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The argument was renamed to `shape` and deprecated since NumPy 1.16,
so the deprecation can now be finalized.
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ENH: Added libdivide for floor divide
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- Update setup.py
- Update pavement.py
- Add 1.21.0-note.rst
- Update npyconfig.h
- Clear release/upcoming_changes
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* ENH,API: Store exported buffer info on the array
This speeds up array deallocation and buffer exports, since it
removes the need to global dictionary lookups. It also somewhat
simplifies the logic. The main advantage is prossibly less the
speedup itself (which is not large compared to most things that
happen in the livetime of an array), but rather that no unnecessary
work is done for shortlived arrays, which never export a buffer.
The downside of this approach is that the ABI changes for anyone
who would be subclassing ndarray in C.
* MAINT: Do not tag the NULL (no buffers exported)
The allocation is not the right place to initialize to anything but
NULL, so take the easy path and do not tag the NULL default.
* TST: Add test for best try RuntimeError on corrupt buffer-info
* Remove NPY_SIZEOF_PYARRAYOBJECT and add some documentation
* Use 3 to tag the pointer and object for a "bad" one in the test
* DEP: deprecate the NPY_SIZEOF_PYARRAYOBJECT macro
* Tune down matti's deprecation to write the error instead.
* Tweak macro, so that clang hopefully doesn't complain.
* Use None instead of NULL in PyErr_WriteUnraisable, pypy seems to have a bug with it
* Just comment it out...
* Apply suggestions from code review
Co-authored-by: Matti Picus <matti.picus@gmail.com>
Co-authored-by: mattip <matti.picus@gmail.com>
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DOC: Add missing release fragments to ``upcoming_changes``.
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ENH: Add where argument to np.mean
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Harmonize the signature of np.mean, np.var np.std, np.any, np.all,
and their respective nd.array methods with np.sum by adding a where
argument, see gh-15818.
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This should fix hopefully all broken links and missing code blocks.
I also slight shortened one of the very long titles.
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The new array coercion code incorrectly promoted to "object" when a promotion was impossible and
a dtype passed in. This likely only was relevant for `dtype="V"` where different void dtypes (structured
or different length) often cannot be promoted to each other.
Previously, this worked in some cases e.g. when the array contained byte strings the correct string
was found and then cast to void. Now the void dtypes are promoted, and this fails (unless the string
lengths match).
The error for promotion is now refined in this case.
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At least for now, lets not import it to the main namespace, since there is
no agreement that this is a good idea.
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* implement sliding_window_view #7753
Test cases are shown in the issue page.
* Add Example Cases
* Add step_size and N-dim support
* Add shape and step_size check. Remove warning.
* Remove shape default
Add step_size default's description.
* Give proper parameter name 'step'
* fix a parameter description mistake
* implement test function for sliding_window_view()
* implement test function for sliding_window_view()
* Fix according to @eric-wieser comments
* Change arange to ogrid in Examples
* remove np.squeeze on return line
* Clarify document to avoid parameter confusion.
* add `writable` and more explanation in docs
* resolve a write conflit
* fixes according to @seberg review
* resolve write hazard
* remove outdated docs.
* change referring according to @mattip.
change 'writeable' to 'readonly' as @seberg suggest.
remove 'step' as @eric-wieser request
* fix test minor error
* DOC: Grammar fixes
* STY: Add missing line break required by PEP8
* + Change readonly parameter to writeable.
+ Update writeable description.
+ Fix a few parameter checks.
+ Other minor improvements.
* Move to new api as proposed by @eric-wieser
- Change api to follow suggestion by Eric Wieser in
https://github.com/numpy/numpy/pull/10771#issuecomment-524715356
- Update docstring
- Add more tests
* Improve documentation
* Add sliding_window_view to documentation index
* Apply suggestions from code review
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
* Fix window shape check
* Add `sliding_window_view` to __all__
* Add tests for error cases
* Add array_function dispatching
* Change dispatcher argument defaults to None
* Simplify array function dispatching
* Added "np." prefix to doctests
* Split tests
* Improved docstring
* Add release note
* Fix docstring formatting
* Fix doctest
* Remove namespacing in documentation indexing
* Improve docstring
* Improved docstring
* Simplified docstring
* Improve docstring to make pseudo code stand out
* Improve docstring
* Add simple application example
* Correct release note
* Improve link with as_strides
* Add note about performance
* Tidy up main doc string
* Make language on performance warning stronger
* Bugfix: pass subok and writeable to as_strided
* Add writeable test
* Add subok test
* Change subok test to use custom array subclass instead of unsupported MaskedArray
* Add version added information
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Fanjin <fjzeng@ucsd.edu>
Co-authored-by: Fanjin Zeng <Fnjn@users.noreply.github.com>
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: fanjin <fjzeng@outlook.com>
Closes gh-7753
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DEP,BUG: Coercion/cast of array to a subarray dtype will be fixed
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This currently appends the subarray dtype dimensions first and then
tries to assign to the result array which uses incorrect broadcasting
(broadcasting against the subarray dimensions instead of repeating
each element according to the subarray dimensions).
