{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.88ZtQBtD/b1/numpy_2.2.3+ds-5_i386.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.88ZtQBtD/b2/numpy_2.2.3+ds-5_i386.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n- 02216633a218f000562e53ac1f56a5d3 5811628 doc optional python-numpy-doc_2.2.3+ds-5_all.deb\n+ c9278d367aee30b020b35857e43c2d80 5811776 doc optional python-numpy-doc_2.2.3+ds-5_all.deb\n b49bca3955ba2733c9eab7427e208faf 29561492 debug optional python3-numpy-dbgsym_2.2.3+ds-5_i386.deb\n 7a879024cf54dda864e64f68093acac0 145792 python optional python3-numpy-dev_2.2.3+ds-5_i386.deb\n bce6078295d4ee3b39c68dd546156b6a 5060120 python optional python3-numpy_2.2.3+ds-5_i386.deb\n"}, {"source1": "python-numpy-doc_2.2.3+ds-5_all.deb", "source2": "python-numpy-doc_2.2.3+ds-5_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 2025-03-09 20:14:24.000000 debian-binary\n--rw-r--r-- 0 0 0 64876 2025-03-09 20:14:24.000000 control.tar.xz\n--rw-r--r-- 0 0 0 5746560 2025-03-09 20:14:24.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 64884 2025-03-09 20:14:24.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 5746700 2025-03-09 20:14:24.000000 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": "./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 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html\n -rw-r--r-- 0 root (0) root (0) 46891 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html\n -rw-r--r-- 0 root (0) root (0) 45382 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html\n -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\n -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\n -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\n -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\n--rw-r--r-- 0 root (0) root (0) 44350 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/new-or-different.html\n+-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\n -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\n -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\n -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\n -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\n -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\n -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\n -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\n@@ -2610,15 +2610,15 @@\n -rw-r--r-- 0 root (0) root (0) 24374 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.matlib.html\n -rw-r--r-- 0 root (0) root (0) 26288 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.other.html\n -rw-r--r-- 0 root (0) root (0) 37419 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials-package.html\n -rw-r--r-- 0 root (0) root (0) 46847 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.chebyshev.html\n -rw-r--r-- 0 root (0) root (0) 51499 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.classes.html\n -rw-r--r-- 0 root (0) root (0) 43104 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.hermite.html\n -rw-r--r-- 0 root (0) root (0) 43639 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.hermite_e.html\n--rw-r--r-- 0 root (0) root (0) 47585 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.html\n+-rw-r--r-- 0 root (0) root (0) 47589 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.html\n -rw-r--r-- 0 root (0) root (0) 43031 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.laguerre.html\n -rw-r--r-- 0 root (0) root (0) 42812 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.legendre.html\n -rw-r--r-- 0 root (0) root (0) 28772 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.poly1d.html\n -rw-r--r-- 0 root (0) root (0) 41877 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polynomial.html\n -rw-r--r-- 0 root (0) root (0) 26623 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.polynomials.polyutils.html\n -rw-r--r-- 0 root (0) root (0) 26761 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.rec.html\n -rw-r--r-- 0 root (0) root (0) 26422 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/routines.set.html\n@@ -2754,15 +2754,15 @@\n -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\n -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\n -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\n -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\n -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\n -rw-r--r-- 0 root (0) root (0) 90523 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/release.html\n -rw-r--r-- 0 root (0) root (0) 12397 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/search.html\n--rw-r--r-- 0 root (0) root (0) 2686442 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js\n+-rw-r--r-- 0 root (0) root (0) 2686433 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js\n drwxr-xr-x 0 root (0) root (0) 0 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/user/\n -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\n -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\n -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\n -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\n -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\n -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\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": "@@ -536,30 +536,30 @@\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\">7.29 ms +- 962 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">14.2 ms +- 147 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">4.47 ms +- 204 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">6.21 ms +- 31.9 us 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\">5.8 ms +- 80.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">16.6 ms +- 73.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">3.78 ms +- 46.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">5.84 ms +- 19.3 us 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\">29.5 ms +- 357 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">42.9 ms +- 18 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">12.4 ms +- 30 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">14.3 ms +- 25.1 us 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@@ -586,21 +586,21 @@\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([0.63916943, 0.41190027, 0.77778762])</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">array([0.44341898, 0.48485351, 0.14821712])</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([0.30106312, 0.18675065, 0.21117067], dtype=float32)</span>\n+<span class=\"gh\">Out[8]: </span><span class=\"go\">array([0.65999645, 0.6686265 , 0.3359151 ], 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([205, 106, 156], dtype=uint8)</span>\n+<span class=\"gh\">Out[9]: </span><span class=\"go\">array([221, 44, 120], 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@@ -613,18 +613,18 @@\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([0.78118065, 0.9207799 ])</span>\n+<span class=\"gh\">Out[12]: </span><span class=\"go\">array([0.52837855, 0.59357937])</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\">[0.78118065 0.9207799 0. 