Numpy2 complex2 #2
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brendan-m-murphy
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Introduced in 4f7d709
Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.10.3 to 1.12.2. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](pypa/gh-action-pypi-publish@v1.10.3...v1.12.2) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <[email protected]>
updates: - [github.com/astral-sh/ruff-pre-commit: v0.7.1 → v0.7.3](astral-sh/ruff-pre-commit@v0.7.1...v0.7.3)
Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.21.2 to 2.21.3. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](pypa/cibuildwheel@v2.21.2...v2.21.3) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: Mohit Kumar <[email protected]>
Numpy 2.0's new scalar promotion rules don't increase the precision of scalars as readily, so there was an overflow in the `test_inner_composite` test in `scalar/test_loop.py`. This didn't happen in Numpy 1.26 because at some point the value was converted to float64, whereas now it remains at the specified precision.
I also reverted a change to the autocaster tests. Now the old behavior is preserved.
I was getting a NameError from the list comprehensions saying that e.g. `pytensor_scalar` was not defined. I'm not sure why, but this is another (more verbose) way to do the same thing.
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From numpy PR numpy/numpy#22449, the repr of scalar values has changed, e.g. from "1" to "np.int64(1)", which caused two doctests to fail. The doctest for tensor.extra_ops.Unique was failing because the output shape for the inverse indices has changed when axis is None: numpy/numpy#20638
`np.asarray(big_number, dtype=too_small_dtype)` raises an OverflowError in numpy 2.0; the code in `TensorType.filter` has been changed to the new recommended way to downcast, and the error type caught by some tests has been changed to OverflowError from TypeError
After this numpy PR: numpy/numpy@44ba7ca `copy` didn't seem to actually make an independent copy of the `np.random.Generator` objects spawned by `RandomStream`. This was causing the "test values" computed by e.g. `RandomStream.uniform` to increment the RNG state, which was causing tests that rely on `RandomStream` to fail.
numpy.random.Generator.__getstate__() now returns none; to see the state of the bit generator, you need to use Generator.bit_generator.state.
Needed to replace `.__getstate__()` with `.bit_generator.state`
In numpy 2.0, if axis=None, then np.unique does not flatten the inverse indices returned if return_inverse=True
Due to changes in numpy conversion rules (NEP 50), overflows are not ignored; in particular, negating a unsigned int causes an overflow error. The test for `neg` has been changed to check that this error is raised.
forgot to convert None to empty dict
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