{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.7UtKB4TY/b1/numpy_2.2.4+ds-1_arm64.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.7UtKB4TY/b2/numpy_2.2.4+ds-1_arm64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n- 5208c9c996c1bf14934e13c1a4714aac 5814136 doc optional python-numpy-doc_2.2.4+ds-1_all.deb\n+ abde7a449ceacf7bfa89ea5cf749aa98 5813872 doc optional python-numpy-doc_2.2.4+ds-1_all.deb\n  ed4ae6572e28f8b1dbd938419975210d 14383292 debug optional python3-numpy-dbgsym_2.2.4+ds-1_arm64.deb\n  3a5b3ecec4a4b617987f388521dd977f 136096 python optional python3-numpy-dev_2.2.4+ds-1_arm64.deb\n  eda4b103877c2256a35928417850a972 3604092 python optional python3-numpy_2.2.4+ds-1_arm64.deb\n"}, {"source1": "python-numpy-doc_2.2.4+ds-1_all.deb", "source2": "python-numpy-doc_2.2.4+ds-1_all.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r--   0        0        0        4 2025-04-01 19:45:23.000000 debian-binary\n--rw-r--r--   0        0        0    64868 2025-04-01 19:45:23.000000 control.tar.xz\n--rw-r--r--   0        0        0  5749076 2025-04-01 19:45:23.000000 data.tar.xz\n+-rw-r--r--   0        0        0    64872 2025-04-01 19:45:23.000000 control.tar.xz\n+-rw-r--r--   0        0        0  5748808 2025-04-01 19:45:23.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: python-numpy-doc\n Source: numpy\n Version: 1:2.2.4+ds-1\n Architecture: all\n Maintainer: Debian Python Team <team+python@tracker.debian.org>\n-Installed-Size: 100719\n+Installed-Size: 100718\n Depends: libjs-sphinxdoc (>= 8.1)\n Suggests: python-imageio-doc, python-pandas-doc, python-pytest-doc, python-scipy-doc, python-skimage-doc, python3-doc\n Section: doc\n Priority: optional\n Multi-Arch: foreign\n Homepage: http://www.numpy.org/\n Description: Python library for numerical computations (Documentation)\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -2578,15 +2578,15 @@\n -rw-r--r--   0 root         (0) root         (0)    42758 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html\n -rw-r--r--   0 root         (0) root         (0)    47423 2025-04-01 19:45:23.000000 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./usr/share/doc/python-numpy/html/reference/routines.array-manipulation.html\n -rw-r--r--   0 root         (0) root         (0)    27535 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.bitwise.html\n -rw-r--r--   0 root         (0) root         (0)    54450 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.char.html\n@@ -2610,15 +2610,15 @@\n -rw-r--r--   0 root         (0) root         (0)    24374 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.matlib.html\n -rw-r--r--   0 root         (0) root         (0)    26288 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.other.html\n -rw-r--r--   0 root         (0) root         (0)    37419 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials-package.html\n -rw-r--r--   0 root         (0) root         (0)    46847 2025-04-01 19:45:23.000000 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./usr/share/doc/python-numpy/html/reference/routines.polynomials.laguerre.html\n -rw-r--r--   0 root         (0) root         (0)    42812 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.legendre.html\n -rw-r--r--   0 root         (0) root         (0)    28772 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.poly1d.html\n -rw-r--r--   0 root         (0) root         (0)    41877 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polynomial.html\n -rw-r--r--   0 root         (0) root         (0)    26623 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polyutils.html\n -rw-r--r--   0 root         (0) root         (0)    26761 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.rec.html\n -rw-r--r--   0 root         (0) root         (0)    26422 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.set.html\n@@ -2755,15 +2755,15 @@\n -rw-r--r--   0 root         (0) root         (0)    31655 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.1-notes.html\n -rw-r--r--   0 root         (0) root         (0)    32348 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.2-notes.html\n -rw-r--r--   0 root         (0) root         (0)    32865 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.3-notes.html\n -rw-r--r--   0 root         (0) root         (0)    32016 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.4-notes.html\n -rw-r--r--   0 root         (0) root         (0)    13407 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/template.html\n -rw-r--r--   0 root         (0) root         (0)    90990 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release.html\n -rw-r--r--   0 root         (0) root         (0)    12397 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/search.html\n--rw-r--r--   0 root         (0) root         (0)  2687993 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/searchindex.js\n+-rw-r--r--   0 root         (0) root         (0)  2687990 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/searchindex.js\n drwxr-xr-x   0 root         (0) root         (0)        0 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/\n -rw-r--r--   0 root         (0) root         (0)   177614 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/absolute_beginners.html\n -rw-r--r--   0 root         (0) root         (0)    50529 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/basics.broadcasting.html\n -rw-r--r--   0 root         (0) root         (0)    33464 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/basics.copies.html\n -rw-r--r--   0 root         (0) root         (0)    64100 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/basics.creation.html\n -rw-r--r--   0 root         (0) root         (0)    65763 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/basics.dispatch.html\n -rw-r--r--   0 root         (0) root         (0)    18746 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/user/basics.html\n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/new-or-different.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/new-or-different.html", "unified_diff": "@@ -536,35 +536,34 @@\n <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>\n \n <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>\n \n <span class=\"gp\">In [3]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> -n 1 rng.standard_normal(100000)\n <span class=\"gp\">   ...: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> -n 1 numpy.random.standard_normal(100000)\n <span class=\"gp\">   ...: </span>\n-<span class=\"go\">The slowest run took 15.74 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n-<span class=\"go\">3.07 ms +- 5.09 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">The slowest run took 12.21 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n-<span class=\"go\">8.77 ms +- 9.45 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">The slowest run took 11.70 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">2.29 ms +- 3.37 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">The slowest run took 4.29 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">3.7 ms +- 2.84 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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)\n <span class=\"gp\">   ...: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> -n 1 numpy.random.standard_exponential(100000)\n <span class=\"gp\">   ...: </span>\n-<span class=\"go\">The slowest run took 9.19 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n-<span class=\"go\">2.15 ms +- 2.82 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">The slowest run took 5.65 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n-<span class=\"go\">4.06 ms +- 3.64 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">817 us +- 38.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">The slowest run took 6.15 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">3.04 ms +- 3.13 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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)\n <span class=\"gp\">   ...: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> -n 1 numpy.random.standard_gamma(3.0, 100000)\n <span class=\"gp\">   ...: </span>\n-<span class=\"go\">The slowest run took 4.32 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n-<span class=\"go\">5.92 ms +- 3.99 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">9.08 ms +- 3.97 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">2.95 ms +- 1.41 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">The slowest run took 4.57 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">9.72 ms +- 6.98 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <ul class=\"simple\">\n <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\n random numbers from a discrete uniform distribution. This replaces both\n <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>\n <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\n@@ -591,21 +590,21 @@\n <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>\n </ul>\n </li>\n </ul>\n <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>\n \n <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>\n-<span class=\"gh\">Out[7]: </span><span class=\"go\">array([0.34194237, 0.91932205, 0.94178031])</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">array([0.94041448, 0.19110624, 0.94988671])</span>\n \n <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>\n-<span class=\"gh\">Out[8]: </span><span class=\"go\">array([0.02471197, 0.9925222 , 0.48664033], dtype=float32)</span>\n+<span class=\"gh\">Out[8]: </span><span class=\"go\">array([0.7870627 , 0.8089857 , 0.85572165], dtype=float32)</span>\n \n <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>\n-<span class=\"gh\">Out[9]: </span><span class=\"go\">array([ 11, 193, 173], dtype=uint8)</span>\n+<span class=\"gh\">Out[9]: </span><span class=\"go\">array([131,  55, 201], dtype=uint8)</span>\n </pre></div>\n </div>\n <ul>\n <li><p>Optional <code class=\"docutils literal notranslate\"><span class=\"pre\">out</span></code> argument that allows existing arrays to be filled for\n select distributions</p>\n <ul class=\"simple\">\n <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>\n@@ -618,18 +617,18 @@\n </li>\n </ul>\n <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>\n \n <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>\n \n <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>\n-<span class=\"gh\">Out[12]: </span><span class=\"go\">array([0.28363179, 0.47329423])</span>\n+<span class=\"gh\">Out[12]: </span><span class=\"go\">array([0.5927328 , 0.86546345])</span>\n \n <span class=\"gp\">In [13]: </span><span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">existing</span><span class=\"p\">)</span>\n-<span class=\"go\">[0.28363179 0.47329423 0.         