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super_state_machine 1.0

Super State Machine gives you utilities to build finite state machines.

Super State Machine gives you utilities to build finite state machines.

Features

  • Fully tested with Python 2.7, 3.3, 3.4 and PyPy.

  • Create finite state machines:

    >>> from enum import Enum
    
    >>> from super_state_machine import machines
    
    
    >>> class Task(machines.StateMachine):
    ...
    ...    class States(Enum):
    ...
    ...         DRAFT = 'draft'
    ...         SCHEDULED = 'scheduled'
    ...         PROCESSING = 'processing'
    ...         SENT = 'sent'
    ...         FAILED = 'failed'
    
    >>> task = Task()
    >>> task.is_draft
    False
    >>> task.set_draft()
    >>> task.state
    'draft'
    >>> task.state = 'scheduled'
    >>> task.is_scheduled
    True
    >>> task.state = 'p'
    >>> task.state
    'processing'
    >>> task.state = 'wrong'
    *** ValueError: Unrecognized value ('wrong').
    
  • Define allowed transitions graph, define additional named transitions and checkers:

    >>> class Task(machines.StateMachine):
    ...
    ...     class States(Enum):
    ...
    ...         DRAFT = 'draft'
    ...         SCHEDULED = 'scheduled'
    ...         PROCESSING = 'processing'
    ...         SENT = 'sent'
    ...         FAILED = 'failed'
    ...
    ...     class Meta:
    ...
    ...         allow_empty = False
    ...         initial_state = 'draft'
    ...         transitions = {
    ...             'draft': ['scheduled', 'failed'],
    ...             'scheduled': ['failed'],
    ...             'processing': ['sent', 'failed']
    ...         }
    ...         named_transitions = [
    ...             ('process', 'processing', ['scheduled']),
    ...             ('fail', 'failed')
    ...         ]
    ...         named_checkers = [
    ...             ('can_be_processed', 'processing'),
    ...         ]
    
    >>> task = Task()
    >>> task.state
    'draft'
    >>> task.process()
    *** TransitionError: Cannot transit from 'draft' to 'processing'.
    >>> task.set_scheduled()
    >>> task.can_be_processed
    True
    >>> task.process()
    >>> task.state
    'processing'
    >>> task.fail()
    >>> task.state
    'failed'
    

    Note, that third argument restricts from which states transition will be added to allowed (in case of process, new allowed transition will be added, from ‘scheduled’ to ‘processing’). No argument means all available states, None or empty list won’t add anything beyond defined ones.

  • You can also define short version of all transition values like:

    >>> class Task(machine.StateMachine):
    ...
    ...     class States(Enum):
    ...
    ...         DRAFT = 'draft'
    ...         SCHEDULED = 'scheduled'
    ...         PROCESSING = 'processing'
    ...         SENT = 'sent'
    ...         FAILED = 'failed'
    ...
    ...     class Meta:
    ...
    ...         allow_empty = False
    ...         initial_state = 'd'
    ...         transitions = {
    ...             'd': ['sc', 'f'],
    ...             'sc': ['f'],
    ...             'p': ['se', 'f']
    ...         }
    ...         named_transitions = [
    ...             ('process', 'p', ['sc']),
    ...             ('fail', 'f')
    ...         ]
    ...         named_checkers = [
    ...             ('can_be_processed', 'p'),
    ...         ]
    

    Result code will behave the same as example above. Note also that you can always pass also enum values instead of strings.

History

1.0 (2014-09-04)

  • Added all basic features.

0.1.0 (2014-08-08)

  • First release on PyPI.
  • Added utilities to create simple state machine.
 
File Type Py Version Uploaded on Size
super_state_machine-1.0-py2.py3-none-any.whl (md5) Python Wheel 2.7 2014-09-04 9KB
super_state_machine-1.0.tar.gz (md5) Source 2014-09-04 23KB
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