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Since we only need to support python 2, we can remove any case where we just
pass a single string literal and use the b prefix instead.
What we can't do is transform asbytes("tests %d" % num), because %-formatting
fails on bytes in python 3.x < 3.5.
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TST: Prove that poly1d coeffs are immutable
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Remove the comment suggesting that they are not
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BUG: Prevent crash in poly1d.__eq__
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Fixes #8760
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BUG: Fix np.average for object arrays
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Fixes #8696
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Previously, these had different rules for unmasking values, and even different
arguments to decide how to do so.
Fixes #8664
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ENH: Allow for an in-place nan_to_num conversion
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This somehow reverts #7618 and solves #6847, #7548 by implementing
support for unevenly spaced data. Now the behaviour is similar to
that of Matlab/Octave function. As argument it can take:
1. A single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
i.e. `dx`, `dy`, `dz`, ...
3. N arrays to specify the coordinates of the values along each
dimension of F. The length of the array must match the size of
the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.
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These were tested by temporarily removing the base classes from AxisError
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closes gh-8637
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BUG: Don't leak internal exceptions when given an empty array
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Fixes #7454
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Also, remove some unused variables
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BUG: Fix crash on 0d return value in apply_along_axis
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Note that this is not a full subsitute for np.ma.apply_along_axis,
as that allows functions to return a mix of np.ma.masked and scalars
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Also:
ENH: Support arbitrary dimensionality of return value
MAINT: remove special casing
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This change makes iscomplexobj compatible with custom array types
using custom dtypes, that are not fully compatible to Numpys dtypes,
which can nevertheless be coerced to a numpy array with asarray
again, as has been the behaviour before PR #7936.
Fixes #8601
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ENH: Vectorize packbits with SSE2
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Larger data to account for future vectorization.
Also add benchmarks for packbits and unpackbits
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the masked nans which are equivalent to valid infs must be replaced
with infs earlier otherwise the inf is lost in the masked sum of the low
and high part.
Closes gh-8487
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* BUG: Closes issue #8419
Fixes issue in apply_along_axis() where func1d() returns a non ndarray
* BUG: Fix apply_along_axis() when func1d() returns a non-ndarray
Closes issue #8419. Fixes issue in apply_along_axis() where
func1d() returns a non ndarray by calling asanyarray() on
result. This commit fixes a too long line in the test case.
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BUG: handle unmasked NaN in ma.median like normal median
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This requires to base masked median on sort(endwith=False) as we need to
distinguish Inf and NaN.
Using Inf as filler element of the sort does not work as then the mask is not
guaranteed to be at the end.
Closes gh-8340
Also fixed 1d ma.median not handling np.inf correctly, the nd variant
was ok.
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Closes gh-8220
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The original dtype converters for bytes and str
did not account for converting objects of str or
bytes dtype respectively. Replace the original
converters with those from numpy.compat, which
are much more robust.
Closes gh-8033.
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This behaviour matches that for most other numpy functions (such as
np.mean). It was initially slated for 1.12, but replaced by a
FutureWarning. Hence, this is for 1.13.
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BUG: np.piecewise not working for scalars
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Fixed meshgrid to return arrays with same dtype as arguments.
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BUG: ediff1d should return subclasses
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Ediff1d performance
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Fix bug in ravel_multi_index for big indices (Issue #7546)
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ufuncs (#8054)
* ENH: add signature argument to vectorize for generalized ufuncs
Example usage (from the docstring):
Vectorized convolution:
>>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
>>> convolve(np.eye(4), [1, 2, 1])
array([[ 1., 2., 1., 0., 0., 0.],
[ 0., 1., 2., 1., 0., 0.],
[ 0., 0., 1., 2., 1., 0.],
[ 0., 0., 0., 1., 2., 1.]])
* Use str.format rather than %
* Fix spelling typo
* BUG: fix np.vectorize for size 0 inputs
* DOC: add vectorize to 1.12.0 release notes
* [ci-skip] Remove outdated comment
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