flats 1.0.0

Minimal library that enables flattening of nested instances of iterable containers.

Minimal library that enables flattening of nested instances of iterable containers.

Purpose

This library provides a function for flattening iterable data structure instances that may be nested (i.e., that contain iterables). The depth up to which flattening occurs can be specified.

Installation and Usage

This library is available as a package on PyPI:

python -m pip install flats

The library can be imported in the usual ways:

import flats
from flats import flats

Examples

This library provides a function that can flatten any instance of a container type that is the root of a tree of nested instances of container types, returning as an iterable the sequence of all objects or values (that are not of a container type) encountered during an in-order traversal. Any instance of the Iterable class or the Generator class is considered to be an instance of a container type by this library:

>>> from flats import flats
>>> list(flats([[1, 2, 3], [4, 5, 6, 7]]))
[1, 2, 3, 4, 5, 6, 7]

The nested instances need not be of the same type:

>>> tuple(flats([{1}, {2}, {3}, frozenset({4}), iter([5, 6, 7])]))
(1, 2, 3, 4, 5, 6, 7)
>>> list(flats(['abc', 'xyz']))
['a', 'b', 'c', 'x', 'y', 'z']
>>> list(flats([range(3), range(3)]))
[0, 1, 2, 0, 1, 2]

It is also possible to limit the depth to which nested instances of a container type are recursively traversed during the flattening process (leaving unmodified the nesting of any instances that are found at a greater depth):

>>> list(flats([[[1, 2], 3], [4, 5, 6, 7]], depth=1))
[[1, 2], 3, 4, 5, 6, 7]
>>> list(flats([[[1, 2], 3], [4, 5, 6, 7]], depth=2))
[1, 2, 3, 4, 5, 6, 7]
>>> list(flats([[[1, [2]], 3], [4, [[[5]]], 6, 7]], depth=float('inf')))
[1, 2, 3, 4, 5, 6, 7]

Development

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]"

Documentation

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

Testing and Conventions

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/flats/flats.py -v

Style conventions are enforced using Pylint:

python -m pip install ".[lint]"
python -m pylint src/flats

Contributions

In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.

Versioning

Beginning with version 0.1.0, 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.

Publishing

This library can be published as a package on PyPI via the GitHub Actions workflow found in .github/workflows/build-publish-sign-release.yml that follows the recommendations found in the Python Packaging User Guide.

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.

To publish the package, create and push a tag for the version being published (replacing ?.?.? with the version number):

git tag ?.?.?
git push origin ?.?.?