Hi,
The numpy.where() function was rewritten in numpy 1.9 to speed it up. I
traced it to this changeset.
https://github.com/numpy/numpy/commit/593e3c30c24f0c61a271dc883c614724d7a57e1e
The weird thing is the 1.9 behavior changed the resulting dtype in some
situations when using scalar values as
What a coincidence! A very related bug just got re-opened today at my
behest: https://github.com/numpy/numpy/issues/5095
Not the same, but I wouldn't be surprised if it stems from the same
sources. The short of it...
np.where(x, 0, x)
where x is a masked array, will return a masked array in
np.uint64(-1)+0
Out[36]: 1.8446744073709552e+19
I often work on signal processing requiring bit-exact integral arithmetic.
Promoting to float is not helpful - I don't understand the logic of the
above example.
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The histogram function supports a weights option, but most others (e.g.,
percentile) do not.
For my problem, I have a trace of the amounts of time (floating point) that
my machine under test is in each of N states. I'd like to produce
histograms, kde, maybe nice pics with seaborn.
I can use
On Wed, Jul 29, 2015 at 1:07 PM, Neal Becker ndbeck...@gmail.com wrote:
np.uint64(-1)+0
Out[36]: 1.8446744073709552e+19
I often work on signal processing requiring bit-exact integral arithmetic.
Promoting to float is not helpful - I don't understand the logic of the
above example.
See this
