--- /srv/reproducible-results/rbuild-debian/r-b-build.PAyWFrfh/b1/numpy_2.2.3+ds-5_amd64.changes
+++ /srv/reproducible-results/rbuild-debian/r-b-build.PAyWFrfh/b2/numpy_2.2.3+ds-5_amd64.changes
├── Files
│ @@ -1,5 +1,5 @@
│
│ - 2c4f609874a98e5ce32979dd167203ba 5811648 doc optional python-numpy-doc_2.2.3+ds-5_all.deb
│ + e6b46326d1ad3ca61c00529de25414ef 5811808 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 64876 2025-03-09 20:14:24.000000 control.tar.xz
│ │ --rw-r--r-- 0 0 0 5746580 2025-03-09 20:14:24.000000 data.tar.xz
│ │ +-rw-r--r-- 0 0 0 64872 2025-03-09 20:14:24.000000 control.tar.xz
│ │ +-rw-r--r-- 0 0 0 5746744 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) 46891 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) 45382 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) 44354 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) 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) 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
│ │ │ │ @@ -2610,15 +2610,15 @@
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 24374 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.matlib.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 26288 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.other.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 37419 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials-package.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 46847 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.chebyshev.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 51499 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.classes.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 43104 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.hermite.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 43639 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.hermite_e.html
│ │ │ │ --rw-r--r-- 0 root (0) root (0) 47585 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.html
│ │ │ │ +-rw-r--r-- 0 root (0) root (0) 47589 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 43031 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.laguerre.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 42812 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.legendre.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 28772 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.poly1d.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 41877 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polynomial.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 26623 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polyutils.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 26761 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.rec.html
│ │ │ │ -rw-r--r-- 0 root (0) root (0) 26422 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.set.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) 2686421 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js
│ │ │ │ +-rw-r--r-- 0 root (0) root (0) 2686399 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">2.08 ms +- 28.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">3.56 ms +- 35.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">1.17 ms +- 17 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">2.06 ms +- 46.3 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">1.05 ms +- 16.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">2.52 ms +- 26.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">520 us +- 9.69 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">1.36 ms +- 10.7 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">3.55 ms +- 32.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ -<span class="go">7.17 ms +- 38 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">2.13 ms +- 19.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>
│ │ │ │ +<span class="go">4.13 ms +- 18.5 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([0.31372568, 0.72167143, 0.11277527])</span>
