transitions-aio 0.9.3.4
A lightweight, anyio-capable, object-oriented state machine implementation
Requires Python: >=3.8
Keywords: state machine

# <a name="transitions-module"></a> transitions_aio

A lightweight, object-oriented, anyio-enabled state machine implementation in Python with many extensions. Compatible with Python 3.8+.

This is a fork of [transitions](https://github.com/pytransitions/transitions).
It replaces `asyncio` with [anyio](https://github.com/agronholm/anyio).

## Installation

    pip install transitions_aio as transitions

... or clone the repo from GitHub and then:

    python -mbuild

## Table of Contents

- [Quickstart](#quickstart)
- [Non-Quickstart](#the-non-quickstart)
  - [Some key concepts](#some-key-concepts)
  - [Basic initialization](#basic-initialization)
  - [States](#states)
    - [Callbacks](#state-callbacks)
    - [Checking state](#checking-state)
    - [Enumerations](#enum-state)
  - [Transitions](#transitions)
    - [Triggering a transition](#triggers)
    - [Automatic transitions](#automatic-transitions-for-all-states)
    - [Transitioning from multiple states](#transitioning-from-multiple-states)
    - [Reflexive transitions from multiple states](#reflexive-from-multiple-states)
    - [Internal transitions](#internal-transitions)
    - [Ordered transitions](#ordered-transitions)
    - [Queued transitions](#queued-transitions)
    - [Conditional transitions](#conditional-transitions)
    - [Check transitions](#check-transitions)
    - [Callbacks](#transition-callbacks)
  - [Callable resolution](#resolution)
  - [Callback execution order](#execution-order)
  - [Passing data](#passing-data)
  - [Alternative initialization patterns](#alternative-initialization-patterns)
  - [Logging](#logging)
  - [(Re-)Storing machine instances](#restoring)
  - [Typing support](#typing-support)
  - [Extensions](#extensions)
    - [Hierarchical State Machine](#hsm)
    - [Diagrams](#diagrams)
    - [Threading](#threading)
    - [Async](#async)
    - [State features](#state-features)
    - [Django](#django-support)
  - [Bug reports etc.](#bug-reports)

## Quickstart

They say [a good example is worth](https://www.google.com/webhp?ie=UTF-8#q=%22a+good+example+is+worth%22&start=20) 100 pages of API documentation, a million directives, or a thousand words.

Well, "they" probably lie... but here's an example anyway:

```python
from transitions import Machine
import random

class NarcolepticSuperhero(object):

    # Define some states. Most of the time, narcoleptic superheroes are just like
    # everyone else. Except for...
    states = ['asleep', 'hanging out', 'hungry', 'sweaty', 'saving the world']

    def __init__(self, name):

        # No anonymous superheroes on my watch! Every narcoleptic superhero gets
        # a name. Any name at all. SleepyMan. SlumberGirl. You get the idea.
        self.name = name

        # What have we accomplished today?
        self.kittens_rescued = 0

        # Initialize the state machine
        self.machine = Machine(model=self, states=NarcolepticSuperhero.states, initial='asleep')

        # Add some transitions. We could also define these using a static list of
        # dictionaries, as we did with states above, and then pass the list to
        # the Machine initializer as the transitions= argument.

        # At some point, every superhero must rise and shine.
        self.machine.add_transition(trigger='wake_up', source='asleep', dest='hanging out')

        # Superheroes need to keep in shape.
        self.machine.add_transition('work_out', 'hanging out', 'hungry')

        # Those calories won't replenish themselves!
        self.machine.add_transition('eat', 'hungry', 'hanging out')

        # Superheroes are always on call. ALWAYS. But they're not always
        # dressed in work-appropriate clothing.
        self.machine.add_transition('distress_call', '*', 'saving the world',
                         before='change_into_super_secret_costume')

        # When they get off work, they're all sweaty and disgusting. But before
        # they do anything else, they have to meticulously log their latest
        # escapades. Because the legal department says so.
        self.machine.add_transition('complete_mission', 'saving the world', 'sweaty',
                         after='update_journal')

        # Sweat is a disorder that can be remedied with water.
        # Unless you've had a particularly long day, in which case... bed time!
        self.machine.add_transition('clean_up', 'sweaty', 'asleep', conditions=['is_exhausted'])
        self.machine.add_transition('clean_up', 'sweaty', 'hanging out')

        # Our NarcolepticSuperhero can fall asleep at pretty much any time.
        self.machine.add_transition('nap', '*', 'asleep')

    def update_journal(self):
        """ Dear Diary, today I saved Mr. Whiskers. Again. """
        self.kittens_rescued += 1

    @property
    def is_exhausted(self):
        """ Basically a coin toss. """
        return random.random() < 0.5

    def change_into_super_secret_costume(self):
        print("Beauty, eh?")
```

