--- /srv/reproducible-results/rbuild-debian/r-b-build.TVBfkVew/b1/numpy_2.2.3+ds-5_amd64.changes
+++ /srv/reproducible-results/rbuild-debian/r-b-build.TVBfkVew/b2/numpy_2.2.3+ds-5_amd64.changes
├── Files
│ @@ -1,5 +1,5 @@
│
│ - bfe62738a3ff38b259fbf7d44ee87d6f 5812116 doc optional python-numpy-doc_2.2.3+ds-5_all.deb
│ + 8e57d52f3041225ba98e20a232b544df 5812184 doc optional python-numpy-doc_2.2.3+ds-5_all.deb
│ d5e1262c6a226e1e3b8c6bc524e59bf3 30549416 debug optional python3-numpy-dbgsym_2.2.3+ds-5_amd64.deb
│ 230604ed18d1586efc7dd03dc04df1a3 138436 python optional python3-numpy-dev_2.2.3+ds-5_amd64.deb
│ 16b3418df31d0fbdaf2c89f5c6f9ac46 5084044 python optional python3-numpy_2.2.3+ds-5_amd64.deb
├── python-numpy-doc_2.2.3+ds-5_all.deb
│ ├── file list
│ │ @@ -1,3 +1,3 @@
│ │ -rw-r--r-- 0 0 0 4 2025-03-09 20:14:24.000000 debian-binary
│ │ --rw-r--r-- 0 0 0 64884 2025-03-09 20:14:24.000000 control.tar.xz
│ │ --rw-r--r-- 0 0 0 5747040 2025-03-09 20:14:24.000000 data.tar.xz
│ │ +-rw-r--r-- 0 0 0 64888 2025-03-09 20:14:24.000000 control.tar.xz
│ │ +-rw-r--r-- 0 0 0 5747104 2025-03-09 20:14:24.000000 data.tar.xz
│ ├── control.tar.xz
│ │ ├── control.tar
│ │ │ ├── ./md5sums
│ │ │ │ ├── ./md5sums
│ │ │ │ │┄ Files differ
│ ├── data.tar.xz
│ │ ├── data.tar
│ │ │ ├── file list
│ │ │ │ @@ -2578,15 +2578,15 @@
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 42758 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 47423 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 45546 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 82403 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generator.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 45982 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/index.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 89078 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/legacy.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 35540 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/multithreading.html
│ │ │ │ --rw-r--r-- 0 root (0) root (0) 44353 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/new-or-different.html
│ │ │ │ +-rw-r--r-- 0 root (0) root (0) 44355 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/new-or-different.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 52723 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/parallel.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 38070 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/performance.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 41915 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/upgrading-pcg64.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 45998 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.array-creation.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 50957 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.array-manipulation.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 27535 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.bitwise.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 54450 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.char.html
│ │ │ │ @@ -2754,15 +2754,15 @@
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 46199 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release/2.2.0-notes.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 31563 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release/2.2.1-notes.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 32256 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release/2.2.2-notes.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 32747 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release/2.2.3-notes.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 13407 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release/template.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 90523 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 12397 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/search.html
│ │ │ │ --rw-r--r-- 0 root (0) root (0) 2686426 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js
│ │ │ │ +-rw-r--r-- 0 root (0) root (0) 2686410 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js
│ │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 177614 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/absolute_beginners.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 50529 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/basics.broadcasting.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 33464 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/basics.copies.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 64100 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/basics.creation.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 65763 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/basics.dispatch.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 18746 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/basics.html
│ │ │ ├── ./usr/share/doc/python-numpy/html/reference/random/new-or-different.html
│ │ │ │ @@ -536,30 +536,30 @@
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">numpy.random</span>
│ │ │ │
│ │ │ │ <span class="gp">In [2]: </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
│ │ │ │
│ │ │ │ <span class="gp">In [3]: </span><span class="o">%</span><span class="k">timeit</span> -n 1 rng.standard_normal(100000)
│ │ │ │ <span class="gp"> ...: </span><span class="o">%</span><span class="k">timeit</span> -n 1 numpy.random.standard_normal(100000)
│ │ │ │ <span class="gp"> ...: </span>
│ │ │ │ -<span class="go">3.49 ms +- 721 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">7.84 ms +- 1.33 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">1.36 ms +- 33.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">2.61 ms +- 17.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="o">%</span><span class="k">timeit</span> -n 1 rng.standard_exponential(100000)
│ │ │ │ <span class="gp"> ...: </span><span class="o">%</span><span class="k">timeit</span> -n 1 numpy.random.standard_exponential(100000)
│ │ │ │ <span class="gp"> ...: </span>
│ │ │ │ -<span class="go">1.93 ms +- 109 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">5.43 ms +- 1.18 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">724 us +- 6.86 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">1.94 ms +- 10.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="o">%</span><span class="k">timeit</span> -n 1 rng.standard_gamma(3.0, 100000)
│ │ │ │ <span class="gp"> ...: </span><span class="o">%</span><span class="k">timeit</span> -n 1 numpy.random.standard_gamma(3.0, 100000)
│ │ │ │ <span class="gp"> ...: </span>
│ │ │ │ -<span class="go">8.56 ms +- 1.23 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">13.7 ms +- 265 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">2.98 ms +- 21.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">5.05 ms +- 12.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <ul class="simple">
│ │ │ │ <li><p><a class="reference internal" href="generated/numpy.random.Generator.integers.html#numpy.random.Generator.integers" title="numpy.random.Generator.integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">integers</span></code></a> is now the canonical way to generate integer
│ │ │ │ random numbers from a discrete uniform distribution. This replaces both
│ │ │ │ <a class="reference internal" href="generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a> and the deprecated <a class="reference internal" href="generated/numpy.random.random_integers.html#numpy.random.random_integers" title="numpy.random.random_integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_integers</span></code></a>.</p></li>
│ │ │ │ <li><p>The <a class="reference internal" href="generated/numpy.random.rand.html#numpy.random.rand" title="numpy.random.rand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand</span></code></a> and <a class="reference internal" href="generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn</span></code></a> methods are only available through the legacy
│ │ │ │ @@ -586,21 +586,21 @@
│ │ │ │ <li><p>Standard Exponentials (<a class="reference internal" href="generated/numpy.random.Generator.standard_exponential.html#numpy.random.Generator.standard_exponential" title="numpy.random.Generator.standard_exponential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standard_exponential</span></code></a>)</p></li>
│ │ │ │ </ul>
│ │ │ │ </li>
│ │ │ │ </ul>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
│ │ │ │
│ │ │ │ <span class="gp">In [7]: </span><span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
│ │ │ │ -<span class="gh">Out[7]: </span><span class="go">array([0.09837248, 0.04465839, 0.2084051 ])</span>