0.        ]</span>\n+<span class=\"go\">[0.5927328  0.86546345 0.         0.        ]</span>\n </pre></div>\n </div>\n <ul class=\"simple\">\n <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>,\n <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\n axis an operation is performed over for multi-dimensional arrays.</p></li>\n </ul>\n@@ -641,25 +640,25 @@\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">array([[ 0,  1,  2,  3],</span>\n <span class=\"go\">       [ 4,  5,  6,  7],</span>\n <span class=\"go\">       [ 8,  9, 10, 11]])</span>\n \n <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>\n <span class=\"gh\">Out[17]: </span>\n-<span class=\"go\">array([[ 3,  1,  2,  1,  0],</span>\n-<span class=\"go\">       [ 7,  5,  6,  5,  4],</span>\n-<span class=\"go\">       [11,  9, 10,  9,  8]])</span>\n+<span class=\"go\">array([[ 2,  3,  3,  2,  2],</span>\n+<span class=\"go\">       [ 6,  7,  7,  6,  6],</span>\n+<span class=\"go\">       [10, 11, 11, 10, 10]])</span>\n \n <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>\n \n <span class=\"gp\">In [19]: </span><span class=\"n\">a</span>\n <span class=\"gh\">Out[19]: </span>\n-<span class=\"go\">array([[ 1,  3,  2,  0],</span>\n-<span class=\"go\">       [ 5,  7,  6,  4],</span>\n-<span class=\"go\">       [ 9, 11, 10,  8]])</span>\n+<span class=\"go\">array([[ 0,  3,  2,  1],</span>\n+<span class=\"go\">       [ 4,  7,  6,  5],</span>\n+<span class=\"go\">       [ 8, 11, 10,  9]])</span>\n </pre></div>\n </div>\n <ul class=\"simple\">\n <li><p>Added a method to sample from the complex normal distribution\n (<em class=\"xref py py-obj\">complex_normal</em>)</p></li>\n </ul>\n </section>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -102,36 +102,34 @@\n In [1]: import numpy.random\n \n In [2]: rng = np.random.default_rng()\n \n In [3]: %timeit -n 1 rng.standard_normal(100000)\n    ...: %timeit -n 1 numpy.random.standard_normal(100000)\n    ...:\n-The slowest run took 15.74 times longer than the fastest. This could mean that\n+The slowest run took 11.70 times longer than the fastest. This could mean that\n an intermediate result is being cached.\n-3.07 ms +- 5.09 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-The slowest run took 12.21 times longer than the fastest. This could mean that\n+2.29 ms +- 3.37 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 4.29 times longer than the fastest. This could mean that\n an intermediate result is being cached.\n-8.77 ms +- 9.45 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.7 ms +- 2.84 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n In [4]: %timeit -n 1 rng.standard_exponential(100000)\n    ...: %timeit -n 1 numpy.random.standard_exponential(100000)\n    ...:\n-The slowest run took 9.19 times longer than the fastest. This could mean that\n+817 us +- 38.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 6.15 times longer than the fastest. This could mean that\n an intermediate result is being cached.\n-2.15 ms +- 2.82 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-The slowest run took 5.65 times longer than the fastest. This could mean that\n-an intermediate result is being cached.\n-4.06 ms +- 3.64 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.04 ms +- 3.13 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)\n    ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)\n    ...:\n-The slowest run took 4.32 times longer than the fastest. This could mean that\n+2.95 ms +- 1.41 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 4.57 times longer than the fastest. This could mean that\n an intermediate result is being cached.\n-5.92 ms +- 3.99 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-9.08 ms +- 3.97 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+9.72 ms +- 6.98 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n     * _\bi_\bn_\bt_\be_\bg_\be_\br_\bs is now the canonical way to generate integer random numbers from\n       a discrete uniform distribution. This replaces both _\br_\ba_\bn_\bd_\bi_\bn_\bt and the\n       deprecated _\br_\ba_\bn_\bd_\bo_\bm_\b__\bi_\bn_\bt_\be_\bg_\be_\br_\bs.\n     * The _\br_\ba_\bn_\bd and _\br_\ba_\bn_\bd_\bn methods are only available through the legacy\n       _\bR_\ba_\bn_\bd_\bo_\bm_\bS_\bt_\ba_\bt_\be.