│ │ │ │ +<span class="gh">Out[7]: </span><span class="go">array([0.20177295, 0.80481925, 0.25923581])</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([0.1022529 , 0.65221405, 0.698686 ], dtype=float32)</span>
│ │ │ │ +<span class="gh">Out[8]: </span><span class="go">array([0.6782615 , 0.70966625, 0.8387832 ], 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([102, 67, 157], dtype=uint8)</span>
│ │ │ │ +<span class="gh">Out[9]: </span><span class="go">array([ 82, 164, 39], 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([0.90602207, 0.89725564])</span>
│ │ │ │ +<span class="gh">Out[12]: </span><span class="go">array([0.56660927, 0.68786807])</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">[0.90602207 0.89725564 0. 0. ]</span>
│ │ │ │ +<span class="go">[0.56660927 0.68786807 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([[ 3, 1, 0, 1, 2],</span>
│ │ │ │ -<span class="go"> [ 7, 5, 4, 5, 6],</span>
│ │ │ │ -<span class="go"> [11, 9, 8, 9, 10]])</span>
│ │ │ │ +<span class="go">array([[ 2, 3, 3, 0, 2],</span>
│ │ │ │ +<span class="go"> [ 6, 7, 7, 4, 6],</span>
│ │ │ │ +<span class="go"> [10, 11, 11, 8, 10]])</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([[ 2, 0, 1, 3],</span>
│ │ │ │ -<span class="go"> [ 6, 4, 5, 7],</span>
│ │ │ │ -<span class="go"> [10, 8, 9, 11]])</span>
│ │ │ │ +<span class="go">array([[ 3, 2, 1, 0],</span>
│ │ │ │ +<span class="go"> [ 7, 6, 5, 4],</span>
│ │ │ │ +<span class="go"> [11, 10, 9, 8]])</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)
│ │ │ │ │ ...:
│ │ │ │ │ -2.08 ms +- 28.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -3.56 ms +- 35.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +1.17 ms +- 17 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +2.06 ms +- 46.3 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)
│ │ │ │ │ ...:
│ │ │ │ │ -1.05 ms +- 16.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -2.52 ms +- 26.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +520 us +- 9.69 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +1.36 ms +- 10.7 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)
│ │ │ │ │ ...:
│ │ │ │ │ -3.55 ms +- 32.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ -7.17 ms +- 38 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +2.13 ms +- 19.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │ +4.13 ms +- 18.5 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([0.31372568, 0.72167143, 0.11277527])
│ │ │ │ │ +Out[7]: array([0.20177295, 0.80481925, 0.25923581])
│ │ │ │ │
│ │ │ │ │ In [8]: rng.random(3, dtype=np.float32)
│ │ │ │ │ -Out[8]: array([0.1022529 , 0.65221405, 0.698686 ], dtype=float32)
│ │ │ │ │ +Out[8]: array([0.6782615 , 0.70966625, 0.8387832 ], dtype=float32)
│ │ │ │ │
│ │ │ │ │ In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
│ │ │ │ │ -Out[9]: array([102, 67, 157], dtype=uint8)
│ │ │ │ │ +Out[9]: array([ 82, 164, 39], 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([0.90602207, 0.89725564])
│ │ │ │ │ +Out[12]: array([0.56660927, 0.68786807])
│ │ │ │ │
│ │ │ │ │ In [13]: print(existing)
│ │ │ │ │ -[0.90602207 0.89725564 0. 0. ]
│ │ │ │ │ +[0.56660927 0.68786807 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([[ 3, 1, 0, 1, 2],
│ │ │ │ │ - [ 7, 5, 4, 5, 6],
│ │ │ │ │ - [11, 9, 8, 9, 10]])
│ │ │ │ │ +array([[ 2, 3, 3, 0, 2],
│ │ │ │ │ + [ 6, 7, 7, 4, 6],
│ │ │ │ │ + [10, 11, 11, 8, 10]])
│ │ │ │ │
│ │ │ │ │ In [18]: rng.shuffle(a, axis=1) # Shuffle in-place
│ │ │ │ │
│ │ │ │ │ In [19]: a
│ │ │ │ │ Out[19]:
│ │ │ │ │ -array([[ 2, 0, 1, 3],
│ │ │ │ │ - [ 6, 4, 5, 7],
│ │ │ │ │ - [10, 8, 9, 11]])
│ │ │ │ │ +array([[ 3, 2, 1, 0],
│ │ │ │ │ + [ 7, 6, 5, 4],
│ │ │ │ │ + [11, 10, 9, 8]])
│ │ │ │ │ * 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([1.13434729, 0.01956587])</span>
│ │ │ │ +<span class="gh">Out[4]: </span><span class="go">array([ 1.0984877 , -0.06986567])</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([5.12412867, 5.1045628 ], domain=[0., 9.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ +<span class="gh">Out[6]: </span><span class="go">Polynomial([4.87332896, 4.94319463], 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([0.01956587, 1.13434729], domain=[-1., 1.], window=[-1., 1.], symbol='x')</span>
