Extensible combinator library for building symbolic Python expressions that are compatible with serialization and can be evaluated at a later time.
In many scenarios that require some form of lazy evaluation, it is sufficient to employ lambda expressions, generators/iterables, or abstract syntax trees (via the ast and/or inspect modules). However, there are certain cases where none of these are an option (for example, employing lambda expressions precludes serialization and employing the ast or inspect modules usually involves introducing boilerplate that expands the solution beyond one line of code). The purpose of this library is to fill those gaps and make it possible to write concise symbolic expressions that are embedded directly within the concrete syntax of the language.
This library is available as a package on PyPI:
python -m pip install symbolism
The library can be imported in the usual ways:
import symbolism from symbolism import *
The library makes it possible to construct symbolic Python expressions (as instances of the symbol class) that can be evaluated at a later time. A symbolic expression involving addition of integers is created in the example below:
>>> from symbolism import * >>> addition = symbol(lambda x, y: x + y) >>> summation = addition(symbol(1), symbol(2))
The expression above can be evaluated at a later time:
>>> summation.evaluate() 3
Instances of symbol are compatible with common built-in infix and prefix arithmetic, logical, and relational operators. When an operator is applied to one or more symbol instances, a new symbol instance is created:
>>> summation = symbol(1) + symbol(2) >>> summation.evaluate() 3
Pre-defined constants are also provided for all built-in operators supported by the symbol class:
>>> conjunction = and_(symbol(True), symbol(False)) >>> conjunction.evaluate() False
All installation and development dependencies are fully specified in pyproject.toml. The project.optional-dependencies object is used to specify optional requirements for various development tasks. This makes it possible to specify additional options (such as docs, lint, and so on) when performing installation using pip:
python -m pip install .[docs,lint]
The documentation can be generated automatically from the source files using Sphinx:
python -m pip install .[docs] cd docs sphinx-apidoc -f -E --templatedir=_templates -o _source .. && make html
All unit tests are executed and their coverage is measured when using pytest (see the pyproject.toml file for configuration details):
python -m pip install .[test] python -m pytest
Alternatively, all unit tests are included in the module itself and can be executed using doctest:
python src/symbolism/symbolism.py -v
Style conventions are enforced using Pylint:
python -m pip install .[lint] python -m pylint src/symbolism
In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.
The version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.
This library can be published as a package on PyPI by a package maintainer. First, install the dependencies required for packaging and publishing:
python -m pip install .[publish]
Ensure that the correct version number appears in pyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has an automation rule that activates and sets as the default all tagged versions. Create and push a tag for this version (replacing ?.?.? with the version number):
git tag ?.?.? git push origin ?.?.?
Remove any old build/distribution files. Then, package the source into a distribution archive:
rm -rf build dist src/*.egg-info python -m build --sdist --wheel .
Finally, upload the package distribution archive to PyPI:
python -m twine upload dist/*