chore(deps): update dependency numpy to v1.23.0 (#1464)

[![Mend Renovate](https://app.renovatebot.com/images/banner.svg)](https://renovatebot.com)

This PR contains the following updates:

| Package | Change | Age | Adoption | Passing | Confidence |
|---|---|---|---|---|---|
| [numpy](https://www.numpy.org) ([source](https://togithub.com/numpy/numpy)) | `==1.22.4` -> `==1.23.0` | [![age](https://badges.renovateapi.com/packages/pypi/numpy/1.23.0/age-slim)](https://docs.renovatebot.com/merge-confidence/) | [![adoption](https://badges.renovateapi.com/packages/pypi/numpy/1.23.0/adoption-slim)](https://docs.renovatebot.com/merge-confidence/) | [![passing](https://badges.renovateapi.com/packages/pypi/numpy/1.23.0/compatibility-slim/1.22.4)](https://docs.renovatebot.com/merge-confidence/) | [![confidence](https://badges.renovateapi.com/packages/pypi/numpy/1.23.0/confidence-slim/1.22.4)](https://docs.renovatebot.com/merge-confidence/) |

---

### Release Notes

<details>
<summary>numpy/numpy</summary>

### [`v1.23.0`](https://togithub.com/numpy/numpy/releases/tag/v1.23.0)

[Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.4...v1.23.0)

### NumPy 1.23.0 Release Notes

The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:

-   Implementation of `loadtxt` in C, greatly improving its performance.
-   Exposing DLPack at the Python level for easy data exchange.
-   Changes to the promotion and comparisons of structured dtypes.
-   Improvements to f2py.

See below for the details,

#### New functions

-   A masked array specialization of `ndenumerate` is now available as
    `numpy.ma.ndenumerate`. It provides an alternative to
    `numpy.ndenumerate` and skips masked values by default.

    ([gh-20020](https://togithub.com/numpy/numpy/pull/20020))

-   `numpy.from_dlpack` has been added to allow easy exchange of data
    using the DLPack protocol. It accepts Python objects that implement
    the `__dlpack__` and `__dlpack_device__` methods and returns a
    ndarray object which is generally the view of the data of the input
    object.

    ([gh-21145](https://togithub.com/numpy/numpy/pull/21145))

#### Deprecations

-   Setting `__array_finalize__` to `None` is deprecated. It must now be
    a method and may wish to call `super().__array_finalize__(obj)`
    after checking for `None` or if the NumPy version is sufficiently
    new.

    ([gh-20766](https://togithub.com/numpy/numpy/pull/20766))

-   Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same
    meaning as `axis=None`. This is deprecated and `axis=None` must be
    used instead.

    ([gh-20920](https://togithub.com/numpy/numpy/pull/20920))

-   The hook function `PyDataMem_SetEventHook` has been deprecated and
    the demonstration of its use in tool/allocation_tracking has been
    removed. The ability to track allocations is now built-in to python
    via `tracemalloc`.

    ([gh-20394](https://togithub.com/numpy/numpy/pull/20394))

-   `numpy.distutils` has been deprecated, as a result of `distutils`
    itself being deprecated. It will not be present in NumPy for
    Python >= 3.12, and will be removed completely 2 years after the
    release of Python 3.12 For more details, see
    `distutils-status-migration`{.interpreted-text role="ref"}.

    ([gh-20875](https://togithub.com/numpy/numpy/pull/20875))

-   `numpy.loadtxt` will now give a `DeprecationWarning` when an integer
    `dtype` is requested but the value is formatted as a floating point number.

    ([gh-21663](https://togithub.com/numpy/numpy/pull/21663))

#### Expired deprecations

-   The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been
    removed as part of the continued removal of Python 2 compatibility.
    This concludes the deprecation from 1.15.

    ([gh-16830](https://togithub.com/numpy/numpy/pull/16830))

-   The `alen` and `asscalar` functions have been removed.

    ([gh-20414](https://togithub.com/numpy/numpy/pull/20414))

-   The `UPDATEIFCOPY` array flag has been removed together with the
    enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated)
    `PyArray_XDECREF_ERR` was also removed. These were all deprecated in
    1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling
    `PyArray_ResoveWritebackIfCopy` before the array is deallocated.