│ │ │ │ +<span class="gh">Out[7]: </span><span class="go">Polynomial([-0.06986567, 1.0984877 ], 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([1.13434729, 0.01956587])
│ │ │ │ │ +Out[4]: array([ 1.0984877 , -0.06986567])
│ │ │ │ │ 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([5.12412867, 5.1045628 ], domain=[0., 9.], window=[-1.,
│ │ │ │ │ +Out[6]: Polynomial([4.87332896, 4.94319463], 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([0.01956587, 1.13434729], domain=[-1., 1.], window=[-1.,
│ │ │ │ │ +Out[7]: Polynomial([-0.06986567, 1.0984877 ], 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 {}
│ │ │ │ │ @@ -32346,15 +32346,14 @@
│ │ │ │ │ "01280782": [2335, 2378, 2425],
│ │ │ │ │ "016": [648, 653],
│ │ │ │ │ "01652764": 2634,
│ │ │ │ │ "01666667": 1544,
│ │ │ │ │ "016j": 2105,
│ │ │ │ │ "01831564": 1660,
│ │ │ │ │ "018318": [2353, 2400, 2450],
│ │ │ │ │ - "01956587": 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,
│ │ │ │ │ @@ -32380,36 +32379,37 @@
│ │ │ │ │ "04": [54, 55, 164, 410, 547, 1586, 2463, 2594, 2658],
│ │ │ │ │ "0400": 360,
│ │ │ │ │ "04097352": 2634,
│ │ │ │ │ "04166667": [1544, 1585],
│ │ │ │ │ "04211c6": 2521,
│ │ │ │ │ "04551152e": 2104,
│ │ │ │ │ "04719755": 1911,
│ │ │ │ │ - "05": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2461, 2647],
│ │ │ │ │ + "05": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2647],
│ │ │ │ │ "0500": 360,
│ │ │ │ │ "05093587": 2634,
│ │ │ │ │ "05208333": 1585,
│ │ │ │ │ "05263157894736836": 2257,
│ │ │ │ │ "0549999999999997": 2083,
│ │ │ │ │ "055": 2083,
│ │ │ │ │ "0596779": 1153,
│ │ │ │ │ - "06": [55, 566, 2083, 2508],
│ │ │ │ │ + "06": [55, 566, 2083, 2461, 2508],
│ │ │ │ │ "0614962j": [439, 453],
│ │ │ │ │ "0625": [418, 624, 1645],
│ │ │ │ │ "06369197489564249": 2458,
│ │ │ │ │ "06381726": 349,
│ │ │ │ │ "0660": [302, 2131],
│ │ │ │ │ "06959433e": [420, 947],
│ │ │ │ │ + "06986567": 2488,
│ │ │ │ │ "07": [55, 164, 547, 896, 897, 1335, 2170, 2508],
│ │ │ │ │ "07106781e": 514,
│ │ │ │ │ "07407407": 1809,
│ │ │ │ │ "07779185": 2458,
│ │ │ │ │ "07937323": 524,
│ │ │ │ │ "07944154": [657, 2654],
│ │ │ │ │ - "08": [55, 91, 147, 410, 523, 548, 896, 1095, 2322, 2366, 2413, 2461, 2525, 2658],
│ │ │ │ │ + "08": [55, 91, 147, 410, 523, 548, 896, 1095, 2322, 2366, 2413, 2525, 2658],
│ │ │ │ │ "0800": 2525,
│ │ │ │ │ "08187135": 54,
│ │ │ │ │ "08333333": [1645, 1871],
│ │ │ │ │ "08405657": 1867,
│ │ │ │ │ "0855": 2641,
│ │ │ │ │ "08553692": 38,
│ │ │ │ │ "085537": 2641,
│ │ │ │ │ @@ -32418,14 +32418,15 @@
│ │ │ │ │ "08703704": [1113, 1543],
│ │ │ │ │ "087300000000000003": [2346, 2392, 2441],
│ │ │ │ │ "09": [55, 2171, 2252, 2323, 2367, 2414],
│ │ │ │ │ "090097550553843": 2641,
│ │ │ │ │ "09417735": [349, 2457, 2638],
│ │ │ │ │ "0943951": 1911,
│ │ │ │ │ "09640474436813": 675,
│ │ │ │ │ + "0984877": 2488,
│ │ │ │ │ "09861229": [657, 2654],
│ │ │ │ │ "0999755859375": 62,
│ │ │ │ │ "099975586": 62,
│ │ │ │ │ "0a1": 577,
│ │ │ │ │ "0a2": 577,
│ │ │ │ │ "0b00000011": [1519, 2235],
│ │ │ │ │ "0b1": [34, 50, 577, 2602],
│ │ │ │ │ @@ -32496,16 +32497,15 @@
│ │ │ │ │ "100j": 1909,
│ │ │ │ │ "100x5": 652,
│ │ │ │ │ "101": [86, 137, 138, 143, 145, 570, 1325, 2077, 2463, 2665],
│ │ │ │ │ "1010": [145, 1519, 2077],
│ │ │ │ │ "10100": [143, 570],
│ │ │ │ │ "1015": 2592,
│ │ │ │ │ "10151": 2560,
│ │ │ │ │ - "102": [2461, 2463, 2636, 2665],
│ │ │ │ │ - "1022529": 2461,
│ │ │ │ │ + "102": [2463, 2636, 2665],
│ │ │ │ │ "1023": [141, 2583],
│ │ │ │ │ "1024": [66, 72, 2657],
│ │ │ │ │ "10240": 977,
│ │ │ │ │ "103": 2463,
│ │ │ │ │ "10330": 55,
│ │ │ │ │ "10339": [2536, 2542],
│ │ │ │ │ "1035": [1643, 1655],
│ │ │ │ │ @@ -32519,15 +32519,14 @@
│ │ │ │ │ "10401": 2536,
│ │ │ │ │ "10403": 2536,
│ │ │ │ │ "10412": 2536,
│ │ │ │ │ "10424": 2536,
│ │ │ │ │ "10425": 2536,
│ │ │ │ │ "10431": 2536,
│ │ │ │ │ "10435": 2536,