\n     * _\bG_\be_\bn_\be_\br_\ba_\bt_\bo_\br_\b._\br_\ba_\bn_\bd_\bo_\bm is now the canonical way to generate floating-point\n       random numbers, which replaces _\bR_\ba_\bn_\bd_\bo_\bm_\bS_\bt_\ba_\bt_\be_\b._\br_\ba_\bn_\bd_\bo_\bm_\b__\bs_\ba_\bm_\bp_\bl_\be, _\bs_\ba_\bm_\bp_\bl_\be, and\n@@ -150,38 +148,38 @@\n           o Uniforms (_\br_\ba_\bn_\bd_\bo_\bm and _\bi_\bn_\bt_\be_\bg_\be_\br_\bs)\n           o Normals (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\bn_\bo_\br_\bm_\ba_\bl)\n           o Standard Gammas (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\bg_\ba_\bm_\bm_\ba)\n           o Standard Exponentials (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\be_\bx_\bp_\bo_\bn_\be_\bn_\bt_\bi_\ba_\bl)\n In [6]: rng = np.random.default_rng()\n \n In [7]: rng.random(3, dtype=np.float64)\n-Out[7]: array([0.34194237, 0.91932205, 0.94178031])\n+Out[7]: array([0.94041448, 0.19110624, 0.94988671])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.02471197, 0.9925222 , 0.48664033], dtype=float32)\n+Out[8]: array([0.7870627 , 0.8089857 , 0.85572165], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 11, 193, 173], dtype=uint8)\n+Out[9]: array([131,  55, 201], dtype=uint8)\n     * Optional out argument that allows existing arrays to be filled for select\n       distributions\n           o Uniforms (_\br_\ba_\bn_\bd_\bo_\bm)\n           o Normals (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\bn_\bo_\br_\bm_\ba_\bl)\n           o Standard Gammas (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\bg_\ba_\bm_\bm_\ba)\n           o Standard Exponentials (_\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd_\b__\be_\bx_\bp_\bo_\bn_\be_\bn_\bt_\bi_\ba_\bl)\n       This allows multithreading to fill large arrays in chunks using suitable\n       BitGenerators in parallel.\n In [10]: rng = np.random.default_rng()\n \n In [11]: existing = np.zeros(4)\n \n In [12]: rng.random(out=existing[:2])\n-Out[12]: array([0.28363179, 0.47329423])\n+Out[12]: array([0.5927328 , 0.86546345])\n \n In [13]: print(existing)\n-[0.28363179 0.47329423 0.         0.        ]\n+[0.5927328  0.86546345 0.         0.        ]\n     * Optional axis argument for methods like _\bc_\bh_\bo_\bi_\bc_\be, _\bp_\be_\br_\bm_\bu_\bt_\ba_\bt_\bi_\bo_\bn and _\bs_\bh_\bu_\bf_\bf_\bl_\be\n       that controls which axis an operation is performed over for multi-\n       dimensional arrays.\n In [14]: rng = np.random.default_rng()\n \n In [15]: a = np.arange(12).reshape((3, 4))\n \n@@ -189,25 +187,25 @@\n Out[16]:\n array([[ 0,  1,  2,  3],\n        [ 4,  5,  6,  7],\n        [ 8,  9, 10, 11]])\n \n In [17]: rng.choice(a, axis=1, size=5)\n Out[17]:\n-array([[ 3,  1,  2,  1,  0],\n-       [ 7,  5,  6,  5,  4],\n-       [11,  9, 10,  9,  8]])\n+array([[ 2,  3,  3,  2,  2],\n+       [ 6,  7,  7,  6,  6],\n+       [10, 11, 11, 10, 10]])\n \n In [18]: rng.shuffle(a, axis=1)        # Shuffle in-place\n \n In [19]: a\n Out[19]:\n-array([[ 1,  3,  2,  0],\n-       [ 5,  7,  6,  4],\n-       [ 9, 11, 10,  8]])\n+array([[ 0,  3,  2,  1],\n+       [ 4,  7,  6,  5],\n+       [ 8, 11, 10,  9]])\n     * Added a method to sample from the complex normal distribution\n       (c\bco\bom\bmp\bpl\ble\bex\bx_\b_n\bno\bor\brm\bma\bal\bl)\n _\bp_\br_\be_\bv_\bi_\bo_\bu_\bs\n _\bM_\bu_\bl_\bt_\bi_\bt_\bh_\br_\be_\ba_\bd_\be_\bd_\b _\bg_\be_\bn_\be_\br_\ba_\bt_\bi_\bo_\bn\n _\bn_\be_\bx_\bt\n _\bP_\be_\br_\bf_\bo_\br_\bm_\ba_\bn_\bc_\be\n \u00a9 Copyright 2008-2025, NumPy Developers.\n"}]}, {"source1": "./usr/share/doc/python-numpy/html/reference/routines.polynomials.html", "source2": "./usr/share/doc/python-numpy/html/reference/routines.polynomials.html", "unified_diff": "@@ -609,31 +609,31 @@\n \n <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>\n </pre></div>\n </div>\n <p>With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)\n 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>\n <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>\n-<span class=\"gh\">Out[4]: </span><span class=\"go\">array([ 1.07557467, -0.16889551])</span>\n+<span class=\"gh\">Out[4]: </span><span class=\"go\">array([0.92122459, 0.40715967])</span>\n </pre></div>\n </div>\n <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>\n class method is preferred:</p>\n <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>\n \n <span class=\"gp\">In [6]: </span><span class=\"n\">p_fitted</span>\n-<span class=\"gh\">Out[6]: </span><span class=\"go\">Polynomial([4.67119052, 4.84008604], domain=[0., 9.], window=[-1.,  1.], symbol=&#39;x&#39;)</span>\n+<span class=\"gh\">Out[6]: </span><span class=\"go\">Polynomial([4.55267033, 4.14551067], domain=[0., 9.], window=[-1.,  1.], symbol=&#39;x&#39;)</span>\n </pre></div>\n </div>\n <p>Note that the coefficients are given <em>in the scaled domain</em> defined by the\n 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>.\n <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\n coefficients in the unscaled data domain.