To help you get started, we've collected the most common ways that Pint is being used within popular public projects.

nanograv / PINT / tests / test_fitter.py View on Github

def test_ftest():
    """Test for fitter class F-test."""
    m = tm.get_model(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.gls.par"))
    t = toa.get_TOAs(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.tim"))
    f = fitter.WLSFitter(toas=t, model=m)
    f.fit_toas()
    # Test adding parameters
    FD4 = param.prefixParameter(
        parameter_type="float", name="FD4", value=0.0, units=u.s, frozen=False
    )
    ft = f.ftest(FD4, "FD", remove=False)
    assert isinstance(ft, float) or isinstance(ft, bool)
    # Test return the full output
    ft, r_rms, chi2, dof = f.ftest(FD4, "FD", remove=False, full_output=True)
    # Test removing parameter
    FD3 = param.prefixParameter(
        parameter_type="float", name="FD3", value=0.0, units=u.s, frozen=False
    )
    ft = f.ftest(FD3, "FD", remove=True)
    assert isinstance(ft, float) or isinstance(ft, bool)

View more ways to use Pint

Pint: makes units easy

Pint is a 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 is distributed with a comprehensive list of physical units, prefixes and constants. Due to its modular design, you can extend (or even rewrite!) the complete list without changing the source code. It supports a lot of numpy mathematical operations without monkey patching or wrapping numpy.

It has a complete test coverage. It runs in Python 3.9+ with no other dependency. It is licensed under BSD.

It is extremely easy and natural to use:

>>> import pint
>>> ureg = pint.UnitRegistry()
>>> 3 * ureg.meter + 4 * ureg.cm

and you can make good use of numpy if you want:

>>> import numpy as np
>>> [3, 4] * ureg.meter + [4, 3] * ureg.cm

>>> np.sum(_)

Quick Installation

To install Pint, simply:

$ pip install pint

or utilizing conda, with the conda-forge channel:

$ conda install -c conda-forge pint

and then simply enjoy it!

Documentation

Full documentation is available at

Command-line converter

A command-line script pint-convert provides a quick way to convert between units or get conversion factors.

Design principles

Although there are already a few very good Python packages to handle physical quantities, no one was really fitting my needs. Like most developers, I programmed Pint to scratch my own itches.

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. Unit name translation is available if Babel is installed.

Free to choose the numerical type: You can use any numerical type (fraction, float, decimal, numpy.ndarray, etc). NumPy is not required but supported.

Awesome 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.

Uncertainties integration: transparently handles calculations with quantities with uncertainties (like 3.14±0.01 meter) via the uncertainties package.

Handle temperature: conversion between units with different reference points, like positions on a map or absolute temperature scales.

Dependency free: it depends only on Python and its standard library. It interacts with other packages like numpy and uncertainties if they are installed

Pandas integration: Thanks to Pandas Extension Types it is now possible to use Pint with Pandas. Operations on DataFrames and between columns are units aware, providing even more convenience for users of Pandas DataFrames. For full details, see the pint-pandas Jupyter notebook.

Pint is maintained by a community of scientists, programmers and enthusiasts around the world. See AUTHORS for a complete list.

To review an ordered list of notable changes for each version of a project, see CHANGES