Quick Start

What makes Automat different?

There are dozens of libraries on PyPI implementing state machines. So it behooves me to say why yet another one would be a good idea.

Automat is designed around this principle: while organizing your code around state machines is a good idea, your callers don’t, and shouldn’t have to, care that you’ve done so. In Python, the “input” to a stateful system is a method call; the “output” may be a method call, if you need to invoke a side effect, or a return value, if you are just performing a computation in memory. Most other state-machine libraries require you to explicitly create an input object, provide that object to a generic “input” method, and then receive results, sometimes in terms of that library’s interfaces and sometimes in terms of classes you define yourself.

For example, a snippet of the coffee-machine example above might be implemented as follows in naive Python:

class CoffeeMachine(object):
    def brew_button(self):
        if self.has_water and self.has_beans and not self.is_lid_open:
            self.heat_the_heating_element()
            # ...

With Automat, you’d create a class with a automat.MethodicalMachine attribute:

from automat import MethodicalMachine

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

and then you would break the above logic into two pieces - the brew_button input, declared like so:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    @_machine.input()
    def brew_button(self):
        "The user pressed the 'brew' button."

It wouldn’t do any good to declare a method body on this, however, because input methods don’t actually execute their bodies when called; doing actual work is the output’s job:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.output()
    def _heat_the_heating_element(self):
        "Heat up the heating element, which should cause coffee to happen."
        self._heating_element.turn_on()

As well as a couple of states - and for simplicity’s sake let’s say that the only two states are have_beans and dont_have_beans:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.state()
    def have_beans(self):
        "In this state, you have some beans."

    @_machine.state(initial=True)
    def dont_have_beans(self):
        "In this state, you don't have any beans."

dont_have_beans is the initial state because CoffeeBrewer starts without beans in it.

(And another input to put some beans in:)

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.input()
    def put_in_beans(self):
        "The user put in some beans."

Finally, you hook everything together with the upon() method of the functions decorated with _machine.state:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    # When we don't have beans, upon putting in beans, we will then have beans
    # (and produce no output)
    dont_have_beans.upon(put_in_beans, enter=have_beans, outputs=[])

    # When we have beans, upon pressing the brew button, we will then not have
    # beans any more (as they have been entered into the brewing chamber) and
    # our output will be heating the heating element.
    have_beans.upon(brew_button, enter=dont_have_beans,
                    outputs=[_heat_the_heating_element])

To users of this coffee machine class though, it still looks like a POPO (Plain Old Python Object):

>>> coffee_machine = CoffeeMachine()
>>> coffee_machine.put_in_beans()
>>> coffee_machine.brew_button()

All of the inputs are provided by calling them like methods, all of the outputs are automatically invoked when they are produced according to the outputs specified to automat.MethodicalState.upon() and all of the states are simply opaque tokens - although the fact that they’re defined as methods like inputs and outputs allows you to put docstrings on them easily to document them.

How do I get the current state of a state machine?

Don’t do that.

One major reason for having a state machine is that you want the callers of the state machine to just provide the appropriate input to the machine at the appropriate time, and not have to check themselves what state the machine is in. So if you are tempted to write some code like this:

if connection_state_machine.state == "CONNECTED":
    connection_state_machine.send_message()
else:
    print("not connected")

Instead, just make your calling code do this:

connection_state_machine.send_message()

and then change your state machine to look like this:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.state()
    def connected(self):
        "connected"
    @_machine.state()
    def not_connected(self):
        "not connected"
    @_machine.input()
    def send_message(self):
        "send a message"
    @_machine.output()
    def _actually_send_message(self):
        self._transport.send(b"message")
    @_machine.output()
    def _report_sending_failure(self):
        print("not connected")
    connected.upon(send_message, enter=connected, [_actually_send_message])
    not_connected.upon(send_message, enter=not_connected, [_report_sending_failure])

so that the responsibility for knowing which state the state machine is in remains within the state machine itself.

Input for Inputs and Output for Outputs

Quite often you want to be able to pass parameters to your methods, as well as inspecting their results. For example, when you brew the coffee, you might expect a cup of coffee to result, and you would like to see what kind of coffee it is. And if you were to put delicious hand-roasted small-batch artisanal beans into the machine, you would expect a better cup of coffee than if you were to use mass-produced beans. You would do this in plain old Python by adding a parameter, so that’s how you do it in Automat as well.

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.input()
    def put_in_beans(self, beans):
        "The user put in some beans."

However, one important difference here is that we can’t add any implementation code to the input method. Inputs are purely a declaration of the interface; the behavior must all come from outputs. Therefore, the change in the state of the coffee machine must be represented as an output. We can add an output method like this:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.output()
    def _save_beans(self, beans):
        "The beans are now in the machine; save them."
        self._beans = beans

and then connect it to the put_in_beans by changing the transition from dont_have_beans to have_beans like so:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    dont_have_beans.upon(put_in_beans, enter=have_beans,
                         outputs=[_save_beans])

Now, when you call:

coffee_machine.put_in_beans("real good beans")

the machine will remember the beans for later.