    ([gh-20589](https://togithub.com/numpy/numpy/pull/20589))

-   Exceptions will be raised during array-like creation. When an object
    raised an exception during access of the special attributes
    `__array__` or `__array_interface__`, this exception was usually
    ignored. This behaviour was deprecated in 1.21, and the exception
    will now be raised.

    ([gh-20835](https://togithub.com/numpy/numpy/pull/20835))

-   Multidimensional indexing with non-tuple values is not allowed.
    Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]`
    produced a `FutureWarning` and was interpreted as a multidimensional
    index (i.e., `arr[tuple(ind)]`). Now this example is treated like an
    array index over a single dimension (`arr[array(ind)]`).
    Multidimensional indexing with anything but a tuple was deprecated
    in NumPy 1.15.

    ([gh-21029](https://togithub.com/numpy/numpy/pull/21029))

-   Changing to a dtype of different size in F-contiguous arrays is no
    longer permitted. Deprecated since Numpy 1.11.0. See below for an
    extended explanation of the effects of this change.

    ([gh-20722](https://togithub.com/numpy/numpy/pull/20722))

#### New Features

##### crackfortran has support for operator and assignment overloading

`crackfortran` parser now understands operator and assignment
definitions in a module. They are added in the `body` list of the module
which contains a new key `implementedby` listing the names of the
subroutines or functions implementing the operator or assignment.

([gh-15006](https://togithub.com/numpy/numpy/pull/15006))

##### f2py supports reading access type attributes from derived type statements

As a result, one does not need to use `public` or `private` statements
to specify derived type access properties.

([gh-15844](https://togithub.com/numpy/numpy/pull/15844))

##### New parameter `ndmin` added to `genfromtxt`

This parameter behaves the same as `ndmin` from `numpy.loadtxt`.

([gh-20500](https://togithub.com/numpy/numpy/pull/20500))

##### `np.loadtxt` now supports quote character and single converter function

`numpy.loadtxt` now supports an additional `quotechar` keyword argument
which is not set by default. Using `quotechar='"'` will read quoted
fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a
dictionary for the `converters` argument.

([gh-20580](https://togithub.com/numpy/numpy/pull/20580))

##### Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size
required that the entire array be C-contiguous. This limitation would
unnecessarily force the user to make contiguous copies of non-contiguous
arrays before being able to change the dtype.

This change affects not only `ndarray.view`, but other construction
mechanisms, including the discouraged direct assignment to
`ndarray.dtype`.

This change expires the deprecation regarding the viewing of
F-contiguous arrays, described elsewhere in the release notes.

([gh-20722](https://togithub.com/numpy/numpy/pull/20722))

##### Deterministic output files for F2PY

For F77 inputs, `f2py` will generate `modname-f2pywrappers.f`
unconditionally, though these may be empty. For free-form inputs,
`modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be
generated unconditionally, and may be empty. This allows writing generic
output rules in `cmake` or `meson` and other build systems. Older
behavior can be restored by passing `--skip-empty-wrappers` to `f2py`.
`f2py-meson`{.interpreted-text role="ref"} details usage.

([gh-21187](https://togithub.com/numpy/numpy/pull/21187))

##### `keepdims` parameter for `average`

The parameter `keepdims` was added to the functions `numpy.average` and
`numpy.ma.average`. The parameter has the same meaning as it does in
reduction functions such as `numpy.sum` or `numpy.mean`.

([gh-21485](https://togithub.com/numpy/numpy/pull/21485))

##### New parameter `equal_nan` added to `np.unique`

`np.unique` was changed in 1.21 to treat all `NaN` values as equal and
return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21
behavior to treat `NaNs` as unique. Defaults to `True`.

([gh-21623](https://togithub.com/numpy/numpy/pull/21623))

#### Compatibility notes

##### 1D `np.linalg.norm` preserves float input types, even for scalar results

Previously, this would promote to `float64` when the `ord` argument was
not one of the explicitly listed values, e.g. `ord=3`:

    >>> f32 = np.float32([1, 2])
    >>> np.linalg.norm(f32, 2).dtype
    dtype('float32')
    >>> np.linalg.norm(f32, 3)
    dtype('float64')  # numpy 1.22
    dtype('float32')  # numpy 1.23

This change affects only `float32` and `float16` vectors with `ord`
other than `-Inf`, `0`, `1`, `2`, and `Inf`.

