Pint 0.6 (Units in Python)
hernan.grecco at gmail.com
Mon Nov 10 21:01:23 CET 2014
We are happy to announce Pint 0.6. Pint is a Python package to define,
operate and manipulate physical quantities: the product of a numerical
value and a unit of measurement.
Check out the blog post for more details about this release:
You can get pint using pip:
$ pip install pint
or get the source code:
and check the docs:
Thanks to the people that contributed bug reports, suggestions and patches
since 0.5. In particular to Matthieu Dartiailh, Ryan Kingsbury, Joel
Mohler, Virgil Dupras, Jonas Olson, John David Reaver and Peter Grayson. A
big thanks should be given to David Linke who did an awesome work with
offset units (Please let me know if I am forgetting someone!)
What is Pint?
Pint is Python package to define, operate and manipulate physical
quantities: the product of a numerical value and a unit of
measurement. It allows arithmetic operations between them and
conversions from and to different units. It supports a lot of numpy
mathematical operations without monkey patching or wrapping numpy.
It is distributed with a comprehensive list of physical units,
prefixes and constants. Due to it’s modular design, you can extend (or
even rewrite!) the complete list without changing the source code.
It has a complete test coverage. It runs in Python 2.6+ and 3.2+ with no
other dependency. It licensed under BSD.
* Unit parsing: prefixed and pluralized forms of units are recognized
without explicitly defining them. In other words: as the prefix kilo
and the unit meter are defined, Pint understands kilometers. This
results in a much shorter and maintainable unit definition list as
compared to other packages.
* Standalone unit definitions: units definitions are loaded from a
text file which is simple and easy to edit. Adding and changing units
and their definitions does not involve changing the code.
* Advanced string formatting: a quantity can be formatted into string
using PEP 3101 syntax. Extended conversion flags are given to provide
symbolic, latex and pretty formatting.
* Free to choose the numerical type: You can use any numerical type
(fraction, float, decimal, numpy.ndarray, etc). NumPy is not required
* NumPy integration: When you choose to use a NumPy ndarray, its
methods and ufuncs are supported including automatic conversion of
units. For example numpy.arccos(q) will require a dimensionless q and
the units of the output quantity will be radian.
* Handle temperature: conversion between units with different
reference points, like positions on a map or absolute temperature
* Small codebase: easy to maintain codebase with a flat hierarchy.
* Dependency free: it depends only on Python and it’s standard library.
* Python 2 and 3: a single codebase that runs unchanged in Python 2.6+ and
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