This also fixes the python scalar pathway `np.array(2, dtype="(2)f4,")`
which previously only filled the first value. I consider that a clear
bug fix.
Closes gh-17511
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BUG: Respect dtype of all-zero argument to poly1d
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MAINT: Cleanup swig for Python 3.
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Replaces ``PyInt_Check`` and ``PyInt_AsLong`` in a few places.
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* ENH: add function to get broadcast shape from a given set of shapes.
Add new function numpy.broadcast_shape which takes tuples
for the shapes to be broadcast against each other.
Return the broadcasted shape as a tuple.
See #17217
* Perform array allocations of size 0 for provided shape tuples
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
* Test for int as input shape
Also update docstring to include both ints and tuples of ints as input
* Remove unnecessary array_function_dispatch
* Add missing set_module
* Add release notes. Add versionadded to docstring.
Also fix up docstring details.
* follow convention for trailing comma
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
* Change name to broadcast_shapes. Also add test case, and type hint.
* follow convention
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
* Update docstring
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
* Add reference to numpy docs on broadcasting to docstring
Also move versionadded
* Fix spelling
Co-authored-by: Warren Weckesser <warren.weckesser@gmail.com>
* Add broadcast_shapes to reference docs and add See Also sections
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
Co-authored-by: Warren Weckesser <warren.weckesser@gmail.com>
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Adds a keyword-only dtype parameter to correlate and coerrcoef to allow
user to specify the dtype of the output.
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
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DEP: Deprecate coercion to subarray dtypes
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When coercing to subarray dtypes, e.g. using `np.array(obj, dtype)`,
but also `arr.astype(dtype)`, the behaviour was only well defined
with tuple inputs, but not with array-like inputs.
In particular, `arr.astype(dtype)` had arguably surprising behaviour
of not converting by element, but rather attempting (and often failing)
to broadcast `arr` to the result array with added dimensions.
This deprecates all of these cases, the main issue would be for users
relying on stranger inputs with broadcasted tuples contained in
sequences:
```
np.array([((0, 1), (1, 2)), ((2,),)], dtype='(2,2)f4')
```
In most cases, where the tuples have the correct output shape,
the new base dtype can be directly used since the discovered shape
should match.
However, there is no work-around for the above case.
Closes gh-17173
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This removes one of the larger changes to array-coercion, which
meant that NumPy scalars were always coerced like a 0-D array
would be (i.e. using normal casting). When the assignment is
explicitly an integer, now `scalar.__int__()` will be used instead
(as was the case previously).
Since previously this was handled differently, a *single* scalar
is still converted using casting:
np.array(np.float64(np.nan), dtype=np.int64)
succeeds, but any other thing fails, such as:
np.array([np.float64(np.nan)], dtype=np.int64)
arr1d_int64[()] = np.float64(np.nan)
np.array(np.array(np.nan), dtype=np.int64)
This does not affect Python scalars, that always raise, because
they always are converted using `scalar.__int__()`.
Unsigned integers always supported casting from their signed
equivalent, so the difference is much less visible for them and
this chooses to always use the casting behaviour.
The main reason for this change is to help pands:
https://github.com/pandas-dev/pandas/issues/35481
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BLD: enabled negation of library choices in NPY_*_ORDER
When users build for a particular order it may be beneficial
to disallow certain libraries.
In particular a user may not care about which accelerated
BLAS library is used, so long as the NetLIB or ATLAS library isn't used.
This is now possible with:
NPY_BLAS_ORDER='^blas,atlas'
or
NPY_BLAS_ORDER='!blas,atlas'
Since we may envision more BLAS/LAPACK libraries to the pool, this
will provide greater flexibility as they enter.
A new (local) method is added in system_info.py which removes duplicate
code and allows for easier usage across libraries.
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* ENH: Allow genfromtxt to unpack structured arrays
genfromtxt failed to transpose output when
unpack=True and `dtype` was structured (or None).
This patch resolves the issue by
returning a list of arrays, as in `loadtxt`.
Co-authored-by: Matti Picus <matti.picus@gmail.com>
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
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[skip ci]
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ENH: Implement concatenate dtype and casting keyword arguments
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Co-authored-by: Matti Picus <matti.picus@gmail.com>
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Unfortunately, the casting was not consistent and sometimes used
force casting (axis=None) while normally same kind casting was used.
This thus deprecates the `force_casting` corner case, so that
casting has to be provided in the future.
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The "do not use" comment has been here since bb0e4f356cce2f199d9c08ffe572fbabadc846d1.
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API: Remove `np.ctypeslib.ctypes_load_library`
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This function has been deprecated since fcee1ad856089a7ecb7b6865d280c0273dacb638 (Numpy v1.0b3).
14 years is more than enough time for users to switch from it.
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MAINT, BUG: Remove uses of PyString_FromString.
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We no longer need to use the compatibility function after dropping
support for Python 2.7. In some cases unicode was the correct string
type rather than the bytes of the compatibility version and bugs in the
array `__complex__` and array `__array_interface__` methods have been
fixed by changing that.
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MAINT: Revert boolean casting back to elementwise comparisons in `trim_zeros`
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