0. ]</span>\n+<span class=\"go\">[0.52837855 0.59357937 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@@ -636,25 +636,25 @@\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([[ 0, 1, 2, 0, 3],</span>\n-<span class=\"go\"> [ 4, 5, 6, 4, 7],</span>\n-<span class=\"go\"> [ 8, 9, 10, 8, 11]])</span>\n+<span class=\"go\">array([[ 3, 2, 1, 2, 0],</span>\n+<span class=\"go\"> [ 7, 6, 5, 6, 4],</span>\n+<span class=\"go\"> [11, 10, 9, 10, 8]])</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([[ 2, 1, 3, 0],</span>\n-<span class=\"go\"> [ 6, 5, 7, 4],</span>\n-<span class=\"go\"> [10, 9, 11, 8]])</span>\n+<span class=\"go\">array([[ 1, 0, 3, 2],</span>\n+<span class=\"go\"> [ 5, 4, 7, 6],</span>\n+<span class=\"go\"> [ 9, 8, 11, 10]])</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": "@@ -102,26 +102,26 @@\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-7.29 ms +- 962 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-14.2 ms +- 147 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.47 ms +- 204 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+6.21 ms +- 31.9 us 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-5.8 ms +- 80.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-16.6 ms +- 73.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.78 ms +- 46.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.84 ms +- 19.3 us 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-29.5 ms +- 357 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-42.9 ms +- 18 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+12.4 ms +- 30 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+14.3 ms +- 25.1 us 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@@ -140,38 +140,38 @@\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([0.63916943, 0.41190027, 0.77778762])\n+Out[7]: array([0.44341898, 0.48485351, 0.14821712])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.30106312, 0.18675065, 0.21117067], dtype=float32)\n+Out[8]: array([0.65999645, 0.6686265 , 0.3359151 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([205, 106, 156], dtype=uint8)\n+Out[9]: array([221, 44, 120], 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([0.78118065, 0.9207799 ])\n+Out[12]: array([0.52837855, 0.59357937])\n \n In [13]: print(existing)\n-[0.78118065 0.9207799 0. 0. ]\n+[0.52837855 0.59357937 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@@ -179,25 +179,25 @@\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([[ 0, 1, 2, 0, 3],\n- [ 4, 5, 6, 4, 7],\n- [ 8, 9, 10, 8, 11]])\n+array([[ 3, 2, 1, 2, 0],\n+ [ 7, 6, 5, 6, 4],\n+ [11, 10, 9, 10, 8]])\n \n In [18]: rng.shuffle(a, axis=1) # Shuffle in-place\n \n In [19]: a\n Out[19]:\n-array([[ 2, 1, 3, 0],\n- [ 6, 5, 7, 4],\n- [10, 9, 11, 8]])\n+array([[ 1, 0, 3, 2],\n+ [ 5, 4, 7, 6],\n+ [ 9, 8, 11, 10]])\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([0.95298507, 0.22234397])</span>\n+<span class=\"gh\">Out[4]: </span><span class=\"go\">array([ 1.07488973, -0.58555718])</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([4.5107768 , 4.28843283], domain=[0., 9.], window=[-1., 1.], symbol='x')</span>\n+<span class=\"gh\">Out[6]: </span><span class=\"go\">Polynomial([4.25144661, 4.83700379], domain=[0., 9.], window=[-1., 1.], symbol='x')</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([0.22234397, 0.95298507], domain=[-1., 1.], window=[-1., 1.], symbol='x')</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">Polynomial([-0.58555718, 1.07488973], domain=[-1., 1.], window=[-1., 1.], symbol='x')</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([0.95298507, 0.22234397])\n+Out[4]: array([ 