3 ms·
Only tangential to this but somebody might find it usefull. I’m doing lots of calculations in Python involving various units. I’m using a similar library called
by tomas789 3y ago
Only tangential to this but somebody might find it usefull. I’m doing lots of calculations in Python involving various units. I’m using a similar library called Pint. https://github.com/hgrecco/pint https://github.com/hgrecco/pint
My business is thermodynamics of power plants. Professionals in the industry tend to use convenient units like C, bars, kJ/kg and so on. But the formulas usualy need basic SI units.
Using this library not only streamlines the conversion process but also keeps track of the unit itself. So instead of variable turbine_output_gj and turbine_output_mw I can have just turbine_output which is convenient.
It is hard to put a value on that but I believe it has already spared me many trivial mistakes that I had to explain to my clients.
- jeramey 3y agoI'll second that. I work in the renewable energy space where we get all manner of atmospheric and power data in a wide variety of units depending on the data source, so Pint is incredibly useful in normalizing them as well as making it clear in the code what and how unit conversion is happening. The fact that it integrates fairly nicely with Pandas and Numpy is great, too.
- jkrubin 3y agoI will third that and add that I use pint regularly and currently work in non-renewable energy.
- vegetablepotpie 3y agoUnrelated question. I was recently appointed to a citizens energy commission and I'm finding my self modeling energy use and generation in Python. I'm curious if you would suggest any models of residential and commercial energy consumption and renewable energy generation. I'm currently generating a lot of my own from scratch and I'm being (rightly) criticized by my colleagues to use something off the shelf.
- jeramey 3y agoIf you're looking for something turn-keyish, you might be able to get some help with this from one or more of the big players in the renewable energy analysis and forecasting space, including (in no particular order) DNV, Vaisala, Bloomberg, and Ventyx. Beware, their offerings can get pretty expensive and there's often no standard list pricing. Also, I strongly encourage you to always do your own validation of any model data they might give you, so the work you're doing to produce your own models is probably not wasted.