8.2.8.22. SubDispatchFunction

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

It converts a Dispatcher into a function.

This function takes a sequence of arguments or a key values as input of the dispatch.

Returns:

A function that executes the dispatch of the given dsp.

Return type:

callable

See also

dispatch(), shrink_dsp()

Example:

A dispatcher with two functions max and min and an unresolved cycle (i.e., a –> max –> c –> min –> a):

Extract a static function node, i.e. the inputs a and b and the output a are fixed:

>>> fun = SubDispatchFunction(dsp, 'myF', ['a', 'b'], ['a'])
>>> fun.__name__
'myF'
>>> fun(b=1, a=2)
0.0

The created function raises a ValueError if un-valid inputs are provided:

>>> fun(1, 0)
Traceback (most recent call last):
...
DispatcherError:
  Unreachable output-targets: ...
  Available outputs: ...

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 Function.
__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().
_parse_inputs
_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, function_id=None, inputs=None, outputs=None, inputs_dist=None, shrink=True, wildcard=True, output_type=None, output_type_kw=None, first_arg_as_kw=False)

Initializes the Sub-dispatch Function.

Parameters:

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

• function_id (str, optional) – Function name.

• inputs (list[str], iterable, optional) – Input data nodes.

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

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

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

• 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.

• 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 (bool) – Extra kwargs to pass to the selector function.

• first_arg_as_kw – Uses the first argument of the __call__ method as kwargs.

Attributes

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

var_keyword = 'kw'

__init__(dsp, function_id=None, inputs=None, outputs=None, inputs_dist=None, shrink=True, wildcard=True, output_type=None, output_type_kw=None, first_arg_as_kw=False)

Initializes the Sub-dispatch Function.

Parameters:

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

• function_id (str, optional) – Function name.

• inputs (list[str], iterable, optional) – Input data nodes.

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

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

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

• 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.

• 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 (bool) – Extra kwargs to pass to the selector function.

• first_arg_as_kw – Uses the first argument of the __call__ method as kwargs.

property __signature__

_parse_inputs(*args, **kw)

__call__(*args, _stopper=None, _executor=False, _sol_name=(), _verbose=False, **kw)

Call self as a function.

__annotations__ = {}

__doc__ = "\n    It converts a :class:`~schedula.dispatcher.Dispatcher` into a function.\n\n    This function takes a sequence of arguments or a key values as input of the\n    dispatch.\n\n    :return:\n        A function that executes the dispatch of the given `dsp`.\n    :rtype: callable\n\n    .. seealso:: :func:`~schedula.dispatcher.Dispatcher.dispatch`,\n       :func:`~schedula.dispatcher.Dispatcher.shrink_dsp`\n\n    **Example**:\n\n    A dispatcher with two functions `max` and `min` and an unresolved cycle\n    (i.e., `a` --> `max` --> `c` --> `min` --> `a`):\n\n    .. dispatcher:: dsp\n       :opt: graph_attr={'ratio': '1'}\n\n        >>> from schedula import Dispatcher\n        >>> dsp = Dispatcher(name='Dispatcher')\n        >>> dsp.add_function('max', max, inputs=['a', 'b'], outputs=['c'])\n        'max'\n        >>> from math import log\n        >>> def my_log(x):\n        ...     return log(x - 1)\n        >>> dsp.add_function('log(x - 1)', my_log, inputs=['c'],\n        ...                  outputs=['a'], input_domain=lambda c: c > 1)\n        'log(x - 1)'\n\n    Extract a static function node, i.e. the inputs `a` and `b` and the\n    output `a` are fixed::\n\n        >>> fun = SubDispatchFunction(dsp, 'myF', ['a', 'b'], ['a'])\n        >>> fun.__name__\n        'myF'\n        >>> fun(b=1, a=2)\n        0.0\n\n    .. dispatcher:: fun\n       :opt: workflow=True, graph_attr={'ratio': '1'}\n\n        >>> fun.dsp.name = 'Created function internal'\n\n    The created function raises a ValueError if un-valid inputs are\n    provided:\n\n    .. dispatcher:: fun\n       :opt: workflow=True, graph_attr={'ratio': '1'}\n       :code:\n\n        >>> fun(1, 0)  # doctest: +IGNORE_EXCEPTION_DETAIL\n        Traceback (most recent call last):\n        ...\n        DispatcherError:\n          Unreachable output-targets: ...\n          Available outputs: ...\n    "

__module__ = 'schedula.utils.dsp'