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Currently, the following code:
```
import numpy as np
with open('foo.npy', 'wb') as f:
for i in range(np.random.randint(10)):
np.save(f, 1)
with open('foo.npy', 'rb') as f:
while True:
np.load(f)
```
Will raise:
```
ValueError: Cannot load file containing pickled data when allow_pickle=False
```
While there is no pickled data in the file.
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DOC #22266 Add examples for diag_indices_from()
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BUG: Handle arrays in `conf_data`
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* `configuration_data` does not support `lists` as the data argument
* ref: https://mesonbuild.com/Reference-manual_functions.html#arguments12
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DOC: Improved nbytes description
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DOC: Improved nbytes description
DOC: Improved nbytes description
DOC: Improved nbytes description
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BUG: Fixup f2py's handling a very little bit
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This clears the error holding only to the type. Since in the other
path the errmessage seemed completely uninitialized, I opted to just
ignore it entirely and keep the old error.
I could fathom to use error chaining here, but overall, I am not even
sure that chaining makes even sense for these errors. This fix is
meant to be minimal (the second one, I just noticed randomly), it
does not make this code clean.
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* fix docstring of real_if_close
* factor out dtype
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BUG: Fix for npyv__trunc_s32_f32 (VXE)
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np.sin(), np.cos() are giving erroneous result for float32 (VXE)
This PR is fixing `npyv_s32 npyv__trunc_s32_f32(npyv_f32 a)`
to resolve the issue.
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BUG: Fix `integer / float` scalar promotion
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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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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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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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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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