8.2.15.1. Solution

class Solution(*args, **kwargs)

Solution class for dispatch result.

Methods

__class_getitem__	See PEP 585
__contains__	True if the dictionary has the specified key, else False.
__deepcopy__
__delattr__	Implement delattr(self, name).
__delitem__	Delete self[key].
__dir__	Default dir() implementation.
__eq__	Return self==value.
__format__	Default object formatter.
__ge__	Return self>=value.
__getattribute__	Return getattr(self, name).
__getitem__	x.__getitem__(y) <==> x[y]
__getstate__	Helper for pickle.
__gt__	Return self>value.
__hash__	Return hash(self).
__init__
__init_subclass__	This method is called when a class is subclassed.
__ior__	Return self|=value.
__iter__	Implement iter(self).
__le__	Return self<=value.
__len__	Return len(self).
__lt__	Return self<value.
__ne__	Return self!=value.
__new__
__or__	Return self|value.
__reduce__	Return state information for pickling
__reduce_ex__	Helper for pickle.
__repr__	Return repr(self).
__reversed__	od.__reversed__() <==> reversed(od)
__ror__	Return value|self.
__setattr__	Implement setattr(self, name, value).
__setitem__	Set self[key] to value.
__sizeof__	Size of object in memory, in bytes.
__str__	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_add_initial_value	Add initial values updating workflow, seen, and fringe.
_add_out_dsp_inputs
_apply_filters
_check_function_domain
_check_sub_dsp_domain
_clean_set
_close
_copy_structure
_dsp_closed_add
_ended
_evaluate_function
_evaluate_node
_get_node_estimations	Returns the data nodes estimations and wait_inputs flag.
_init_sub_dsp	Initialize the dispatcher as sub-dispatcher and update the fringe.
_init_workflow
_remove_unused_nodes	Removes unused function and sub-dispatcher nodes.
_remove_wait_in
_run
_see_node	See a node, updating seen and fringe.
_see_remote_link_node	See data remote links of the node (set output to remote links).
_set_data_node_output	Set the data node output from node estimations.
_set_dsp_features
_set_function_node_output	Set the function node output from node inputs.
_set_inputs
_set_node_output	Set the node outputs from node inputs.
_set_sub_dsp_node_input	Initializes the sub-dispatcher and set its inputs.
_set_wildcards	Update wildcards set with the input data nodes that are also outputs.
_started
_update_fut_results
_update_meeting
_update_methods
_verbose
_visit_nodes	Visits a node, updating workflow, seen, and fringe..
_warning	Handles the error messages.
check_targets	Terminates ArciDispatch algorithm when all targets have been visited.
check_wait_in	Stops the search of the investigated node of the ArciDispatch algorithm, until all inputs are satisfied.
clear	od.clear() -> None.
copy	od.copy() -> a shallow copy of od
form	Creates a dispatcher Form Flask app.
fromkeys	Create a new ordered dictionary with keys from iterable and values set to value.
get	Return the value for key if key is in the dictionary, else default.
get_node	Returns a sub node of a dispatcher.
get_sub_dsp_from_workflow	Returns the sub-dispatcher induced by the workflow from sources.
items	D.items() -> a set-like object providing a view on D's items
keys	D.keys() -> a set-like object providing a view on D's keys
move_to_end	Move an existing element to the end (or beginning if last is false).
plot	Plots the Dispatcher with a graph in the DOT language with Graphviz.
pop	od.pop(key[,default]) -> v, remove specified key and return the corresponding value.
popitem	Remove and return a (key, value) pair from the dictionary.
result	Set all asynchronous results.
setdefault	Insert key with a value of default if key is not in the dictionary.
update	D.update([E, ]**F) -> None.
values	D.values() -> an object providing a view on D's values
web	Creates a dispatcher Flask app.

__init__(dsp=None, inputs=None, outputs=None, wildcard=False, inputs_dist=None, no_call=False, rm_unused_nds=False, wait_in=None, no_domain=False, _empty=False, index=(-1,), full_name=(), verbose=False, excluded_defaults=())

Attributes

__annotations__
__dict__
__doc__
__module__
__weakref__	list of weak references to the object
pipe	Returns the full pipe of a dispatch run.

__hash__()

Return hash(self).

__repr__()

Return repr(self).

__init__(dsp=None, inputs=None, outputs=None, wildcard=False, inputs_dist=None, no_call=False, rm_unused_nds=False, wait_in=None, no_domain=False, _empty=False, index=(-1,), full_name=(), verbose=False, excluded_defaults=())

_set_dsp_features(dsp)

_set_inputs(inputs, initial_dist, excluded_defaults=())

_set_wildcards(wildcard, inputs=None, outputs=None)

Update wildcards set with the input data nodes that are also outputs.

Parameters:

• wildcard (bool, int) – 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.

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

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

_update_methods()

check_wait_in(wait_in, n_id)

Stops the search of the investigated node of the ArciDispatch algorithm, until all inputs are satisfied.

Parameters:

• wait_in (bool) – If True the node is waiting input estimations.

• n_id (str) – Data or function node id.

Returns:

True if all node inputs are satisfied, otherwise False.

Return type:

bool

_clean_set()

_init_workflow(inputs=None, inputs_dist=None, initial_dist=0.0, clean=True)

_close(cached_ids)

static _update_fut_results(futs, fut, data, key)

result(timeout=None)

Set all asynchronous results.

Parameters:

timeout (float) – The number of seconds to wait for the result if the futures aren’t done. If None, then there is no limit on the wait time.

Returns:

Update Solution.