│ │ │ │ │ - "1045628": 2488,
│ │ │ │ │ "1049": 2098,
│ │ │ │ │ "105": 2463,
│ │ │ │ │ "10534": 2536,
│ │ │ │ │ "10536": 2536,
│ │ │ │ │ "10537": 2536,
│ │ │ │ │ "10539": 2536,
│ │ │ │ │ "10540": 2536,
│ │ │ │ │ @@ -32609,15 +32608,14 @@
│ │ │ │ │ "11203": 2539,
│ │ │ │ │ "11210": 2542,
│ │ │ │ │ "11211": 2539,
│ │ │ │ │ "11218": 2542,
│ │ │ │ │ "11219": 2539,
│ │ │ │ │ "11251": 2539,
│ │ │ │ │ "11274": 2540,
│ │ │ │ │ - "11277527": 2461,
│ │ │ │ │ "11294": 2540,
│ │ │ │ │ "113": [674, 2463, 2665],
│ │ │ │ │ "11308": 2542,
│ │ │ │ │ "1138": 2612,
│ │ │ │ │ "114": 2463,
│ │ │ │ │ "11407192": 1822,
│ │ │ │ │ "1142": [2353, 2400, 2450],
│ │ │ │ │ @@ -32716,15 +32714,14 @@
│ │ │ │ │ "123456789a12": 25,
│ │ │ │ │ "123456789a123456789b": 25,
│ │ │ │ │ "1234567e": 2172,
│ │ │ │ │ "12346": 2462,
│ │ │ │ │ "1235": 2091,
│ │ │ │ │ "123abc": [302, 305, 2131, 2134],
│ │ │ │ │ "124": [98, 905],
│ │ │ │ │ - "12412867": 2488,
│ │ │ │ │ "125": [470, 660, 1114, 1142, 1645, 1651, 1899, 1900, 2239, 2339, 2382, 2429, 2460, 2491, 2658, 2665],
│ │ │ │ │ "12589991e": 645,
│ │ │ │ │ "126": [863, 1048, 1116, 1904],
│ │ │ │ │ "1261": 2612,
│ │ │ │ │ "12658": 2560,
│ │ │ │ │ "12697628": 2634,
│ │ │ │ │ "127": [62, 66, 514, 863, 1048, 1102, 1116, 1904, 2301, 2302, 2462, 2463, 2464, 2583, 2638],
│ │ │ │ │ @@ -32760,15 +32757,14 @@
│ │ │ │ │ "133": [527, 2330, 2374, 2421],
│ │ │ │ │ "13392": 2551,
│ │ │ │ │ "13394": 2551,
│ │ │ │ │ "13396": 2551,
│ │ │ │ │ "134": [542, 968, 969],
│ │ │ │ │ "13406828": 2458,
│ │ │ │ │ "13421": 2566,
│ │ │ │ │ - "13434729": 2488,
│ │ │ │ │ "1344": [270, 880, 1069, 1229, 1312, 1466, 1986],
│ │ │ │ │ "13450292": 54,
│ │ │ │ │ "135": [105, 130],
│ │ │ │ │ "13516": 2572,
│ │ │ │ │ "13523": 2551,
│ │ │ │ │ "13533528": 1774,
│ │ │ │ │ "13549": 2551,
│ │ │ │ │ @@ -33032,15 +33028,15 @@
│ │ │ │ │ "15648": 2566,
│ │ │ │ │ "15666": 2572,
│ │ │ │ │ "15675": 2562,
│ │ │ │ │ "15676": 2562,
│ │ │ │ │ "15677": 2562,
│ │ │ │ │ "15679": 2562,
│ │ │ │ │ "15685": 2566,
│ │ │ │ │ - "157": [2461, 2463],
│ │ │ │ │ + "157": 2463,
│ │ │ │ │ "15715": 2566,
│ │ │ │ │ "15722": 2562,
│ │ │ │ │ "15729": 2562,
│ │ │ │ │ "1573": 2612,
│ │ │ │ │ "15734": 2562,
│ │ │ │ │ "15759": 2572,
│ │ │ │ │ "15769": 2566,
│ │ │ │ │ @@ -33103,15 +33099,15 @@
│ │ │ │ │ "16200": 2572,
│ │ │ │ │ "1621": [1913, 2236],
│ │ │ │ │ "1622776601683795": 637,
│ │ │ │ │ "16232": 2572,
│ │ │ │ │ "16236208e": 2104,
│ │ │ │ │ "16350": 2572,
│ │ │ │ │ "16354": 2659,
│ │ │ │ │ - "164": 675,
│ │ │ │ │ + "164": [675, 2461],
│ │ │ │ │ "16434881e": 2104,
│ │ │ │ │ "16439": 2565,
│ │ │ │ │ "16441": 2565,
│ │ │ │ │ "16476": 2572,
│ │ │ │ │ "16484065": 1154,
│ │ │ │ │ "16515": 2572,
│ │ │ │ │ "16519": 2572,
│ │ │ │ │ @@ -33554,14 +33550,15 @@
│ │ │ │ │ "2012": [55, 162, 163],
│ │ │ │ │ "2013": 2620,
│ │ │ │ │ "20140822061353": [24, 32],
│ │ │ │ │ "2015": [14, 55, 2526, 2542, 2594],
│ │ │ │ │ "2016": [55, 557],
│ │ │ │ │ "2017": [2533, 2534, 2612],
│ │ │ │ │ "20172": 50,
│ │ │ │ │ + "20177295": 2461,
│ │ │ │ │ "2018": [2535, 2599],
│ │ │ │ │ "2019": [5, 42, 65, 2328, 2463, 2535, 2550, 2554, 2599, 2659],
│ │ │ │ │ "202": [2463, 2665],
│ │ │ │ │ "2020": [21, 55, 57, 2270, 2535, 2547, 2572, 2573],
│ │ │ │ │ "20201": 2583,
│ │ │ │ │ "2021": [8, 31, 42, 45, 55, 2573, 2585, 2612],
│ │ │ │ │ "20217": 2583,
│ │ │ │ │ @@ -34323,21 +34320,22 @@
│ │ │ │ │ "25802": 2622,
│ │ │ │ │ "25812": 2622,
│ │ │ │ │ "25816": 2622,
│ │ │ │ │ "2584": 28,
│ │ │ │ │ "25866": 2622,
│ │ │ │ │ "25911": 2622,
│ │ │ │ │ "25914": 2622,
│ │ │ │ │ + "25923581": 2461,
│ │ │ │ │ "25943": 2622,
│ │ │ │ │ "25954": 2622,
│ │ │ │ │ "25d0": 32,
│ │ │ │ │ "25j": 2658,
│ │ │ │ │ "25t03": 55,
│ │ │ │ │ "25x": 138,
│ │ │ │ │ - "26": [29, 30, 40, 50, 54, 55, 58, 63, 79, 146, 944, 2204, 2208, 2310, 2333, 2377, 2424, 2461, 2513, 2519, 2534, 2536, 2537, 2577, 2599, 2601, 2622, 2625, 2629, 2640, 2656, 2657, 2665],
│ │ │ │ │ + "26": [29, 30, 40, 50, 54, 55, 58, 63, 79, 146, 944, 2204, 2208, 2310, 2333, 2377, 2424, 2513, 2519, 2534, 2536, 2537, 2577, 2599, 2601, 2622, 2625, 2629, 2640, 2656, 2657, 2665],