│ │ │ │ +<span class="gh">Out[7]: </span><span class="go">array([0.55420097, 0.45697908, 0.69487323])</span>
│ │ │ │
│ │ │ │ <span class="gp">In [8]: </span><span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
│ │ │ │ -<span class="gh">Out[8]: </span><span class="go">array([0.13149399, 0.6899817 , 0.9949636 ], dtype=float32)</span>
│ │ │ │ +<span class="gh">Out[8]: </span><span class="go">array([0.5988971 , 0.18808335, 0.86917627], dtype=float32)</span>
│ │ │ │
│ │ │ │ <span class="gp">In [9]: </span><span class="n">rng</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
│ │ │ │ -<span class="gh">Out[9]: </span><span class="go">array([119, 176, 212], dtype=uint8)</span>
│ │ │ │ +<span class="gh">Out[9]: </span><span class="go">array([252, 61, 65], dtype=uint8)</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <ul>
│ │ │ │ <li><p>Optional <code class="docutils literal notranslate"><span class="pre">out</span></code> argument that allows existing arrays to be filled for
│ │ │ │ select distributions</p>
│ │ │ │ <ul class="simple">
│ │ │ │ <li><p>Uniforms (<a class="reference internal" href="generated/numpy.random.Generator.random.html#numpy.random.Generator.random" title="numpy.random.Generator.random"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a>)</p></li>
│ │ │ │ @@ -613,18 +613,18 @@
│ │ │ │ </li>
│ │ │ │ </ul>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
│ │ │ │
│ │ │ │ <span class="gp">In [11]: </span><span class="n">existing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
│ │ │ │
│ │ │ │ <span class="gp">In [12]: </span><span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">out</span><span class="o">=</span><span class="n">existing</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
│ │ │ │ -<span class="gh">Out[12]: </span><span class="go">array([0.55876893, 0.61901174])</span>
│ │ │ │ +<span class="gh">Out[12]: </span><span class="go">array([0.97290577, 0.00500125])</span>
│ │ │ │
│ │ │ │ <span class="gp">In [13]: </span><span class="nb">print</span><span class="p">(</span><span class="n">existing</span><span class="p">)</span>
│ │ │ │ -<span class="go">[0.55876893 0.61901174 0. 0. ]</span>
│ │ │ │ +<span class="go">[0.97290577 0.00500125 0. 0. ]</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <ul class="simple">
│ │ │ │ <li><p>Optional <code class="docutils literal notranslate"><span class="pre">axis</span></code> argument for methods like <a class="reference internal" href="generated/numpy.random.Generator.choice.html#numpy.random.Generator.choice" title="numpy.random.Generator.choice"><code class="xref py py-obj docutils literal notranslate"><span class="pre">choice</span></code></a>,
│ │ │ │ <a class="reference internal" href="generated/numpy.random.Generator.permutation.html#numpy.random.Generator.permutation" title="numpy.random.Generator.permutation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permutation</span></code></a> and <a class="reference internal" href="generated/numpy.random.Generator.shuffle.html#numpy.random.Generator.shuffle" title="numpy.random.Generator.shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shuffle</span></code></a> that controls which
│ │ │ │ axis an operation is performed over for multi-dimensional arrays.</p></li>
│ │ │ │ </ul>
│ │ │ │ @@ -636,25 +636,25 @@
│ │ │ │ <span class="gh">Out[16]: </span>
│ │ │ │ <span class="go">array([[ 0, 1, 2, 3],</span>
│ │ │ │ <span class="go"> [ 4, 5, 6, 7],</span>
│ │ │ │ <span class="go"> [ 8, 9, 10, 11]])</span>
│ │ │ │
│ │ │ │ <span class="gp">In [17]: </span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
│ │ │ │ <span class="gh">Out[17]: </span>
│ │ │ │ -<span class="go">array([[ 0, 3, 3, 1, 3],</span>
│ │ │ │ -<span class="go"> [ 4, 7, 7, 5, 7],</span>
│ │ │ │ -<span class="go"> [ 8, 11, 11, 9, 11]])</span>
│ │ │ │ +<span class="go">array([[ 2, 3, 2, 0, 0],</span>
│ │ │ │ +<span class="go"> [ 6, 7, 6, 4, 4],</span>
│ │ │ │ +<span class="go"> [10, 11, 10, 8, 8]])</span>
│ │ │ │
│ │ │ │ <span class="gp">In [18]: </span><span class="n">rng</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># Shuffle in-place</span>
│ │ │ │
│ │ │ │ <span class="gp">In [19]: </span><span class="n">a</span>
│ │ │ │ <span class="gh">Out[19]: </span>
│ │ │ │ -<span class="go">array([[ 0, 3, 2, 1],</span>
│ │ │ │ -<span class="go"> [ 4, 7, 6, 5],</span>
│ │ │ │ -<span class="go"> [ 8, 11, 10, 9]])</span>
│ │ │ │ +<span class="go">array([[ 2, 0, 1, 3],</span>
│ │ │ │ +<span class="go"> [ 6, 4, 5, 7],</span>
│ │ │ │ +<span class="go"> [10, 8, 9, 11]])</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <ul class="simple">
│ │ │ │ <li><p>Added a method to sample from the complex normal distribution
│ │ │ │ (<em class="xref py py-obj">complex_normal</em>)</p></li>
│ │ │ │ </ul>
│ │ │ │ </section>
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -102,26 +102,26 @@
│ │ │ │ │ In [1]: import numpy.random
│ │ │ │ │
│ │ │ │ │ In [2]: rng = np.random.default_rng()
│ │ │ │ │
│ │ │ │ │ In [3]: %timeit -n 1 rng.standard_normal(100000)
│ │ │ │ │ ...: %timeit -n 1 numpy.random.standard_normal(100000)
│ │ │ │ │ ...:
│ │ │ │ │ -3.49 ms +- 721 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -7.84 ms +- 1.33 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +1.36 ms +- 33.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +2.61 ms +- 17.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ In [4]: %timeit -n 1 rng.standard_exponential(100000)
│ │ │ │ │ ...: %timeit -n 1 numpy.random.standard_exponential(100000)
│ │ │ │ │ ...:
│ │ │ │ │ -1.93 ms +- 109 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -5.43 ms +- 1.18 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +724 us +- 6.86 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +1.94 ms +- 10.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)
│ │ │ │ │ ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)
│ │ │ │ │ ...:
│ │ │ │ │ -8.56 ms +- 1.23 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -13.7 ms +- 265 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +2.98 ms +- 21.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +5.05 ms +- 12.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ * _i_n_t_e_g_e_r_s is now the canonical way to generate integer random numbers from
│ │ │ │ │ a discrete uniform distribution. This replaces both _r_a_n_d_i_n_t and the
│ │ │ │ │ deprecated _r_a_n_d_o_m___i_n_t_e_g_e_r_s.
│ │ │ │ │ * The _r_a_n_d and _r_a_n_d_n methods are only available through the legacy
│ │ │ │ │ _R_a_n_d_o_m_S_t_a_t_e.
│ │ │ │ │ * _G_e_n_e_r_a_t_o_r_._r_a_n_d_o_m is now the canonical way to generate floating-point
│ │ │ │ │ random numbers, which replaces _R_a_n_d_o_m_S_t_a_t_e_._r_a_n_d_o_m___s_a_m_p_l_e, _s_a_m_p_l_e, and
│ │ │ │ │ @@ -140,38 +140,38 @@
│ │ │ │ │ o Uniforms (_r_a_n_d_o_m and _i_n_t_e_g_e_r_s)
│ │ │ │ │ o Normals (_s_t_a_n_d_a_r_d___n_o_r_m_a_l)
│ │ │ │ │ o Standard Gammas (_s_t_a_n_d_a_r_d___g_a_m_m_a)
│ │ │ │ │ o Standard Exponentials (_s_t_a_n_d_a_r_d___e_x_p_o_n_e_n_t_i_a_l)
│ │ │ │ │ In [6]: rng = np.random.default_rng()
│ │ │ │ │
│ │ │ │ │ In [7]: rng.random(3, dtype=np.float64)
│ │ │ │ │ -Out[7]: array([0.09837248, 0.04465839, 0.2084051 ])
│ │ │ │ │ +Out[7]: array([0.55420097, 0.45697908, 0.69487323])
│ │ │ │ │
│ │ │ │ │ In [8]: rng.random(3, dtype=np.float32)
│ │ │ │ │ -Out[8]: array([0.13149399, 0.6899817 , 0.9949636 ], dtype=float32)
│ │ │ │ │ +Out[8]: array([0.5988971 , 0.18808335, 0.86917627], dtype=float32)
│ │ │ │ │
│ │ │ │ │ In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
│ │ │ │ │ -Out[9]: array([119, 176, 212], dtype=uint8)
│ │ │ │ │ +Out[9]: array([252, 61, 65], dtype=uint8)
│ │ │ │ │ * Optional out argument that allows existing arrays to be filled for select
│ │ │ │ │ distributions
│ │ │ │ │ o Uniforms (_r_a_n_d_o_m)
│ │ │ │ │ o Normals (_s_t_a_n_d_a_r_d___n_o_r_m_a_l)
│ │ │ │ │ o Standard Gammas (_s_t_a_n_d_a_r_d___g_a_m_m_a)
│ │ │ │ │ o Standard Exponentials (_s_t_a_n_d_a_r_d___e_x_p_o_n_e_n_t_i_a_l)
│ │ │ │ │ This allows multithreading to fill large arrays in chunks using suitable
│ │ │ │ │ BitGenerators in parallel.
│ │ │ │ │ In [10]: rng = np.random.default_rng()
│ │ │ │ │
│ │ │ │ │ In [11]: existing = np.zeros(4)
│ │ │ │ │
│ │ │ │ │ In [12]: rng.random(out=existing[:2])
│ │ │ │ │ -Out[12]: array([0.55876893, 0.61901174])
│ │ │ │ │ +Out[12]: array([0.97290577, 0.00500125])
│ │ │ │ │
│ │ │ │ │ In [13]: print(existing)
│ │ │ │ │ -[0.55876893 0.61901174 0. 0. ]
│ │ │ │ │ +[0.97290577 0.00500125 0. 0. ]
│ │ │ │ │ * Optional axis argument for methods like _c_h_o_i_c_e, _p_e_r_m_u_t_a_t_i_o_n and _s_h_u_f_f_l_e
│ │ │ │ │ that controls which axis an operation is performed over for multi-
│ │ │ │ │ dimensional arrays.