Return type:

Solution

static _dsp_closed_add(dsp_closed, s)

_run(stopper=None, executor=False)

get_sub_dsp_from_workflow(sources, reverse=False, add_missing=False, check_inputs=True)

Returns the sub-dispatcher induced by the workflow from sources.

The induced sub-dispatcher of the dsp contains the reachable nodes and edges evaluated with breadth-first-search on the workflow graph from source nodes.

Parameters:

• sources (list[str], iterable) – Source nodes for the breadth-first-search. A container of nodes which will be iterated through once.

• reverse (bool, optional) – If True the workflow graph is assumed as reversed.

• add_missing (bool, optional) – If True, missing function’ inputs are added to the sub-dispatcher.

• check_inputs (bool, optional) – If True the missing function’ inputs are not checked.

Returns:

A sub-dispatcher.

Return type:

schedula.dispatcher.Dispatcher

property pipe

Returns the full pipe of a dispatch run.

_copy_structure(**kwargs)

__deepcopy__(memo)

_add_out_dsp_inputs()

check_targets(node_id)

Terminates ArciDispatch algorithm when all targets have been visited.

Parameters:

node_id (str) – Data or function node id.

Returns:

True if all targets have been visited, otherwise False.

Return type:

bool

_get_node_estimations(node_attr, node_id)

Returns the data nodes estimations and wait_inputs flag.

Parameters:

• node_attr (dict) – Dictionary of node attributes.

• node_id (str) – Data node’s id.

Returns:

• node estimations with minimum distance from the starting node, and

• wait_inputs flag

Return type:

(dict[str, T], bool)

_remove_wait_in()

_set_node_output(node_id, no_call, next_nds=None, **kw)

Set the node outputs from node inputs.

Parameters:

• node_id (str) – Data or function node id.

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

Returns:

If the output have been evaluated correctly.

Return type:

bool

_evaluate_function(args, node_id, node_attr, attr, stopper=None, executor=False)

_check_function_domain(args, node_attr, node_id)

_evaluate_node(args, node_attr, node_id, skip_func=False, **kw)

_set_data_node_output(node_id, node_attr, no_call, next_nds=None, **kw)

Set the data node output from node estimations.

Parameters:

• node_id (str) – Data node id.

• node_attr (dict[str, T]) – Dictionary of node attributes.

• no_call (bool) – If True data node estimations are not used.

Returns:

If the output have been evaluated correctly.

Return type:

bool

_apply_filters(res, node_id, node_attr, attr, stopper=None, executor=False)

_started(attr, node_id)

_ended(attr, node_id)

_verbose(node_id, attr, end=False)

_set_function_node_output(node_id, node_attr, no_call, next_nds=None, **kw)

Set the function node output from node inputs.

Parameters:

• node_id (str) – Function node id.

• node_attr (dict[str, T]) – Dictionary of node attributes.

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

Returns:

If the output have been evaluated correctly.

Return type:

bool

_add_initial_value(data_id, value, initial_dist=0.0, fringe=None, no_call=None)

Add initial values updating workflow, seen, and fringe.

Parameters:

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

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

• data_id (str) – Data node id.

• value (dict[str, T]) – Data node value e.g., {‘value’: val}.

• initial_dist (float, int, optional) – Data node initial distance in the ArciDispatch algorithm.

Returns:

True if the data has been visited, otherwise false.

Return type:

bool

_update_meeting(node_id, dist)

_visit_nodes(node_id, dist, fringe, no_call=False, **kw)

Visits a node, updating workflow, seen, and fringe..

Parameters:

• node_id (str) – Node id to visit.

• dist (float, int) – Distance from the starting node.

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

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

Returns:

False if all dispatcher targets have been reached, otherwise True.

Return type:

bool

_see_node(node_id, fringe, dist, w_wait_in=0)

See a node, updating seen and fringe.

Parameters:

• node_id (str) – Node id to see.

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

• dist (float, int) – Distance from the starting node.

• w_wait_in (int, float) – Additional weight for sorting correctly the nodes in the fringe.

Returns:

True if the node is visible, otherwise False.

Return type:

bool

_remove_unused_nodes()

Removes unused function and sub-dispatcher nodes.

_init_sub_dsp(dsp, fringe, outputs, no_call, initial_dist, index, full_name, excluded_defaults)

Initialize the dispatcher as sub-dispatcher and update the fringe.

Parameters:

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

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

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

_see_remote_link_node(node_id, fringe=None, dist=None, check_dsp=<function Solution.<lambda>>)

See data remote links of the node (set output to remote links).

Parameters:

• node_id (str) – Node id.

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

• dist (float, int) – Distance from the starting node.

• check_dsp ((Dispatcher) -> bool) – A function to check if the remote dispatcher is ok.

_check_sub_dsp_domain(dsp_id, node, pred, kw)

_set_sub_dsp_node_input(node_id, dsp_id, fringe, no_call, initial_dist)

Initializes the sub-dispatcher and set its inputs.

Parameters:

• node_id (str) – Input node to set.

• dsp_id (str) – Sub-dispatcher node id.

• fringe (list[(float | int, bool, (str, Dispatcher)]) – Heapq of closest available nodes.

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

• initial_dist (int, float) – Distance to reach the sub-dispatcher node.

Returns:

If the input have been set.

Return type:

bool

_warning(msg, node_id, ex, *args, **kwargs)

Handles the error messages.

Note

If self.raises is True the dispatcher interrupt the dispatch when an error occur, otherwise if raises != ‘’ it logs a warning.

__annotations__ = {}

__doc__ = 'Solution class for dispatch result.'

__module__ = 'schedula.utils.sol'