So how do we get the beans back out again? One of our outputs needs to have a return value. It would make sense if our brew_button method returned the cup of coffee that it made, so we should add an output. So, in addition to heating the heating element, let’s add a return value that describes the coffee. First a new output:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.output()
    def _describe_coffee(self):
        return "A cup of coffee made with {}.".format(self._beans)

Note that we don’t need to check first whether self._beans exists or not, because we can only reach this output method if the state machine says we’ve gone through a set of states that sets this attribute.

Now, we need to hook up _describe_coffee to the process of brewing, so change the brewing transition to:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    have_beans.upon(brew_button, enter=dont_have_beans,
                    outputs=[_heat_the_heating_element,
                             _describe_coffee])

Now, we can call it:

>>> coffee_machine.brew_button()
[None, 'A cup of coffee made with real good beans.']

Except… wait a second, what’s that None doing there?

Since every input can produce multiple outputs, in automat, the default return value from every input invocation is a list. In this case, we have both _heat_the_heating_element and _describe_coffee outputs, so we’re seeing both of their return values. However, this can be customized, with the collector argument to upon(); the collector is a callable which takes an iterable of all the outputs’ return values and “collects” a single return value to return to the caller of the state machine.

In this case, we only care about the last output, so we can adjust the call to upon() like this:

class CoffeeBrewer(object):
    _machine = MethodicalMachine()

    # ...

    have_beans.upon(brew_button, enter=dont_have_beans,
                    outputs=[_heat_the_heating_element,
                             _describe_coffee],
                    collector=lambda iterable: list(iterable)[-1]
    )

And now, we’ll get just the return value we want:

>>> coffee_machine.brew_button()
'A cup of coffee made with real good beans.'

If I can’t get the state of the state machine, how can I save it to (a database, an API response, a file on disk…)

There are APIs for serializing the state machine.

First, you have to decide on a persistent representation of each state, via the serialized= argument to the MethodicalMachine.state() decorator.

Let’s take this very simple “light switch” state machine, which can be on or off, and flipped to reverse its state:

class LightSwitch(object):
    _machine = MethodicalMachine()

    @_machine.state(serialized="on")
    def on_state(self):
        "the switch is on"

    @_machine.state(serialized="off", initial=True)
    def off_state(self):
        "the switch is off"

    @_machine.input()
    def flip(self):
        "flip the switch"

    on_state.upon(flip, enter=off_state, outputs=[])
    off_state.upon(flip, enter=on_state, outputs=[])

In this case, we’ve chosen a serialized representation for each state via the serialized argument. The on state is represented by the string “on”, and the off state is represented by the string “off”.

Now, let’s just add an input that lets us tell if the switch is on or not.

from operator import itemgetter

first = itemgetter(0)

class LightSwitch(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.input()
    def query_power(self):
        "return True if powered, False otherwise"

    @_machine.output()
    def _is_powered(self):
        return True

    @_machine.output()
    def _not_powered(self):
        return False

    on_state.upon(
        query_power, enter=on_state, outputs=[_is_powered], collector=first
    )
    off_state.upon(
        query_power, enter=off_state, outputs=[_not_powered], collector=first
    )

To save the state, we have the MethodicalMachine.serializer() method. A method decorated with @serializer() gets an extra argument injected at the beginning of its argument list: the serialized identifier for the state. In this case, either “on” or “off”. Since state machine output methods can also affect other state on the object, a serializer method is expected to return all relevant state for serialization.

For our simple light switch, such a method might look like this:

class LightSwitch(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.serializer()
    def save(self, state):
        return {"is-it-on": state}

Serializers can be public methods, and they can return whatever you like. If necessary, you can have different serializers - just multiple methods decorated with @_machine.serializer() - for different formats; return one data-structure for JSON, one for XML, one for a database row, and so on.

When it comes time to unserialize, though, you generally want a private method, because an unserializer has to take a not-fully-initialized instance and populate it with state. It is expected to return the serialized machine state token that was passed to the serializer, but it can take whatever arguments you like. Of course, in order to return that, it probably has to take it somewhere in its arguments, so it will generally take whatever a paired serializer has returned as an argument.

So our unserializer would look like this:

class LightSwitch(object):
    _machine = MethodicalMachine()

    # ...

    @_machine.unserializer()
    def _restore(self, blob):
        return blob["is-it-on"]

Generally you will want a classmethod deserialization constructor which you write yourself to call this, so that you know how to create an instance of your own object, like so:

class LightSwitch(object):
    _machine = MethodicalMachine()

    # ...

    @classmethod
    def from_blob(cls, blob):
        self = cls()
        self._restore(blob)
        return self

Saving and loading our LightSwitch along with its state-machine state can now be accomplished as follows:

>>> switch1 = LightSwitch()
>>> switch1.query_power()
False
>>> switch1.flip()
[]
>>> switch1.query_power()
True
>>> blob = switch1.save()
>>> switch2 = LightSwitch.from_blob(blob)
>>> switch2.query_power()
True

More comprehensive (tested, working) examples are present in docs/examples.

Go forth and machine all the state!