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Aforementioned module was accidently marked as public
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With `as_strided`, and the newly introduced `sliding_window_view` function, there are currently 2 public objects that can:
a. Only be imported from a private module
b. Are publicly documented to-be imported from aforementioned module
Both observations are problematic and in need of rectification.
This commit therefore moves `np.lib.stride_tricks` to the `PUBLIC_MODULES` list.
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* Add doc for deprecate_with_doc
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
Co-authored-by: Matti Picus <matti.picus@gmail.com>
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This adds a warning when the main NumPy module is reloaded
with the assumption that in this case objects such as `np.matrix`,
`np._NoValue` or exceptions may be cached internally.
It also gives a warning when NumPy is imported in a sub-interpreter.
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The new tags look like '1.21.0.dev0+98.gaa0453721f', where '98' is the
number of commits since the 1.21.0 branch was started and 'aa0453721f'.
The chosen form may be specified in the 'setup.cfg' file. This PR adds
two new files 'numpy/_version.py' and 'numpy/version.py'. The latter
is kept because it is part of the public API and is actually used by
some downstream projects, but it is no longer dynamically created.
See https://github.com/python-versioneer/python-versioneer/ for more
information.
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MAINT: Use numpy version for f2py version.
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Because f2py is released as part of numpy, the old svn versioning
no longer makes sense and it was decided to use the numpy version
instead.
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ENH: Support for the NVIDIA HPC SDK nvfortran compiler
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Move them into a new `numpy/typing/tests directory`
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ENH: Add annotations to 3 functions in `np.core.function_base`
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Co-Authored-By: Eric Wieser <eric-wieser@users.noreply.github.com>
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MAINT: Fix various issues with the `np.generic` annotations
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Addresses https://github.com/numpy/numpy/pull/17214#discussion_r481239468
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See https://bugs.python.org/issue20092
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ENH: Add placeholder stubs for all sub-modules
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MAINT: use sysconfig not distutils.sysconfig where possible
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Using `np.random` can cause mysterious test failures in mypy `api.run`
with a report that 'error: Module has no attribute "rand"'. The fix
here is to use `np.empty` instead of `np.random.rand`. Why this works
is unknown at present.
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counterparts (#17222)
* DOC: redistribute docstring-only content from numpy/doc
* DOC: post-transition clean-up
* DOC, MAINT: reskip doctests, fix a few easy ones
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* MAINT: Added missing methods to `np.flatiter`
* DOC: Added a comment about weird `flatiter.__getitem__` behavior
Contrary to `ndarray.__getitem__` its counterpart in `flatiter` does not accept objects with the `__array__` or `__index__` protocols;
boolean indexing is just plain broken (gh-17175)
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* Added annotations for 8 new functions
The 8 respective functions, all located in `np.core.fromnumeric`, consist of:
* `prod`
* `cumprod`
* `ndim`
* `size`
* `around`
* `mean`
* `std`
* `var`
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
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As explained in NEP 32, the financial functions are to be removed
from version 1.20.
They are now replaced with module level `__getattr__` to give a useful
error message for those surprised by the `AttributeError`.
This only works for Python 3.7+, but it is expected that by the 1.20 release
Python 3.6 will not be supported.
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This pull requests addresses and implement two previous TODO's in `np.typing._DTypeLike`:
* Added a protocol, `_SupportsDtype`, for objects with the `dtype` attribute.
* Replaced an old dictionary, representing array field parameter, with a `TypedDict`.
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* MAINT: Added the `order` argument to `np.array()`
* MAINT: Made a number of `np.array()` arguments keyword-only
* TST: Added a `np.array` test with >2 positional arguments
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Affected generic subclasses:
* `floating`
* `complexfloating`
* `datetime64`
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Newly annotated functions:
* `clip`
* `sum`
* `all`
* `any`
* `cumsum`
* `ptp`
* `amax`
* `amin`
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`numpy.abs` is alias of `numpy.absolute`. I added its type annotation.
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See #16612
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ENH: enable multi-platform SIMD compiler optimizations
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Implement new distutils class `CCompilerOpt`, used for handling
the CPU/hardware optimization, starting from parsing the
command arguments, to managing the relationship between the CPU baseline
and dispatch-able features, also generating the required C headers
and ending with compiling the sources with proper compiler's flags.
`CCompilerOpt` mainly used as a helper class for `CCompiler`,
and doesn't provide any runtime detection for the CPU features,
instead only focuses on the compiler side, but it generates
abstract C headers that can be used later for the final
runtime dispatching process.
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