A functional composition framework that supports:
- State - functions which retain state for their next turn of action.
- Ambiguity - non deterministic composition with priorities.
- Injection of compositions into long pipelines (deep dependency injection).
- Non cancerous
asynciosupport.
pip install computation-graph
To deploy: python setup.py sdist bdist_wheel; twine upload dist/*; rm -rf dist/;
Debugging
We need graphviz to visualize computation graphs:
sudo apt update && apt install graphviz
pip install pygraphviz
Debugging is possible by replacing to_callable with run.to_callable_with_side_effect with debug.debugger(filename) as the first argument.
This 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.
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twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.8.2
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