│ │ │ │ │ In [14]: rng = np.random.default_rng()
│ │ │ │ │
│ │ │ │ │ In [15]: a = np.arange(12).reshape((3, 4))
│ │ │ │ │
│ │ │ │ │ @@ -179,25 +179,25 @@
│ │ │ │ │ Out[16]:
│ │ │ │ │ array([[ 0, 1, 2, 3],
│ │ │ │ │ [ 4, 5, 6, 7],
│ │ │ │ │ [ 8, 9, 10, 11]])
│ │ │ │ │
│ │ │ │ │ In [17]: rng.choice(a, axis=1, size=5)
│ │ │ │ │ Out[17]:
│ │ │ │ │ -array([[ 0, 3, 3, 1, 3],
│ │ │ │ │ - [ 4, 7, 7, 5, 7],
│ │ │ │ │ - [ 8, 11, 11, 9, 11]])
│ │ │ │ │ +array([[ 2, 3, 2, 0, 0],
│ │ │ │ │ + [ 6, 7, 6, 4, 4],
│ │ │ │ │ + [10, 11, 10, 8, 8]])
│ │ │ │ │
│ │ │ │ │ In [18]: rng.shuffle(a, axis=1) # Shuffle in-place
│ │ │ │ │
│ │ │ │ │ In [19]: a
│ │ │ │ │ Out[19]:
│ │ │ │ │ -array([[ 0, 3, 2, 1],
│ │ │ │ │ - [ 4, 7, 6, 5],
│ │ │ │ │ - [ 8, 11, 10, 9]])
│ │ │ │ │ +array([[ 2, 0, 1, 3],
│ │ │ │ │ + [ 6, 4, 5, 7],
│ │ │ │ │ + [10, 8, 9, 11]])
│ │ │ │ │ * Added a method to sample from the complex normal distribution
│ │ │ │ │ (ccoommpplleexx__nnoorrmmaall)
│ │ │ │ │ _p_r_e_v_i_o_u_s
│ │ │ │ │ _M_u_l_t_i_t_h_r_e_a_d_e_d_ _g_e_n_e_r_a_t_i_o_n
│ │ │ │ │ _n_e_x_t
│ │ │ │ │ _P_e_r_f_o_r_m_a_n_c_e
│ │ │ │ │ © Copyright 2008-2025, NumPy Developers.
│ │ │ ├── ./usr/share/doc/python-numpy/html/reference/routines.polynomials.html
│ │ │ │ @@ -609,31 +609,31 @@
│ │ │ │
│ │ │ │ <span class="gp">In [3]: </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">+</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <p>With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)
│ │ │ │ could be applied to these data with <a class="reference internal" href="generated/numpy.polyfit.html#numpy.polyfit" title="numpy.polyfit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polyfit</span></code></a>:</p>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">deg</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │ │ -<span class="gh">Out[4]: </span><span class="go">array([1.06648364, 0.00958763])</span>
│ │ │ │ +<span class="gh">Out[4]: </span><span class="go">array([0.89327527, 0.30236465])</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <p>With the new polynomial API, the <a class="reference internal" href="generated/numpy.polynomial.polynomial.Polynomial.fit.html#numpy.polynomial.polynomial.Polynomial.fit" title="numpy.polynomial.polynomial.Polynomial.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>
│ │ │ │ class method is preferred:</p>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">p_fitted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polynomial</span><span class="o">.</span><span class="n">Polynomial</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">deg</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │ │
│ │ │ │ <span class="gp">In [6]: </span><span class="n">p_fitted</span>
│ │ │ │ -<span class="gh">Out[6]: </span><span class="go">Polynomial([4.80876402, 4.79917639], domain=[0., 9.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ +<span class="gh">Out[6]: </span><span class="go">Polynomial([4.32210337, 4.01973872], domain=[0., 9.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ <p>Note that the coefficients are given <em>in the scaled domain</em> defined by the
│ │ │ │ linear mapping between the <code class="docutils literal notranslate"><span class="pre">window</span></code> and <code class="docutils literal notranslate"><span class="pre">domain</span></code>.
│ │ │ │ <a class="reference internal" href="generated/numpy.polynomial.polynomial.Polynomial.convert.html#numpy.polynomial.polynomial.Polynomial.convert" title="numpy.polynomial.polynomial.Polynomial.convert"><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert</span></code></a> can be used to get the
│ │ │ │ coefficients in the unscaled data domain.</p>
│ │ │ │ <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">p_fitted</span><span class="o">.</span><span class="n">convert</span><span class="p">()</span>
│ │ │ │ -<span class="gh">Out[7]: </span><span class="go">Polynomial([0.00958763, 1.06648364], domain=[-1., 1.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ +<span class="gh">Out[7]: </span><span class="go">Polynomial([0.30236465, 0.89327527], domain=[-1., 1.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ </pre></div>
│ │ │ │ </div>
│ │ │ │ </section>
│ │ │ │ </section>
│ │ │ │ <section id="documentation-for-the-polynomial-package">
│ │ │ │ <h2>Documentation for the <a class="reference internal" href="routines.polynomials-package.html#module-numpy.polynomial" title="numpy.polynomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polynomial</span></code></a> package<a class="headerlink" href="#documentation-for-the-polynomial-package" title="Link to this heading">#</a></h2>
│ │ │ │ <p>In addition to standard power series polynomials, the polynomial package
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -150,26 +150,26 @@
│ │ │ │ │
│ │ │ │ │ In [2]: x = np.arange(10)
│ │ │ │ │
│ │ │ │ │ In [3]: y = np.arange(10) + rng.standard_normal(10)
│ │ │ │ │ With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)
│ │ │ │ │ could be applied to these data with _p_o_l_y_f_i_t:
│ │ │ │ │ In [4]: np.polyfit(x, y, deg=1)
│ │ │ │ │ -Out[4]: array([1.06648364, 0.00958763])
│ │ │ │ │ +Out[4]: array([0.89327527, 0.30236465])
│ │ │ │ │ With the new polynomial API, the _f_i_t class method is preferred:
│ │ │ │ │ In [5]: p_fitted = np.polynomial.Polynomial.fit(x, y, deg=1)
│ │ │ │ │
│ │ │ │ │ In [6]: p_fitted
│ │ │ │ │ -Out[6]: Polynomial([4.80876402, 4.79917639], domain=[0., 9.], window=[-1.,
│ │ │ │ │ +Out[6]: Polynomial([4.32210337, 4.01973872], domain=[0., 9.], window=[-1.,
│ │ │ │ │ 1.], symbol='x')
