| Commit message (Collapse) | Author | Age | Files | Lines |
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* Added chain exception in _pocketfft.py
* Added chain exception in format.py
* Added chain exception in make_lite.py
* Added chain exception in mrecords.py
* added from e for exceptions
* Minor update for _read_array_header exception msg
* Removed \n from excp msg and e from msg format.
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
* Update numpy/linalg/lapack_lite/make_lite.py
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Charles Harris <charlesr.harris@gmail.com>
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* Added chain exception in _bits_of func
* Added chain exception
* Added chain exception in unixccompiler.py
* Added chain exception in config.py
* Added chain exception in fcompiler __init__.py
* Added chain exception in compaq.py
* Added chain exception in format.py
* Updated raise chain exception
* STY: Break long line.
Co-authored-by: Charles Harris <charlesr.harris@gmail.com>
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Follow up to [this commit](https://github.com/numpy/numpy/commit/36f71788620bb17d123669cf03f086fb2b87b3a8), which missed the second pronoun in this sentence.
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DOC: fixed docstring for descr_to_dtype
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Per #16500 discussion, adds link updates to PR.
Replaces NEP URLs with intersphinx links in these
files:
* doc/HOWTO_RELEASE.rst.txt
* doc/source/dev/howto-docs.rst
* numpy/doc/dispatch.py
* numpy/lib/format.py
and incorporates @rossbar's #16502 change of `config.py`.
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types (#15816)
Cleanup from the dropping of python 2
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The bug occurs since numpy 1.16. Before that empty descr corresponds to
`np.dtype([])`. This fixes the problem by following numpy 1.15's
behavior.
Closes gh-15396
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As numpy is Python 3 only, these import statements are now unnecessary
and don't alter runtime behavior.
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Address gh-14142 for the 1.18 release: warn when saving a dtype with metadata that cannot be loaded.
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All of this code was already correct, this just tidies it a little
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This version encodes the dtype as utf8 instead of latin1.
Unfortunately we need to create a new version to make this change, because we did not limit ourselves to ASCII in versions 1 and 2.
Fixes gh-7391
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This works towards adding a third format that supports utf8
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a partial mitigation of #12759.
see also https://nvd.nist.gov/vuln/detail/CVE-2019-6446
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`"Invalid version %r" % (1, 2)` would fail with `TypeError: not all arguments converted during string formatting`
The `Header is not a dictionary` error had a similar problem.
Fixed by changing this entire function to use `.format` in place of `%`, which does not have this gotcha.
Found using LGTM.com
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The pickle module was being imported from numpy.core.numeric. It was
defined there in order to use pickle5 when available in Python3 and
cpickle in Python2. The numpy.compat module seems a better place for
that.
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* ENH: Improve support for pathlib.Path objects in more functions
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All imports of pickle from numpy modules are now done this way:
>>> from numpy.core.numeric import pickle
Also, some loops on protocol numbers are added over pickle tests that
were not caught from #12090
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Previously, saving format version 1 would align to 16 bytes,
and saving version 2 would align improperly (bug #8085).
Alignment is now always at least 64 bytes in either version,
which supports memory mapping of the saved files on Linux,
where mmap() offset must be a multiple of the page size.
Why 64 bytes? Simply because we don't know of a case where
more is needed. AVX alignment is 32 bytes; AVX-512 is 64.
Fixes #8085, closes #8598.
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Since we only need to support python 2, we can remove any case where we just
pass a single string literal and use the b prefix instead.
What we can't do is transform asbytes("tests %d" % num), because %-formatting
fails on bytes in python 3.x < 3.5.
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with empty dtypes. See #6430
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Prevents overflow errors for large arrays on systems
where the default int type is int32.
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Previously the passed in version was ignored
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Previously read_array_header_1_0 & read_array_header_2_0 were not
returning the documented results.
Closes #5602
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Add pickle compatibility flags to numpy.save and numpy.load. Allow only
combinations that cannot corrupt binary data in Numpy arrays. Use the
same default values as Python pickle.
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