There, now you've baked a state machine into `NarcolepticSuperhero`. Let's take him/her/it out for a spin...

```python
>>> batman = NarcolepticSuperhero("Batman")
>>> batman.state
'asleep'

>>> batman.wake_up()
>>> batman.state
'hanging out'

>>> batman.nap()
>>> batman.state
'asleep'

>>> batman.clean_up()
MachineError: "Can't trigger event clean_up from state asleep!"

>>> batman.wake_up()
>>> batman.work_out()
>>> batman.state
'hungry'

# Batman still hasn't done anything useful...
>>> batman.kittens_rescued
0

# We now take you live to the scene of a horrific kitten entreement...
>>> batman.distress_call()
'Beauty, eh?'
>>> batman.state
'saving the world'

# Back to the crib.
>>> batman.complete_mission()
>>> batman.state
'sweaty'

>>> batman.clean_up()
>>> batman.state
'asleep'   # Too tired to shower!

# Another productive day, Alfred.
>>> batman.kittens_rescued
1
```

While we cannot read the mind of the actual batman, we surely can visualize the current state of our `NarcolepticSuperhero`.

![batman diagram](https://user-images.githubusercontent.com/205986/104932302-c2f24580-59a7-11eb-8963-5dce738b9305.png)

Have a look at the [Diagrams](#diagrams) extensions if you want to know how.

## The non-quickstart

A state machine is a _model_ of behavior composed of a finite number of _states_ and _transitions_ between those states. Within each state and transition some _action_ can be performed. A state machine needs to start at some _initial state_. When using `transitions`, a state machine may consist of multiple objects where some (_machines_) contain definitions for the manipulation of other (_models_). Below, we will look at some core concepts and how to work with them.

### Some key concepts

- **State**. A state represents a particular condition or stage in the state machine. It's a distinct mode of behavior or phase in a process.

- **Transition**. This is the process or event that causes the state machine to change from one state to another.

- **Model**. The actual stateful structure. It's the entity that gets updated during transitions. It may also define _actions_ that will be executed during transitions. For instance, right before a transition or when a state is entered or exited.

- **Machine**. This is the entity that manages and controls the model, states, transitions, and actions. It's the conductor that orchestrates the entire process of the state machine.

- **Trigger**. This is the event that initiates a transition, the method that sends the signal to start a transition.

- **Action**. Specific operation or task that is performed when a certain state is entered, exited, or during a transition. The action is implemented through _callbacks_, which are functions that get executed when some event happens.

### Basic initialization

Getting a state machine up and running is pretty simple. Let's say you have the object `lump` (an instance of class `Matter`), and you want to manage its states:

```python
class Matter(object):
    pass

lump = Matter()
```

You can initialize a (_minimal_) working state machine bound to the model `lump` like this:

```python
from transitions import Machine
machine = Machine(model=lump, states=['solid', 'liquid', 'gas', 'plasma'], initial='solid')

# Lump now has a new state attribute!
lump.state
>>> 'solid'
```

An alternative is to not explicitly pass a model to the `Machine` initializer:

```python

machine = Machine(states=['solid', 'liquid', 'gas', 'plasma'], initial='solid')

# The machine instance itself now acts as a model
machine.state
>>> 'solid'
```

Note that this time I did not pass the `lump` model as an argument. The first argument passed to `Machine` acts as a model. So when I pass something there, all the convenience functions will be added to the object. If no model is provided then the `machine` instance itself acts as a model.

When at the beginning I said "minimal", it was because while this state machine is technically operational, it doesn't actually _do_ anything. It starts in the `'solid'` state, but won't ever move into another state, because no transitions are defined... yet!

Let's try again.

```python
# The states
states=['solid', 'liquid', 'gas', 'plasma']

# And some transitions between states. We're lazy, so we'll leave out
# the inverse phase transitions (freezing, condensation, etc.).
transitions = [
    { 'trigger': 'melt', 'source': 'solid', 'dest': 'liquid' },
    { 'trigger': 'evaporate', 'source': 'liquid', 'dest': 'gas' },
    { 'trigger': 'sublimate', 'source': 'solid', 'dest': 'gas' },
    { 'trigger': 'ionize', 'source': 'gas', 'dest': 'plasma' }
]

# Initialize
machine = Machine(lump, states=states, transitions=transitions, initial='liquid')

# Now lump maintains state...
lump.state
>>> 'liquid'

# And that state can change...
# Either calling the shiny new trigger methods
lump.evaporate()
lump.state
>>> 'gas'

# Or by calling the trigger method directly
lump.trigger('ionize')
lump.state
>>> 'plasma'
```

Notice the shiny new methods attached to the `Matter` instance (`evaporate()`, `ionize()`, etc.).
Each method triggers the corresponding transition.
Transitions can also be triggered _dynamically_ by calling the `trigger()` method provided with the name of the transition, as shown above.
More on this in the [Triggering a transition](#triggers) section.


