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Integer true division converted the other type directly to the output.
This is correct if both operands are integers, but since the output
of integer division is double precision, it is incorrect when the
other operand is a float32 or float16.
The solution is that we must convert to the same type (as always)
and only the output type is adjusted, but not the inputs.
This means that `integer / float` will correctly defer to the float
which leads to correct promotion.
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MAINT: changing the method of checking for nan / inf values linalg.eig
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Changing numpy to use the same function from numpy.lib.function_base as scipy. Tests pass.
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This PR moves our repo setup to onCreateCommand from postCreateCommand in order to resolve a race condition between conda init and the default shell in VS Code. You can test this new devcontainer.json by creating a codespace for my fork/branch: https://github.com/codespaces/new?hide_repo_select=true&ref=jungaretti%2Fupdate-devcontainer&repo=576410652
postCreateCommand doesn't block interaction with your codespace. You can use other terminals and move around editors while it runs. On the other hand, onCreateCommand runs before you interact with your codespace. You won't be able to open a terminal until it finishes. onCreateCommand is a better choice for running our setup script because we need to run conda init before using the terminal.
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Add functionality to autogenerate build information for a Meson-based build.
In order to add new information, do the following:
- Add the information as an argument in `numpy/meson.build`
- Modify `__config__.py.in` to accept the new argument
Note that SIMD information is added to config, but is WIP/empty,
because `__cpu*` lists are not yet populated as meson does not build
SIMD features yet.
There are two display modes:
- `stdout`: Uses `PyYaml` to display in a human friendly
format. Uses `json` if `PyYaml` is not installed
- `dicts`: Returns a `dict` object
Things will work fine without `pyyaml` installed, an unobtrusive
warning is displayed that the printed output will look better
with `pyyaml`.
[ci skip]
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Convert several methods to the vectorcall convention. The conversions give a performance improvement, see #20790 (comment)
Some notes:
* For vdot the METH_KEYWORDS was removed, as the C vdot method was positional only.
* The add_docstring is converted with an additional check. It was parsed as if (!PyArg_ParseTuple(args, "OO!:add_docstring", &obj, &PyUnicode_Type, &str)), but there is no support for the ! in the npy_parse_arguments
* CI was complaining about coverage of _get_ndarray_c_version. A test was added, but only to provide coverage
* In function_base.py a redundant check in def place was removed
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
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BUG: fix broken numpy.distutils Fortran handling
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The actual output in the GitHub UI gets truncated, so with
`-vv` it is not possible to see what failed without downloading
the log as a zip file.
[skip circle] [skip azp] [skip cirrus]
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The `Path` and `COMMON_FIXED_EXTENSIONS` variables were not
defined at all. This code is untested, and SciPy doesn't build
with NumPy `main` before this fix. The issue was introduced
a few days ago in gh-22885.
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Adds protection against deploying empty docs when sphinx build fails
in circleCI.
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DOC: Fixup docs after the code removal
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CircleCI is currently not set up to fail when docs fail, so this
slipped through the cracks.
It may be that the testing docs should get a bit more changes then
just removing these things.
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DEP: Remove the deprecated ``utils.py`` shim.
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The shim has been deprecated since 2019, the proper place to import
utils funtions is directly from numpy.testing.
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ENH: Move identity to the ArrayMethod to allow customization
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It should only be used by the legacy method, so also reflect that
in the field name.
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Co-authored-by: Matti Picus <matti.picus@gmail.com>
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As requested by Matti in review.
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Its a new version (it changed ;)), plus I took the liberty to move
things around a bit ABI wise.
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value
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Need to look into whether to cut out the dynamic discovery of
reorderability though.
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Also add it to the wrapped array-method (ufunc) implementation
so that a Unit dtype can reasonably use an identity for reductions.
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This also fixes/changes that the identity value is defined by the
reduce dtype and not by the result dtype. That does not make a
different in any sane use-case, but there is a theoretical change:
arr = np.array([])
out = np.array(None) # object array
np.add.reduce(arr, out=out, dtype=np.float64)
Where the output result was previously an integer 0, due to the
output being of `object` dtype, but now it is the correct float
due to the _operation_ being done in `float64`, so that the output
should be `np.zeros((), dtype=np.float64).astype(object)`.
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[ci skip]
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API: Allow SciPy to get away with assuming `trapz` is a Python function
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This wraps `trapz` into a proper python function and then copies all
attributes expected on a Python function over from the "fake" version
to the real one.
This allows SciPy to pretend `trapz` is a Python function to create
their own version.
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MAINT: Move unused import into hook for pyinstaller
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pyinstaller should pack it to allow running the tests, but doesn't
pack the tests themselves and thus doesn't find the `import` statements
that use `numpy.core._multiarray_tests`.
This makes sure that pyinstaller will ship `numpy.core._multiarray_tests`
in any case.
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MAINT: Remove all nose testing support.
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NumPy switched to using pytest in 2018 and nose has been unmaintained
for many years. We have kept NumPy's nose support to avoid breaking
downstream projects who might have been using it and not yet switched to
pytest or some other testing framework. With the arrival of Python 3.12,
unpatched nose will raise an error. It it time to move on.
Decorators removed
- raises
- slow
- setastest
- skipif
- knownfailif
- deprecated
- parametrize
- _needs_refcount
These are not to be confused with pytest versions with similar names,
e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize.
Functions removed
- Tester
- import_nose
- run_module_suite
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MAINT: dtype.name works for NEP 42 dtypes
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Deprecate np.finfo(None), it may be that we should more generally deprecate `np.dtype(None)` but this is a start and particularly weird maybe.
Closes gh-14684
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BUG: Implement `ArrayFunctionDispatcher.__get__`
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While functions should not normally need this, Python functions do
provide it (C functions do not, but we are a fatter object anyway).
By implementing `__get__` we also ensure that `inspect.isroutine()`
passes. And by that we ensure that Sphinx considers these a `py:function:`
role.
Closes gh-23032
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DOC: Ignore attribute FutureWarning in doc build
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