│ │ │ │ │ "260": [162, 1328, 2238, 2576, 2665],
│ │ │ │ │ "26064346e": 147,
│ │ │ │ │ "26081": 2625,
│ │ │ │ │ "26103": 2625,
│ │ │ │ │ "26137788e": 2104,
│ │ │ │ │ "26157": 2625,
│ │ │ │ │ "262": 2665,
│ │ │ │ │ @@ -34508,15 +34506,15 @@
│ │ │ │ │ "27t00": 360,
│ │ │ │ │ "27t01": 360,
│ │ │ │ │ "27t02": 360,
│ │ │ │ │ "27t04": 360,
│ │ │ │ │ "27t05": 360,
│ │ │ │ │ "27t06": 360,
│ │ │ │ │ "27t07": 360,
│ │ │ │ │ - "28": [36, 54, 55, 146, 409, 661, 1695, 1704, 1707, 1862, 2168, 2208, 2225, 2461, 2463, 2513, 2538, 2539, 2540, 2543, 2629, 2640, 2648, 2656, 2658, 2665],
│ │ │ │ │ + "28": [36, 54, 55, 146, 409, 661, 1695, 1704, 1707, 1862, 2168, 2208, 2225, 2463, 2513, 2538, 2539, 2540, 2543, 2629, 2640, 2648, 2656, 2658, 2665],
│ │ │ │ │ "28000000e": 1335,
│ │ │ │ │ "2800000e": 1335,
│ │ │ │ │ "28006": 2630,
│ │ │ │ │ "28007": 2630,
│ │ │ │ │ "2801": 2613,
│ │ │ │ │ "28021": 2630,
│ │ │ │ │ "28044": 2630,
│ │ │ │ │ @@ -34662,30 +34660,29 @@
│ │ │ │ │ "3125": 2081,
│ │ │ │ │ "3128": 2614,
│ │ │ │ │ "313": 2665,
│ │ │ │ │ "3131": 2614,
│ │ │ │ │ "3134": 2614,
│ │ │ │ │ "3135": 2614,
│ │ │ │ │ "3136": 2614,
│ │ │ │ │ - "31372568": 2461,
│ │ │ │ │ "314": [420, 947],
│ │ │ │ │ "3141": 562,
│ │ │ │ │ "3153": 2615,
│ │ │ │ │ "31534378e": 660,
│ │ │ │ │ "316": [680, 1923, 2658],
│ │ │ │ │ "3160": 2615,
│ │ │ │ │ "3168": 2615,
│ │ │ │ │ "3173": 2617,
│ │ │ │ │ "3175": 2617,
│ │ │ │ │ "317811": 28,
│ │ │ │ │ "317j": [411, 617],
│ │ │ │ │ "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, 2461, 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],
│ │ │ │ │ + "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,
│ │ │ │ │ "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,
│ │ │ │ │ @@ -34731,22 +34728,22 @@
│ │ │ │ │ "34784527": 2634,
│ │ │ │ │ "3480": 2615,
│ │ │ │ │ "3484692283495345": [648, 653],
│ │ │ │ │ "34889999999999999": [2325, 2369, 2416],
│ │ │ │ │ "34890909": 523,
│ │ │ │ │ "34960421": 680,
│ │ │ │ │ "3497": 2615,
│ │ │ │ │ - "35": [409, 489, 669, 870, 1056, 2204, 2325, 2369, 2416, 2461, 2572, 2634, 2640, 2656, 2665],
│ │ │ │ │ + "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,
│ │ │ │ │ @@ -34765,15 +34762,15 @@
│ │ │ │ │ "3743": 2615,
│ │ │ │ │ "375": [1634, 1663],
│ │ │ │ │ "37601032": [2387, 2434],
│ │ │ │ │ "377": 28,
│ │ │ │ │ "378": 652,
│ │ │ │ │ "379": 533,
│ │ │ │ │ "3793": 2615,
│ │ │ │ │ - "38": [59, 542, 968, 969, 1545, 1752, 1761, 2107, 2204, 2208, 2313, 2333, 2361, 2377, 2408, 2424, 2461, 2510, 2572, 2656, 2665],
│ │ │ │ │ + "38": [59, 542, 968, 969, 1545, 1752, 1761, 2107, 2204, 2208, 2313, 2333, 2361, 2377, 2408, 2424, 2510, 2572, 2656, 2665],
│ │ │ │ │ "380": [2238, 2576],
│ │ │ │ │ "38268343": 642,
│ │ │ │ │ "38268343j": 642,
│ │ │ │ │ "3832": 2615,
│ │ │ │ │ "38434191e": 660,
│ │ │ │ │ "38446749": 1867,
│ │ │ │ │ "385": [2270, 2300],
│ │ │ │ │ @@ -34782,15 +34779,15 @@
│ │ │ │ │ "3871": 2615,
│ │ │ │ │ "38777878e": [147, 1651],
│ │ │ │ │ "38791518e": [421, 948],
│ │ │ │ │ "38885": [2361, 2408],
│ │ │ │ │ "389056": 2641,
│ │ │ │ │ "3890561": [38, 2665],
│ │ │ │ │ "3891": 2641,
│ │ │ │ │ - "39": [30, 58, 2208, 2463, 2640, 2656],
│ │ │ │ │ + "39": [30, 58, 2208, 2461, 2463, 2640, 2656],
│ │ │ │ │ "390": [2270, 2300],
│ │ │ │ │ "3900": 2615,
│ │ │ │ │ "3900x": 2463,
│ │ │ │ │ "39015": 2316,
│ │ │ │ │ "39211752": 1153,
│ │ │ │ │ "39337286e": 1149,
│ │ │ │ │ "3971": 2615,
│ │ │ │ │ @@ -34926,15 +34923,15 @@
│ │ │ │ │ "4532": [409, 661, 2168],
│ │ │ │ │ "4545724517479104": 2460,
│ │ │ │ │ "45560727e": 54,
│ │ │ │ │ "456": 1921,
│ │ │ │ │ "4567": 2643,
│ │ │ │ │ "45674898e": 566,
│ │ │ │ │ "45a3d84": 2521,
│ │ │ │ │ - "46": [409, 523, 905, 1707, 2204, 2208, 2640, 2656],
│ │ │ │ │ + "46": [409, 523, 905, 1707, 2204, 2208, 2461, 2640, 2656],
│ │ │ │ │ "460": [2238, 2576],
│ │ │ │ │ "46009194e": 566,
│ │ │ │ │ "4602": 2618,
│ │ │ │ │ "4610935": 457,
│ │ │ │ │ "4613": 2618,
│ │ │ │ │ "4628": 2618,
│ │ │ │ │ "46351241j": 2081,
│ │ │ │ │ @@ -35048,15 +35045,16 @@
│ │ │ │ │ "51658839e": 1586,
│ │ │ │ │ "517": 10,
