On Mon, Apr 04, 2022 at 07:46:12AM -0000, Brian McCall wrote:
> Now do it for NumPy arrays
In response to Greg:
[quoting Greg]
I'm not convinced there's a need for new syntax here.
63*lbs
77*km/hr
With appropriate definitions of lbs, km and hr these
can be made to construct numbers with attached units.
[end quote]
Numpy arrays support array*scalar, which multiplies each
element of the array by the scalar.
>>> import numpy as np
>>> arr = np.array([2, 3, 4, 5])
>>> arr*1.5
array([3. , 4.5, 6. , 7.5])
So we're part way there.
However, I suspect that having an array of unit objects rather than
low-level machine ints or floats will reduce the performance of numpy a
lot. This is probably unavoidable: there is no way you can do numeric
computations and track units as cheaply as doing numeric computations
*without* tracking units.
But performance should be the least of our concerns at this point.
--
Steve
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