Update numpy requirement from 1.26.4 to 2.0.0

[dependabot[bot]]

Updates the requirements on numpy to permit the latest version.

Release notes

Sourced from numpy's releases.

v2.0.0

NumPy 2.0.0 Release Notes

NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs.

This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0, in addition to these release notes, include:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for downstream package authors

Highlights

Highlights of this release include:

• New features:
  • A new variable-length string dtype, numpy.dtypes.StringDType and a new numpy.strings namespace with performant ufuncs for string operations,
  • Support for float32 and longdouble in all numpy.fft functions,
  • Support for the array API standard in the main numpy namespace.
• Performance improvements:
  • Sorting functions sort, argsort, partition, argpartition have been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
  • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
  • numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.
• Python API improvements:

... (truncated)

Commits

• 1d49c7f Merge pull request #26698 from charris/prepare-2.0.0
• 2103511 DOC: Remove duplicate in author list.
• db8030e BUG: Change cibuildwheel version [wheel build]
• 1a68264 REL: Prepare for the NumPy 2.0.0 release [wheel build]
• c8665ba Merge pull request #26696 from charris/backport-26582
• 103f4dd Merge pull request #26697 from charris/backport-25963
• c193dcd Merge pull request #26695 from charris/backport-26667
• 8fa8191 BUG: Fix bug in numpy.pad() (#25963)
• ece3559 BUG: weighted nanpercentile, nanquantile and multi-dim q (#26582)
• b31e195 BUG: Adds asanyarray to start of linalg.cross (#26667)
• Additional commits viewable in compare view

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[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 97.6%. Comparing base (4882eea) to head (f9a2190).

Additional details and impacted files

```diff @@ Coverage Diff @@ ## master #642 +/- ## ====================================== Coverage 97.6% 97.6% ====================================== Files 162 162 Lines 3212 3212 Branches 787 787 ====================================== Hits 3138 3138 Misses 48 48 Partials 26 26 ```

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[purva-thakre]

@vprusso We need to undo this and work on fixing the failures. I noticed they popped up in another PR.

Screenshot from this PR.

[purva-thakre]

We could use #645 or #638 to fix these failures.

[vprusso]

My bad! I was under the impression that some changes in numpy were impacting the docs to make 0.0 not equal to np.float(0.0), etc. If either of those diffs would fix the failures, let me get to reviewing those when ready and we can merge those in. Thanks, @purva-thakre !

[vprusso]

Hmm, it's not actually clear to me why these failures are occurring. Is this perhaps some type of numpy 2.0-specific issue?

[purva-thakre]

Hmm, it's not actually clear to me why these failures are occurring

Me neither. I think it might be better to undo this merge and the dependabot ones following this one.

I just created a new local environment for python3.12. pytest toqito/ failed with errors. I don't understand why we didn't see the same behavior in this PR.

(CVXPY) Jul 02 07:13:58 PM: Encountered unexpected exception importing solver CVXOPT:
ImportError('libopenblas.so.0: cannot open shared object file: No such file or directory')
(CVXPY) Jul 02 07:13:58 PM: Encountered unexpected exception importing solver GLPK:
ImportError('libopenblas.so.0: cannot open shared object file: No such file or directory')
(CVXPY) Jul 02 07:13:58 PM: Encountered unexpected exception importing solver GLPK_MI:
ImportError('libopenblas.so.0: cannot open shared object file: No such file or directory')

I don't see the same behavior for a python3.11 environment.

[purva-thakre]

Hmm, it's not actually clear to me why these failures are occurring. Is this perhaps some type of numpy 2.0-specific issue?

Yup, it is. The default integer type has been changed and np.int has been deprecated for a while.

I tested out an example in channel_props/choi_rank. Doctest expects the output to be np.int64(4). Using int(choi_rank(kraus_ops)) gives the expected output of 4. This feels like an easier workaround. WDYT?

If I use np.int(choi_rank(kraus_ops)), it fails due to

----> 1 np.int(choi_rank(kraus_ops))

File ~/anaconda3/envs/toqito_env/lib/python3.11/site-packages/numpy/__init__.py:394, in __getattr__(attr)
    389     warnings.warn(
    390         f"In the future `np.{attr}` will be defined as the "
    391         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    393 if attr in __former_attrs__:
--> 394     raise AttributeError(__former_attrs__[attr])
    396 if attr in __expired_attributes__:
    397     raise AttributeError(
    398         f"`np.{attr}` was removed in the NumPy 2.0 release. "
    399         f"{__expired_attributes__[attr]}"
    400     )

AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

Will add more to this as I make my way through the other failures.