│ │ │ │ │ "5170": 2620,
│ │ │ │ │ "5184": 2620,
│ │ │ │ │ "51851852": 1809,
│ │ │ │ │ "519928": 2634,
│ │ │ │ │ "51992837": 2634,
│ │ │ │ │ - "52": [10, 50, 62, 457, 1638, 1647, 1650, 2204, 2208, 2461, 2554, 2634, 2640, 2647, 2656, 2658, 2659, 2665],
│ │ │ │ │ + "52": [10, 50, 62, 457, 1638, 1647, 1650, 2204, 2208, 2554, 2634, 2640, 2647, 2656, 2658, 2659, 2665],
│ │ │ │ │ + "520": 2461,
│ │ │ │ │ "5203": 2620,
│ │ │ │ │ "5225": 2620,
│ │ │ │ │ "5231": 2620,
│ │ │ │ │ "52338984": [2345, 2391, 2439],
│ │ │ │ │ "52359878": 1911,
│ │ │ │ │ "52380952e": 1816,
│ │ │ │ │ "5240": 2620,
│ │ │ │ │ @@ -35095,35 +35093,36 @@
│ │ │ │ │ "5470": [2353, 2400, 2450],
│ │ │ │ │ "5481": 2621,
│ │ │ │ │ "5492": 2621,
│ │ │ │ │ "54930614": [106, 131],
│ │ │ │ │ "5493061443340549": [413, 619],
│ │ │ │ │ "54959369": 2665,
│ │ │ │ │ "54999924": 1247,
│ │ │ │ │ - "55": [28, 59, 60, 73, 893, 1083, 1143, 1525, 1905, 2090, 2204, 2240, 2461, 2622, 2656],
│ │ │ │ │ + "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,
│ │ │ │ │ "55458479": 349,
│ │ │ │ │ "55490914e": 2665,
│ │ │ │ │ "5555555555555554": 1349,
│ │ │ │ │ "55627469": 349,
│ │ │ │ │ "55645993": 2634,
│ │ │ │ │ "5580": 2535,
│ │ │ │ │ "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],
│ │ │ │ │ + "56660927": 2461,
│ │ │ │ │ "567": 2643,
│ │ │ │ │ "56826729e": 2104,
│ │ │ │ │ "56917101": 2634,
│ │ │ │ │ "57": [58, 669, 2204, 2322, 2366, 2413, 2463, 2491, 2636, 2656],
│ │ │ │ │ "5707963267948966": [102, 125],
│ │ │ │ │ "57079633": [94, 105, 130, 1911, 2238, 2665],
│ │ │ │ │ "5708": [412, 618],
│ │ │ │ │ @@ -35234,15 +35233,14 @@
│ │ │ │ │ "650": 2618,
│ │ │ │ │ "6500": 2522,
│ │ │ │ │ "6501": 2522,
│ │ │ │ │ "65028784": 2665,
│ │ │ │ │ "65143654": 1154,
│ │ │ │ │ "6515": [2353, 2400, 2450],
│ │ │ │ │ "65200189e": 566,
│ │ │ │ │ - "65221405": 2461,
│ │ │ │ │ "6526": 2522,
│ │ │ │ │ "6527": 2522,
│ │ │ │ │ "6530": 2522,
│ │ │ │ │ "6532": 2522,
│ │ │ │ │ "6536": 2522,
│ │ │ │ │ "6537": 2522,
│ │ │ │ │ "6538": 2522,
│ │ │ │ │ @@ -35301,15 +35299,15 @@
│ │ │ │ │ "6678": 2522,
│ │ │ │ │ "6686": 2522,
│ │ │ │ │ "6689502": 2576,
│ │ │ │ │ "669": 669,
│ │ │ │ │ "6695": 2522,
│ │ │ │ │ "6697": 2522,
│ │ │ │ │ "6698": 2522,
│ │ │ │ │ - "67": [409, 670, 671, 672, 1901, 2461, 2542, 2643, 2656, 2665],
│ │ │ │ │ + "67": [409, 670, 671, 672, 1901, 2542, 2643, 2656, 2665],
│ │ │ │ │ "67046769e": 147,
│ │ │ │ │ "6717": 2522,
│ │ │ │ │ "6718": 2522,
│ │ │ │ │ "6719": 2522,
│ │ │ │ │ "6721": 2522,
│ │ │ │ │ "6726": 2522,
│ │ │ │ │ "6735": 2522,
│ │ │ │ │ @@ -35318,14 +35316,15 @@
│ │ │ │ │ "6756": 2522,
│ │ │ │ │ "6757": 2522,
│ │ │ │ │ "6765": 28,
│ │ │ │ │ "6771": 2522,
│ │ │ │ │ "6775": 2522,
│ │ │ │ │ "6780": 2522,
│ │ │ │ │ "6781": 2522,
│ │ │ │ │ + "6782615": 2461,
│ │ │ │ │ "6783": 2522,
│ │ │ │ │ "6785": 2522,
│ │ │ │ │ "68": [2656, 2658, 2665],
│ │ │ │ │ "6805": [2353, 2400, 2450],
│ │ │ │ │ "6807": 2522,
│ │ │ │ │ "68080986": 349,
│ │ │ │ │ "6813": 2522,
│ │ │ │ │ @@ -35333,18 +35332,19 @@
│ │ │ │ │ "6819": 2522,
│ │ │ │ │ "68206631e": 2104,
│ │ │ │ │ "684": [2463, 2572],
│ │ │ │ │ "6840": 2524,
│ │ │ │ │ "6843": 2524,
│ │ │ │ │ "68456316": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "68482974": 1153,
│ │ │ │ │ + "68786807": 2461,
│ │ │ │ │ "6884": 2524,
│ │ │ │ │ "68862757": 660,
│ │ │ │ │ "6888893": [2352, 2399, 2449],
│ │ │ │ │ - "69": [58, 431, 2656, 2665],
│ │ │ │ │ + "69": [58, 431, 2461, 2656, 2665],
│ │ │ │ │ "6916": 2524,
│ │ │ │ │ "6922": 2524,
│ │ │ │ │ "6924": 2524,
│ │ │ │ │ "69312169": [1113, 1543],
│ │ │ │ │ "69314718": [76, 657, 2654],
│ │ │ │ │ "69314718055994529": 657,
│ │ │ │ │ "6931471805599453": 2257,
│ │ │ │ │ @@ -35353,15 +35353,14 @@
│ │ │ │ │ "6943": 2524,
│ │ │ │ │ "6949": 2524,
│ │ │ │ │ "6950": 2524,
│ │ │ │ │ "6952": 2524,
│ │ │ │ │ "69583040e": [420, 947],
│ │ │ │ │ "69646919": 1153,
│ │ │ │ │ "69736803": [349, 2457, 2638],
│ │ │ │ │ - "698686": 2461,
│ │ │ │ │ "699": 520,
│ │ │ │ │ "6ad92e5": 13,
│ │ │ │ │ "6e17": 55,
│ │ │ │ │ "6j": [538, 851, 866, 1025, 1026, 1031, 1051, 1891, 1909, 1914, 2241],