([gh-17709](https://togithub.com/numpy/numpy/pull/17709))

##### Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion
for structured dtypes by promoting the subtypes of each field instead of
raising an exception:

    >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
    dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced,
however padding is ignored. Promotion involving structured dtypes now
always ensures native byte-order for all fields (which may change the
result of `np.concatenate`) and ensures that the result will be
"packed", i.e. all fields are ordered contiguously and padding is
removed. See
`structured_dtype_comparison_and_promotion`{.interpreted-text
role="ref"} for further details.

The `repr` of aligned structures will now never print the long form
including `offsets` and `itemsize` unless the structure includes padding
not guaranteed by `align=True`.

In alignment with the above changes to the promotion logic, the casting
safety has been updated:

-   `"equiv"` enforces matching names and titles. The itemsize is
    allowed to differ due to padding.
-   `"safe"` allows mismatching field names and titles
-   The cast safety is limited by the cast safety of each included
    field.
-   The order of fields is used to decide cast safety of each individual
    field. Previously, the field names were used and only unsafe casts
    were possible when names mismatched.

The main important change here is that name mismatches are now
considered "safe" casts.

([gh-19226](https://togithub.com/numpy/numpy/pull/19226))

##### `NPY_RELAXED_STRIDES_CHECKING` has been removed

NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore.
Relaxed strides have been the default for many years and the option was
initially introduced to allow a smoother transition.

([gh-20220](https://togithub.com/numpy/numpy/pull/20220))

##### `np.loadtxt` has recieved several changes

The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully
empty lines in the file, but counted them towards `max_rows`. When
`max_rows` is used and the file contains empty lines, these will now not
be counted. Previously, it was possible that the result contained fewer
than `max_rows` rows even though more data was available to be read. If
the old behaviour is required, `itertools.islice` may be used:

    import itertools
    lines = itertools.islice(open("file"), 0, max_rows)
    result = np.loadtxt(lines, ...)

While generally much faster and improved, `numpy.loadtxt` may now fail
to converter certain strings to numbers that were previously
successfully read. The most important cases for this are:

-   Parsing floating point values such as `1.0` into integers is now
    deprecated.
-   Parsing hexadecimal floats such as `0x3p3` will fail
-   An `_` was previously accepted as a thousands delimiter `100_000`.
    This will now result in an error.

If you experience these limitations, they can all be worked around by
passing appropriate `converters=`. NumPy now supports passing a single
converter to be used for all columns to make this more convenient. For
example, `converters=float.fromhex` can read hexadecimal float numbers
and `converters=int` will be able to read `100_000`.

Further, the error messages have been generally improved. However, this
means that error types may differ. In particularly, a `ValueError` is
now always raised when parsing of a single entry fails.

([gh-20580](https://togithub.com/numpy/numpy/pull/20580))

#### Improvements

##### `ndarray.__array_finalize__` is now callable

This means subclasses can now use `super().__array_finalize__(obj)`
without worrying whether `ndarray` is their superclass or not. The
actual call remains a no-op.

([gh-20766](https://togithub.com/numpy/numpy/pull/20766))

##### Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power
ISA 3.1 can be used to accelerate some NumPy operations, e.g.,
floor_divide, modulo, etc.

([gh-20821](https://togithub.com/numpy/numpy/pull/20821))

##### `np.fromiter` now accepts objects and subarrays

The `numpy.fromiter` function now supports object and subarray dtypes.
Please see he function documentation for examples.

([gh-20993](https://togithub.com/numpy/numpy/pull/20993))

##### Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the
underlying libc supports certain math operations. Previously this code
did not respect the proper signatures. Fixing this enables compilation
for the `wasm-ld` backend (compilation for web assembly) and reduces the
number of warnings.

([gh-21154](https://togithub.com/numpy/numpy/pull/21154))

##### `np.kron` now maintains subclass information

`np.kron` maintains subclass information now such as masked arrays while
computing the Kronecker product of the inputs

```python
>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
  data=[[1, --, --, --],
        [--, 4, --, --],
        [--, --, 4, --],
        [--, --, --, 16]],
  mask=[[False,  True,  True,  True],
        [ True, False,  True,  True],
        [ True,  True, False,  True],
        [ True,  True,  True, False]],
  fill_value=999999)
```

:warning: Warning, `np.kron` output now follows `ufunc` ordering (`multiply`) to determine
the output class type

```python
>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True
```

([gh-21262](https://togithub.com/numpy/numpy/pull/21262))

#### Performance improvements and changes

##### Faster `np.loadtxt`

`numpy.loadtxt` is now generally much faster than previously as most of
it is now implemented in C.

([gh-20580](https://togithub.com/numpy/numpy/pull/20580))

##### Faster reduction operators

Reduction operations like `numpy.sum`, `numpy.prod`, `numpy.add.reduce`,
`numpy.logical_and.reduce` on contiguous integer-based arrays are now
much faster.

([gh-21001](https://togithub.com/numpy/numpy/pull/21001))

##### Faster `np.where`

`numpy.where` is now much faster than previously on unpredictable/random
input data.

([gh-21130](https://togithub.com/numpy/numpy/pull/21130))

##### Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although
rare operations (e.g. with 0-D arrays rather than scalars) may be slower
in some cases. However, even with these improvements users who want the
best performance for their scalars, may want to convert a known NumPy
scalar into a Python one using `scalar.item()`.

([gh-21188](https://togithub.com/numpy/numpy/pull/21188))

##### Faster `np.kron`

`numpy.kron` is about 80% faster as the product is now computed using
broadcasting.

([gh-21354](https://togithub.com/numpy/numpy/pull/21354))

#### Checksums

##### MD5

    21839aaeab3088e685d7c8d0e1856a23  numpy-1.23.0-cp310-cp310-macosx_10_9_x86_64.whl
    e657684ea521c50de0197aabfb44e78d  numpy-1.23.0-cp310-cp310-macosx_11_0_arm64.whl
    219017660861fdec59b852630e3fef2a  numpy-1.23.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    03c3df83b8327910482a7d24ebe9213b  numpy-1.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    b8f06ce4054acc147845a9643bd36082  numpy-1.23.0-cp310-cp310-win32.whl
    877322db5a62634eef4e351db99a070d  numpy-1.23.0-cp310-cp310-win_amd64.whl
    7bb54f95e74306eff733466b6343695f  numpy-1.23.0-cp38-cp38-macosx_10_9_x86_64.whl
    5514a0030e5cf065e916950737d6d129  numpy-1.23.0-cp38-cp38-macosx_11_0_arm64.whl
    22d43465791814fe50e03ded430bd80c  numpy-1.23.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    771a1f7e488327645bac5b54dd2f6286  numpy-1.23.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    449bfa2d55aff3e722d2fc85a7549620  numpy-1.23.0-cp38-cp38-win32.whl
    60c7d27cf92dadb6d206df6e65b1032f  numpy-1.23.0-cp38-cp38-win_amd64.whl
    dc2a5c5d2223f7b45a45f7f760d0f2db  numpy-1.23.0-cp39-cp39-macosx_10_9_x86_64.whl
    ba5729353c3521ed7ee72c796e77a546  numpy-1.23.0-cp39-cp39-macosx_11_0_arm64.whl
    06d5cd49de096482944dead2eb92d783  numpy-1.23.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    6ff50a994f6006349b5f1415e4da6f45  numpy-1.23.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    49185f219512403ef23d43d6f2adbefd  numpy-1.23.0-cp39-cp39-win32.whl
    ff126a84dcf91700f9ca13ff606d109f  numpy-1.23.0-cp39-cp39-win_amd64.whl
    e1462428487dc599cdffb723dec642c4  numpy-1.23.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
    fef1d20265135737fbc0f91ca4441990  numpy-1.23.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    4f8142288202a32c682d01921d6c2c78  numpy-1.23.0-pp38-pypy38_pp73-win_amd64.whl
    513e4241d06b8fae5732cd049cdf3b57  numpy-1.23.0.tar.gz

