It converts a Dispatcher() into a function.
This function takes a sequence of arguments as input of the dispatch.
| Returns: | A function that executes the pipe of the given dsp, updating its
workflow. |
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| Return type: | callable |
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Note
This wrapper is not thread safe, because it overwrite the solution.
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 = DispatchPipe(dsp, 'myF', ['a', 'b'], ['a'])
>>> fun.__name__
'myF'
>>> fun(2, 1)
1
The created function raises a ValueError if un-valid inputs are
provided:
Methods
__init__ |
Initializes the Sub-dispatch Function. |
copy |
|
get_node |
Returns a sub node of a dispatcher. |
parse_inputs |
|
plot |
Plots the Dispatcher with a graph in the DOT language with Graphviz. |
search_node_description |
|
web |
Creates a dispatcher Flask app. |
-
__init__(dsp, function_id, inputs, outputs=None, cutoff=None, inputs_dist=None, no_domain=True, wildcard=True)
Initializes the Sub-dispatch Function.
| Parameters: |
- dsp (schedula.Dispatcher) – A dispatcher that identifies the model adopted.
- function_id (str) – Function name.
- inputs (list[str], iterable) – Input data nodes.
- outputs (list[str], iterable, optional) – Ending data nodes.
- cutoff (float, int, optional) – Depth to stop the search.
- inputs_dist (dict[str, int | float], optional) – Initial distances of input data nodes.
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