### <a name="states"></a>States

The soul of any good state machine (and of many bad ones, no doubt) is a set of states. Above, we defined the valid model states by passing a list of strings to the `Machine` initializer. But internally, states are actually represented as `State` objects.

You can initialize and modify States in a number of ways. Specifically, you can:

- pass a string to the `Machine` initializer giving the name(s) of the state(s), or
- directly initialize each new `State` object, or
- pass a dictionary with initialization arguments

The following snippets illustrate several ways to achieve the same goal:

```python
# import Machine and State class
from transitions import Machine, State

# Create a list of 3 states to pass to the Machine
# initializer. We can mix types; in this case, we
# pass one State, one string, and one dict.
states = [
    State(name='solid'),
    'liquid',
    { 'name': 'gas'}
    ]
machine = Machine(lump, states)

# This alternative example illustrates more explicit
# addition of states and state callbacks, but the net
# result is identical to the above.
machine = Machine(lump)
solid = State('solid')
liquid = State('liquid')
gas = State('gas')
machine.add_states([solid, liquid, gas])
```

States are initialized _once_ when added to the machine and will persist until they are removed from it. In other words: if you alter the attributes of a state object, this change will NOT be reset the next time you enter that state. Have a look at how to [extend state features](#state-features) in case you require some other behaviour.

#### <a name="state-callbacks"></a>Callbacks

But just having states and being able to move around between them (transitions) isn't very useful by itself. What if you want to do something, perform some _action_ when you enter or exit a state? This is where _callbacks_ come in.

A `State` can also be associated with a list of `enter` and `exit` callbacks, which are called whenever the state machine enters or leaves that state. You can specify callbacks during initialization by passing them to a `State` object constructor, in a state property dictionary, or add them later.

For convenience, whenever a new `State` is added to a `Machine`, the methods `on_enter_«state name»` and `on_exit_«state name»` are dynamically created on the Machine (not on the model!), which allow you to dynamically add new enter and exit callbacks later if you need them.

```python
# Our old Matter class, now with  a couple of new methods we
# can trigger when entering or exit states.
class Matter(object):
    def say_hello(self): print("hello, new state!")
    def say_goodbye(self): print("goodbye, old state!")

lump = Matter()

# Same states as above, but now we give StateA an exit callback
states = [
    State(name='solid', on_exit=['say_goodbye']),
    'liquid',
    { 'name': 'gas', 'on_exit': ['say_goodbye']}
    ]

machine = Machine(lump, states=states)
machine.add_transition('sublimate', 'solid', 'gas')

# Callbacks can also be added after initialization using
# the dynamically added on_enter_ and on_exit_ methods.
# Note that the initial call to add the callback is made
# on the Machine and not on the model.
machine.on_enter_gas('say_hello')

# Test out the callbacks...
machine.set_state('solid')
lump.sublimate()
>>> 'goodbye, old state!'
>>> 'hello, new state!'
```

Note that `on_enter_«state name»` callback will _not_ fire when a Machine is first initialized. For example if you have an `on_enter_A()` callback defined, and initialize the `Machine` with `initial='A'`, `on_enter_A()` will not be fired until the next time you enter state `A`. (If you need to make sure `on_enter_A()` fires at initialization, you can simply create a dummy initial state and then explicitly call `to_A()` inside the `__init__` method.)

In addition to passing in callbacks when initializing a `State`, or adding them dynamically, it's also possible to define callbacks in the model class itself, which may increase code clarity. For example:

```python
class Matter(object):
    def say_hello(self): print("hello, new state!")
    def say_goodbye(self): print("goodbye, old state!")
    def on_enter_A(self): print("We've just entered state A!")

lump = Matter()
machine = Machine(lump, states=['A', 'B', 'C'])
```

Now, any time `lump` transitions to state `A`, the `on_enter_A()` method defined in the `Matter` class will fire.

