Unit Conversion in Python

I think you should use quantities, because a quantity has some units associated with it.

Pressure, for example, will be a quantity that could be entered and converted in and to different units (Pa, psi, atm, etc). Probably you could create new quantities specifics for your application.

It looks like another package has come out for doing this as well, written by Massimo DiPierro of web2py fame, called Buckingham.

Also of note, Brian has had something like this for some time.

Note that quantities has very bad support for temperature:

>>> (100 * pq.degC).rescale(pq.degF)
array(179.99999999999997) * degF
>>> (0 * pq.degC).rescale(pq.degF)
array(0.0) * degF

0 degrees Celsius isn’t 0 degrees Fahrenheit. Their framework doesn’t support any kind of conversion that isn’t just multiplying by a factor.

Pint has recently come onto the field. Anybody care to share their experiences? Looks good. FYI: It looks like Pint will be integrated with Uncertainties in the near future.

You may want to look at a new package called natu. It addresses the three issues that @ChristopherBruns listed. It’s available in PyPI.

I’m the author of that package, and I’d appreciate any comments or suggestions.

I am surprised that nobody mentioned SymPy yet. SymPy is a mature and well-maintained symbolic mathematics library for Python that is moreover a NumFOCUS-sponsored project.

It has a Physics module with many useful classes and functions for “solving problems in physics”. Most relevant for you, it has a Unit sub-module that contains everything you need, I think; just read the excellent documentation.

There is another package called unyt from the yt-project. The authors of unyt acknowledge the existence of Pint and astropy.units. Conversions from and to these other packages is supported.

The selling point of unyt is speed. It is faster than the other two. The unit packages are compared in several benchmarks in this paper.

The benchmarks are disappointing for anyone obsessed with performance. 🙁 The slowdown of calculations with any of these unit systems is large. The slowdown factor is 6-10 for arrays with 1000 entries (worse for smaller arrays).

Disclaimer: I am not affiliated with unyt, I just want to share what I learned about unit systems.

Thought to mention the units package which is part of the Astropy package.

It’s well maintained, easy to use, and has all the basic units (as well as astrophysics-related units).
It provides tools for both units and quantities. And there’s also a module for physical constants.

I’d like to point to a separate library for dealing with units: Barril

https://github.com/ESSS/barril

Docs at: https://barril.readthedocs.io/en/latest/

While it does have support for creating "random" units from computation (such as Pint, unum, etc), it’s more tailored to having a database of units (which the library has by default — see: https://barril.readthedocs.io/en/latest/units.html and the implementation: https://github.com/ESSS/barril/blob/master/src/barril/units/posc.py) and then you can query and transform based on the related units.

One thing it supports that does a lot of difference in that regard is dealing with unit conversions which would be "dimentionless" — such as m3/m3 (i.e.:volume per volume) and then converting to cm3/m3 and keeping the dimension.

i.e.: in pint:

>>> import pint
>>> ureg = pint.UnitRegistry()
>>> m = ureg.meter
>>> v = 1 * (m*3)/(m*3)
>>> v
<Quantity(1.0, 'dimensionless')>

And then, after that (as far as I know), it’s not really possible to do additional unit conversions properly knowing that it was m3/m3.

In barril:

>>> from barril.units import Scalar
>>> a = Scalar(3, 'm3/m3')
>>> a.GetValue('cm3/m3')
3000000.0
>>> a.category
'volume per volume'
>>> a.unit
'm3/m3'

and something as a.GetValue('m3') (with an invalid value) would give an error saying that the conversion is actually invalid.

The unit database (which was initially based on the POSC Units of Measure Dictionary) is a bit more tailored for the Oil & Gas field, but should be usable outside of it too.

