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Manipulate physical quantities in Python

Project description


Binder PyPI version

This python package allows you to manipulate physical quantities, basically considering in the association of a value (scalar, numpy.ndarray and more) and a physical unit (like meter or joule).

>>> from physipy.quickstart import nm, hp, c, J
>>> E_ph = hp * c / (500 * nm)
>>> print(E_ph)
3.9728916483435158e-19 kg*m**2/s**2
>>> E_ph.favunit = J
>>> print(E_ph)
3.9728916483435158e-19 J

For a quickstart, check the quickstart notebook on the homepage Get a live session at Binder


pip install physipy


  • Few LOC
  • Simple architecture, with only 2 classes (namely Dimension and Quantity)
  • High numpy compatibility
  • Human-readable syntax (fast syntax !)

Use case

  • Define scalar and arrays of physical quantities
  • Compute operation between them : add, sub, mul, div, pow, and so on
  • Display physical quantities in various “units”

Implementation approach

The implementation is pretty simple :

  • a Dimension object represents a physical dimension. For now, these dimension are based on the SI unit. It is basically a dictionary where the keys represent the base dimensions, and the values are the exponent these dimensions.
  • a Quantity object is simply the association of a value, scalar or array (or more!), and a Dimension object. Note that this Quantity classe does not sub-class numpy.ndarray (although Quantity objects are compatible with numpy's ufuncs). Most of the work is done by this class.
  • By default, a Quantity is displayed in term of SI untis. To express a Quantity in another unit, just set the "favunit", which stands for "favourite unit" of the Quantity : my_toe_length.favunit = mm.
  • Plenty of common units (ex : Watt) and constants (ex : speed of light) are packed in. Your physical quantities (my_toe_length), units (kg), and constants (kB) are all Quantity objects.

Numpy's support

Numpy is almost fully and transparently handled in physipy : basic operations, indexing, numpy functions and universal functions are handled. There are more than 150 functions implemented ! Some limitation still exist but can be can be circumvented. See the dedicated notebook :

Matplotlib's units support

Matplotlib allows defining a physical units interface, which can be turned on using just setup_matplotlib, all plot involving a physical quantity will automatically label the axis accordingly :

import numpy as np
import matplotlib.pyplot as plt
from physipy import s, m, units, setup_matplotlib
setup_matplotlib() # make matplotlib physipy's units aware
mm = units["mm"]   # get millimiter
ms = units["ms"]   # get millisecond

y = np.linspace(0, 30) * mm
x = np.linspace(0, 5) * s
y.favunit = mm # no need to call ax.yaxis.set_units(mm)
x.favunit = ms # no need to call ax.xaxis.set_units(ms)

fig, ax = plt.subplots()
ax.plot(x, y)

Checkout the dedicated notebook on matplotlib support.


Some ipywidgets are provided to make your physical researches and results more interactive : Checkout the dedicated notebook on ipywidgets.

Known issues

See the dedicated notebook.


Benchmark results using asv are available at :


See also the corresponding notebook at :

About angles and units

See : Astropy's base units :

Alternative packages

A quick performance benchmark show that physipy is just as fast (or faster) than other well-known physical packages, both when computing scalars (int or float) and numpy arrays :

For a more in-depth comparison, checkout this repository (not maintenained be it should!) : :

There are plenty of python packages that handle physical quantities computation. Some of them are full packages while some are just plain python module. Here is a list of those I could find (approximately sorted by guessed-popularity) :

If you know another package that is not in this list yet, feel free to contribute ! Also, if you are interested in the subject of physical quantities packages in python, check this quantities-comparison repo and this talk. Also check this comparison table and this takl.

Some C/C++ alternatives :


This project is licensed under the MIT License - see the file for details


Thumbs up to phicem and his pysics package, on which this package was highly inspired. Check it out !

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