--- /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=&#39;x&#39;)</span>
│ │ │ │ +<span class="gh">Out[6]: </span><span class="go">Polynomial([4.32210337, 4.01973872], domain=[0., 9.], window=[-1.,  1.], symbol=&#39;x&#39;)</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=&#39;x&#39;)</span>
│ │ │ │ +<span class="gh">Out[7]: </span><span class="go">Polynomial([0.30236465, 0.89327527], domain=[-1.,  1.], window=[-1.,  1.], symbol=&#39;x&#39;)</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,
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│ │ │ │ │          "23031": 2596,
│ │ │ │ │ @@ -34223,14 +34220,15 @@
│ │ │ │ │          "25186": 2622,
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│ │ │ │ │          "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,
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│ │ │ │ │          "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,
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│ │ │ │ │          "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],
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│ │ │ │ │          "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 @@
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│ │ │ │ │          "7976931348623157e": 1335,
│ │ │ │ │          "79769313e": 1335,
│ │ │ │ │ -        "79917639": 2488,
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│ │ │ │ │          "7e17": 55,
│ │ │ │ │          "7x": [2566, 2599],
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│ │ │ │ │ @@ -35530,15 +35529,14 @@
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│ │ │ │ │ @@ -35561,15 +35559,15 @@
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│ │ │ │ │ @@ -35584,24 +35582,25 @@
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│ │ │ │ │ +        "86917627": 2461,
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│ │ │ │ │ @@ -35616,14 +35615,15 @@
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│ │ │ │ │ @@ -35657,15 +35657,15 @@
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│ │ │ │ │ @@ -35676,15 +35676,15 @@
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│ │ │ │ │ @@ -35710,14 +35710,15 @@
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│ │ │ │ │ +        "97290577": 2461,
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│ │ │ │ │ @@ -35733,15 +35734,15 @@
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│ │ │ │ │ @@ -35762,15 +35763,14 @@
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│ │ │ │ │          "99885316e": 566,
│ │ │ │ │          "999": [543, 844, 864, 887, 1020, 1049, 1077, 1125, 2535, 2643],