1.07488973, -0.58555718])\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([4.5107768 , 4.28843283], domain=[0., 9.], window=[-1.,\n+Out[6]: Polynomial([4.25144661, 4.83700379], 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([0.22234397, 0.95298507], domain=[-1., 1.], window=[-1.,\n+Out[7]: Polynomial([-0.58555718, 1.07488973], 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": "@@ -32397,14 +32397,15 @@\n \"06369197489564249\": 2458,\n \"06381726\": 349,\n \"0660\": [302, 2131],\n \"06959433e\": [420, 947],\n \"07\": [55, 164, 547, 896, 897, 1335, 2170, 2508],\n \"07106781e\": 514,\n \"07407407\": 1809,\n+ \"07488973\": 2488,\n \"07779185\": 2458,\n \"07937323\": 524,\n \"07944154\": [657, 2654],\n \"08\": [55, 91, 147, 410, 523, 548, 896, 1095, 2322, 2366, 2413, 2525, 2658],\n \"0800\": 2525,\n \"08187135\": 54,\n \"08333333\": [1645, 1871],\n@@ -32536,15 +32537,15 @@\n \"10558\": 2536,\n \"10559\": 2536,\n \"10560\": 2536,\n \"10561\": 2536,\n \"10562\": 2536,\n \"10563\": 2536,\n \"1057109529998157\": 2665,\n- \"106\": [55, 524, 2461],\n+ \"106\": [55, 524],\n \"10607\": 2536,\n \"10608\": 2536,\n \"10609\": 2536,\n \"10610\": 2536,\n \"10612\": 2536,\n \"10615\": 2599,\n \"10622\": 2536,\n@@ -32665,15 +32666,15 @@\n \"11987\": 2541,\n \"11992\": 2544,\n \"11995\": 2541,\n \"11e\": 457,\n \"11ee694744f2552d\": 13,\n \"11x\": 1527,\n \"12\": [8, 25, 28, 29, 40, 47, 52, 54, 55, 58, 59, 61, 67, 78, 79, 98, 107, 108, 134, 136, 140, 141, 147, 153, 215, 270, 336, 354, 355, 363, 364, 366, 367, 371, 374, 375, 403, 409, 435, 439, 441, 453, 489, 520, 523, 524, 530, 538, 566, 568, 628, 661, 669, 680, 863, 880, 881, 905, 916, 941, 963, 1027, 1048, 1069, 1070, 1107, 1116, 1135, 1159, 1160, 1161, 1164, 1166, 1167, 1193, 1194, 1195, 1198, 1204, 1205, 1206, 1212, 1213, 1228, 1229, 1236, 1240, 1247, 1249, 1281, 1312, 1435, 1466, 1510, 1527, 1574, 1577, 1588, 1591, 1601, 1637, 1644, 1650, 1656, 1658, 1707, 1715, 1752, 1765, 1772, 1829, 1864, 1871, 1876, 1885, 1890, 1902, 1904, 1911, 1912, 1913, 1919, 1955, 1986, 2089, 2168, 2204, 2205, 2206, 2208, 2209, 2210, 2223, 2224, 2225, 2237, 2246, 2248, 2299, 2313, 2327, 2328, 2332, 2341, 2342, 2372, 2376, 2384, 2388, 2419, 2423, 2431, 2435, 2457, 2461, 2463, 2477, 2491, 2513, 2514, 2519, 2525, 2531, 2532, 2533, 2534, 2535, 2545, 2547, 2549, 2554, 2557, 2560, 2569, 2570, 2572, 2573, 2575, 2583, 2587, 2588, 2595, 2597, 2599, 2601, 2603, 2604, 2605, 2606, 2622, 2623, 2624, 2625, 2628, 2629, 2630, 2634, 2636, 2637, 2640, 2641, 2644, 2648, 2656, 2658, 2665, 2667],\n- \"120\": [32, 356, 358, 363, 568, 1212, 2225, 2606],\n+ \"120\": [32, 356, 358, 363, 568, 1212, 2225, 2461, 2606],\n \"120131\": [1644, 1656],\n \"12065\": 2594,\n \"12080\": 2545,\n \"12083\": 2545,\n \"12084\": 2545,\n \"12085\": 2545,\n \"121\": [431, 2463],\n@@ -32928,26 +32929,26 @@\n \"14644661\": 1756,\n \"14673309e\": 2665,\n \"14682\": 2560,\n \"14686\": 2553,\n \"14687\": 2557,\n \"14692469\": 2634,\n \"146925\": 2634,\n- \"147\": 2461,\n \"14717\": 2560,\n \"14718\": 2560,\n \"14720\": 2560,\n \"14730\": 2560,\n \"14758\": 2558,\n \"14771\": 2560,\n \"14777\": 2560,\n \"14781\": 2558,\n \"14787\": 2569,\n \"148\": [2344, 2386, 2433, 2463],\n \"14812\": 3,\n+ \"14821712\": 2461,\n \"148293216\": 2665,\n \"14841\": 2560,\n \"14851\": 2558,\n \"14852\": 2558,\n \"14853\": 2553,\n \"14854\": 2553,\n \"14855\": 2558,\n@@ -33020,15 +33021,14 @@\n \"15355\": 2566,\n \"15385\": 2566,\n \"154\": [2463, 2665],\n \"15427\": 2566,\n \"15463\": 2566,\n \"155\": [2463, 2636],\n \"15534\": 2566,\n- \"156\": 2461,\n \"15648\": 2566,\n \"15666\": 2572,\n \"15675\": 