Developers

Install Poetry

The first thing is to check you have Poetry installed.

$ poetry --version

If you don't, then please install Poetry.

It will help to make sure Poetry's bin directory is in your PATH environment variable.

But in any case, make sure you know the path to the poetry executable. The Poetry installer tells you where it has been installed, and how to configure your shell.

Please refer to the Poetry docs for guidance on using Poetry.

Setup for PyCharm users

You can easily obtain the project files using PyCharm (menu "Git > Clone..."). PyCharm will then usually prompt you to open the project.

Open the project in a new window. PyCharm will then usually prompt you to create a new virtual environment.

Create a new Poetry virtual environment for the project. If PyCharm doesn't already know where your poetry executable is, then set the path to your poetry executable in the "New Poetry Environment" form input field labelled "Poetry executable". In the "New Poetry Environment" form, you will also have the opportunity to select which Python executable will be used by the virtual environment.

PyCharm will then create a new Poetry virtual environment for your project, using a particular version of Python, and also install into this virtual environment the project's package dependencies according to the pyproject.toml file, or the poetry.lock file if that exists in the project files.

You can add different Poetry environments for different Python versions, and switch between them using the "Python Interpreter" settings of PyCharm. If you want to use a version of Python that isn't installed, either use your favourite package manager, or install Python by downloading an installer for recent versions of Python directly from the Python website.

Once project dependencies have been installed, you should be able to run tests from within PyCharm (right-click on the tests folder and select the 'Run' option).

Because of a conflict between pytest and PyCharm's debugger and the coverage tool, you may need to add --no-cov as an option to the test runner template. Alternatively, just use the Python Standard Library's unittest module.

You should also be able to open a terminal window in PyCharm, and run the project's Makefile commands from the command line (see below).

Setup from command line

Obtain the project files, using Git or suitable alternative.

In a terminal application, change your current working directory to the root folder of the project files. There should be a Makefile in this folder.

Use the Makefile to create a new Poetry virtual environment for the project and install the project's package dependencies into it, using the following command.

$ make install-packages

It's also possible to also install the project in 'editable mode'.

$ make install

Please note, if you create the virtual environment in this way, and then try to open the project in PyCharm and configure the project to use this virtual environment as an "Existing Poetry Environment", PyCharm sometimes has some issues (don't know why) which might be problematic. If you encounter such issues, you can resolve these issues by deleting the virtual environment and creating the Poetry virtual environment using PyCharm (see above).

Project Makefile commands

You can run tests using the following command.

$ make test

You can check the formatting of the code using the following command.

$ make lint

You can reformat the code using the following command.

$ make fmt

Tests belong in ./tests. Code-under-test belongs in ./stackbased_fsm.

See the Python eventsourcing project for more information and guidance about developing event-sourced applications.

Edit package dependencies in pyproject.toml. Update installed packages (and the poetry.lock file) using the following command.

$ make update-packages