You can make use of `on_final` callbacks which will be triggered when a state with `final=True` is entered.
```python
from transitions import Machine, State

states = [State(name='idling'),
          State(name='rescuing_kitten'),
          State(name='offender_gone', final=True),
          State(name='offender_caught', final=True)]

transitions = [["called", "idling", "rescuing_kitten"],  # we will come when  called
               {"trigger": "intervene",
                "source": "rescuing_kitten",
                "dest": "offender_gone",  # we
                "conditions": "offender_is_faster"},  # unless they are faster
               ["intervene", "rescuing_kitten", "offender_caught"]]


class FinalSuperhero(object):

    def __init__(self, speed):
        self.machine = Machine(self, states=states, transitions=transitions, initial="idling", on_final="claim_success")
        self.speed = speed

    def offender_is_faster(self, offender_speed):
        return self.speed < offender_speed

    def claim_success(self, **kwargs):
        print("The kitten is safe.")


hero = FinalSuperhero(speed=10)  # we are not in shape today
hero.called()
assert hero.is_rescuing_kitten()
hero.intervene(offender_speed=15)
# >>> 'The kitten is safe'
assert hero.machine.get_state(hero.state).final  # it's over
assert hero.is_offender_gone()  # maybe next time ...
```

#### <a name="checking-state"></a>Checking state

You can always check the current state of the model by either:

- inspecting the `.state` attribute, or
- calling `is_«state name»()`

And if you want to retrieve the actual `State` object for the current state, you can do that through the `Machine` instance's `get_state()` method.

```python
lump.state
>>> 'solid'
lump.is_gas()
>>> False
lump.is_solid()
>>> True
machine.get_state(lump.state).name
>>> 'solid'
```

If you'd like you can choose your own state attribute name by passing the `model_attribute` argument while initializing the `Machine`. This will also change the name of `is_«state name»()` to `is_«model_attribute»_«state name»()` though. Similarly, auto transitions will be named `to_«model_attribute»_«state name»()` instead of `to_«state name»()`. This is done to allow multiple machines to work on the same model with individual state attribute names.

```python
lump = Matter()
machine = Machine(lump, states=['solid', 'liquid', 'gas'],  model_attribute='matter_state', initial='solid')
lump.matter_state
>>> 'solid'
# with a custom 'model_attribute', states can also be checked like this:
lump.is_matter_state_solid()
>>> True
lump.to_matter_state_gas()
>>> True
```

#### <a name="enum-state"></a>Enumerations

So far we have seen how we can give state names and use these names to work with our state machine.
If you favour stricter typing and more IDE code completion (or you just can't type 'sesquipedalophobia' any longer because the word scares you) using [Enumerations](https://docs.python.org/3/library/enum.html) might be what you are looking for:

```python
import enum
from transitions import Machine

class States(enum.Enum):
    ERROR = 0
    RED = 1
    YELLOW = 2
    GREEN = 3

transitions = [['proceed', States.RED, States.YELLOW],
               ['proceed', States.YELLOW, States.GREEN],
               ['error', '*', States.ERROR]]

m = Machine(states=States, transitions=transitions, initial=States.RED)
assert m.is_RED()
assert m.state is States.RED
state = m.get_state(States.RED)  # get transitions.State object
print(state.name)  # >>> RED
m.proceed()
m.proceed()
assert m.is_GREEN()
m.error()
assert m.state is States.ERROR
```

You can mix enums and strings if you like (e.g. `[States.RED, 'ORANGE', States.YELLOW, States.GREEN]`) but note that internally, `transitions` will still handle states by name (`enum.Enum.name`).
Thus, it is not possible to have the states `'GREEN'` and `States.GREEN` at the same time.

### <a name="transitions"></a>Transitions

Some of the above examples already illustrate the use of transitions in passing, but here we'll explore them in more detail.

As with states, each transition is represented internally as its own object – an instance of class `Transition`. The quickest way to initialize a set of transitions is to pass a dictionary, or list of dictionaries, to the `Machine` initializer. We already saw this above:

```python
transitions = [
    { 'trigger': 'melt', 'source': 'solid', 'dest': 'liquid' },
    { 'trigger': 'evaporate', 'source': 'liquid', 'dest': 'gas' },
    { 'trigger': 'sublimate', 'source': 'solid', 'dest': 'gas' },
    { 'trigger': 'ionize', 'source': 'gas', 'dest': 'plasma' }
]
machine = Machine(model=Matter(), states=states, transitions=transitions)
```

Defining transitions in dictionaries has the benefit of clarity, but can be cumbersome. If you're after brevity, you might choose to define transitions using lists. Just make sure that the elements in each list are in the same order as the positional arguments in the `Transition` initialization (i.e., `trigger`, `source`, `destination`, etc.).

