misu is short for "misura", which means measurement (in
misu is a package for doing calculations with in consistent
units of measurement.
On Windows, precompiled wheels are provided so all you have to do is this:
pip install misu
On Linux, you have to install from a source distribution (sdist). This is also on PyPI, but you must already have Cython and numpy present in your target environment. This is because they are required to build misu. Thus, you need something like this on Linux:
$ python3.7 -m venv venv $ source venv/bin/activate (venv) $ pip install Cython numpy (venv) $ pip install misu <lots of compiler output>
If you have have experience with making manylinux wheels for Linux, I would love to get your help to make them for misu too!
Most of the time you will probably work with
misu interactively, and
it will be most convenient to import the entire namespace:
from misu import * mass = 100*kg print(mass >> lb)
kg got imported from the
misu package. We redefine
the shift operator to perform inline conversions. The code above
There are many units already defined, and it is easy to add more. Here we convert the same quantity into ounces:
print(mass >> oz)
What you see above would be useless on its own. What you really need is to be able to perform consistent calculations with quantities expressed in different, but compatible units:
mass = 10*kg + 20*lb print(mass)
For addition and subtraction,
misu will ensure that only consistent
units can be used. Multiplication and division will produce new units:
distance = 100*metres time = 9.2*seconds speed = distance / time print(speed)
As before, it is trivially easy to express that quantity in different units of compatible dimensions:
print(speed >> km/hr)
misu is a package of handling physical quantities with dimensions.
This means performing calculations with all the units being tracked
correctly. It is possible to add kilograms per hour to ounces per
minute, obtain the correct answer, and have that answer be reported in,
say, pounds per week.
misu grew out of a personal need. I have used this code personally
in a (chemical) engineering context for well over a year now (at time of
writing, Feb 2015). Every feature has been added in response to a
- Speed optimized.
misuis very fast! Heavy math code in Python will be around only 5X slower when used with
misu. This is much faster than other quantities packages for Python.
- Written as a Cython extension module. Speed benefits carry over when
misufrom your own Cython module (a
.pxdis provided for linking).
- When an operation involving incompatible units is attempted, an
EIncompatibleUnitsexception is raised, with a clear explanation message about which units were inconsistent.
- Decorators for functions to enforce dimensions
@dimensions(x='Length', y='Mass') def f(x, y): return x/y f(2*m, 3*kg) # Works f(200*feet, 3*tons) # Works f(2*joules, 3*kelvin) # raises AssertionError f(2*m, 3) # raises AssertionError
- An operator for easily stripping the units component to obtain a plain numerical value
mass = 100 * kg mass_lb = mass >> lb duty = 50 * MW duty_BTU_hr = duty >> BTU / hr
- An enormous amount of redundancy in the naming of various units. This
METRESwill all work. The reason for this is that from my own experience, when working interactively (e.g. in the IPython Notebook) it can be very distracting to incorrectly guess the name for a particular unit, and have to look it up.
m**3and so on.
- You can specify a reporting unit for a dimension, meaning that you could have all lengths be reported in "feet" by default for example.
- You can specify a reporting format for a particular unit.
There are other projects, why
There are several units systems for Python, but the primary motivating
use-case is that
misu is written as a Cython module and is by far
the fastest* for managing units available in Python.
*Except for ``NumericalUnits``, which is a special case
**I haven't actually checked that this statement is true for all of them yet.
For speed-critical code, the application of unit operations can still be too slow. In these situations it is typical to first cast quantities into numerical values (doubles, say), perform the speed-critical calculations (perhaps call into a C-library), and then re-cast the result back into a quantity and return that from a function.
@dimensions(x='Length', y='Mass') def f(x, y): x = x >> metre y = y >> ounces <code that assumes meters and ounces, returns value in BTU> return answer * BTU
This way you can still easily wrap performance-critical calculations with robust unit-handling.
The inspiration for
Frink by Alan Eliasen. It is
wonderful, but I need to work with units in the IPython Notebook, and
with all my other Python code.
There are a bunch of other similar projects. I have not used any of them enough yet to provide a fair comparison: