Bump numpy from 1.26.4 to 2.0.0 (#40)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.4 to 2.0.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/numpy/numpy/releases">numpy's
releases</a>.</em></p>
<blockquote>
<h2>v2.0.0</h2>
<h1>NumPy 2.0.0 Release Notes</h1>
<p>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.</p>
<p>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:</p>
<ul>
<li>The <a
href="https://numpy.org/devdocs/numpy_2_0_migration_guide.html">numpy-2-migration-guide</a></li>
<li>The Numpy 2.0-specific advice in <a
href="https://numpy.org/devdocs/dev/depending_on_numpy.html">for
downstream package authors</a></li>
</ul>
<h2>Highlights</h2>
<p>Highlights of this release include:</p>
<ul>
<li>New features:
<ul>
<li>A new variable-length string dtype,
<code>numpy.dtypes.StringDType</code> and a new
<code>numpy.strings</code> namespace with performant ufuncs for string
operations,</li>
<li>Support for <code>float32</code> and <code>longdouble</code> in all
<code>numpy.fft</code> functions,</li>
<li>Support for the array API standard in the main <code>numpy</code>
namespace.</li>
</ul>
</li>
<li>Performance improvements:
<ul>
<li>Sorting functions <code>sort</code>, <code>argsort</code>,
<code>partition</code>, <code>argpartition</code> have been
accelerated through the use of the Intel x86-simd-sort and
Google Highway libraries, and may see large (hardware-specific)
speedups,</li>
<li>macOS Accelerate support and binary wheels for macOS &gt;=14, with
significant performance improvements for linear algebra
operations on macOS, and wheels that are about 3 times smaller,</li>
<li><code>numpy.char</code> fixed-length string operations have
been accelerated by implementing ufuncs that also support
<code>numpy.dtypes.StringDType</code> in addition to the
fixed-length string dtypes,</li>
<li>A new tracing and introspection API,
<code>numpy.lib.introspect.opt_func_info</code>, to determine
which hardware-specific kernels are available and will be
dispatched to.</li>
<li><code>numpy.save</code> 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.</li>
</ul>
</li>
<li>Python API improvements:</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/numpy/numpy/commit/1d49c7f7ff527c696fc26ab2278ad51632a66660"><code>1d49c7f</code></a>
Merge pull request <a
href="https://redirect.github.com/numpy/numpy/issues/26698">#26698</a>
from charris/prepare-2.0.0</li>
<li><a
href="https://github.com/numpy/numpy/commit/2103511c0fa1b336f38d43920e3eb5440af9ea15"><code>2103511</code></a>
DOC: Remove duplicate in author list.</li>
<li><a
href="https://github.com/numpy/numpy/commit/db8030e9bb082466f89714c9a97c87f99dabb60d"><code>db8030e</code></a>
BUG: Change cibuildwheel version [wheel build]</li>
<li><a
href="https://github.com/numpy/numpy/commit/1a68264bb5f6859fc85c985984fc682034320503"><code>1a68264</code></a>
REL: Prepare for the NumPy 2.0.0 release [wheel build]</li>
<li><a
href="https://github.com/numpy/numpy/commit/c8665baa495d13e40eee0d39db841b0cbfde04a1"><code>c8665ba</code></a>
Merge pull request <a
href="https://redirect.github.com/numpy/numpy/issues/26696">#26696</a>
from charris/backport-26582</li>
<li><a
href="https://github.com/numpy/numpy/commit/103f4dd40725bcf610b3ff19a324e1ca099a47c4"><code>103f4dd</code></a>
Merge pull request <a
href="https://redirect.github.com/numpy/numpy/issues/26697">#26697</a>
from charris/backport-25963</li>
<li><a
href="https://github.com/numpy/numpy/commit/c193dcd1dcd01989088475349e1b717d2b82b07b"><code>c193dcd</code></a>
Merge pull request <a
href="https://redirect.github.com/numpy/numpy/issues/26695">#26695</a>
from charris/backport-26667</li>
<li><a
href="https://github.com/numpy/numpy/commit/8fa81913477d19c78b5f8660830615b31a95a7de"><code>8fa8191</code></a>
BUG: Fix bug in numpy.pad() (<a
href="https://redirect.github.com/numpy/numpy/issues/25963">#25963</a>)</li>
<li><a
href="https://github.com/numpy/numpy/commit/ece3559d4c0b9fc22c2d272d07cef1d62b4b65b2"><code>ece3559</code></a>
BUG: weighted nanpercentile, nanquantile and multi-dim q (<a
href="https://redirect.github.com/numpy/numpy/issues/26582">#26582</a>)</li>
<li><a
href="https://github.com/numpy/numpy/commit/b31e19550fbb374efc6246e2f114124eee6a3714"><code>b31e195</code></a>
BUG: Adds asanyarray to start of linalg.cross (<a
href="https://redirect.github.com/numpy/numpy/issues/26667">#26667</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/numpy/numpy/compare/v1.26.4...v2.0.0">compare
view</a></li>
</ul>
</details>
<br />


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tests/requirements.txt

@@ -1 +1 @@
-numpy==1.26.4
+numpy==2.0.0