</p>\n <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>\n-<span class=\"gh\">Out[7]: </span><span class=\"go\">Polynomial([-0.16889551,  1.07557467], domain=[-1.,  1.], window=[-1.,  1.], symbol=&#39;x&#39;)</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">Polynomial([0.40715967, 0.92122459], domain=[-1.,  1.], window=[-1.,  1.], symbol=&#39;x&#39;)</span>\n </pre></div>\n </div>\n </section>\n </section>\n <section id=\"documentation-for-the-polynomial-package\">\n <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>\n <p>In addition to standard power series polynomials, the polynomial package\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -150,26 +150,26 @@\n \n In [2]: x = np.arange(10)\n \n In [3]: y = np.arange(10) + rng.standard_normal(10)\n With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)\n could be applied to these data with _\bp_\bo_\bl_\by_\bf_\bi_\bt:\n In [4]: np.polyfit(x, y, deg=1)\n-Out[4]: array([ 1.07557467, -0.16889551])\n+Out[4]: array([0.92122459, 0.40715967])\n With the new polynomial API, the _\bf_\bi_\bt class method is preferred:\n In [5]: p_fitted = np.polynomial.Polynomial.fit(x, y, deg=1)\n \n In [6]: p_fitted\n-Out[6]: Polynomial([4.67119052, 4.84008604], domain=[0., 9.], window=[-1.,\n+Out[6]: Polynomial([4.55267033, 4.14551067], domain=[0., 9.], window=[-1.,\n 1.], symbol='x')\n Note that the coefficients are given i\bin\bn t\bth\bhe\be s\bsc\bca\bal\ble\bed\bd d\bdo\bom\bma\bai\bin\bn defined by the linear\n mapping between the window and domain. _\bc_\bo_\bn_\bv_\be_\br_\bt can be used to get the\n coefficients in the unscaled data domain.\n In [7]: p_fitted.convert()\n-Out[7]: Polynomial([-0.16889551,  1.07557467], domain=[-1.,  1.], window=[-1.,\n+Out[7]: Polynomial([0.40715967, 0.92122459], domain=[-1.,  1.], window=[-1.,\n 1.], symbol='x')\n *\b**\b**\b**\b**\b* D\bDo\boc\bcu\bum\bme\ben\bnt\bta\bat\bti\bio\bon\bn f\bfo\bor\br t\bth\bhe\be _\bp\bp_\bo\bo_\bl\bl_\by\by_\bn\bn_\bo\bo_\bm\bm_\bi\bi_\ba\ba_\bl\bl p\bpa\bac\bck\bka\bag\bge\be_\b#\b# *\b**\b**\b**\b**\b*\n In addition to standard power series polynomials, the polynomial package\n provides several additional kinds of polynomials including Chebyshev, Hermite\n (two subtypes), Laguerre, and Legendre polynomials. Each of these has an\n associated c\bco\bon\bnv\bve\ben\bni\bie\ben\bnc\bce\be c\bcl\bla\bas\bss\bs available from the _\bn_\bu_\bm_\bp_\by_\b._\bp_\bo_\bl_\by_\bn_\bo_\bm_\bi_\ba_\bl namespace that\n provides a consistent interface for working with polynomials regardless of\n"}]}, {"source1": "./usr/share/doc/python-numpy/html/searchindex.js", "source2": "./usr/share/doc/python-numpy/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -32361,15 +32361,14 @@\n         \"01t12\": 55,\n         \"02\": [54, 55, 147, 162, 163, 410, 547, 566, 669, 1335, 1586, 1601, 1658, 1715, 1772, 1816, 1829, 1885, 2104],\n         \"020995\": 57,\n         \"02284196\": 1697,\n         \"02294967\": 1640,\n         \"023\": 2644,\n         \"02332573e\": 54,\n-        \"02471197\": 2461,\n         \"0253\": 2652,\n         \"02654825\": 1891,\n         \"02658058e\": 2666,\n         \"02755911\": 2666,\n         \"027559113243068367\": 2666,\n         \"02785049\": 1867,\n         \"02i\": [513, 2644],\n@@ -32378,56 +32377,55 @@\n         \"0326911\": [2335, 2378, 2425],\n         \"0361\": 2607,\n         \"03703704\": 1809,\n         \"03943254e\": 2104,\n         \"03968254\": [1113, 1543],\n         \"0396842\": 680,\n         \"03t13\": 55,\n-        \"04\": [54, 55, 164, 410, 547, 1586, 2463, 2594, 2659],\n+        \"04\": [54, 55, 164, 410, 547, 1586, 2461, 2463, 2594, 2659],\n         \"0400\": 360,\n         \"04097352\": 2635,\n         \"04166667\": [1544, 1585],\n         \"04211c6\": 2521,\n         \"04551152e\": 2104,\n         \"04719755\": 1911,\n         \"05\": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2648],\n         \"0500\": 360,\n         \"05093587\": 2635,\n         \"05208333\": 1585,\n         \"05263157894736836\": 2257,\n         \"0549999999999997\": 2083,\n         \"055\": 2083,\n         \"0596779\": 1153,\n-        \"06\": [55, 566, 2083, 2461, 2508],\n+        \"06\": [55, 566, 2083, 2508],\n         \"0614962j\": [439, 453],\n         \"0625\": [418, 624, 1645],\n         \"06369197489564249\": 2458,\n         \"06381726\": 349,\n         \"0660\": [302, 2131],\n         \"06959433e\": [420, 947],\n-        \"07\": [55, 164, 547, 896, 897, 1335, 2170, 2461, 2508],\n+        \"07\": [55, 164, 547, 896, 897, 1335, 2170, 2508],\n         \"07106781e\": 514,\n         \"07407407\": 1809,\n-        \"07557467\": 2488,\n         \"07779185\": 2458,\n         \"07937323\": 524,\n         \"07944154\": [657, 2655],\n-        \"08\": [55, 91, 147, 410, 523, 548, 896, 1095, 2322, 2366, 2413, 2461, 2525, 2659],\n+        \"08\": [55, 91, 147, 410, 523, 548, 896, 1095, 2322, 2366, 2413, 2525, 2659],\n         \"0800\": 2525,\n         \"08187135\": 54,\n         \"08333333\": [1645, 1871],\n         \"08405657\": 1867,\n         \"0855\": 2642,\n         \"08553692\": 38,\n         \"085537\": 2642,\n         \"0856306\": 1154,\n         \"08618131\": 1526,\n         \"08703704\": [1113, 1543],\n         \"087300000000000003\": [2346, 2392, 2441],\n-        \"09\": [55, 2171, 2323, 2367, 2414, 2461],\n+        \"09\": [55, 2171, 2323, 2367, 2414],\n         \"090097550553843\": 2642,\n         \"09417735\": [349, 2457, 2639],\n         \"0943951\": 1911,\n         \"09640474436813\": 