| Commit message (Collapse) | Author | Age | Files | Lines |
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ENH: umath: ensure ufuncs are well-defined with memory overlapping inputs
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It's clearer to explicitly specify which operands are accessed
elementwise, rather than to list exceptions.
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Add a new NPY_ITER_COPY_IF_OVERLAP iterator flag to NpyIter, which
instructs it to check if read operands overlap with write operands in
memory, and make temporary copies to eliminate detected overlap.
Thanks to Sebastian Berg.
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Came across this in the online docs
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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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Closes gh-8214.
Closes gh-8215.
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DEP: Deprecate the keepdims argument to accumulate
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DOC: Fix bad partition example.
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The strings in error messages were left untouched
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Closes gh-391.
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Note that the newly added `ndim` property is an alias for `nd` and
not available in numpy versions earlier than 1.12.
Add back the tests for `nd`. They can be removed if/when `nd` is
dropped.
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Both `ndarray` and `nditer` spell this property `ndim`, so broadcast
objects should too. The existing property remains for compatibility
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Attempt to clarify the sometimes convoluted behavior of the various
order options 'K', 'A', 'C', 'F'.
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ndarray.fill (not fill) is not appropriate here because it is a list how
to create arrays not how to fill them. [ci skip]
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Fixes gh-7010
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Some of the documentation for newbyteorder, copy and pasted in several
spots, had paragraphs ending in `::`, initiating a sphinx generated
Verbatim environment and resulting in "LaTeX Error: Too deeply nested".
The user_array.container class needed non-empty class documentation.
That that caused a problem is probably a numpydoc bug, but it is easy to
fix.
[skip ci]
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Closes gh-6863.
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The bug traces to the PyArray_OrderConverter
method in conversion_utils.c, where no errors
are thrown if the ORDER parameter passed in
is not of the string data-type or has a string
value of length greater than one. This commit
causes a DeprecationWarning to be raised, which
will later be turned into a TypeError or another
type of error in a future release.
Closes gh-6598.
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Behavior goes back at least to 1.6.2.
Fixes #6367.
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Most of these fixes involve putting blank lines around
.. versionadded:: x.x.x
and
.. deprecated:: x.x.x
Some of the examples were also fixed.
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Update docs for boolean array indexing and nonzero order.
Add links to row-major and column-major terms where they appear.
Closes #3177
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Document the matmul function and add '@' to the operator
section of the reference manual.
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Closes #5927
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ENH: add np.stack
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The motivation here is to present a uniform and N-dimensional interface for
joining arrays along a new axis, similarly to how `concatenate` provides a
uniform and N-dimensional interface for joining arrays along an existing axis.
Background
~~~~~~~~~~
Currently, users can choose between `hstack`, `vstack`, `column_stack` and
`dstack`, but none of these functions handle N-dimensional input. In my
opinion, it's also difficult to keep track of the differences between these
methods and to predict how they will handle input with different
dimensions.
In the past, my preferred approach has been to either construct the result
array explicitly and use indexing for assignment, to or use `np.array` to
stack along the first dimension and then use `transpose` (or a similar method)
to reorder dimensions if necessary. This is pretty awkward.
I brought this proposal up a few weeks on the numpy-discussion list:
http://mail.scipy.org/pipermail/numpy-discussion/2015-February/072199.html
I also received positive feedback on Twitter:
https://twitter.com/shoyer/status/565937244599377920
Implementation notes
~~~~~~~~~~~~~~~~~~~~
The one line summaries for `concatenate` and `stack` have been (re)written to
mirror each other, and to make clear that the distinction between these functions
is whether they join over an existing or new axis.
In general, I've tweaked the documentation and docstrings with an eye toward
pointing users to `concatenate`/`stack`/`split` as a fundamental set of basic
array manipulation routines, and away from
`array_split`/`{h,v,d}split`/`{h,v,d,column_}stack`
I put this implementation in `numpy.core.shape_base` alongside `hstack`/`vstack`,
but it appears that there is also a `numpy.lib.shape_base` module that contains
another larger set of functions, including `dstack`. I'm not really sure where
this belongs (or if it even matters).
Finally, it might be a good idea to write a masked array version of `stack`.
But I don't use masked arrays, so I'm not well motivated to do that.
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Modified the docstrings to all, any, sum, prod, mean, var, std, min, max
to add keepdims argument.
Added 'out' keyword parameter to numpy.argmin, numpy.argmax, to mirror
ndarray methods.
Updated ndarray.clip docstring to give correct parameter description.
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Array docstring now lists correct order default
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Fixes #5306
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