8.2.8.21. SubDispatch

class SubDispatch(dsp=None, *args, **kwargs)

It dispatches a given Dispatcher like a function.

This function takes a sequence of dictionaries as input that will be combined before the dispatching.

Returns:

A function that executes the dispatch of the given Dispatcher.

Return type:

callable

See also

dispatch(), combine_dicts()

Example:

>>> from schedula import Dispatcher
>>> sub_dsp = Dispatcher(name='Sub-dispatcher')
...
>>> def fun(a):
...     return a + 1, a - 1
...
>>> sub_dsp.add_function('fun', fun, ['a'], ['b', 'c'])
'fun'
>>> dispatch = SubDispatch(sub_dsp, ['a', 'b', 'c'], output_type='dict')
>>> dsp = Dispatcher(name='Dispatcher')
>>> dsp.add_function('Sub-dispatch', dispatch, ['d'], ['e'])
'Sub-dispatch'

The Dispatcher output is:

>>> o = dsp.dispatch(inputs={'d': {'a': 3}})

while, the Sub-dispatch is:

>>> sol = o.workflow.nodes['Sub-dispatch']['solution']
>>> sol
Solution({'a': 3, 'b': 4, 'c': 2})
>>> sol == o['e']
True

Methods

__call__	Call self as a function.
__deepcopy__
__delattr__	Implement delattr(self, name).
__dir__	Default dir() implementation.
__eq__	Return self==value.
__format__	Default object formatter.
__ge__	Return self>=value.
__getattribute__	Return getattr(self, name).
__getstate__	Helper for pickle.
__gt__	Return self>value.
__hash__	Return hash(self).
__init__	Initializes the Sub-dispatch.
__init_subclass__	This method is called when a class is subclassed.
__le__	Return self<=value.
__lt__	Return self<value.
__ne__	Return self!=value.
__new__
__reduce__	Helper for pickle.
__reduce_ex__	Helper for pickle.
__repr__	Return repr(self).
__setattr__	Implement setattr(self, name, value).
__setstate__
__sizeof__	Size of object in memory, in bytes.
__str__	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_return
blue	Constructs a Blueprint out of the current object.
copy
form	Creates a dispatcher Form Flask app.
get_node	Returns a sub node of a dispatcher.
plot	Plots the Dispatcher with a graph in the DOT language with Graphviz.
web	Creates a dispatcher Flask app.

__init__(dsp, outputs=None, inputs_dist=None, wildcard=False, no_call=False, shrink=False, rm_unused_nds=False, output_type='all', function_id=None, output_type_kw=None)

Initializes the Sub-dispatch.

Parameters:

• dsp (schedula.Dispatcher | schedula.utils.blue.BlueDispatcher) – A dispatcher that identifies the model adopted.

• outputs (list[str], iterable) – Ending data nodes.

• inputs_dist (dict[str, int | float], optional) – Initial distances of input data nodes.

• wildcard (bool, int, optional) – If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. If it is equal to 2, the the data node that cannot be calculated are excluded by the wildcard condition.

• no_call (bool, optional) – If True data node estimation function is not used.

• shrink (bool, optional) – If True the dispatcher is shrink before the dispatch.

• rm_unused_nds (bool, optional) – If True unused function and sub-dispatcher nodes are removed from workflow.

• output_type (str, optional) –

  Type of function output:

  • ’all’: a dictionary with all dispatch outputs.

  • ’list’: a list with all outputs listed in outputs.

  • ’dict’: a dictionary with any outputs listed in outputs.

• output_type_kw (dict, optional) – Extra kwargs to pass to the selector function.

• function_id (str, optional) – Function name.

Attributes

__annotations__
__dict__
__doc__
__module__
__weakref__	list of weak references to the object

static __new__(cls, dsp=None, *args, **kwargs)

__getstate__()

Helper for pickle.

__setstate__(d)

__init__(dsp, outputs=None, inputs_dist=None, wildcard=False, no_call=False, shrink=False, rm_unused_nds=False, output_type='all', function_id=None, output_type_kw=None)

Initializes the Sub-dispatch.

Parameters:

• dsp (schedula.Dispatcher | schedula.utils.blue.BlueDispatcher) – A dispatcher that identifies the model adopted.

• outputs (list[str], iterable) – Ending data nodes.

• inputs_dist (dict[str, int | float], optional) – Initial distances of input data nodes.

• wildcard (bool, int, optional) – If True, when the data node is used as input and target in the ArciDispatch algorithm, the input value will be used as input for the connected functions, but not as output. If it is equal to 2, the the data node that cannot be calculated are excluded by the wildcard condition.

• no_call (bool, optional) – If True data node estimation function is not used.

• shrink (bool, optional) – If True the dispatcher is shrink before the dispatch.

• rm_unused_nds (bool, optional) – If True unused function and sub-dispatcher nodes are removed from workflow.

• output_type (str, optional) –

  Type of function output:

  • ’all’: a dictionary with all dispatch outputs.

  • ’list’: a list with all outputs listed in outputs.

  • ’dict’: a dictionary with any outputs listed in outputs.

• output_type_kw (dict, optional) – Extra kwargs to pass to the selector function.

• function_id (str, optional) – Function name.

__doc__ = "\n    It dispatches a given :class:`~schedula.dispatcher.Dispatcher` like a\n    function.\n\n    This function takes a sequence of dictionaries as input that will be\n    combined before the dispatching.\n\n    :return:\n        A function that executes the dispatch of the given\n        :class:`~schedula.dispatcher.Dispatcher`.\n    :rtype: callable\n\n    .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`,\n       :func:`combine_dicts`\n\n    Example:\n\n    .. dispatcher:: dsp\n       :opt: graph_attr={'ratio': '1'}, depth=-1\n       :code:\n\n        >>> from schedula import Dispatcher\n        >>> sub_dsp = Dispatcher(name='Sub-dispatcher')\n        ...\n        >>> def fun(a):\n        ...     return a + 1, a - 1\n        ...\n        >>> sub_dsp.add_function('fun', fun, ['a'], ['b', 'c'])\n        'fun'\n        >>> dispatch = SubDispatch(sub_dsp, ['a', 'b', 'c'], output_type='dict')\n        >>> dsp = Dispatcher(name='Dispatcher')\n        >>> dsp.add_function('Sub-dispatch', dispatch, ['d'], ['e'])\n        'Sub-dispatch'\n\n    The Dispatcher output is:\n\n    .. dispatcher:: o\n       :opt: graph_attr={'ratio': '1'}, depth=-1\n       :code:\n\n        >>> o = dsp.dispatch(inputs={'d': {'a': 3}})\n\n    while, the Sub-dispatch is:\n\n    .. dispatcher:: sol\n       :opt: graph_attr={'ratio': '1'}, depth=-1\n       :code:\n\n        >>> sol = o.workflow.nodes['Sub-dispatch']['solution']\n        >>> sol\n        Solution({'a': 3, 'b': 4, 'c': 2})\n        >>> sol == o['e']\n        True\n\n    "

blue(memo=None, depth=-1)

Constructs a Blueprint out of the current object.

Parameters:

• memo (dict[T,schedula.utils.blue.Blueprint]) – A dictionary to cache Blueprints.

• depth (int, optional) – Depth of sub-dispatch blue. If negative all levels are bluprinted.

Returns:

A Blueprint of the current object.

Return type:

schedula.utils.blue.Blueprint

__call__(*input_dicts, copy_input_dicts=False, _stopper=None, _executor=False, _sol_name=(), _verbose=False)

Call self as a function.

_return(solution)

copy()

__annotations__ = {}

__module__ = 'schedula.utils.dsp'