│ │ │ │ │ Note that the coefficients are given iinn tthhee ssccaalleedd ddoommaaiinn defined by the linear
│ │ │ │ │ mapping between the window and domain. _c_o_n_v_e_r_t can be used to get the
│ │ │ │ │ coefficients in the unscaled data domain.
│ │ │ │ │ In [7]: p_fitted.convert()
│ │ │ │ │ -Out[7]: Polynomial([0.00958763, 1.06648364], domain=[-1., 1.], window=[-1.,
│ │ │ │ │ +Out[7]: Polynomial([0.30236465, 0.89327527], domain=[-1., 1.], window=[-1.,
│ │ │ │ │ 1.], symbol='x')
│ │ │ │ │ ********** DDooccuummeennttaattiioonn ffoorr tthhee _pp_oo_ll_yy_nn_oo_mm_ii_aa_ll ppaacckkaaggee_## **********
│ │ │ │ │ In addition to standard power series polynomials, the polynomial package
│ │ │ │ │ provides several additional kinds of polynomials including Chebyshev, Hermite
│ │ │ │ │ (two subtypes), Laguerre, and Legendre polynomials. Each of these has an
│ │ │ │ │ associated ccoonnvveenniieennccee ccllaassss available from the _n_u_m_p_y_._p_o_l_y_n_o_m_i_a_l namespace that
│ │ │ │ │ provides a consistent interface for working with polynomials regardless of
│ │ │ ├── ./usr/share/doc/python-numpy/html/searchindex.js
│ │ │ │ ├── js-beautify {}
│ │ │ │ │ @@ -32324,21 +32324,21 @@
│ │ │ │ │ "001637457145670096": 2315,
│ │ │ │ │ "001j": [1596, 1653, 1710, 1767, 1824, 1879, 1893],
│ │ │ │ │ "002": [1335, 1602, 1659, 1716, 1773, 1830, 1886],
│ │ │ │ │ "0024": 477,
│ │ │ │ │ "003": [2327, 2372, 2419],
│ │ │ │ │ "005": 2188,
│ │ │ │ │ "0050000029": [357, 359],
│ │ │ │ │ + "00500125": 2461,
│ │ │ │ │ "0050045159": [357, 359],
│ │ │ │ │ "00578369": 1754,
│ │ │ │ │ "00625": 1585,
│ │ │ │ │ "007000999997857": 1541,
│ │ │ │ │ "00867716": [2319, 2363, 2410],
│ │ │ │ │ "00950034": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ - "00958763": 2488,
│ │ │ │ │ "009a87ff44bf": 42,
│ │ │ │ │ "00j": [514, 642, 2167],
│ │ │ │ │ "01": [41, 42, 45, 54, 55, 62, 147, 162, 163, 164, 335, 361, 410, 514, 547, 557, 566, 669, 897, 1088, 1094, 1586, 1594, 1601, 1658, 1715, 1772, 1829, 1885, 2104, 2160, 2323, 2329, 2367, 2373, 2414, 2420, 2525, 2657],
│ │ │ │ │ "010": [653, 1602, 1659, 1716, 1773, 1830, 1886],
│ │ │ │ │ "0100": 1519,
│ │ │ │ │ "01041667": 1585,
│ │ │ │ │ "011": [1602, 1659, 1716, 1773, 1830, 1886],
│ │ │ │ │ @@ -32347,14 +32347,15 @@
│ │ │ │ │ "01280782": [2335, 2378, 2425],
│ │ │ │ │ "016": [648, 653],
│ │ │ │ │ "01652764": 2634,
│ │ │ │ │ "01666667": 1544,
│ │ │ │ │ "016j": 2105,
│ │ │ │ │ "01831564": 1660,
│ │ │ │ │ "018318": [2353, 2400, 2450],
│ │ │ │ │ + "01973872": 2488,
│ │ │ │ │ "01j": 514,
│ │ │ │ │ "01t00": [55, 62, 361, 2525],
│ │ │ │ │ "01t08": 2525,
│ │ │ │ │ "01t12": 55,
│ │ │ │ │ "02": [54, 55, 147, 162, 163, 410, 547, 566, 669, 1335, 1586, 1601, 1658, 1715, 1772, 1816, 1829, 1885, 2104],
│ │ │ │ │ "020995": 57,
│ │ │ │ │ "02284196": 1697,
│ │ │ │ │ @@ -32378,32 +32379,30 @@
│ │ │ │ │ "0396842": 680,
│ │ │ │ │ "03t13": 55,
│ │ │ │ │ "04": [54, 55, 164, 410, 547, 1586, 2463, 2594, 2658],
│ │ │ │ │ "0400": 360,
│ │ │ │ │ "04097352": 2634,
│ │ │ │ │ "04166667": [1544, 1585],
│ │ │ │ │ "04211c6": 2521,
│ │ │ │ │ - "04465839": 2461,
│ │ │ │ │ "04551152e": 2104,
│ │ │ │ │ "04719755": 1911,
│ │ │ │ │ - "05": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2647],
│ │ │ │ │ + "05": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2461, 2647],
│ │ │ │ │ "0500": 360,
│ │ │ │ │ "05093587": 2634,
│ │ │ │ │ "05208333": 1585,
│ │ │ │ │ "05263157894736836": 2257,
│ │ │ │ │ "0549999999999997": 2083,
│ │ │ │ │ "055": 2083,
│ │ │ │ │ "0596779": 1153,
│ │ │ │ │ "06": [55, 566, 2083, 2508],
│ │ │ │ │ "0614962j": [439, 453],
│ │ │ │ │ "0625": [418, 624, 1645],
│ │ │ │ │ "06369197489564249": 2458,
│ │ │ │ │ "06381726": 349,
│ │ │ │ │ "0660": [302, 2131],
│ │ │ │ │ - "06648364": 2488,
│ │ │ │ │ "06959433e": [420, 947],
│ │ │ │ │ "07": [55, 164, 547, 896, 897, 1335, 2170, 2508],
│ │ │ │ │ "07106781e": 514,
│ │ │ │ │ "07407407": 1809,
│ │ │ │ │ "07779185": 2458,
│ │ │ │ │ "07937323": 524,
│ │ │ │ │ "07944154": [657, 2654],
│ │ │ │ │ @@ -32420,15 +32419,14 @@
│ │ │ │ │ "08703704": [1113, 1543],
│ │ │ │ │ "087300000000000003": [2346, 2392, 2441],
│ │ │ │ │ "09": [55, 2171, 2252, 2323, 2367, 2414],
│ │ │ │ │ "090097550553843": 2641,
│ │ │ │ │ "09417735": [349, 2457, 2638],
│ │ │ │ │ "0943951": 1911,
│ │ │ │ │ "09640474436813": 675,
│ │ │ │ │ - "09837248": 2461,
│ │ │ │ │ "09861229": [657, 2654],
│ │ │ │ │ "0999755859375": 62,
│ │ │ │ │ "099975586": 62,
│ │ │ │ │ "0a1": 577,
│ │ │ │ │ "0a2": 577,
│ │ │ │ │ "0b00000011": [1519, 2235],
│ │ │ │ │ "0b1": [34, 50, 577, 2602],
│ │ │ │ │ @@ -32566,15 +32564,15 @@
│ │ │ │ │ "1073741821": [420, 421, 947, 948, 1923],
│ │ │ │ │ "1078": 2622,
│ │ │ │ │ "108": [524, 2463],
│ │ │ │ │ "10862": 2538,
│ │ │ │ │ "10868": 2538,
│ │ │ │ │ "1088311": 2343,
│ │ │ │ │ "10898": 2542,
│ │ │ │ │ - "109": [136, 523, 2461, 2463],
│ │ │ │ │ + "109": [136, 523, 2463],
│ │ │ │ │ "10905": 2538,
│ │ │ │ │ "10941": 2328,
│ │ │ │ │ "10946": 28,
│ │ │ │ │ "10947": 2538,
│ │ │ │ │ "10959": 2538,
│ │ │ │ │ "10960": 2538,
│ │ │ │ │ "10961": 2538,
│ │ │ │ │ @@ -32656,15 +32654,15 @@
│ │ │ │ │ "11785": 2543,
│ │ │ │ │ "11786": 2543,
│ │ │ │ │ "1179": 2463,
│ │ │ │ │ "118": 2463,
│ │ │ │ │ "1180339887498949": 2115,
│ │ │ │ │ "11803399": 1228,
│ │ │ │ │ "11885628817151628": 2458,
│ │ │ │ │ - "119": [2461, 2616],
│ │ │ │ │ + "119": 2616,
│ │ │ │ │ "11902": 2544,
│ │ │ │ │ "11981": 2544,
│ │ │ │ │ "11982": 2544,
│ │ │ │ │ "11985": 2541,
│ │ │ │ │ "11986": 2541,
│ │ │ │ │ "11987": 2541,
│ │ │ │ │ "11992": 2544,
│ │ │ │ │ @@ -32749,15 +32747,14 @@
│ │ │ │ │ "13026": 2549,
│ │ │ │ │ "13028": 2549,
│ │ │ │ │ "13038": 2549,
