physipy

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 import units, constants
>>> nm = units['nm']    # nanometer
>>> hp = constants['h'] # Planck's constant
>>> c  = constants['c'] # speed of light
>>> E_ph = hp * c / (500 * nm) # energy of a photon at wavelength 500nm
>>> print(E_ph)
3.9728916483435158e-19 kg*m**2/s**2
>>> J = units['J'] # Joule
>>> E_ph.favunit = J # set the favourite unit for display/print
>>> print(E_ph)
3.9728916483435158e-19 J

For a quickstart, check the quickstart notebook on the homepage

Documentation

Full documentation of physipy is available here : https://physipy.readthedocs.io/en/latest/, generated with mkdocs and hosted on readthedocs.

Try physipy online now

Get a live python session with physipy by clicking here. After a while, you'll get an interactive notebook session, then open the quiskstart.ipynb notebook in the left panel.

Installation

The latest release of physipy is available on [pypi] at https://pypi.org/project/physipy/. Hence the easiest way to install physipy is using pip :

pip install physipy

Latest source code is hosted on Github at https://github.com/mocquin/physipy/. You can download and un-zip the package localy, or clone the git repository with :

git clone https://github.com/mocquin/physipy

For more information, see here.

Why choose this package

Here are some reasons that might encourage you to choose this package for quantity/physical/units handling in python :

• Light-weight package (2 classes, few helper functions - the rest is convenience)
• Great numpy compatibility (see below)
• Great pandas compatibility (see below)
• Great matplotlib compatibility (see below)
• As fast (if not faster) than the main other units packages (see below)

Also :

• lots of unit tests
• computation performances tracked with airspeed-velocity (see below)
• Jupyter widgets that handle units (as ipywidgets and Qt, see below)

Goals of the project

The project focuses on keeping these goals in the center of any new development :

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

Implementation approach

If you're only interested in using physipy, you don't need to understand this part (thou it wouldn't hurt to read it) :

• 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 class does not sub-class numpy's ndarray (although Quantity instances are compatible with numpy's ufuncs, see below). 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 "favorit 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

One the biggest strength of physipy is its numpy support :

import numpy as np
from physipy import m, units

mm = units['mm']

lengths = np.linspace(-3*m, 4.5*m, 12*mm)
print(lengths[4])
print(lengths.mean())

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 limitations still exist but can be can be circumvented. See the documentation for numpy support.

Pandas' support

Pandas can be interfaced with physipy through the extension API exposed by pandas. For this, just install the package physipandas. You can then use pd.Series and pd.DataFrame whilst keeping the meaningfull units. Checkout the dedicated repo for physipandas for more information.

import pandas as pd
import numpy as np
from physipy import m
from physipandas import QuantityDtype, QuantityArray

# definition is a bit verbose...
c = pd.Series(QuantityArray(np.arange(10)*m), 
              dtype=QuantityDtype(m))

print(type(c))                 # --> <class 'pandas.core.series.Series'>
print(c.physipy.dimension)     # --> : L
print(c.physipy.values.mean()) # --> : 4.5 m
c

0   0
1   1
2   2
3   3
4   4
5   5
6   6
7   7
8   8
9   9
dtype: physipy[1 m]

Matplotlib's units support

Matplotlib allows defining a physical units interface, which can be turned-on using physipy's 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

# physipy work
x = np.linspace(0, 5) * s
x.favunit = ms 
y = np.linspace(0, 30) * mm
y.favunit = mm 

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

Checkout the matplotlib page on physipy documentation.

Widgets

Some ipywidgets and PyQt widgets are provided to make your physical researches and results more interactive : everything is stored in a separate package.

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 but it should!) : https://github.com/mocquin/quantities-comparison :

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) :

• astropy
• sympy
• pint
• forallpeople
• unyt
• python-measurement
• Unum
• scipp
• magnitude
• physics.py : there are actually several packages based on the same core code : ipython-physics (python 2 only) and python3-physics (python 3 only)
• ScientificPython.Scientific.Physics.PhysicalQuantities
• numericalunits
• dimensions.py (python 2 only)
• buckingham
• units
• quantities
• physical-quantities
• brian
• quantiphy
• parampy
• pynbody
• python-units
• pyansys-units (forked from pint?)
• natu
• misu
• units
• physunits
• openscn
• and finally pysics from which this package was inspired

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 talk.

Some C/C++ alternatives :

• units

Performance

Performance of physipy are tracked using airspeedvelocity. Results are available at https://mocquin.github.io/physipy/.

License

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

Acknowledgment

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