The following list-of-lists is functionally equivalent to the list-of-dictionaries above:

```python
transitions = [
    ['melt', 'solid', 'liquid'],
    ['evaporate', 'liquid', 'gas'],
    ['sublimate', 'solid', 'gas'],
    ['ionize', 'gas', 'plasma']
]
```

Alternatively, you can add transitions to a `Machine` after initialization:

```python
machine = Machine(model=lump, states=states, initial='solid')
machine.add_transition('melt', source='solid', dest='liquid')
```

#### <a name="triggers"></a>Triggering a transition

For a transition to be executed, some event needs to _trigger_ it. There are two ways to do this:

1. Using the automatically attached method in the base model:
    ```python
    >>> lump.melt()
    >>> lump.state
    'liquid'
    >>> lump.evaporate()
    >>> lump.state
    'gas'
    ```

    Note how you don't have to explicitly define these methods anywhere; the name of each transition is bound to the model passed to the `Machine` initializer (in this case, `lump`). This also means that your model **should not** already contain methods with the same name as event triggers since `transitions` will only attach convenience methods to your model if the spot is not already taken. If you want to modify that behaviour, have a look at the [FAQ](examples/Frequently%20asked%20questions.ipynb).
2. Using the `trigger` method, now attached to your model (if it hasn't been there before). This method lets you execute transitions by name in case dynamic triggering is required:
    ```python
    >>> lump.trigger('melt')
    >>> lump.state
    'liquid'
    >>> lump.trigger('evaporate')
    >>> lump.state
    'gas'
    ```

#### Triggering invalid transitions

By default, triggering an invalid transition will raise an exception:

```python
>>> lump.to_gas()
>>> # This won't work because only objects in a solid state can melt
>>> lump.melt()
transitions.core.MachineError: "Can't trigger event melt from state gas!"
```

This behavior is generally desirable, since it helps alert you to problems in your code. But in some cases, you might want to silently ignore invalid triggers. You can do this by setting `ignore_invalid_triggers=True` (either on a state-by-state basis, or globally for all states):

```python
>>> # Globally suppress invalid trigger exceptions
>>> m = Machine(lump, states, initial='solid', ignore_invalid_triggers=True)
>>> # ...or suppress for only one group of states
>>> states = ['new_state1', 'new_state2']
>>> m.add_states(states, ignore_invalid_triggers=True)
>>> # ...or even just for a single state. Here, exceptions will only be suppressed when the current state is A.
>>> states = [State('A', ignore_invalid_triggers=True), 'B', 'C']
>>> m = Machine(lump, states)
>>> # ...this can be inverted as well if just one state should raise an exception
>>> # since the machine's global value is not applied to a previously initialized state.
>>> states = ['A', 'B', State('C')] # the default value for 'ignore_invalid_triggers' is False
>>> m = Machine(lump, states, ignore_invalid_triggers=True)
```

If you need to know which transitions are valid from a certain state, you can use `get_triggers`:

```python
m.get_triggers('solid')
>>> ['melt', 'sublimate']
m.get_triggers('liquid')
>>> ['evaporate']
m.get_triggers('plasma')
>>> []
# you can also query several states at once
m.get_triggers('solid', 'liquid', 'gas', 'plasma')
>>> ['melt', 'evaporate', 'sublimate', 'ionize']
```

If you have followed this documentation from the beginning, you will notice that `get_triggers` actually returns more triggers than the explicitly defined ones shown above, such as `to_liquid` and so on.
These are called `auto-transitions` and will be introduced in the next section.

#### <a name="automatic-transitions-for-all-states"></a>Automatic transitions for all states

In addition to any transitions added explicitly, a `to_«state»()` method is created automatically whenever a state is added to a `Machine` instance. This method transitions to the target state no matter which state the machine is currently in:

```python
lump.to_liquid()
lump.state
>>> 'liquid'
lump.to_solid()
lump.state
>>> 'solid'
```

If you desire, you can disable this behavior by setting `auto_transitions=False` in the `Machine` initializer.

#### <a name="transitioning-from-multiple-states"></a>Transitioning from multiple states

A given trigger can be attached to multiple transitions, some of which can potentially begin or end in the same state. For example:

```python
machine.add_transition('transmogrify', ['solid', 'liquid', 'gas'], 'plasma')
machine.add_transition('transmogrify', 'plasma', 'solid')
# This next transition will never execute
machine.add_transition('transmogrify', 'plasma', 'gas')
```

In this case, calling `transmogrify()` will set the model's state to `'solid'` if it's currently `'plasma'`, and set it to `'plasma'` otherwise. (Note that only the _first_ matching transition will execute; thus, the transition defined in the last line above won't do anything.)