│ │ │ │ │ "13041": 2549,
│ │ │ │ │ "13090": [186, 827, 999, 1172, 1259, 1414, 1933],
│ │ │ │ │ "131": [527, 2463],
│ │ │ │ │ "1312": 485,
│ │ │ │ │ - "13149399": 2461,
│ │ │ │ │ "13182611e": [420, 947],
│ │ │ │ │ "132": [527, 2330, 2374, 2421, 2463, 2636],
│ │ │ │ │ "1324274851176597e": 1867,
│ │ │ │ │ "133": [527, 2330, 2374, 2421],
│ │ │ │ │ "13392": 2551,
│ │ │ │ │ "13394": 2551,
│ │ │ │ │ "13396": 2551,
│ │ │ │ │ @@ -33219,15 +33216,15 @@
│ │ │ │ │ "17545176": 1153,
│ │ │ │ │ "17568": 2569,
│ │ │ │ │ "17577": [2572, 2659],
│ │ │ │ │ "17580": 2572,
│ │ │ │ │ "17582": 2583,
│ │ │ │ │ "17586": 2576,
│ │ │ │ │ "17596": 2572,
│ │ │ │ │ - "176": [2461, 2463],
│ │ │ │ │ + "176": 2463,
│ │ │ │ │ "17647": 2569,
│ │ │ │ │ "17652": 2569,
│ │ │ │ │ "17653": 2569,
│ │ │ │ │ "17660": 2569,
│ │ │ │ │ "17679": 2570,
│ │ │ │ │ "17680": 2570,
│ │ │ │ │ "177": [514, 566, 680, 2658],
│ │ │ │ │ @@ -33333,14 +33330,15 @@
│ │ │ │ │ "1875": 1585,
│ │ │ │ │ "18763": 2575,
│ │ │ │ │ "18764": 2575,
│ │ │ │ │ "18768": 2575,
│ │ │ │ │ "18769": 2575,
│ │ │ │ │ "18794": 2575,
│ │ │ │ │ "188": [2463, 2636],
│ │ │ │ │ + "18808335": 2461,
│ │ │ │ │ "1885": 2611,
│ │ │ │ │ "18874": 2576,
│ │ │ │ │ "1887902": 1911,
│ │ │ │ │ "18880": 2576,
│ │ │ │ │ "18884": 2583,
│ │ │ │ │ "18887": 2575,
│ │ │ │ │ "189": 2327,
│ │ │ │ │ @@ -33634,15 +33632,14 @@
│ │ │ │ │ "20806": 2584,
│ │ │ │ │ "20807": 2584,
│ │ │ │ │ "20814": 2584,
│ │ │ │ │ "20815": 2584,
│ │ │ │ │ "20819": 2584,
│ │ │ │ │ "20821": 2588,
│ │ │ │ │ "20835": 2588,
│ │ │ │ │ - "2084051": 2461,
│ │ │ │ │ "20842": 2585,
│ │ │ │ │ "20843": 2585,
│ │ │ │ │ "20844": 2585,
│ │ │ │ │ "20845": 2585,
│ │ │ │ │ "20875": 2588,
│ │ │ │ │ "20880": 65,
│ │ │ │ │ "20906": 2585,
│ │ │ │ │ @@ -33665,15 +33662,15 @@
│ │ │ │ │ "20985": 2585,
│ │ │ │ │ "20986": 2585,
│ │ │ │ │ "20992": 2585,
│ │ │ │ │ "20993": 2588,
│ │ │ │ │ "20_ver": 0,
│ │ │ │ │ "20count": 141,
│ │ │ │ │ "20d03bcfd": 0,
│ │ │ │ │ - "21": [21, 26, 28, 30, 31, 40, 47, 54, 55, 58, 74, 98, 162, 163, 270, 336, 357, 359, 377, 388, 396, 403, 476, 477, 628, 661, 669, 880, 905, 944, 1069, 1229, 1312, 1466, 1869, 1881, 1986, 2091, 2168, 2172, 2208, 2225, 2230, 2237, 2238, 2342, 2343, 2463, 2513, 2515, 2517, 2519, 2567, 2568, 2572, 2583, 2585, 2588, 2594, 2627, 2628, 2632, 2634, 2636, 2640, 2641, 2648, 2653, 2656, 2664, 2665, 2667],
│ │ │ │ │ + "21": [21, 26, 28, 30, 31, 40, 47, 54, 55, 58, 74, 98, 162, 163, 270, 336, 357, 359, 377, 388, 396, 403, 476, 477, 628, 661, 669, 880, 905, 944, 1069, 1229, 1312, 1466, 1869, 1881, 1986, 2091, 2168, 2172, 2208, 2225, 2230, 2237, 2238, 2342, 2343, 2461, 2463, 2513, 2515, 2517, 2519, 2567, 2568, 2572, 2583, 2585, 2588, 2594, 2627, 2628, 2632, 2634, 2636, 2640, 2641, 2648, 2653, 2656, 2664, 2665, 2667],
│ │ │ │ │ "210": 363,
│ │ │ │ │ "21001": 2588,
│ │ │ │ │ "21029": 2588,
│ │ │ │ │ "210306068529402873165736369884012333109": [2275, 2280],
│ │ │ │ │ "21048": 2586,
│ │ │ │ │ "211": 2491,
│ │ │ │ │ "21106": 2586,
│ │ │ │ │ @@ -33690,15 +33687,15 @@
│ │ │ │ │ "21148": 2586,
│ │ │ │ │ "21154": 2588,
│ │ │ │ │ "21187": 2588,
│ │ │ │ │ "21188": 2588,
│ │ │ │ │ "21191": 2587,
│ │ │ │ │ "21192": 2587,
│ │ │ │ │ "21199": 2622,
│ │ │ │ │ - "212": [2461, 2622],
│ │ │ │ │ + "212": 2622,
│ │ │ │ │ "21243": 2587,
│ │ │ │ │ "21245": 2587,
│ │ │ │ │ "21262": 2588,
│ │ │ │ │ "21275": 2587,
│ │ │ │ │ "21277": 2587,
│ │ │ │ │ "213": [12, 2491],
│ │ │ │ │ "21336384": 2658,
│ │ │ │ │ @@ -33896,15 +33893,15 @@
│ │ │ │ │ "22972": 2596,
│ │ │ │ │ "22976": 2596,
│ │ │ │ │ "22982": 2599,
│ │ │ │ │ "22989": 2596,
│ │ │ │ │ "22997": 2599,
│ │ │ │ │ "22998": 2599,
│ │ │ │ │ "22e": 457,
│ │ │ │ │ - "23": [1, 8, 10, 18, 32, 43, 47, 54, 55, 58, 62, 63, 75, 79, 98, 135, 139, 163, 270, 287, 329, 336, 483, 513, 628, 669, 880, 905, 917, 1029, 1069, 1104, 1229, 1241, 1312, 1324, 1466, 1478, 1545, 1707, 1748, 1777, 1903, 1913, 1986, 1998, 2154, 2208, 2235, 2237, 2256, 2321, 2340, 2342, 2365, 2383, 2412, 2430, 2461, 2513, 2519, 2542, 2552, 2595, 2622, 2627, 2634, 2636, 2640, 2644, 2647, 2656, 2665],
│ │ │ │ │ + "23": [1, 8, 10, 18, 32, 43, 47, 54, 55, 58, 62, 63, 75, 79, 98, 135, 139, 163, 270, 287, 329, 336, 483, 513, 628, 669, 880, 905, 917, 1029, 1069, 1104, 1229, 1241, 1312, 1324, 1466, 1478, 1545, 1707, 1748, 1777, 1903, 1913, 1986, 1998, 2154, 2208, 2235, 2237, 2256, 2321, 2340, 2342, 2365, 2383, 2412, 2430, 2513, 2519, 2542, 2552, 2595, 2622, 2627, 2634, 2636, 2640, 2644, 2647, 2656, 2665],
│ │ │ │ │ "230": 409,
│ │ │ │ │ "230000071797338e": 477,
│ │ │ │ │ "23011": 2599,
│ │ │ │ │ "23019": 2599,
│ │ │ │ │ "23020": 2599,
│ │ │ │ │ "23030": 2596,
│ │ │ │ │ "23031": 2596,
│ │ │ │ │ @@ -34223,14 +34220,15 @@
│ │ │ │ │ "25186": 2622,
│ │ │ │ │ "25188": 2605,
│ │ │ │ │ "25189": 2605,
│ │ │ │ │ "25190": 2605,
│ │ │ │ │ "25191": 2605,
│ │ │ │ │ "25192": 2605,
│ │ │ │ │ "25193": 2622,
│ │ │ │ │ + "252": 2461,
│ │ │ │ │ "25201": 2605,
│ │ │ │ │ "25202": 2605,
│ │ │ │ │ "25203": 2605,
│ │ │ │ │ "25204": 2605,
│ │ │ │ │ "25205": 2605,
│ │ │ │ │ "25217": 2605,
│ │ │ │ │ "25218": 2605,
│ │ │ │ │ @@ -34344,15 +34342,14 @@
│ │ │ │ │ "26268": 2625,
│ │ │ │ │ "26285": 2625,
│ │ │ │ │ "26292": 2625,
│ │ │ │ │ "26313": 2625,
│ │ │ │ │ "26388": 2625,
│ │ │ │ │ "26393": 2622,
│ │ │ │ │ "26452": 2625,
│ │ │ │ │ - "265": 2461,
│ │ │ │ │ "26501": 2625,
│ │ │ │ │ "26579": 2625,
│ │ │ │ │ "26580": 2625,
│ │ │ │ │ "26590556": 2634,
│ │ │ │ │ "26598632": 1228,
│ │ │ │ │ "266": 2463,
│ │ │ │ │ "26606588": 533,
│ │ │ │ │ @@ -34634,14 +34631,15 @@
│ │ │ │ │ "30000": 533,
│ │ │ │ │ "3000488281": 476,
│ │ │ │ │ "3003003": 1999,
│ │ │ │ │ "3006": 2614,
