Django States

Description

State engine for django models. Define a state graph for a model and remember the state of each object. State transitions can be logged for objects.

Note

This fork provides for changes specifically for Pivotal Energy Solutions

Installation

pip install pivotal-django-states

Usage example

    from django_states.fields import StateField
    from django_states.machine import StateMachine, StateDefinition, StateTransition

    class PurchaseStateMachine(StateMachine):
       log_transitions = True

       # possible states
       class initiated(StateDefinition):
           description = _('Purchase initiated')
           initial = True

       class paid(StateDefinition):
           description = _('Purchase paid')

           def handler(self, instance):
               code_to_execute_when_arriving_in_this_state()

       class shipped(StateDefinition):
           description = _('Purchase shipped')

       # state transitions
       class mark_paid(StateTransition):
           from_state = 'initiated'
           to_state = 'paid'
           description = 'Mark this purchase as paid'

       class ship(StateTransition):
           from_state = 'paid'
           to_state = 'shipped'
           description = 'Ship purchase'

           def handler(transition, instance, user):
               code_to_execute_during_this_transition()

           def has_permission(transition, instance, user):
               return true_when_user_can_make_this_transition()

    class Purchase(StateModel):
        purchase_state = StateField(machine=PurchaseStateMachine, default='initiated')
        ... (other fields for a purchase)

If log_transitions is enabled, another model is created. Everything should be compatible with South_ for migrations.

Note: If you're creating a DataMigration in South <http://south.aeracode.org/>__, remember to use obj.save(no_state_validation=True)

Usage example:

   p = Purchase()

   # Will automatically create state object for this purchase, in the
   # initial state.
   p.save()
   p.get_purchase_state_info().make_transition('mark_paid', request.user) # User parameter is optional
   p.state # Will return 'paid'
   p.get_purchase_state_info().description # Will return 'Purchase paid'

   # Returns an iterator of possible transitions for this purchase.
   p.get_purchase_state_info().possible_transitions()

   # Which can be used like this..
   [x.get_name() for x in p.possible_transitions]

For better transition control, override:

• has_permission(self, instance, user): Check whether this user is allowed to make this transition.
• handler(self, instance, user): Code to run during this transition. When an exception has been raised in here, the transition will not be made.

Get all objects in a certain state::

Purchase.objects.filter(state='initiated')

Validation

You can add a test that needs to pass before a state transition can be executed. Well, you can add 2: one based on the current user (has_permission) and one generic (validate).

So on a StateTransition-object you need to specify an extra validate function (signature is validate(cls, instance)). This should yield TransitionValidationError, this way you can return multiple errors on that need to pass before the transition can happen.

The has_permission function (signature has_permission(transition, instance, user)) should check whether the given user is allowed to make the transition. E.g. a super user can moderate all comments while other users can only moderate comments on their blog-posts.

Groups

Sometimes you want to group several states together, since for a certain view (or other content) it doesn't really matter which of the states it is. We support 2 different state groups, inclusive (only these) or exclusive (everything but these):

  class is_paid(StateGroup):
      states = ['paid', 'shipped']

  class is_paid(StateGroup):
      exclude_states = ['initiated']

State graph

You can get a graph of your states by running the graph_states management command.

   python manage.py graph_states myapp.Purchase.state

This requires graphviz and python bindings for graphviz: pygraphviz and yapgvb.