You can also make a trigger cause a transition from _all_ states to a particular destination by using the `'*'` wildcard:

```python
machine.add_transition('to_liquid', '*', 'liquid')
```

Note that wildcard transitions will only apply to states that exist at the time of the add_transition() call. Calling a wildcard-based transition when the model is in a state added after the transition was defined will elicit an invalid transition message, and will not transition to the target state.

#### <a name="reflexive-from-multiple-states"></a>Reflexive transitions from multiple states

A reflexive trigger (trigger that has the same state as source and destination) can easily be added specifying `=` as destination.
This is handy if the same reflexive trigger should be added to multiple states.
For example:

```python
machine.add_transition('touch', ['liquid', 'gas', 'plasma'], '=', after='change_shape')
```

This will add reflexive transitions for all three states with `touch()` as trigger and with `change_shape` executed after each trigger.

#### <a name="internal-transitions"></a>Internal transitions

In contrast to reflexive transitions, internal transitions will never actually leave the state.
This means that transition-related callbacks such as `before` or `after` will be processed while state-related callbacks `exit` or `enter` will not.
To define a transition to be internal, set the destination to `None`.

```python
machine.add_transition('internal', ['liquid', 'gas'], None, after='change_shape')
```

#### <a name="ordered-transitions"></a> Ordered transitions

A common desire is for state transitions to follow a strict linear sequence. For instance, given states `['A', 'B', 'C']`, you might want valid transitions for `A` → `B`, `B` → `C`, and `C` → `A` (but no other pairs).

To facilitate this behavior, Transitions provides an `add_ordered_transitions()` method in the `Machine` class:

```python
states = ['A', 'B', 'C']
 # See the "alternative initialization" section for an explanation of the 1st argument to init
machine = Machine(states=states, initial='A')
machine.add_ordered_transitions()
machine.next_state()
print(machine.state)
>>> 'B'
# We can also define a different order of transitions
machine = Machine(states=states, initial='A')
machine.add_ordered_transitions(['A', 'C', 'B'])
machine.next_state()
print(machine.state)
>>> 'C'
# Conditions can be passed to 'add_ordered_transitions' as well
# If one condition is passed, it will be used for all transitions
machine = Machine(states=states, initial='A')
machine.add_ordered_transitions(conditions='check')
# If a list is passed, it must contain exactly as many elements as the
# machine contains states (A->B, ..., X->A)
machine = Machine(states=states, initial='A')
machine.add_ordered_transitions(conditions=['check_A2B', ..., 'check_X2A'])
# Conditions are always applied starting from the initial state
machine = Machine(states=states, initial='B')
machine.add_ordered_transitions(conditions=['check_B2C', ..., 'check_A2B'])
# With `loop=False`, the transition from the last state to the first state will be omitted (e.g. C->A)
# When you also pass conditions, you need to pass one condition less (len(states)-1)
machine = Machine(states=states, initial='A')
machine.add_ordered_transitions(loop=False)
machine.next_state()
machine.next_state()
machine.next_state() # transitions.core.MachineError: "Can't trigger event next_state from state C!"
```

#### <a name="queued-transitions"></a>Queued transitions

The default behaviour in Transitions is to process events instantly. This means events within an `on_enter` method will be processed _before_ callbacks bound to `after` are called.

```python
def go_to_C():
    global machine
    machine.to_C()

def after_advance():
    print("I am in state B now!")

def entering_C():
    print("I am in state C now!")

states = ['A', 'B', 'C']
machine = Machine(states=states, initial='A')

# we want a message when state transition to B has been completed
machine.add_transition('advance', 'A', 'B', after=after_advance)

# call transition from state B to state C
machine.on_enter_B(go_to_C)

# we also want a message when entering state C
machine.on_enter_C(entering_C)
machine.advance()
>>> 'I am in state C now!'
>>> 'I am in state B now!' # what?
```

The execution order of this example is

```
prepare -> before -> on_enter_B -> on_enter_C -> after.
```

If queued processing is enabled, a transition will be finished before the next transition is triggered:

```python
machine = Machine(states=states, queued=True, initial='A')
...
machine.advance()
>>> 'I am in state B now!'
>>> 'I am in state C now!' # That's better!
```

This results in

```
prepare -> before -> on_enter_B -> queue(to_C) -> after  -> on_enter_C.
```