I find the units packages to be more than what want. It doesn’t take much code to start building your own functions that refer back to the very few basic fundamental numbers. Also, It forces you to do the dimensional analysis to prevent errors.

def FtoC(Tf):
    return (Tf-32)*5/9
def CtoF(Tc):
    return 9*Tc/5+32
def CtoK(Tc):
    return Tc+273.15
def INCHtoCM(Inch):
    return 2.54 * Inch
def CMtoINCH(cm):
    return cm / INCHtoCM(1)
def INCHtoMETER(inch):
    return .01*INCHtoCM(inch)
def FOOTtoMETER(foot):
    return INCHtoMETER(12*foot)
def METERtoINCH(Meter):
    return CMtoINCH(100 * Meter)
def METERtoFOOT(Meter):
    return METERtoINCH(Meter)/12
def M3toINCH3(M3):
    return (METERtoINCH(M3))**3
def INCH3toGALLON(Inch3):
    return Inch3 / 231
def M3toGALLON(M3):
    return INCH3toGALLON(M3toINCH3(M3))
def KG_M3toLB_GALLON(KGperM3):
    return KGtoLBM(KGperM3) / M3toGALLON(1)
def BARtoPASCAL(bar):
    return 100000 * bar
def KGtoLBM(kilogram):
    return kilogram * 2.20462262185
def LBMtoKG(lbm):
    return lbm/KGtoLBM(1)
def NEWTONtoLBF(newton):
    return newton * KGtoLBM(1) * METERtoFOOT(1) / STANDARD_GRAVITY_IMPERIAL()
def LBFtoNEWTON(lbf):
    return lbf * STANDARD_GRAVITY_IMPERIAL() * LBMtoKG(1) * FOOTtoMETER(1)
def STANDARD_GRAVITY_IMPERIAL():
    return 32.174049
def STANDARD_GRAVITY_SI():
    return 9.80665
def PASCALtoPSI(pascal):
    return pascal * NEWTONtoLBF(1) / METERtoINCH(1)**2
def PSItoPASCAL(psi):
    return psi * LBFtoNEWTON(1) / INCHtoMETER(1)**2

Then let’s say you want to plot the static head pressure of 1,3 Butadiene @44 F and use gauges in PSI because you live in the US but the density tables are in SI units as they should be……………………

# butadiene temperature in Fahrenheit
Tf = 44

# DIPPR105 Equation Parameters (Density in kg/m3, T in K)
# valid in temperature 165 to 424 Kelvin
A=66.9883
B=0.272506
C=425.17
D=0.288139

# array of pressures in psi
Pscale = np.arange(0,5,.1, dtype=float)
Tk = CtoK(FtoC(44))
Density = A / (B**(1+(1-Tk/C)**D)) # KG/M3
Height = [PSItoPASCAL(P) / (Density * STANDARD_GRAVITY_SI()) for P in Pscale]
Height_inch =  METERtoINCH(1) * np.array(Height, dtype=np.single)

Another package to mention is Axiompy.

Installation: pip install axiompy

from axiompy import Units
units = Units()
print(units.unit_convert(3 * units.metre, units.foot))
>>> <Value (9.84251968503937 <Unit (foot)>)>

My preferred package is QuantiPhy. It takes a different approach than most other packages. With QuantiPhy the units are simply strings, and the package is largely used when reading or writing quantities. As such, it much easier to incorporate into your software. QuantiPhy supports unit and scale factor conversion both when creating quantities and when rendering them to strings. Here is an example that reads and then writes a table of times and temperatures, converting from minutes/°F to seconds/K on the way in and back to the original units on the way out:

>>> from quanitphy import Quantity

>>> rawdata = '0 450, 10 400, 20 360'
>>> data = []
>>> for pair in rawdata.split(','):
...     time, temp = pair.split()
...     time = Quantity(time, 'min', scale='s')
...     temp = Quantity(temp, '°F', scale='K')
...     data += [(time, temp)]

>>> for time, temp in data:
...     print(f'{time:9q} {temp:9q}')
      0 s  505.37 K
    600 s  477.59 K
   1.2 ks  455.37 K

>>> for time, temp in data:
...     print(f"{time:<7smin} {temp:s°F}")
0 min   450 °F
10 min  400 °F
20 min  360 °F