computation-graph 19

No project description provided

A functional composition framework that supports:

1. State - functions which retain state for their next turn of action.
2. Ambiguity - non deterministic composition with priorities.
3. Injection of compositions into long pipelines (deep dependency injection).
4. Non cancerous asyncio support.

pip install computation-graph

To deploy: python setup.py sdist bdist_wheel; twine upload dist/*; rm -rf dist/;

Type checking

The runner will type check all outputs for nodes with return type annotations. In case of a wrong typing, it will log the node at fault.

Debugging

Available debuggers:

1. graphviz.computation_trace
2. mermaid.computation_trace
3. ascii.computation_trace

To use, replace to_callable with run.to_callable_with_side_effect with your selected debugger as the first argument.

Graphviz debugger

This debugger will save a file on each graph execution to current working directory.

You can use this file in a graph viewer like gephi. Nodes colored red are part of the 'winning' computation path. Each of these nodes has the attributes 'result' and 'state'. 'result' is the output of the node, and 'state' is the new state of the node.

In gephi you can filter for the nodes participating in calculation of final result by filtering on result != null.