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DEP: Fix escaped string characters deprecated in Python 3.6.
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In Python 3.6 a number of escape sequences that were previously accepted
-- for instance "\(" that was translated to "\\(" -- are deprecated. To
retain the previous behavior either raw strings must be used or the
backslash must be properly escaped itself.
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Old value was erroneously obtained on a sorted array which is a best
case for the median of 3 pivoted introsort.
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BUG: handle unmasked NaN in ma.median like normal median
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The apply_along_axis path is significantly more expensive than currently
accounted for in the check. Increase the minimum axis size from 400 to
1000 elements.
Either apply_along_axis got more expensive over time or the original
benchmarking was flawed.
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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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DOC: See also np.load and np.memmap in np.lib.format.open_memmap
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np.load supports memmap_mode to open existing .npy files as
memory-mapped files. np.memmap supports creating or loading raw
binary files as memory-mapped files. This adds a link to a
function for creating memory-mapped files in .npy format.
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BUG: ediff1d should return subclasses
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Special case to_begin and to_end both equal to None, avoiding
subsequent steps. Particulary faster for small arrays where
overhead plays a big role.
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Ediff1d performance
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Eliminate a copy operation when to_begin or to_end is given. Also
use ravel instead of flatiter which is much faster.
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Fix bug in ravel_multi_index for big indices (Issue #7546)
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Closes #8166.
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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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DOC: Improve histogram2d() example.
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Closes #8115.
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This commit modifies the numpy.apply_along_axis() function so that if
it is called with an ndarray subclass, the internal func1d calls
receive subclass instances and the overall function returns an instance
of the subclass. There are two new tests for these two behaviours.
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Formerly, I got DeprecationWarnings about callable being None when I tested
on Python 3.
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BUG: financial.pmt modifies input #8055
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financial.pmt masked rate array in place, changing rate array.
Changed to mask to a new array, preserving the original rate array.
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np.roots() does not always return complex roots, the
roots of a polynomial depend its coefficients and
therefore may be in the real or complex domain.
e.g.
```
>>> (np.roots([1,2,3])).dtype
dtype('complex128')
>>> (np.roots([1,2,])).dtype
dtype('float64')
```
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Minor fix: Change isinf description in "See Also" section of nan_to_num documentation to POSITIVE or negative infinity. Must have been a typo.
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Simplify the expansion of the pad_width argument by using `broadcast_to()`.
This fixes the problem reported in gh-7808, where, for example,
`pad_width=((1, 2),)` resulted in an error.
Closes gh-7808.
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The strings in error messages were left untouched
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In some places, just remove aparently unnecessary filters.
After this, all cases of ignore filters should be removed from
the tests, making testing (even multiple runs) normally fully
predictable.
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Comment mentions a speedup, but it seems unsure why it should
be there. Instead use an error state in divide_by_count.
Some extra complex warnings had to be ignored (but those seemed correct)
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