2562,\n \"15676\": 2562,\n \"15677\": 2562,\n \"15679\": 2562,\n \"15685\": 2566,\n@@ -33316,15 +33316,14 @@\n \"18638\": 2574,\n \"18657\": 2576,\n \"18658\": 2576,\n \"18661\": 2574,\n \"18666\": 2576,\n \"1867\": 1643,\n \"18671\": 2574,\n- \"18675065\": 2461,\n \"18695\": 2576,\n \"18697\": 2576,\n \"187\": [2332, 2376, 2423],\n \"1870\": 2611,\n \"18718\": 2576,\n \"18737\": 2576,\n \"1874\": 2611,\n@@ -33582,14 +33581,15 @@\n \"20300\": 2580,\n \"20302\": 2580,\n \"20314\": 2583,\n \"20324493e\": 54,\n \"2033\": 2612,\n \"20357\": 2581,\n \"20394\": 2588,\n+ \"204\": 2461,\n \"20414\": 2588,\n \"2045\": 2612,\n \"2046\": 2612,\n \"20462\": 2581,\n \"20463\": 2581,\n \"20464\": 2581,\n \"20465\": 2581,\n@@ -33597,15 +33597,14 @@\n \"20467\": 2581,\n \"20477401j\": [439, 453],\n \"2048\": 2612,\n \"204840\": 2634,\n \"20484034\": 2634,\n \"20495\": 2581,\n \"20497\": 2581,\n- \"205\": 2461,\n \"20500\": 2588,\n \"2052\": 2615,\n \"20580\": 2588,\n \"20589\": 2588,\n \"206\": 2463,\n \"20613\": 2581,\n \"20618\": 2581,\n@@ -33663,23 +33662,22 @@\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, 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],\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, 2634, 2636, 2640, 2641, 2648, 2653, 2656, 2664, 2665, 2667],\n \"210\": 363,\n \"21001\": 2588,\n \"21029\": 2588,\n \"210306068529402873165736369884012333109\": [2275, 2280],\n \"21048\": 2586,\n \"211\": 2491,\n \"21106\": 2586,\n- \"21117067\": 2461,\n \"21120\": 2599,\n \"21130\": 2588,\n \"21137\": 2586,\n \"21138\": 2586,\n \"21139\": 2586,\n \"21140\": 2586,\n \"21141\": 2586,\n@@ -33766,14 +33764,15 @@\n \"22038\": 2590,\n \"22039\": 2590,\n \"2204460492503131e\": 1915,\n \"22046\": 2594,\n \"22055\": 2594,\n \"22057\": 2590,\n \"22078\": 2590,\n+ \"221\": 2461,\n \"22106\": 2590,\n \"22107\": 2590,\n \"22115\": 2590,\n \"22135945e\": 1149,\n \"22136\": 2591,\n \"22137\": 2599,\n \"22139\": 2594,\n@@ -33790,15 +33789,14 @@\n \"22221\": 2591,\n \"22222\": 2591,\n \"222222\": 1335,\n \"22223\": 2591,\n \"22224\": 2591,\n \"22228\": 2594,\n \"22230\": 2591,\n- \"22234397\": 2488,\n \"22313\": 2594,\n \"22314355\": 2654,\n \"22315\": 2599,\n \"22316\": 2594,\n \"22357\": 2594,\n \"22368\": 2592,\n \"22370\": 2592,\n@@ -34173,15 +34171,15 @@\n \"2495\": 2615,\n \"24955\": 2604,\n \"24962\": 2604,\n \"24971\": 2604,\n \"24972\": 2604,\n \"24977\": 2604,\n \"24978\": 2622,\n- \"25\": [10, 50, 54, 55, 56, 58, 69, 140, 361, 377, 409, 418, 440, 481, 489, 520, 526, 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2604,\n \"25043\": 2604,\n \"25049\": 2604,\n \"25054\": 2622,\n@@ -34207,14 +34205,15 @@\n \"25118\": 2604,\n \"25119\": 2604,\n \"25120\": 2604,\n \"25121\": 2604,\n \"25122\": 2622,\n \"25130\": 2605,\n \"25138\": 2622,\n+ \"25144661\": 2488,\n \"25145\": 2622,\n \"25149\": 2622,\n \"25155\": 2622,\n \"25156\": 2622,\n \"25158\": 2622,\n \"25161\": 2622,\n \"25168\": 2622,\n@@ -34568,20 +34567,19 @@\n \"28324\": 2632,\n \"285\": [566, 637],\n \"286\": 354,\n \"28613933\": 1153,\n \"28657\": 28,\n \"28790332\": 2665,\n \"2883\": [1643, 1655],\n- \"28843283\": 2488,\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, 2461, 2463, 2464, 2513, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2587, 2602, 2622, 2640, 2656, 2665],\n+ \"29\": [10, 21, 45, 54, 55, 76, 144, 163, 1638, 1647, 2208, 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1308, 1462, 1543, 1638, 1906, 1911, 1982, 2089, 2103, 2115, 2165, 2204, 2216, 2224, 2241, 2316, 2319, 2322, 2323, 2325, 2326, 2329, 2339, 2340, 2344, 2371, 2373, 2382, 2383, 