**Important note:** when processing events in a queue, the trigger call will _always_ return `True`, since there is no way to determine at queuing time whether a transition involving queued calls will ultimately complete successfully. This is true even when only a single event is processed.

```python
machine.add_transition('jump', 'A', 'C', conditions='will_fail')
...
# queued=False
machine.jump()
>>> False
# queued=True
machine.jump()
>>> True
```

When a model is removed from the machine, `transitions` will also remove all related events from the queue.

```python
class Model:
    def on_enter_B(self):
        self.to_C()  # add event to queue ...
        self.machine.remove_model(self)  # aaaand it's gone
```

#### <a name="conditional-transitions"></a>Conditional transitions

Sometimes you only want a particular transition to execute if a specific condition occurs. You can do this by passing a method, or list of methods, in the `conditions` argument:

```python
# Our Matter class, now with a bunch of methods that return booleans.
class Matter(object):
    def is_flammable(self): return False
    def is_really_hot(self): return True

machine.add_transition('heat', 'solid', 'gas', conditions='is_flammable')
machine.add_transition('heat', 'solid', 'liquid', conditions=['is_really_hot'])
```

In the above example, calling `heat()` when the model is in state `'solid'` will transition to state `'gas'` if `is_flammable` returns `True`. Otherwise, it will transition to state `'liquid'` if `is_really_hot` returns `True`.

For convenience, there's also an `'unless'` argument that behaves exactly like conditions, but inverted:

```python
machine.add_transition('heat', 'solid', 'gas', unless=['is_flammable', 'is_really_hot'])
```

In this case, the model would transition from solid to gas whenever `heat()` fires, provided that both `is_flammable()` and `is_really_hot()` return `False`.

Note that condition-checking methods will passively receive optional arguments and/or data objects passed to triggering methods. For instance, the following call:

```python
lump.heat(temp=74)
# equivalent to lump.trigger('heat', temp=74)
```

... would pass the `temp=74` optional kwarg to the `is_flammable()` check (possibly wrapped in an `EventData` instance). For more on this, see the [Passing data](#passing-data) section below.

#### <a name="check-transitions"></a>Check transitions

If you want to make sure a transition is possible before you go ahead with it, you can use the `may_<trigger_name>` functions that have been added to your model.
Your model also contains the `may_trigger` function to check a trigger by name:

```python
# check if the current temperature is hot enough to trigger a transition
if lump.may_heat():
# if lump.may_trigger("heat"):
    lump.heat()
```

This will execute all `prepare` callbacks and evaluate the conditions assigned to the potential transitions.
Transition checks can also be used when a transition's destination is not available (yet):

```python
machine.add_transition('elevate', 'solid', 'spiritual')
assert not lump.may_elevate()  # not ready yet :(
assert not lump.may_trigger("elevate")  # same result for checks via trigger name
```

#### <a name="transition-callbacks"></a>Callbacks

You can attach callbacks to transitions as well as states. Every transition has `'before'` and `'after'` attributes that contain a list of methods to call before and after the transition executes:

```python
class Matter(object):
    def make_hissing_noises(self): print("HISSSSSSSSSSSSSSSS")
    def disappear(self): print("where'd all the liquid go?")

transitions = [
    { 'trigger': 'melt', 'source': 'solid', 'dest': 'liquid', 'before': 'make_hissing_noises'},
    { 'trigger': 'evaporate', 'source': 'liquid', 'dest': 'gas', 'after': 'disappear' }
]

lump = Matter()
machine = Machine(lump, states, transitions=transitions, initial='solid')
lump.melt()
>>> "HISSSSSSSSSSSSSSSS"
lump.evaporate()
>>> "where'd all the liquid go?"
```

There is also a `'prepare'` callback that is executed as soon as a transition starts, before any `'conditions'` are checked or other callbacks are executed.

```python
class Matter(object):
    heat = False
    attempts = 0
    def count_attempts(self): self.attempts += 1
    def heat_up(self): self.heat = random.random() < 0.25
    def stats(self): print('It took you %i attempts to melt the lump!' %self.attempts)

    @property
    def is_really_hot(self):
        return self.heat


states=['solid', 'liquid', 'gas', 'plasma']

transitions = [
    { 'trigger': 'melt', 'source': 'solid', 'dest': 'liquid', 'prepare': ['heat_up', 'count_attempts'], 'conditions': 'is_really_hot', 'after': 'stats'},
]

lump = Matter()
machine = Machine(lump, states, transitions=transitions, initial='solid')
lump.melt()
lump.melt()
lump.melt()
lump.melt()
>>> "It took you 4 attempts to melt the lump!"
```

Note that `prepare` will not be called unless the current state is a valid source for the named transition.