│ │ │ │ │ "3007": 2614,
│ │ │ │ │ "301": [438, 1923, 2477],
│ │ │ │ │ "30220482": [2345, 2391, 2439],
│ │ │ │ │ + "30236465": 2488,
│ │ │ │ │ "30278462": 1154,
│ │ │ │ │ "3039": 2614,
│ │ │ │ │ "304": 22,
│ │ │ │ │ "30464": 2094,
│ │ │ │ │ "30498948e": 2665,
│ │ │ │ │ "305": [421, 948],
│ │ │ │ │ "3057": 2615,
│ │ │ │ │ @@ -34681,14 +34679,15 @@
│ │ │ │ │ "318": 1526,
│ │ │ │ │ "3192": 2615,
│ │ │ │ │ "31962608": [196, 836, 1008, 1179, 1266, 1421, 1940],
│ │ │ │ │ "32": [1, 13, 21, 50, 54, 55, 56, 59, 61, 62, 63, 65, 69, 74, 137, 144, 215, 270, 336, 390, 434, 514, 584, 661, 880, 893, 1027, 1069, 1083, 1143, 1198, 1229, 1249, 1281, 1312, 1345, 1348, 1435, 1466, 1519, 1884, 1886, 1902, 1955, 1986, 2076, 2091, 2168, 2204, 2208, 2225, 2240, 2261, 2262, 2268, 2269, 2272, 2273, 2274, 2277, 2278, 2279, 2282, 2283, 2284, 2287, 2288, 2299, 2300, 2301, 2302, 2303, 2304, 2314, 2331, 2375, 2422, 2458, 2459, 2460, 2462, 2508, 2513, 2520, 2521, 2522, 2535, 2543, 2544, 2545, 2546, 2547, 2557, 2562, 2564, 2572, 2574, 2579, 2582, 2587, 2588, 2599, 2602, 2606, 2607, 2617, 2622, 2636, 2638, 2640, 2644, 2645, 2647, 2648, 2651, 2656, 2657, 2658, 2665],
│ │ │ │ │ "320": 1149,
│ │ │ │ │ "32000": 2094,
│ │ │ │ │ "32119158": 1867,
│ │ │ │ │ + "32210337": 2488,
│ │ │ │ │ "323": [260, 421, 948, 1059, 1222, 1305, 1350, 1459, 1579, 1590, 1604, 1605, 1639, 1649, 1661, 1662, 1696, 1706, 1718, 1719, 1753, 1763, 1775, 1776, 1810, 1820, 1832, 1833, 1866, 1875, 1887, 1888, 1979, 2312],
│ │ │ │ │ "3263": 2617,
│ │ │ │ │ "32767": 535,
│ │ │ │ │ "32768": 535,
│ │ │ │ │ "32_767": 62,
│ │ │ │ │ "32_768": 62,
│ │ │ │ │ "32bit": [50, 61, 62, 2364, 2377, 2379, 2388, 2411, 2424, 2426, 2435, 2517, 2572, 2589],
│ │ │ │ │ @@ -34738,15 +34737,15 @@
│ │ │ │ │ "35": [409, 489, 669, 870, 1056, 2204, 2325, 2369, 2416, 2572, 2634, 2640, 2656, 2665],
│ │ │ │ │ "350": [544, 635],
│ │ │ │ │ "3504": 2617,
│ │ │ │ │ "3534857623790153": 666,
│ │ │ │ │ "35355339": 1636,
│ │ │ │ │ "3541": 2615,
│ │ │ │ │ "35489284e": 2104,
│ │ │ │ │ - "36": [58, 137, 355, 1752, 1761, 2204, 2225, 2323, 2367, 2414, 2463, 2491, 2536, 2648, 2656, 2658, 2665],
│ │ │ │ │ + "36": [58, 137, 355, 1752, 1761, 2204, 2225, 2323, 2367, 2414, 2461, 2463, 2491, 2536, 2648, 2656, 2658, 2665],
│ │ │ │ │ "360": [544, 2103, 2238, 2576],
│ │ │ │ │ "36045180e": 147,
│ │ │ │ │ "3608": 2615,
│ │ │ │ │ "361": [1344, 1346, 1522, 1908],
│ │ │ │ │ "362": 12,
│ │ │ │ │ "3628523": 2458,
│ │ │ │ │ "36363636": 136,
│ │ │ │ │ @@ -34877,15 +34876,15 @@
│ │ │ │ │ "42667924": 1154,
│ │ │ │ │ "4267": 2617,
│ │ │ │ │ "4270": 2617,
│ │ │ │ │ "4276": 2617,
│ │ │ │ │ "429": 136,
│ │ │ │ │ "4294967293": 2638,
│ │ │ │ │ "4294967296": [196, 836, 1008, 1179, 1266, 1421, 1940],
│ │ │ │ │ - "43": [2208, 2461, 2583, 2634, 2640, 2656, 2665],
│ │ │ │ │ + "43": [2208, 2583, 2634, 2640, 2656, 2665],
│ │ │ │ │ "430148": 2634,
│ │ │ │ │ "43014843": 2634,
│ │ │ │ │ "43181166": 2458,
│ │ │ │ │ "4354": 2617,
│ │ │ │ │ "4359": 2617,
│ │ │ │ │ "4368": [287, 1241, 1324, 1478, 1998],
│ │ │ │ │ "4375": 2491,
│ │ │ │ │ @@ -34925,14 +34924,15 @@
│ │ │ │ │ "4520525295346629": 1228,
│ │ │ │ │ "4532": [409, 661, 2168],
│ │ │ │ │ "4545724517479104": 2460,
│ │ │ │ │ "45560727e": 54,
│ │ │ │ │ "456": 1921,
│ │ │ │ │ "4567": 2643,
│ │ │ │ │ "45674898e": 566,
│ │ │ │ │ + "45697908": 2461,
│ │ │ │ │ "45a3d84": 2521,
│ │ │ │ │ "46": [409, 523, 905, 1707, 2204, 2208, 2640, 2656],
│ │ │ │ │ "460": [2238, 2576],
│ │ │ │ │ "46009194e": 566,
│ │ │ │ │ "4602": 2618,
│ │ │ │ │ "4610935": 457,
│ │ │ │ │ "4613": 2618,
│ │ │ │ │ @@ -34977,15 +34977,15 @@
│ │ │ │ │ "4836": 2618,
│ │ │ │ │ "484": [2517, 2583, 2625],
│ │ │ │ │ "4853": 2618,
│ │ │ │ │ "4861946401452": 2334,
│ │ │ │ │ "4874": [409, 661, 2168],
│ │ │ │ │ "4882": [287, 1241, 1324, 1478, 1998],
│ │ │ │ │ "4896": 2620,
│ │ │ │ │ - "49": [71, 2208, 2323, 2367, 2414, 2461, 2567, 2568, 2640, 2656, 2665],
│ │ │ │ │ + "49": [71, 2208, 2323, 2367, 2414, 2567, 2568, 2640, 2656, 2665],
│ │ │ │ │ "4928": [409, 661, 2168],
│ │ │ │ │ "49293": 1644,
│ │ │ │ │ "49313049": [2387, 2434],
│ │ │ │ │ "49401501": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "496041986": [420, 947],
│ │ │ │ │ "49778714": 2238,
│ │ │ │ │ "49876311": [2352, 2399, 2449],
│ │ │ │ │ @@ -35101,23 +35101,23 @@
│ │ │ │ │ "54999924": 1247,
│ │ │ │ │ "55": [28, 59, 60, 73, 893, 1083, 1143, 1525, 1905, 2090, 2204, 2240, 2622, 2656],
│ │ │ │ │ "55000000074505806": 1247,
│ │ │ │ │ "5510652": 2634,
│ │ │ │ │ "55111512e": 642,
│ │ │ │ │ "55131477": 1153,
│ │ │ │ │ "5524": 2621,
│ │ │ │ │ + "55420097": 2461,
│ │ │ │ │ "55458479": 349,
│ │ │ │ │ "55490914e": 2665,
│ │ │ │ │ "5555555555555554": 1349,
│ │ │ │ │ "55627469": 349,
│ │ │ │ │ "55645993": 2634,
│ │ │ │ │ "5580": 2535,
│ │ │ │ │ - "55876893": 2461,
│ │ │ │ │ "55914881e": 2104,
│ │ │ │ │ - "56": [52, 55, 61, 544, 1764, 2204, 2461, 2463, 2656, 2658],
│ │ │ │ │ + "56": [52, 55, 61, 544, 1764, 2204, 2463, 2656, 2658],
│ │ │ │ │ "5612": 2621,
│ │ │ │ │ "5614": 2522,
│ │ │ │ │ "562": [680, 2658],
│ │ │ │ │ "5620499351813308": 86,
│ │ │ │ │ "5625": 2491,
│ │ │ │ │ "56294995342131": 2083,
│ │ │ │ │ "5640": [2353, 2400, 2450],
│ │ │ │ │ @@ -35149,14 +35149,15 @@
│ │ │ │ │ "5910": [287, 1241, 1324, 1478, 1998],
│ │ │ │ │ "595726": 2634,
│ │ │ │ │ "59572603": 2634,
│ │ │ │ │ "5969": [99, 906],
│ │ │ │ │ "598150": 2641,
│ │ │ │ │ "59815003": 38,
│ │ │ │ │ "5982": 2641,
│ │ │ │ │ + "5988971": 2461,
│ │ │ │ │ "59903635e": 147,
│ │ │ │ │ "5d0": 32,
│ │ │ │ │ "5e": [114, 117, 119, 260, 669, 674, 675, 1059, 1222, 1305, 1350, 1459, 1579, 1590, 1596, 1604, 1605, 1639, 1649, 1653, 1661, 1662, 1696, 1706, 1710, 1718, 1719, 1753, 1763, 1767, 1775, 1776, 1810, 1820, 1824, 1832, 1833, 1866, 1875, 1879, 1887, 1888, 1893, 1979],