2386, 2394, 2418, 2420, 2429, 2430, 2433, 2444, 2461, 2463, 2513, 2531, 2587, 2608, 2636, 2640, 2644, 2656, 2665],\n \"300\": [66, 363, 2238, 2576, 2644],\n \"3000\": 2594,\n \"30000\": 533,\n \"3000488281\": 476,\n \"3003003\": 1999,\n \"3006\": 2614,\n \"3007\": 2614,\n \"301\": [438, 1923, 2477],\n- \"30106312\": 2461,\n \"30220482\": [2345, 2391, 2439],\n \"30278462\": 1154,\n \"3039\": 2614,\n \"304\": 22,\n \"30464\": 2094,\n \"30498948e\": 2665,\n \"305\": [421, 948],\n \"3057\": 2615,\n \"307\": [1149, 2665],\n \"308\": 1335,\n \"3083169284\": [850, 1030],\n \"308j\": 1335,\n \"309\": [420, 421, 947, 948, 1149, 2460],\n \"30z\": 360,\n- \"31\": [54, 55, 146, 336, 514, 669, 1088, 1094, 1638, 1711, 1867, 1913, 2204, 2236, 2327, 2331, 2375, 2422, 2513, 2520, 2636, 2640, 2656, 2665],\n+ \"31\": [54, 55, 146, 336, 514, 669, 1088, 1094, 1638, 1711, 1867, 1913, 2204, 2236, 2327, 2331, 2375, 2422, 2461, 2513, 2520, 2636, 2640, 2656, 2665],\n \"31018314\": 2458,\n \"3105\": 2614,\n \"3108\": 2614,\n \"3117\": 2614,\n \"3118\": [57, 2547],\n \"31183145\": 2665,\n \"312088\": 1867,\n@@ -34708,14 +34705,15 @@\n \"33333334\": 2171,\n \"3333333e\": 1335,\n \"33339\": 2173,\n \"33340274885464394\": 2202,\n \"3340\": [287, 1241, 1324, 1478, 1998],\n \"3348\": 2617,\n \"33486982e\": 438,\n+ \"3359151\": 2461,\n \"3361\": [99, 906],\n \"3364\": 2615,\n \"3373\": 2615,\n \"33872321e\": 2104,\n \"34\": [12, 28, 144, 441, 1880, 1919, 2208, 2463, 2491, 2566, 2583, 2640, 2656, 2665],\n \"340\": [2238, 2576],\n \"34132519\": [680, 2658],\n@@ -34739,15 +34737,14 @@\n \"35\": [409, 489, 669, 870, 1056, 2204, 2325, 2369, 2416, 2572, 2634, 2640, 2656, 2665],\n \"350\": [544, 635],\n \"3504\": 2617,\n \"3534857623790153\": 666,\n \"35355339\": 1636,\n \"3541\": 2615,\n \"35489284e\": 2104,\n- \"357\": 2461,\n \"36\": [58, 137, 355, 1752, 1761, 2204, 2225, 2323, 2367, 2414, 2463, 2491, 2536, 2648, 2656, 2658, 2665],\n \"360\": [544, 2103, 2238, 2576],\n \"36045180e\": 147,\n \"3608\": 2615,\n \"361\": [1344, 1346, 1522, 1908],\n \"362\": 12,\n \"3628523\": 2458,\n@@ -34833,15 +34830,14 @@\n \"40918587e\": 2104,\n \"40919914\": 2665,\n \"4093\": 2617,\n \"4094\": [2615, 2617],\n \"4096\": [72, 2092],\n \"41\": [941, 1114, 1123, 1124, 1131, 1913, 2104, 2107, 2208, 2236, 2585, 2656, 2665],\n \"4109\": 2617,\n- \"41190027\": 2461,\n \"41211849\": 2665,\n \"4123\": [2615, 2617],\n \"4134\": 2617,\n \"41357986e\": 2175,\n \"4138\": 2617,\n \"4141\": 2615,\n \"41421356\": [653, 1756, 2665],\n@@ -34856,15 +34852,15 @@\n \"4167\": 1643,\n \"4170\": 2617,\n \"4176\": 2617,\n \"4181\": [28, 2621],\n \"4187\": 2617,\n \"4191\": 2617,\n \"4197\": 2617,\n- \"42\": [31, 58, 63, 147, 349, 669, 896, 897, 974, 1029, 2090, 2208, 2256, 2332, 2376, 2423, 2457, 2461, 2566, 2605, 2622, 2634, 2638, 2656, 2663, 2664, 2665],\n+ \"42\": [31, 58, 63, 147, 349, 669, 896, 897, 974, 1029, 2090, 2208, 2256, 2332, 2376, 2423, 2457, 2566, 2605, 2622, 2634, 2638, 2656, 2663, 2664, 2665],\n \"420\": [2238, 2576],\n \"42016704\": 2665,\n \"4206\": 2617,\n \"4220\": 2617,\n \"4223\": 2617,\n \"4225\": 2617,\n \"423\": 55,\n@@ -34891,25 +34887,26 @@\n \"4354\": 2617,\n \"4359\": 2617,\n \"4368\": [287, 1241, 1324, 1478, 1998],\n \"4375\": 2491,\n \"43857224\": 1153,\n \"43887844\": [349, 2457, 2638],\n \"43999999999998\": 1114,\n- \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2463, 2624, 2656, 2657, 2658, 2665],\n+ \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2461, 2463, 2624, 2656, 2657, 2658, 2665],\n \"440\": [10, 2520],\n \"4400\": [409, 661, 2168],\n \"44069024\": 349,\n \"4408\": 2617,\n \"44089210e\": 1527,\n \"4408921e\": [1765, 2175],\n \"4409e\": 2091,\n \"442\": 2615,\n \"4428\": 2617,\n \"4434142\": 349,\n+ \"44341898\": 2461,\n \"443469\": 680,\n \"444\": 485,\n \"4465\": 2620,\n \"4466\": 2617,\n \"4472136\": 642,\n \"4472136j\": 642,\n \"4476\": 2621,\n@@ -34929,15 +34926,15 @@\n \"4532\": [409, 661, 2168],\n \"4545724517479104\": 2460,\n \"45560727e\": 54,\n \"456\": 1921,\n \"4567\": 2643,\n \"45674898e\": 566,\n \"45a3d84\": 2521,\n- \"46\": [409, 523, 905, 1707, 2204, 2208, 2640, 2656],\n+ \"46\": [409, 523, 905, 1707, 2204, 2208, 2461, 2640, 2656],\n \"460\": [2238, 2576],\n \"46009194e\": 566,\n \"4602\": 2618,\n \"4610935\": 457,\n \"4613\": 2618,\n \"4628\": 2618,\n \"46351241j\": 2081,\n@@ -34950,15 +34947,15 @@\n \"46666491\": 349,\n \"46716228e\": 1586,\n \"46755891e\": 54,\n \"468\": [136, 147],\n \"4685006\": [2352, 2399, 2449],\n \"4686\": 12,\n \"46957616e\": [470, 1899, 1900],\n- \"47\": [52, 2208, 2323, 2367, 2414, 2656, 2665],\n+ \"47\": [52, 2208, 2323, 2367, 2414, 2461, 2656, 2665],\n \"4702687338396767\": 2348,\n \"471\": [136, 147, 2463],\n \"47108547995356098\": [2345, 2391, 2439],\n \"47140452j\": 2491,\n \"472\": 55,\n \"4722\": 2618,\n \"4723\": 2621,\n@@ -34975,14 +34972,15 @@\n \"4800\": 57,\n \"48000000\": 2634,\n \"4809319\": 1153,\n \"4816\": [99, 906],\n \"48219694e\": [421, 948],\n \"4836\": 2618,\n \"484\": [2517, 2583, 2625],\n+ \"48485351\": 2461,\n \"4853\": 2618,\n \"4861946401452\": 2334,\n \"4874\": [409, 661, 2168],\n \"4882\": [287, 1241, 1324, 1478, 1998],\n \"4896\": 2620,\n \"49\": [71, 2208, 2323, 2367, 2414, 2567, 2568, 2640, 2656, 2665],\n \"4928\": [409, 661, 2168],\n@@ -35031,15 +35029,14 @@\n \"5067\": 2620,\n \"5082\": 2620,\n \"5095\": 2620,\n \"51\": [58, 520, 1654, 1752, 1761, 1867, 1870, 2083, 2204, 2331, 2339, 2356, 2375, 2382, 2403, 2422, 2429, 2453, 2622, 2656, 2665],\n \"510\": [143, 570],\n \"5100\": 2620,\n \"5104\": 2620,\n- \"5107768\": 2488,\n \"51182162\": 2665,\n \"512\": [69, 893, 1083, 1143, 1247, 2115, 2240, 2542, 2547, 2665],\n \"51327412\": 1891,\n \"5136\": 2620,\n \"5138\": 2620,\n \"514229\": 28,\n \"5147\": 2620,\n@@ -35064,14 +35061,15 @@\n \"52359878\": 1911,\n \"52380952e\": 1816,\n \"5240\": 2620,\n \"5251\": 2620,\n \"526\": 2463,\n \"5260\": [2353, 2400, 2450],\n \"5271\": 42,\n+ \"52837855\": 2461,\n \"53\": [162, 457, 1638, 2208, 2551, 2554, 2656, 2665],\n \"5306\": [409, 661, 2168],\n \"5313\": 2621,\n \"5316\": 2621,\n \"5324j\": [416, 417, 622, 623],\n \"5354\": 2621,\n \"5359\": 2621,\n@@ -35142,18 +35140,20 @@\n \"579365079365115\": [1113, 1543],\n \"57x\": 2594,\n \"58\": [669, 1748, 1777, 2204, 2208, 2332, 2376, 2423, 2464, 2572, 2634, 2656],\n \"582892\": 2634,\n \"58289208\": 2634,\n \"584388\": 55,\n \"585\": [378, 1358, 1514, 2583],\n+ \"58555718\": 2488,\n \"58578644\": 1756,\n \"58826587\": 349,\n \"59\": [55, 2208, 2602, 2656],\n \"5910\": [287, 1241, 1324, 1478, 1998],\n+ \"59357937\": 2461,\n \"595726\": 2634,\n \"59572603\": 2634,\n \"5969\": [99, 906],\n \"598150\": 2641,\n \"59815003\": 38,\n \"5982\": 2641,\n \"59903635e\": 147,\n@@ -35205,15 +35205,14 @@\n \"631198588\": 55,\n \"63317787e\": [2166, 2167],\n \"63526532\": 2458,\n \"636363636364\": [2353, 2400, 2450],\n \"63696169\": 2634,\n \"6376\": 2522,\n \"6390\": [2353, 2400, 2450],\n- \"63916943\": 2461,\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, 2462, 2464, 2513, 2520, 2542, 2547, 2578, 2579, 2580, 2583, 2599, 2602, 2603, 2610, 2615, 2616, 2634, 2638, 2647, 2648, 2656, 2658, 2664, 2665],\n \"64023025\": 2098,\n \"6416010000000001\": 