##### SHA256

    58bfd40eb478f54ff7a5710dd61c8097e169bc36cc68333d00a9bcd8def53b38  numpy-1.23.0-cp310-cp310-macosx_10_9_x86_64.whl
    196cd074c3f97c4121601790955f915187736f9cf458d3ee1f1b46aff2b1ade0  numpy-1.23.0-cp310-cp310-macosx_11_0_arm64.whl
    f1d88ef79e0a7fa631bb2c3dda1ea46b32b1fe614e10fedd611d3d5398447f2f  numpy-1.23.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    d54b3b828d618a19779a84c3ad952e96e2c2311b16384e973e671aa5be1f6187  numpy-1.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    2b2da66582f3a69c8ce25ed7921dcd8010d05e59ac8d89d126a299be60421171  numpy-1.23.0-cp310-cp310-win32.whl
    97a76604d9b0e79f59baeca16593c711fddb44936e40310f78bfef79ee9a835f  numpy-1.23.0-cp310-cp310-win_amd64.whl
    d8cc87bed09de55477dba9da370c1679bd534df9baa171dd01accbb09687dac3  numpy-1.23.0-cp38-cp38-macosx_10_9_x86_64.whl
    f0f18804df7370571fb65db9b98bf1378172bd4e962482b857e612d1fec0f53e  numpy-1.23.0-cp38-cp38-macosx_11_0_arm64.whl
    ac86f407873b952679f5f9e6c0612687e51547af0e14ddea1eedfcb22466babd  numpy-1.23.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    ae8adff4172692ce56233db04b7ce5792186f179c415c37d539c25de7298d25d  numpy-1.23.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    fe8b9683eb26d2c4d5db32cd29b38fdcf8381324ab48313b5b69088e0e355379  numpy-1.23.0-cp38-cp38-win32.whl
    5043bcd71fcc458dfb8a0fc5509bbc979da0131b9d08e3d5f50fb0bbb36f169a  numpy-1.23.0-cp38-cp38-win_amd64.whl
    1c29b44905af288b3919803aceb6ec7fec77406d8b08aaa2e8b9e63d0fe2f160  numpy-1.23.0-cp39-cp39-macosx_10_9_x86_64.whl
    98e8e0d8d69ff4d3fa63e6c61e8cfe2d03c29b16b58dbef1f9baa175bbed7860  numpy-1.23.0-cp39-cp39-macosx_11_0_arm64.whl
    79a506cacf2be3a74ead5467aee97b81fca00c9c4c8b3ba16dbab488cd99ba10  numpy-1.23.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
    092f5e6025813e64ad6d1b52b519165d08c730d099c114a9247c9bb635a2a450  numpy-1.23.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    d6ca8dabe696c2785d0c8c9b0d8a9b6e5fdbe4f922bde70d57fa1a2848134f95  numpy-1.23.0-cp39-cp39-win32.whl
    fc431493df245f3c627c0c05c2bd134535e7929dbe2e602b80e42bf52ff760bc  numpy-1.23.0-cp39-cp39-win_amd64.whl
    f9c3fc2adf67762c9fe1849c859942d23f8d3e0bee7b5ed3d4a9c3eeb50a2f07  numpy-1.23.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
    d0d2094e8f4d760500394d77b383a1b06d3663e8892cdf5df3c592f55f3bff66  numpy-1.23.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
    94b170b4fa0168cd6be4becf37cb5b127bd12a795123984385b8cd4aca9857e5  numpy-1.23.0-pp38-pypy38_pp73-win_amd64.whl
    bd3fa4fe2e38533d5336e1272fc4e765cabbbde144309ccee8675509d5cd7b05  numpy-1.23.0.tar.gz

</details>

---

### Configuration

📅 **Schedule**: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 **Automerge**: Disabled by config. Please merge this manually once you are satisfied.

♻ **Rebasing**: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 **Ignore**: Close this PR and you won't be reminded about this update again.

---

 - [ ] <!-- rebase-check -->If you want to rebase/retry this PR, click this checkbox.

---

This PR has been generated by [Mend Renovate](https://www.mend.io/free-developer-tools/renovate/). View repository job log [here](https://app.renovatebot.com/dashboard#github/googleapis/synthtool).

synthtool/gcp/templates/python_notebooks_testing_pipeline/.cloud-build/requirements.txt

@@ -2,7 +2,7 @@ ipython==8.4.0
 jupyter==1.0
 nbconvert==6.5.0
 papermill==2.3
-numpy==1.22.4
+numpy==1.23.0
 pandas==1.4.2
 matplotlib==3.5.2
 tabulate==0.8.10