│ │ │ │ │ "5e16": 55,
│ │ │ │ │ "5f": 2086,
│ │ │ │ │ "5j": [350, 541, 544, 1326, 1515, 2108, 2658],
│ │ │ │ │ "5th": [513, 669, 2640],
│ │ │ │ │ @@ -35171,24 +35172,23 @@
│ │ │ │ │ "60546483": 523,
│ │ │ │ │ "60663578": 2634,
│ │ │ │ │ "607": [2339, 2382, 2429],
│ │ │ │ │ "60860684e": 1594,
│ │ │ │ │ "609": 2583,
│ │ │ │ │ "6094": 2522,
│ │ │ │ │ "60x": 2599,
│ │ │ │ │ - "61": [13, 2656],
│ │ │ │ │ + "61": [13, 2461, 2656],
│ │ │ │ │ "610": 28,
│ │ │ │ │ "61119": 2098,
│ │ │ │ │ "614064523559687": 2115,
│ │ │ │ │ "6143849206349179": [1113, 1543],
│ │ │ │ │ "6176": [99, 906],
│ │ │ │ │ "61799388": 1911,
│ │ │ │ │ "618": 669,
│ │ │ │ │ "6180": [2353, 2400, 2450],
│ │ │ │ │ - "61901174": 2461,
│ │ │ │ │ "61988120985": [2323, 2367, 2414],
│ │ │ │ │ "61c54bbd": 42,
│ │ │ │ │ "62": [542, 968, 969, 2323, 2333, 2367, 2377, 2414, 2424, 2656],
│ │ │ │ │ "6208": 2522,
│ │ │ │ │ "6227766": 514,
│ │ │ │ │ "623": [2394, 2444],
│ │ │ │ │ "62318272": [2319, 2363, 2410],
│ │ │ │ │ @@ -35228,15 +35228,15 @@
│ │ │ │ │ "6488": 2522,
│ │ │ │ │ "6491": 2522,
│ │ │ │ │ "6495": 2522,
│ │ │ │ │ "6497": 2522,
│ │ │ │ │ "6498": 2522,
│ │ │ │ │ "6499": 2522,
│ │ │ │ │ "64bit": [50, 61, 62, 2364, 2377, 2379, 2388, 2411, 2424, 2426, 2435, 2517, 2572, 2594],
│ │ │ │ │ - "65": 2656,
│ │ │ │ │ + "65": [2461, 2656],
│ │ │ │ │ "650": 2618,
│ │ │ │ │ "6500": 2522,
│ │ │ │ │ "6501": 2522,
│ │ │ │ │ "65028784": 2665,
│ │ │ │ │ "65143654": 1154,
│ │ │ │ │ "6515": [2353, 2400, 2450],
│ │ │ │ │ "65200189e": 566,
│ │ │ │ │ @@ -35337,26 +35337,26 @@
│ │ │ │ │ "6840": 2524,
│ │ │ │ │ "6843": 2524,
│ │ │ │ │ "68456316": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "68482974": 1153,
│ │ │ │ │ "6884": 2524,
│ │ │ │ │ "68862757": 660,
│ │ │ │ │ "6888893": [2352, 2399, 2449],
│ │ │ │ │ - "6899817": 2461,
│ │ │ │ │ "69": [58, 431, 2656, 2665],
│ │ │ │ │ "6916": 2524,
│ │ │ │ │ "6922": 2524,
│ │ │ │ │ "6924": 2524,
│ │ │ │ │ "69312169": [1113, 1543],
│ │ │ │ │ "69314718": [76, 657, 2654],
│ │ │ │ │ "69314718055994529": 657,
│ │ │ │ │ "6931471805599453": 2257,
│ │ │ │ │ "6937": 2524,
│ │ │ │ │ "6942": 2524,
│ │ │ │ │ "6943": 2524,
│ │ │ │ │ + "69487323": 2461,
│ │ │ │ │ "6949": 2524,
│ │ │ │ │ "6950": 2524,
│ │ │ │ │ "6952": 2524,
│ │ │ │ │ "69583040e": [420, 947],
│ │ │ │ │ "69646919": 1153,
│ │ │ │ │ "69736803": [349, 2457, 2638],
│ │ │ │ │ "699": 520,
│ │ │ │ │ @@ -35387,17 +35387,17 @@
│ │ │ │ │ "718282": 2641,
│ │ │ │ │ "7183": 2641,
│ │ │ │ │ "7185": [99, 906],
│ │ │ │ │ "71946897": 1153,
│ │ │ │ │ "72": [13, 355, 544, 669, 1764, 2463, 2572, 2656, 2658],
│ │ │ │ │ "720": [55, 356, 358, 1897, 2225, 2238, 2576],
│ │ │ │ │ "72075441": 680,
│ │ │ │ │ - "721": 2461,
│ │ │ │ │ "721fc64": 13,
│ │ │ │ │ "72375": 1644,
│ │ │ │ │ + "724": 2461,
│ │ │ │ │ "72538256": [103, 126],
│ │ │ │ │ "72686684e": 566,
│ │ │ │ │ "72717132": 2658,
│ │ │ │ │ "72727273": 136,
│ │ │ │ │ "72847407": 1154,
│ │ │ │ │ "729": 2665,
│ │ │ │ │ "72904971": 1153,
│ │ │ │ │ @@ -35510,15 +35510,14 @@
│ │ │ │ │ "7954": 2527,
│ │ │ │ │ "7955": 2527,
│ │ │ │ │ "79579319e": 1867,
│ │ │ │ │ "79694221e": 1586,
│ │ │ │ │ "7972": 2527,
│ │ │ │ │ "7976931348623157e": 1335,
│ │ │ │ │ "79769313e": 1335,
│ │ │ │ │ - "79917639": 2488,
│ │ │ │ │ "79ff": [102, 125],
│ │ │ │ │ "7e": 669,
│ │ │ │ │ "7e13": 55,
│ │ │ │ │ "7e17": 55,
│ │ │ │ │ "7x": [2566, 2599],
│ │ │ │ │ "8": [9, 11, 13, 17, 18, 24, 26, 28, 30, 31, 32, 34, 36, 38, 39, 43, 47, 50, 56, 57, 58, 59, 60, 61, 62, 69, 72, 73, 74, 76, 89, 91, 96, 97, 98, 99, 114, 117, 118, 119, 132, 135, 141, 142, 149, 150, 169, 171, 172, 173, 209, 215, 229, 234, 261, 262, 270, 295, 313, 315, 320, 331, 332, 334, 337, 348, 355, 364, 365, 366, 367, 369, 370, 375, 385, 387, 391, 396, 399, 401, 406, 409, 410, 416, 417, 432, 435, 438, 440, 454, 455, 467, 470, 478, 486, 514, 520, 529, 530, 533, 538, 541, 543, 544, 546, 548, 554, 565, 566, 577, 608, 622, 623, 639, 640, 645, 651, 653, 656, 657, 659, 660, 661, 662, 664, 665, 666, 667, 680, 830, 854, 860, 863, 864, 865, 873, 880, 881, 883, 886, 887, 888, 893, 896, 903, 904, 905, 906, 912, 913, 914, 917, 919, 920, 921, 924, 926, 927, 936, 943, 950, 952, 953, 957, 963, 1002, 1027, 1029, 1035, 1044, 1048, 1049, 1050, 1060, 1061, 1069, 1070, 1072, 1076, 1077, 1078, 1083, 1100, 1104, 1106, 1107, 1111, 1112, 1113, 1116, 1119, 1122, 1123, 1124, 1135, 1142, 1143, 1145, 1148, 1159, 1160, 1161, 1164, 1166, 1167, 1193, 1194, 1195, 1198, 1202, 1203, 1204, 1205, 1206, 1212, 1213, 1223, 1224, 1228, 1229, 1236, 1240, 1242, 1243, 1246, 1249, 1281, 1285, 1286, 1306, 1307, 1312, 1334, 1435, 1439, 1440, 1460, 1461, 1466, 1482, 1510, 1512, 1518, 1520, 1521, 1543, 1574, 1578, 1579, 1585, 1586, 1590, 1591, 1604, 1605, 1637, 1639, 1644, 1649, 1656, 1661, 1662, 1696, 1706, 1718, 1719, 1752, 1753, 1758, 1761, 1763, 1765, 1770, 1775, 1776, 1809, 1810, 1816, 1819, 1820, 1832, 1833, 1865, 1866, 1867, 1870, 1873, 1875, 1882, 1884, 1886, 1887, 1888, 1899, 1900, 1902, 1904, 1908, 1912, 1913, 1914, 1916, 1955, 1959, 1960, 1980, 1981, 1986, 2076, 2079, 2082, 2085, 2087, 2091, 2095, 2104, 2107, 2110, 2115, 2116, 2124, 2141, 2143, 2148, 2156, 2157, 2159, 2164, 2168, 2172, 2200, 2202, 2204, 2205, 2206, 2208, 2209, 2210, 2223, 2224, 2225, 2236, 2237, 2238, 2239, 2240, 2242, 2246, 2248, 2256, 2257, 2290, 2308, 2312, 2313, 2320, 2328, 2329, 2334, 2335, 2341, 2342, 2347, 2354, 2373, 2378, 2384, 2388, 2390, 2394, 2395, 2401, 2420, 2425, 2431, 2435, 2438, 2444, 2445, 2451, 2457, 2458, 2460, 2461, 2463, 2470, 2508, 2513, 2519, 2525, 2535, 2538, 2542, 2544, 2545, 2547, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2571, 2572, 2573, 2576, 2579, 2580, 2583, 2584, 2585, 2586, 2587, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2613, 2615, 2619, 2622, 2623, 2631, 2634, 2636, 2637, 2638, 2640, 2641, 2643, 2644, 2645, 2647, 2648, 2652, 2654, 2656, 2657, 2658, 2659, 2664, 2665],