Default actions meant to be executed before or after _every_ transition can be passed to `Machine` during initialization with
`before_state_change` and `after_state_change` respectively:

```python
class Matter(object):
    def make_hissing_noises(self): print("HISSSSSSSSSSSSSSSS")
    def disappear(self): print("where'd all the liquid go?")

states=['solid', 'liquid', 'gas', 'plasma']

lump = Matter()
m = Machine(lump, states, before_state_change='make_hissing_noises', after_state_change='disappear')
lump.to_gas()
>>> "HISSSSSSSSSSSSSSSS"
>>> "where'd all the liquid go?"
```

There are also two keywords for callbacks which should be executed _independently_ a) of how many transitions are possible,
b) if any transition succeeds and c) even if an error is raised during the execution of some other callback.
Callbacks passed to `Machine` with `prepare_event` will be executed _once_ before processing possible transitions
(and their individual `prepare` callbacks) takes place.
Callbacks of `finalize_event` will be executed regardless of the success of the processed transitions.
Note that if an error occurred it will be attached to `event_data` as `error` and can be retrieved with `send_event=True`.

```python
from transitions import Machine

class Matter(object):
    def raise_error(self, event): raise ValueError("Oh no")
    def prepare(self, event): print("I am ready!")
    def finalize(self, event): print("Result: ", type(event.error), event.error)

states=['solid', 'liquid', 'gas', 'plasma']

lump = Matter()
m = Machine(lump, states, prepare_event='prepare', before_state_change='raise_error',
            finalize_event='finalize', send_event=True)
try:
    lump.to_gas()
except ValueError:
    pass
print(lump.state)

# >>> I am ready!
# >>> Result:  <class 'ValueError'> Oh no
# >>> initial
```

Sometimes things just don't work out as intended and we need to handle exceptions and clean up the mess to keep things going.
We can pass callbacks to `on_exception` to do this:

```python
from transitions import Machine

class Matter(object):
    def raise_error(self, event): raise ValueError("Oh no")
    def handle_error(self, event):
        print("Fixing things ...")
        del event.error  # it did not happen if we cannot see it ...

states=['solid', 'liquid', 'gas', 'plasma']

lump = Matter()
m = Machine(lump, states, before_state_change='raise_error', on_exception='handle_error', send_event=True)
try:
    lump.to_gas()
except ValueError:
    pass
print(lump.state)

# >>> Fixing things ...
# >>> initial
```

### <a name="resolution"></a>Callable resolution

As you have probably already realized, the standard way of passing callables to states, conditions and transitions is by name. When processing callbacks and conditions, `transitions` will use their name to retrieve the related callable from the model. If the method cannot be retrieved and it contains dots, `transitions` will treat the name as a path to a module function and try to import it. Alternatively, you can pass names of properties or attributes. They will be wrapped into functions but cannot receive event data for obvious reasons. You can also pass callables such as (bound) functions directly. As mentioned earlier, you can also pass lists/tuples of callables names to the callback parameters. Callbacks will be executed in the order they were added.

```python
from transitions import Machine
from mod import imported_func

import random


class Model(object):

    def a_callback(self):
        imported_func()

    @property
    def a_property(self):
        """ Basically a coin toss. """
        return random.random() < 0.5

    an_attribute = False


model = Model()
machine = Machine(model=model, states=['A'], initial='A')
machine.add_transition('by_name', 'A', 'A', conditions='a_property', after='a_callback')
machine.add_transition('by_reference', 'A', 'A', unless=['a_property', 'an_attribute'], after=model.a_callback)
machine.add_transition('imported', 'A', 'A', after='mod.imported_func')

model.by_name()
model.by_reference()
model.imported()
```

The callable resolution is done in `Machine.resolve_callable`.
This method can be overridden in case more complex callable resolution strategies are required.

**Example**

```python
class CustomMachine(Machine):
    @staticmethod
    def resolve_callable(func, event_data):
        # manipulate arguments here and return func, or super() if no manipulation is done.
        super(CustomMachine, CustomMachine).resolve_callable(func, event_data)
```

### <a name="execution-order"></a>Callback execution order

In summary, there are currently three ways to trigger events. You can call a model's convenience functions like `lump.melt()`,
execute triggers by name such as `lump.trigger("melt")` or dispatch events on multiple models with `machine.dispatch("melt")`
(see section about multiple models in [alternative initialization patterns](#alternative-initialization-patterns)).
Callbacks on transitions are then executed in the following order:

| Callback                        |    Current State     | Comments                                                                                    |
|---------------------------------| :------------------: |---------------------------------------------------------------------------------------------|
| `'machine.prepare_event'`       |       `sourc