│ │ │ │ │ "6th": [513, 669],
│ │ │ │ │ "6x": [1527, 2599],
│ │ │ │ │ "7": [12, 31, 32, 38, 42, 47, 54, 55, 56, 58, 59, 60, 61, 63, 66, 68, 74, 75, 89, 91, 96, 97, 98, 99, 117, 118, 132, 135, 137, 139, 140, 141, 147, 149, 150, 160, 170, 171, 213, 227, 234, 261, 270, 337, 348, 353, 355, 357, 359, 364, 365, 366, 367, 369, 370, 372, 375, 408, 435, 467, 471, 472, 478, 514, 520, 527, 530, 542, 543, 544, 548, 565, 566, 567, 577, 608, 628, 640, 648, 651, 653, 659, 661, 664, 666, 669, 830, 853, 857, 860, 863, 864, 865, 880, 881, 883, 886, 887, 888, 893, 896, 897, 903, 904, 906, 912, 913, 914, 917, 919, 920, 921, 924, 926, 927, 928, 941, 943, 946, 950, 952, 953, 957, 963, 968, 969, 1002, 1033, 1040, 1044, 1048, 1049, 1050, 1060, 1069, 1070, 1072, 1076, 1077, 1078, 1083, 1101, 1106, 1107, 1116, 1119, 1127, 1135, 1143, 1148, 1149, 1157, 1159, 1160, 1161, 1164, 1166, 1167, 1191, 1193, 1194, 1195, 1200, 1203, 1204, 1205, 1206, 1212, 1213, 1223, 1228, 1229, 1230, 1236, 1240, 1242, 1248, 1249, 1278, 1283, 1286, 1306, 1312, 1331, 1334, 1342, 1344, 1346, 1433, 1437, 1440, 1460, 1466, 1510, 1512, 1520, 1521, 1522, 1554, 1615, 1638, 1644, 1647, 1656, 1672, 1695, 1704, 1705, 1729, 1758, 1770, 1786, 1805, 1816, 1843, 1870, 1880, 1882, 1904, 1908, 1912, 1913, 1914, 1916, 1953, 1957, 1960, 1980, 1986, 2071, 2076, 2078, 2079, 2081, 2082, 2085, 2087, 2091, 2096, 2098, 2104, 2107, 2110, 2115, 2116, 2162, 2163, 2164, 2168, 2171, 2172, 2204, 2205, 2206, 2208, 2209, 2210, 2211, 2212, 2222, 2224, 2225, 2235, 2236, 2237, 2238, 2240, 2241, 2246, 2248, 2257, 2315, 2320, 2323, 2328, 2333, 2339, 2341, 2342, 2347, 2352, 2367, 2377, 2382, 2384, 2388, 2389, 2395, 2399, 2414, 2424, 2429, 2431, 2435, 2436, 2445, 2449, 2457, 2458, 2460, 2461, 2463, 2488, 2491, 2513, 2518, 2519, 2520, 2521, 2522, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2572, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2589, 2591, 2592, 2593, 2594, 2600, 2610, 2612, 2616, 2619, 2622, 2626, 2634, 2636, 2637, 2638, 2640, 2641, 2643, 2644, 2646, 2648, 2655, 2656, 2657, 2659, 2665],
│ │ │ │ │ @@ -35372,29 +35371,29 @@
│ │ │ │ │ "703": 2625,
│ │ │ │ │ "7054": 2535,
│ │ │ │ │ "706": 520,
│ │ │ │ │ "70710678": [216, 247, 641, 1199, 1216, 1282, 1299, 1436, 1453, 1636, 1642, 1956, 1973, 2103],
│ │ │ │ │ "70710678118654746": 637,
│ │ │ │ │ "70710678j": 641,
│ │ │ │ │ "7083": 2529,
│ │ │ │ │ + "70966625": 2461,
│ │ │ │ │ "71": [354, 650, 2460, 2656],
│ │ │ │ │ "71080601": 2658,
│ │ │ │ │ "71238898": 1911,
│ │ │ │ │ "71387850e": 1586,
│ │ │ │ │ "718281": 431,
│ │ │ │ │ "71828182845904523536028747135266249775724709369995": 76,
│ │ │ │ │ "71828183": [38, 2665],
│ │ │ │ │ "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,
│ │ │ │ │ - "72167143": 2461,
│ │ │ │ │ "721fc64": 13,
│ │ │ │ │ "72375": 1644,
│ │ │ │ │ "72538256": [103, 126],
│ │ │ │ │ "72686684e": 566,
│ │ │ │ │ "72717132": 2658,
│ │ │ │ │ "72727273": 136,
│ │ │ │ │ "72847407": 1154,
│ │ │ │ │ @@ -35527,25 +35526,26 @@
│ │ │ │ │ "801": [941, 1114, 1123, 1124, 1131],
│ │ │ │ │ "8010": 2527,
│ │ │ │ │ "8020": 2527,
│ │ │ │ │ "8024": 2527,
│ │ │ │ │ "8031": 2527,
│ │ │ │ │ "804": 2508,
│ │ │ │ │ "8044": 2527,
│ │ │ │ │ + "80481925": 2461,
│ │ │ │ │ "8058837395885292": 666,
│ │ │ │ │ "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],
│ │ │ │ │ "81814867": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "8192": [72, 517, 2092, 2619],
│ │ │ │ │ - "82": [1650, 2323, 2367, 2414, 2463, 2656, 2665],
│ │ │ │ │ + "82": [1650, 2323, 2367, 2414, 2461, 2463, 2656, 2665],
│ │ │ │ │ "8207540608310198": [2353, 2400, 2450],
│ │ │ │ │ "82276161": 349,
│ │ │ │ │ "8230": [2353, 2400, 2450],
│ │ │ │ │ "82485143": 2634,
│ │ │ │ │ "82502011": 349,
│ │ │ │ │ "8255": 2566,
│ │ │ │ │ "826716f": 2521,
│ │ │ │ │ @@ -35558,14 +35558,15 @@
│ │ │ │ │ "83314899": 1154,
│ │ │ │ │ "83333333": 1702,
│ │ │ │ │ "833333333333333": [893, 1083, 1143, 2240],
│ │ │ │ │ "8341": 2528,
│ │ │ │ │ "8346": 2528,
│ │ │ │ │ "83571711": 349,
│ │ │ │ │ "83697020e": [470, 1899, 1900],
│ │ │ │ │ + "8387832": 2461,
│ │ │ │ │ "84": [2656, 2658],