675,\n         \"09861229\": [657, 2655],\n         \"0999755859375\": 62,\n         \"099975586\": 62,\n@@ -32750,15 +32748,15 @@\n         \"1300000\": 2635,\n         \"13020\": 2549,\n         \"13026\": 2549,\n         \"13028\": 2549,\n         \"13038\": 2549,\n         \"13041\": 2549,\n         \"13090\": [186, 827, 999, 1172, 1259, 1414, 1933],\n-        \"131\": [527, 2463],\n+        \"131\": [527, 2461, 2463],\n         \"1312\": 485,\n         \"13182611e\": [420, 947],\n         \"132\": [527, 2330, 2374, 2421, 2463, 2637],\n         \"1324274851176597e\": 1867,\n         \"133\": [527, 2330, 2374, 2421],\n         \"13392\": 2551,\n         \"13394\": 2551,\n@@ -32917,14 +32915,15 @@\n         \"14510\": 2560,\n         \"14512\": 2557,\n         \"14513\": 2557,\n         \"14515\": 2557,\n         \"14518\": 2560,\n         \"14519\": 2557,\n         \"14527\": 2560,\n+        \"14551067\": 2488,\n         \"14557\": 2557,\n         \"14563\": 2557,\n         \"14567\": 2560,\n         \"14578\": 2557,\n         \"14583\": 2560,\n         \"14593\": 2559,\n         \"14596\": 2560,\n@@ -33157,15 +33156,14 @@\n         \"168\": 2463,\n         \"16815\": 2572,\n         \"16830\": 2588,\n         \"16832\": 2567,\n         \"16841\": 2572,\n         \"16872\": 2567,\n         \"16875\": 2567,\n-        \"16889551\": 2488,\n         \"16904\": 2567,\n         \"16905\": 2567,\n         \"16906\": 2567,\n         \"16916\": 2567,\n         \"16922\": 2567,\n         \"16938\": 2572,\n         \"16959\": 2568,\n@@ -33200,15 +33198,15 @@\n         \"17244\": 2568,\n         \"17245\": 2568,\n         \"17269252e\": [421, 948],\n         \"17272\": 2568,\n         \"17284\": 2572,\n         \"17296777\": 2635,\n         \"17298\": 2569,\n-        \"173\": [669, 2461, 2637],\n+        \"173\": [669, 2637],\n         \"17336\": 2569,\n         \"17344\": 2572,\n         \"17394\": 2572,\n         \"174\": 2463,\n         \"17446\": 2569,\n         \"17450\": 2569,\n         \"17456\": 2572,\n@@ -33371,26 +33369,27 @@\n         \"19029263503854454\": 2348,\n         \"19031\": 2576,\n         \"19049\": 2576,\n         \"1905\": 2612,\n         \"19062\": 2583,\n         \"1908\": [2353, 2400, 2450],\n         \"19083\": 2583,\n+        \"19110624\": 2461,\n         \"19135\": 2583,\n         \"19151\": 2583,\n         \"191614240\": 95,\n         \"1918\": 2612,\n         \"192\": [69, 669, 1519, 2463],\n         \"19211\": 2583,\n         \"192163377\": 2098,\n         \"19226\": 2588,\n         \"1923875335537315\": [2352, 2389, 2399, 2436, 2449],\n         \"19249760\": [420, 947],\n         \"19259\": 2583,\n-        \"193\": [2461, 2463, 2637],\n+        \"193\": [2463, 2637],\n         \"19311\": 2577,\n         \"1932\": [2359, 2406, 2456],\n         \"19324\": 2577,\n         \"19330\": 2577,\n         \"19342\": 2577,\n         \"19343\": 2577,\n         \"19347\": 2577,\n@@ -33542,15 +33541,15 @@\n         \"20080\": 2579,\n         \"20080221202153\": 2490,\n         \"20081\": 2579,\n         \"20082\": 2579,\n         \"2009\": 55,\n         \"20090423014010\": [2357, 2404, 2454],\n         \"20090514091424\": [2346, 2392, 2441],\n-        \"201\": [2103, 2666],\n+        \"201\": [2103, 2461, 2666],\n         \"2010\": [55, 67, 361, 2531],\n         \"20100312\": 67,\n         \"20100814160313\": 2609,\n         \"20100817112505\": 2609,\n         \"20106\": 2579,\n         \"2011\": [40, 55, 162, 163, 164, 547, 2303],\n         \"2012\": [55, 162, 163],\n@@ -33667,15 +33666,15 @@\n         \"20985\": 2585,\n         \"20986\": 2585,\n         \"20992\": 2585,\n         \"20993\": 2588,\n         \"20_ver\": 0,\n         \"20count\": 141,\n         \"20d03bcfd\": 0,\n-        \"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, 2635, 2637, 2641, 2642, 2649, 2654, 2657, 2665, 2666, 2668],\n+        \"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, 2635, 2637, 2641, 2642, 2649, 2654, 2657, 2665, 2666, 2668],\n         \"210\": 363,\n         \"21001\": 2588,\n         \"21029\": 2588,\n         \"210306068529402873165736369884012333109\": [2275, 2280],\n         \"21048\": 2586,\n         \"211\": 2491,\n         \"21106\": 2586,\n@@ -34566,15 +34565,14 @@\n         \"28305\": 2632,\n         \"283185307179586\": 2238,\n         \"28318531\": [1891, 2238],\n         \"28321\": 2632,\n         \"28324\": 2632,\n         \"28333\": 2633,\n         \"28348\": 2633,\n-        \"28363179\": 2461,\n         \"28377\": 2633,\n         \"28379\": 2633,\n         \"28385\": 2633,\n         \"28420\": 2633,\n         \"28422\": 2633,\n         \"28423\": 2633,\n         \"28424\": 2633,\n@@ -34599,15 +34597,15 @@\n         \"28657\": 28,\n         \"28790332\": 2666,\n         \"2883\": [1643, 1655],\n         \"28853036\": 1867,\n         \"28904982231052\": 675,\n         \"2892\": 2615,\n         \"28mathematical_const\": 76,\n-        \"29\": [10, 21, 45, 54, 55, 76, 144, 163, 1638, 1647, 2208, 2328, 2463, 2464, 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\"34194237\": 2461,\n         \"3421\": 2615,\n         \"343\": 