1114,\n \"64386512\": 349,\n \"64402274e\": 660,\n \"6446\": [2550, 2554],\n \"646\": [2517, 2576],\n@@ -35273,14 +35272,15 @@\n \"6577\": 2522,\n \"6579\": 2522,\n \"6586976\": 1149,\n \"6590\": 2522,\n \"6596\": 2522,\n \"6596288841243357\": 2458,\n \"659885634118668e\": 645,\n+ \"65999645\": 2461,\n \"65_535\": 62,\n \"65x\": 2594,\n \"66\": [13, 58, 1922, 2547, 2656, 2665],\n \"6600475\": 1149,\n \"6602\": 2522,\n \"6606\": 2522,\n \"6611\": 2522,\n@@ -35300,14 +35300,15 @@\n \"66666667e\": 1816,\n \"66669964e\": 2171,\n \"6667\": 2522,\n \"6668\": 2522,\n \"6675\": 2522,\n \"6678\": 2522,\n \"6686\": 2522,\n+ \"6686265\": 2461,\n \"6689502\": 2576,\n \"669\": 669,\n \"6695\": 2522,\n \"6697\": 2522,\n \"6698\": 2522,\n \"67\": [409, 670, 671, 672, 1901, 2542, 2643, 2656, 2665],\n \"67046769e\": 147,\n@@ -35399,15 +35400,15 @@\n \"72686684e\": 566,\n \"72717132\": 2658,\n \"72727273\": 136,\n \"72847407\": 1154,\n \"729\": 2665,\n \"72904971\": 1153,\n \"72949656\": 2634,\n- \"73\": [2461, 2463, 2656],\n+ \"73\": [2463, 2656],\n \"7320508075688772j\": 59,\n \"73472348e\": 1816,\n \"73496154e\": 1867,\n \"73603959e\": 2665,\n \"73799541\": 1153,\n \"74\": [2463, 2513, 2514, 2656],\n \"74000000\": 2634,\n@@ -35472,20 +35473,18 @@\n \"77447664948076\": 675,\n \"7755575615628914e\": 2647,\n \"77555756e\": 1877,\n \"77598074\": 349,\n \"77714685\": 349,\n \"7774613834041182\": 2315,\n \"7776\": [99, 906],\n- \"77778762\": 2461,\n \"7778\": 2527,\n \"7793\": 2527,\n- \"78\": [2091, 2643, 2656],\n+ \"78\": [2091, 2461, 2643, 2656],\n \"78096262\": [2352, 2399, 2449],\n- \"78118065\": 2461,\n \"7816\": 2527,\n \"7821\": 2527,\n \"7824\": 2527,\n \"7835\": 2527,\n \"784\": 2463,\n \"7847\": 2527,\n \"784728999999999\": 1114,\n@@ -35519,15 +35518,15 @@\n \"79769313e\": 1335,\n \"79ff\": [102, 125],\n \"7e\": 669,\n \"7e13\": 55,\n \"7e17\": 55,\n \"7x\": [2566, 2599],\n \"8\": [9, 11, 13, 17, 18, 24, 26, 28, 30, 31, 32, 34, 36, 38, 39, 43, 47, 50, 56, 57, 58, 59, 60, 61, 62, 69, 72, 73, 74, 76, 89, 91, 96, 97, 98, 99, 114, 117, 118, 119, 132, 135, 141, 142, 149, 150, 169, 171, 172, 173, 209, 215, 229, 234, 261, 262, 270, 295, 313, 315, 320, 331, 332, 334, 337, 348, 355, 364, 365, 366, 367, 369, 370, 375, 385, 387, 391, 396, 399, 401, 406, 409, 410, 416, 417, 432, 435, 438, 440, 454, 455, 467, 470, 478, 486, 514, 520, 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\"84147098\": 2665,\n \"8414709848078965\": 2639,\n \"84147098j\": 2665,\n@@ -35641,15 +35641,14 @@\n \"90909091\": 136,\n \"909297\": 2641,\n \"90929743\": 2665,\n \"91\": 2656,\n \"91275558\": 2634,\n \"916666666666666\": 1240,\n \"92\": [98, 2656, 2658],\n- \"9207799\": 2461,\n \"921fb54442d18p\": 2520,\n \"9223372036854775807\": 2647,\n \"9223372036854775808\": 2647,\n \"92346708\": 349,\n \"92362781e\": 2104,\n \"92387953\": 642,\n \"92387953j\": 642,\n@@ -35690,25 +35689,23 @@\n \"9475673279178444\": 2348,\n \"94864945\": 2665,\n \"94909878\": [2387, 2434],\n \"95\": [37, 39, 646, 2353, 2400, 2450, 2634, 2653, 2656],\n \"9504637\": 2665,\n \"950684\": 2634,\n \"95068423\": 2634,\n- \"95298507\": 2488,\n \"9555\": [2533, 2534],\n \"9556\": [2533, 2534],\n \"9557\": [2533, 2534],\n \"9558\": [2533, 2534],\n \"9559\": [2533, 2534],\n \"9580\": [2533, 2534],\n \"95892427\": 2665,\n \"96\": [62, 69, 1577, 1867, 2303, 2463, 2566, 2647, 2656, 2658, 2667],\n \"96139749\": 2665,\n- \"962\": 2461,\n \"96202397\": [2335, 2378, 2425],\n \"96360618\": [2387, 2434],\n \"9665554\": 349,\n \"96767474e\": 54,\n \"96875\": 2491,\n \"96979195\": 2634,\n \"969792\": 2634,\n"}]}]}]}]}]}