│ │ │ │ │ "80": [69, 396, 409, 446, 977, 2248, 2346, 2357, 2383, 2392, 2404, 2430, 2441, 2454, 2588, 2622, 2647, 2648, 2656],
│ │ │ │ │ @@ -35530,15 +35529,14 @@
│ │ │ │ │ "8010": 2527,
│ │ │ │ │ "8020": 2527,
│ │ │ │ │ "8024": 2527,
│ │ │ │ │ "8031": 2527,
│ │ │ │ │ "804": 2508,
│ │ │ │ │ "8044": 2527,
│ │ │ │ │ "8058837395885292": 666,
│ │ │ │ │ - "80876402": 2488,
│ │ │ │ │ "80b3a34": 2614,
│ │ │ │ │ "81": [1650, 1884, 2634, 2640, 2644, 2656, 2665],
│ │ │ │ │ "81299683": 2634,
│ │ │ │ │ "812997": 2634,
│ │ │ │ │ "813": [270, 880, 1069, 1229, 1312, 1466, 1986],
│ │ │ │ │ "81327024": 2634,
│ │ │ │ │ "81349206": [1113, 1543],
│ │ │ │ │ @@ -35561,15 +35559,15 @@
│ │ │ │ │ "83314899": 1154,
│ │ │ │ │ "83333333": 1702,
│ │ │ │ │ "833333333333333": [893, 1083, 1143, 2240],
│ │ │ │ │ "8341": 2528,
│ │ │ │ │ "8346": 2528,
│ │ │ │ │ "83571711": 349,
│ │ │ │ │ "83697020e": [470, 1899, 1900],
│ │ │ │ │ - "84": [2461, 2656, 2658],
│ │ │ │ │ + "84": [2656, 2658],
│ │ │ │ │ "840": 1212,
│ │ │ │ │ "84057254": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "84090247": 2458,
│ │ │ │ │ "84123594": 523,
│ │ │ │ │ "84147098": 2665,
│ │ │ │ │ "8414709848078965": 2639,
│ │ │ │ │ "84147098j": 2665,
│ │ │ │ │ @@ -35584,24 +35582,25 @@
│ │ │ │ │ "85602287": [2335, 2378, 2425],
│ │ │ │ │ "857": 410,
│ │ │ │ │ "8570331885190563e": [648, 653],
│ │ │ │ │ "85715698e": 2171,
│ │ │ │ │ "8577": 2539,
│ │ │ │ │ "85859792": [349, 2457, 2638],
│ │ │ │ │ "8595784": 2634,
│ │ │ │ │ - "86": [59, 88, 101, 103, 106, 126, 131, 542, 968, 969, 2323, 2367, 2414, 2491, 2656],
│ │ │ │ │ + "86": [59, 88, 101, 103, 106, 126, 131, 542, 968, 969, 2323, 2367, 2414, 2461, 2491, 2656],
│ │ │ │ │ "8601": [55, 62, 67, 2613],
│ │ │ │ │ "86260211e": 54,
│ │ │ │ │ "8630830": 13,
│ │ │ │ │ "86399": 55,
│ │ │ │ │ "86400": 55,
│ │ │ │ │ "86401": 55,
│ │ │ │ │ "8660254": 2103,
│ │ │ │ │ "86820401": [2345, 2391, 2439],
│ │ │ │ │ "86864911e": 1586,
│ │ │ │ │ + "86917627": 2461,
│ │ │ │ │ "87": [2616, 2656],
│ │ │ │ │ "875": [478, 2491],
│ │ │ │ │ "8755": [186, 827, 999, 1172, 1259, 1414, 1933],
│ │ │ │ │ "87649168120691": 674,
│ │ │ │ │ "8770": [2353, 2400, 2450],
│ │ │ │ │ "88": [408, 2462, 2463, 2656, 2658, 2667],
│ │ │ │ │ "8801": [99, 906],
│ │ │ │ │ @@ -35616,14 +35615,15 @@
│ │ │ │ │ "88975": 1644,
│ │ │ │ │ "89": [28, 2091, 2643, 2656],
│ │ │ │ │ "890": 2643,
│ │ │ │ │ "8900451": 1154,
│ │ │ │ │ "89086505": [2352, 2399, 2449],
│ │ │ │ │ "89206682e": 2104,
│ │ │ │ │ "8922078": 2458,
│ │ │ │ │ + "89327527": 2488,
│ │ │ │ │ "89442719": 642,
│ │ │ │ │ "89442719j": 642,
│ │ │ │ │ "89721355": 349,
│ │ │ │ │ "89804309e": 2104,
│ │ │ │ │ "89920014": [2319, 2363, 2410],
│ │ │ │ │ "8999999999999999": 2107,
│ │ │ │ │ "8b2": 2554,
│ │ │ │ │ @@ -35657,15 +35657,15 @@
│ │ │ │ │ "9262": 2532,
│ │ │ │ │ "9263": 2532,
│ │ │ │ │ "9267": 2532,
│ │ │ │ │ "92676499": [349, 2638],
│ │ │ │ │ "927": [539, 554],
│ │ │ │ │ "92771843": 1154,
│ │ │ │ │ "9299": 2532,
│ │ │ │ │ - "93": [2461, 2656],
│ │ │ │ │ + "93": 2656,
│ │ │ │ │ "9304e": 2091,
│ │ │ │ │ "931": 2587,
│ │ │ │ │ "9317": 2532,
│ │ │ │ │ "9319": 2532,
│ │ │ │ │ "9339": 2532,
│ │ │ │ │ "9340": 2532,
│ │ │ │ │ "934284": 349,
│ │ │ │ │ @@ -35676,15 +35676,15 @@
│ │ │ │ │ "9374": 2532,
│ │ │ │ │ "9376": 2532,
│ │ │ │ │ "9377": 2532,
│ │ │ │ │ "93773029": 349,
│ │ │ │ │ "9378": 2532,
│ │ │ │ │ "9379": 2532,
│ │ │ │ │ "9390": [2533, 2534],
│ │ │ │ │ - "94": [409, 669, 2634, 2656],
│ │ │ │ │ + "94": [409, 669, 2461, 2634, 2656],
│ │ │ │ │ "940": 2587,
│ │ │ │ │ "941257": 2634,
│ │ │ │ │ "94125714": 2634,
│ │ │ │ │ "94708397920832": 2641,
│ │ │ │ │ "9475673279178444": 2348,
│ │ │ │ │ "94864945": 2665,
│ │ │ │ │ "94909878": [2387, 2434],
│ │ │ │ │ @@ -35710,14 +35710,15 @@
│ │ │ │ │ "969792": 2634,
│ │ │ │ │ "96e7": 42,
│ │ │ │ │ "97": [2463, 2634, 2656, 2665],
│ │ │ │ │ "9700": 2665,
│ │ │ │ │ "9701": 2665,
│ │ │ │ │ "9702": 2665,
│ │ │ │ │ "97074098": 349,
│ │ │ │ │ + "97290577": 2461,
│ │ │ │ │ "973": [941, 1114, 1123, 1124, 1131],
│ │ │ │ │ "9732": [2533, 2534],
│ │ │ │ │ "9736": [2533, 2534],
│ │ │ │ │ "9742": [2533, 2534],
│ │ │ │ │ "9744": [2533, 2534],
│ │ │ │ │ "9745": [2533, 2534],
│ │ │ │ │ "9746": [2533, 2534],
│ │ │ │ │ @@ -35733,15 +35734,15 @@
│ │ │ │ │ "97719": 1644,
│ │ │ │ │ "9772": 2534,
│ │ │ │ │ "9794": 2534,
│ │ │ │ │ "9797": 2665,
│ │ │ │ │ "97974649": 524,
│ │ │ │ │ "9798": 2665,
│ │ │ │ │ "9799": 2665,
│ │ │ │ │ - "98": [481, 647, 1157, 2634, 2656, 2665],
│ │ │ │ │ + "98": [481, 647, 1157, 2461, 2634, 2656, 2665],
│ │ │ │ │ "9800": 2665,
│ │ │ │ │ "9801": 2665,
│ │ │ │ │ "9802": 2665,
│ │ │ │ │ "98024613": 680,
│ │ │ │ │ "9807642": 1153,
│ │ │ │ │ "981": 2592,
│ │ │ │ │ "98136677": 523,
│ │ │ │ │ @@ -35762,15 +35763,14 @@
│ │ │ │ │ "99091858": [2345, 2391, 2439],
│ │ │ │ │ "99149989": [2345, 2391, 2439],
│ │ │ │ │ "9917": 2343,
│ │ │ │ │ "99256089": 349,
│ │ │ │ │ "99322285": [88, 101],
│ │ │ │ │ "99394529": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "99417356": 1754,
│ │ │ │ │ - "9949636": 2461,
│ │ │ │ │ "99507202": 349,
│ │ │ │ │ "99734545": 1154,
│ │ │ │ │ "998": 2535,
│ │ │ │ │ "99822295": [103, 126],
│ │ │ │ │ "99827656": 1754,
│ │ │ │ │ "99885316e": 566,
│ │ │ │ │ "999": [543, 844, 864, 887, 1020, 1049, 1077, 1125, 2535, 2643],