10000 MAINT: More Histogramdd cleanup by eric-wieser · Pull Request #10863 · numpy/numpy · GitHub
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MAINT: More Histogramdd cleanup #10863

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Merged
merged 8 commits into from
Apr 9, 2018
Merged

MAINT: More Histogramdd cleanup #10863

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40938e4
MAINT: Remove redundant operations in 1d masking
eric-wieser Apr 8, 2018
4a178b2
MAINT: Remove pointless axis permutation
eric-wieser Apr 8, 2018
ad53a1f
MAINT: Remove dead code.
eric-wieser Apr 8, 2018
dbaca82
MAINT: Remove asarray call on a known array
eric-wieser Apr 8, 2018
f4df81f
MAINT: Don't use a dict with numeric keys when a tuple would do fine
eric-wieser Apr 8, 2018
b3a2b02
MAINT: Don't reimplement `ravel_multi_index`
eric-wieser Apr 8, 2018
59d7878
MAINT: Use the minlength argument of bincount to avoid a copy into a …
eric-wieser Apr 8, 2018
cc1107e
MAINT: Don't use np.arange just because `range` is shadowed
eric-wieser Apr 8, 2018
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MAINT: Don't use a dict with numeric keys when a tuple would do fine
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@eric-wieser
eric-wieser committed Apr 8, 2018
commit f4df81f8dcd5c7361a273b7c6cf294a9e8c0841c
7 changes: 4 additions & 3 deletions numpy/lib/histograms.py
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Expand Up @@ -899,9 +899,10 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
return np.zeros(nbin-2), edges

# Compute the bin number each sample falls into.
Ncount = {}
for i in np.arange(D):
Ncount[i] = np.digitize(sample[:, i], edges[i])
Ncount = tuple(
np.digitize(sample[:, i], edges[i])
for i in np.arange(D)
)
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I think these look better when aligned with the arguments.


# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
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