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Simple function for determining the memory usage of common Python values and objects.

Project description

Simple function for determining the memory usage of common Python values and objects.

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Installation and Usage

This library is available as a package on PyPI:

python -m pip install sizeof

The library can be imported in the usual ways:

import sizeof
from sizeof import sizeof

The sizeof function can be applied to any value or object. By default, the function returns the memory consumed by that value or object (and not by any of the objects to which it may contain references):

>>> from sizeof import sizeof
>>> sizeof(123.0123)
16

The amount of memory consumed for any given value or object is in part determined by the host architecture and the version of Python being used. The arch function can be used to determine whether the architecture is 32-bit or 64-bit:

>>> from sizeof import arch
>>> arch()
32

The optional deep argument of the sizeof function makes it possible to calculate the memory consumed by an object and all of it descendants by reference:

>>> sizeof([]) # Size of an empty list.
28
>>> sizeof([1]), sizeof([1, 2]) # Size of reference is 4.
(32, 36)
>>> sizeof(3) # Size of an integer.
14
>>> sizeof([1, 2, 3]) == 28 + (3 * 4)
True
>>> sizeof([1, 2, 3], deep=True) == 28 + (3 * (4 + 14))
True

Note that all of the examples above may return different results on your system and in your environment.

Development

All installation and development dependencies are managed using setuptools and are fully specified in setup.py. The extras_require parameter 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 .. ../setup.py && make html

Testing and Conventions

All unit tests are executed and their coverage is measured when using pytest (see setup.cfg 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 sizeof/sizeof.py -v

Style conventions are enforced using Pylint:

python -m pip install .[lint]
python -m pylint sizeof

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

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 by a package maintainer. First, install the dependencies required for packaging and publishing:

python -m pip install .[publish]

Remove any old build/distribution files. Then, package the source into a distribution archive using the wheel package:

rm -rf dist *.egg-info
python setup.py sdist bdist_wheel

Finally, upload the package distribution archive to PyPI using the twine package:

python -m twine upload dist/*

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