│ │ │ │ │ "840": 1212,
│ │ │ │ │ "84057254": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],
│ │ │ │ │ "84090247": 2458,
│ │ │ │ │ "84123594": 523,
│ │ │ │ │ "84147098": 2665,
│ │ │ │ │ "8414709848078965": 2639,
│ │ │ │ │ @@ -35592,14 +35593,15 @@
│ │ │ │ │ "86399": 55,
│ │ │ │ │ "86400": 55,
│ │ │ │ │ "86401": 55,
│ │ │ │ │ "8660254": 2103,
│ │ │ │ │ "86820401": [2345, 2391, 2439],
│ │ │ │ │ "86864911e": 1586,
│ │ │ │ │ "87": [2616, 2656],
│ │ │ │ │ + "87332896": 2488,
│ │ │ │ │ "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],
│ │ │ │ │ "88031624": 2665,
│ │ │ │ │ @@ -35616,30 +35618,28 @@
│ │ │ │ │ "8900451": 1154,
│ │ │ │ │ "89086505": [2352, 2399, 2449],
│ │ │ │ │ "89206682e": 2104,
│ │ │ │ │ "8922078": 2458,
│ │ │ │ │ "89442719": 642,
│ │ │ │ │ "89442719j": 642,
│ │ │ │ │ "89721355": 349,
│ │ │ │ │ - "89725564": 2461,
│ │ │ │ │ "89804309e": 2104,
│ │ │ │ │ "89920014": [2319, 2363, 2410],
│ │ │ │ │ "8999999999999999": 2107,
│ │ │ │ │ "8b2": 2554,
│ │ │ │ │ "8b3": 2555,
│ │ │ │ │ "8b4": 2556,
│ │ │ │ │ "8d": 11,
│ │ │ │ │ "8e": [533, 669],
│ │ │ │ │ "8f": [386, 406],
│ │ │ │ │ "8j": [2101, 2241],
│ │ │ │ │ "9": [10, 12, 26, 30, 31, 32, 38, 42, 47, 54, 55, 57, 58, 59, 60, 79, 89, 91, 96, 97, 98, 114, 117, 118, 119, 132, 141, 147, 149, 150, 158, 171, 175, 202, 227, 261, 270, 287, 299, 305, 312, 320, 321, 322, 337, 348, 355, 357, 359, 364, 365, 366, 367, 369, 372, 375, 409, 420, 435, 441, 442, 447, 460, 478, 483, 514, 520, 529, 530, 543, 548, 565, 566, 573, 577, 608, 640, 641, 642, 644, 646, 648, 653, 655, 656, 659, 660, 661, 664, 666, 669, 680, 823, 830, 838, 839, 853, 860, 863, 864, 865, 880, 881, 886, 887, 888, 893, 896, 903, 904, 905, 908, 912, 913, 914, 919, 920, 921, 924, 926, 927, 936, 939, 947, 950, 952, 953, 957, 963, 993, 1002, 1010, 1013, 1033, 1044, 1048, 1049, 1050, 1060, 1069, 1070, 1076, 1077, 1078, 1083, 1104, 1106, 1107, 1108, 1113, 1116, 1119, 1135, 1142, 1143, 1145, 1148, 1149, 1157, 1159, 1160, 1161, 1164, 1166, 1167, 1184, 1193, 1194, 1195, 1200, 1204, 1205, 1206, 1212, 1213, 1223, 1228, 1229, 1236, 1240, 1241, 1246, 1247, 1249, 1271, 1283, 1306, 1312, 1324, 1334, 1335, 1342, 1348, 1426, 1437, 1460, 1466, 1478, 1483, 1510, 1512, 1521, 1527, 1540, 1543, 1545, 1586, 1644, 1650, 1656, 1707, 1752, 1762, 1808, 1816, 1870, 1876, 1882, 1884, 1885, 1886, 1903, 1904, 1907, 1908, 1912, 1913, 1914, 1916, 1945, 1957, 1980, 1986, 1998, 2079, 2085, 2087, 2091, 2104, 2107, 2110, 2111, 2115, 2116, 2118, 2128, 2134, 2140, 2148, 2149, 2150, 2161, 2168, 2171, 2204, 2205, 2206, 2208, 2209, 2210, 2211, 2223, 2224, 2225, 2236, 2237, 2238, 2239, 2240, 2246, 2248, 2257, 2303, 2316, 2319, 2327, 2328, 2332, 2334, 2341, 2342, 2347, 2361, 2363, 2376, 2384, 2388, 2395, 2408, 2410, 2423, 2431, 2435, 2445, 2457, 2461, 2463, 2488, 2489, 2491, 2508, 2513, 2519, 2520, 2521, 2522, 2525, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2576, 2577, 2578, 2580, 2583, 2586, 2587, 2590, 2592, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2616, 2622, 2623, 2624, 2625, 2630, 2632, 2634, 2637, 2638, 2640, 2643, 2644, 2646, 2647, 2648, 2656, 2657, 2664, 2665, 2667],
│ │ │ │ │ "90": [25, 29, 33, 37, 39, 55, 68, 78, 363, 1910, 2082, 2103, 2248, 2463, 2610, 2653, 2656],
│ │ │ │ │ "90384387e": 2104,
│ │ │ │ │ "90476190e": 1816,
│ │ │ │ │ - "90602207": 2461,
│ │ │ │ │ "90909091": 136,
│ │ │ │ │ "909297": 2641,
│ │ │ │ │ "90929743": 2665,
│ │ │ │ │ "91": 2656,
│ │ │ │ │ "91275558": 2634,
│ │ │ │ │ "916666666666666": 1240,
│ │ │ │ │ "92": [98, 2656, 2658],
│ │ │ │ │ @@ -35679,14 +35679,15 @@
│ │ │ │ │ "9378": 2532,
│ │ │ │ │ "9379": 2532,
│ │ │ │ │ "9390": [2533, 2534],
│ │ │ │ │ "94": [409, 669, 2634, 2656],
│ │ │ │ │ "940": 2587,
│ │ │ │ │ "941257": 2634,
│ │ │ │ │ "94125714": 2634,
│ │ │ │ │ + "94319463": 2488,
│ │ │ │ │ "94708397920832": 2641,
│ │ │ │ │ "9475673279178444": 2348,
│ │ │ │ │ "94864945": 2665,
│ │ │ │ │ "94909878": [2387, 2434],
│ │ │ │ │ "95": [37, 39, 646, 2353, 2400, 2450, 2634, 2653, 2656],
│ │ │ │ │ "9504637": 2665,
│ │ │ │ │ "950684": 2634,