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authorEric Wieser <wieser.eric@gmail.com>2018-04-08 14:06:36 -0700
committerEric Wieser <wieser.eric@gmail.com>2018-04-08 14:06:36 -0700
commit40938e4367b3f6f0f368d71ec3e4722f58592cd8 (patch)
tree21519059db46e8719d7b7b7d2aab554b52216950 /numpy/lib
parentc8a5f560f1eedd510ca5656b06dd1ca2ba9322bd (diff)
downloadnumpy-40938e4367b3f6f0f368d71ec3e4722f58592cd8.tar.gz
MAINT: Remove redundant operations in 1d masking
`x1d[nonzero(mask1d)[0]]` is just a less readable way to spell `x1d[mask1d]`
Diffstat (limited to 'numpy/lib')
-rw-r--r--numpy/lib/histograms.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 66e2ccda1..a770e3d31 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -918,7 +918,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
on_edge = (np.around(sample[:, i], decimal) ==
np.around(edges[i][-1], decimal))
# Shift these points one bin to the left.
- Ncount[i][np.nonzero(on_edge & not_smaller_than_edge)[0]] -= 1
+ Ncount[i][on_edge & not_smaller_than_edge] -= 1
# Flattened histogram matrix (1D)
# Reshape is used so that overlarge arrays