2666,\n         \"34317802\": 1153,\n         \"34376245\": 2635,\n         \"3456\": 13,\n         \"3458\": 2615,\n         \"3471280\": 1923,\n@@ -34778,29 +34775,29 @@\n         \"3628523\": 2458,\n         \"36363636\": 136,\n         \"3644j\": [415, 621],\n         \"365\": [544, 1344, 1346, 1522, 1908],\n         \"366\": 55,\n         \"36674\": 2098,\n         \"36787944\": [1660, 1774],\n-        \"37\": [59, 60, 136, 1913, 2083, 2204, 2236, 2463, 2641, 2657, 2666],\n+        \"37\": [59, 60, 136, 1913, 2083, 2204, 2236, 2461, 2463, 2641, 2657, 2666],\n         \"370\": 652,\n         \"3701\": 2615,\n         \"37079802\": [349, 2639],\n         \"3712\": 2615,\n         \"3728\": 2615,\n         \"3728973198\": 2594,\n         \"3743\": 2615,\n         \"375\": [1634, 1663],\n         \"37601032\": [2387, 2434],\n         \"377\": 28,\n         \"378\": 652,\n         \"379\": 533,\n         \"3793\": 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2107, 2208, 2236, 2585, 2657, 2666],\n+        \"41\": [941, 1114, 1123, 1124, 1131, 1913, 2104, 2107, 2208, 2236, 2461, 2585, 2657, 2666],\n         \"4109\": 2617,\n         \"41211849\": 2666,\n         \"4123\": [2615, 2617],\n         \"4134\": 2617,\n         \"41357986e\": 2175,\n         \"4138\": 2617,\n         \"4141\": 2615,\n@@ -34939,15 +34937,15 @@\n         \"4476\": 2621,\n         \"4483\": 2617,\n         \"4485\": 2617,\n         \"4486\": 2617,\n         \"449294e\": 2170,\n         \"44948974\": 2659,\n         \"44999999925494177\": 2115,\n-        \"45\": [12, 18, 38, 58, 94, 105, 130, 339, 666, 851, 866, 944, 1025, 1026, 1031, 1051, 1212, 1882, 1886, 2103, 2204, 2461, 2637, 2644, 2657, 2666],\n+        \"45\": [12, 18, 38, 58, 94, 105, 130, 339, 666, 851, 866, 944, 1025, 1026, 1031, 1051, 1212, 1882, 1886, 2103, 2204, 2637, 2644, 2657, 2666],\n         \"450\": 55,\n         \"45000005\": 2115,\n         \"45038594\": [349, 2639],\n         \"45053314\": 2635,\n         \"4511316\": 2666,\n         \"4520525295346629\": 1228,\n         \"4532\": [409, 661, 2168],\n@@ -34983,15 +34981,14 @@\n         \"471\": [136, 147, 2463],\n         \"47108547995356098\": [2345, 2391, 2439],\n         \"47140452j\": 2491,\n         \"472\": 55,\n         \"4722\": 2618,\n         \"4723\": 2621,\n         \"4730\": [409, 661, 2168],\n-        \"47329423\": 2461,\n         \"4733\": 2618,\n         \"47458546\": 349,\n         \"47536961\": 349,\n         \"47613949\": [2335, 2378, 2425],\n         \"4774\": 2618,\n         \"479\": 2620,\n         \"4796\": [409, 661, 2168],\n@@ -35002,15 +34999,14 @@\n         \"4809319\": 1153,\n         \"4816\": [99, 906],\n         \"48219694e\": [421, 948],\n         \"4836\": 2618,\n         \"484\": [2517, 2583, 2625],\n         \"4853\": 2618,\n         \"4861946401452\": 2334,\n-        \"48664033\": 2461,\n         \"4874\": [409, 661, 2168],\n         \"4882\": [287, 1241, 1324, 1478, 1998],\n         \"4896\": 2620,\n         \"49\": [71, 2208, 2323, 2367, 2414, 2567, 2568, 2641, 2657, 2666],\n         \"4928\": [409, 661, 2168],\n         \"49293\": 1644,\n         \"49313049\": [2387, 2434],\n@@ -35124,20 +35120,21 @@\n         \"5470\": [2353, 2400, 2450],\n         \"5481\": 2621,\n         \"5492\": 2621,\n         \"54930614\": [106, 131],\n         \"5493061443340549\": [413, 619],\n         \"54959369\": 2666,\n         \"54999924\": 1247,\n-        \"55\": [28, 59, 60, 73, 893, 1083, 1143, 1525, 1905, 2090, 2204, 2240, 2622, 2657],\n+        \"55\": [28, 59, 60, 73, 893, 1083, 1143, 1525, 1905, 2090, 2204, 2240, 2461, 2622, 2657],\n         \"55000000074505806\": 1247,\n         \"5510652\": 2635,\n         \"55111512e\": 642,\n         \"55131477\": 1153,\n         \"5524\": 2621,\n+        \"55267033\": 2488,\n         \"55458479\": 349,\n         \"55490914e\": 2666,\n         \"5555555555555554\": 1349,\n         \"55627469\": 349,\n         \"55645993\": 2635,\n         \"5580\": 2535,\n         \"55914881e\": 2104,\n@@ -35148,15 +35145,15 @@\n         \"5620499351813308\": 86,\n         \"5625\": 2491,\n         \"56294995342131\": 2083,\n         \"5640\": [2353, 2400, 2450],\n         \"567\": 2644,\n         \"56826729e\": 2104,\n         \"56917101\": 2635,\n-        \"57\": [58, 669, 2204, 2322, 2366, 2413, 2463, 2491, 2637, 2657],\n+        \"57\": [58, 669, 2204, 2322, 2366, 2413, 2461, 2463, 2491, 2637, 2657],\n         \"5707963267948966\": [102, 125],\n         \"57079633\": [94, 105, 130, 1911, 2238, 2666],\n         \"5708\": [412, 618],\n         \"57115742\": 524,\n         \"57136612e\": 2173,\n         \"57510612\": 660,\n         \"576\": 2615,\n@@ -35171,14 +35168,15 @@\n         \"58289208\": 2635,\n         \"584388\": 55,\n         \"585\": [378, 1358, 1514, 2583],\n         \"58578644\": 1756,\n         \"58826587\": 349,\n         \"59\": [55, 2208, 2602, 2657],\n         \"5910\": [287, 1241, 1324, 1478, 1998],\n+        \"5927328\": 2461,\n         \"595726\": 2635,\n         \"59572603\": 2635,\n         \"5969\": [99, 906],\n         \"598150\": 2642,\n         \"59815003\": 38,\n         \"5982\": 2642,\n         \"59903635e\": 147,\n@@ -35230,15 +35228,15 @@\n         \"631198588\": 55,\n         \"63317787e\": [2166, 2167],\n         \"63526532\": 2458,\n         \"636363636364\": [2353, 2400, 2450],\n         \"63696169\": 2635,\n         \"6376\": 2522,\n         \"6390\": [2353, 2400, 2450],\n-        \"64\": [1, 5, 13, 21, 30, 50, 55, 56, 59, 61, 62, 63, 65, 66, 69, 74, 79, 315, 339, 409, 457, 470, 514, 584, 660, 669, 944, 1345, 1348, 1519, 1899, 1900, 2076, 2083, 2143, 2261, 2262, 2264, 2268, 2269, 2273, 2274, 2278, 2279, 2283, 2284, 2287, 2288, 2299, 2300, 2303, 2304, 2314, 2328, 2458, 2461, 2462, 2464, 2513, 2520, 2542, 2547, 2578, 2579, 2580, 2583, 2599, 2602, 2603, 2610, 2615, 2616, 2635, 2639, 2648, 2649, 2657, 2659, 2665, 2666],\n+        \"64\": [1, 5, 13, 21, 30, 50, 55, 56, 59, 61, 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 \"76991118\": 1891,\n-        \"77\": [29, 36, 37, 38, 39, 2461, 2463, 2635, 2654, 2657],\n+        \"77\": [29, 36, 37, 38, 39, 2463, 2635, 2654, 2657],\n         \"770\": [287, 1241, 1324, 1478, 1998, 2650],\n         \"77086955\": 1526,\n         \"7724\": 2526,\n         \"7725\": [2353, 2400, 2450],\n         \"7731\": 2526,\n         \"7736\": 2527,\n         \"7737\": 2526,\n@@ -35516,14 +35513,15 @@\n         \"78497f4\": 2521,\n         \"7851\": 2527,\n         \"78539816\": [94, 104, 105, 129, 130, 2238],\n         \"78539816339744828\": [104, 129],\n         \"7854j\": [413, 619],\n         \"78571429e\": 1816,\n         \"78606431\": [349, 2457, 2639],\n+        \"7870627\": 2461,\n         \"789\": 2644,\n         \"7896\": 2527,\n         \"79\": [13, 87, 100, 104, 129, 2657],\n         \"79033856e\": 1149,\n         \"7904\": 2527,\n         \"7917\": 2527,\n         \"7919\": 2527,\n@@ -35555,24 +35553,26 @@\n         \"8010\": 2527,\n         \"8020\": 2527,\n         \"8024\": 2527,\n         \"8031\": 2527,\n         \"804\": 2508,\n         \"8044\": 2527,\n         \"8058837395885292\": 666,\n+        \"8089857\": 2461,\n         \"80b3a34\": 2614,\n         \"81\": [1650, 1884, 2635, 2641, 2645, 2657, 2666],\n         \"81299683\": 2635,\n         \"812997\": 2635,\n         \"813\": [270, 880, 1069, 1229, 1312, 1466, 1986],\n         \"81327024\": 2635,\n         \"81349206\": [1113, 1543],\n+        \"817\": 2461,\n         \"81814867\": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],\n         \"8192\": [72, 517, 2092, 2619],\n-        \"82\": [1650, 2323, 2367, 2414, 2461, 2463, 2657, 2666],\n+        \"82\": [1650, 2323, 2367, 2414, 2463, 2657, 2666],\n         \"8207540608310198\": [2353, 2400, 2450],\n         \"82276161\": 349,\n         \"8230\": [2353, 2400, 2450],\n         \"82485143\": 2635,\n         \"82502011\": 349,\n         \"8255\": 2566,\n         \"826716f\": 2521,\n@@ -35585,45 +35585,46 @@\n         \"83314899\": 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647, 1157, 2461, 2635, 2657, 2666],\n         \"9800\": 2666,\n         \"9801\": 2666,\n         \"9802\": 2666,\n         \"98024613\": 680,\n         \"9807642\": 1153,\n         \"981\": 2592,\n         \"98136677\": 523,\n         \"987\": 28,\n         \"987654321\": 2393,\n         \"98935825\": 2666,\n         \"9897\": 2666,\n         \"9898\": 2666,\n         \"9899\": [105, 130, 2666],\n-        \"99\": [12, 302, 408, 544, 672, 1096, 1906, 2131, 2460, 2461, 2635, 2657, 2658, 2666],\n+        \"99\": [12, 302, 408, 544, 672, 1096, 1906, 2131, 2460, 2635, 2657, 2658, 2666],\n         \"9900\": 2666,\n         \"99004057\": 349,\n         \"9901\": 2666,\n         \"9902\": 2666,\n         \"990278\": 2635,\n         \"99027828\": 2635,\n         \"99060736\": 2666,\n         \"99091858\": [2345, 2391, 2439],\n         \"99149989\": [2345, 2391, 2439],\n         \"9917\": 2343,\n-        \"9925222\": 2461,\n         \"99256089\": 349,\n         \"99322285\": [88, 101],\n         \"99394529\": [2339, 2352, 2382, 2389, 2399, 2429, 2436, 2449],\n         \"99417356\": 1754,\n         \"99507202\": 349,\n         \"99734545\": 1154,\n         \"998\": 2535,\n@@ -45984,15 +45985,15 @@\n         \"typevar\": 2517,\n         \"typic\": [1, 4, 5, 6, 8, 9, 10, 11, 12, 13, 19, 20, 21, 22, 36, 39, 42, 54, 55, 58, 59, 61, 63, 65, 70, 72, 74, 78, 91, 114, 159, 161, 354, 409, 422, 427, 443, 457, 521, 522, 548, 571, 572, 579, 582, 583, 627, 655, 814, 850, 1030, 1513, 1526, 1577, 1578, 1580, 1585, 1588, 1640, 1645, 1697, 1702, 1754, 1759, 1811, 1816, 1867, 2089, 2094, 2123, 2126, 2127, 2138, 2139, 2145, 2219, 2223, 2226, 2251, 2258, 2290, 2334, 2462, 2473, 2507, 2513, 2514, 2515, 2517, 2518, 2542, 2599, 2616, 2635, 2637, 2644, 2645, 2647, 2648, 2652, 2653, 2654, 2668, 2669],\n         \"typo\": [13, 16, 2535, 2556, 2573, 2587, 2595, 2614, 2617],\n         \"tzinfo\": [67, 360],\n         \"tzoffset\": 67,\n         \"t\\u2081\": 1895,\n         \"t\\u2082\": 1895,\n-        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\"u10\": [56, 1922, 2645],\n         \"u11\": [2320, 2364, 2411],\n         \"u12\": 669,\n         \"u13\": [2469, 2503],\n         \"u16\": [56, 389],\n"}]}]}]}]}]}