{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.smvmLap4/b1/numpy_2.2.3+ds-5_arm64.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.smvmLap4/b2/numpy_2.2.3+ds-5_arm64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n- 81590e859a535d93064dfdcdf6cb0f20 5811684 doc optional python-numpy-doc_2.2.3+ds-5_all.deb\n+ 1dc0f98d6d6bd7a7754f86da8f60c722 5811696 doc optional python-numpy-doc_2.2.3+ds-5_all.deb\n 3378b7732053d210d6587f3e4f54a631 14376308 debug optional python3-numpy-dbgsym_2.2.3+ds-5_arm64.deb\n 2fb069c89762b4d9883e4eb3c4b60efe 136044 python optional python3-numpy-dev_2.2.3+ds-5_arm64.deb\n 8c85ab08528f6f73d38fe6acd7144aeb 3595824 python optional python3-numpy_2.2.3+ds-5_arm64.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 64888 2025-03-09 20:14:24.000000 control.tar.xz\n--rw-r--r-- 0 0 0 5746604 2025-03-09 20:14:24.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 64868 2025-03-09 20:14:24.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 5746636 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) 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) 44354 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@@ -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) 2686410 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/searchindex.js\n+-rw-r--r-- 0 root (0) root (0) 2686428 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\">1.02 ms +- 53.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">2.57 ms +- 68.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">935 us +- 46.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">2.54 ms +- 43.2 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\">1 ms +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">1.88 ms +- 18.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">799 us +- 18.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">1.88 ms +- 14.3 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\">2.47 ms +- 22.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n-<span class=\"go\">7.95 ms +- 3.34 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">2.34 ms +- 29.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">4.47 ms +- 19.3 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([0.26314191, 0.23305205, 0.21018643])</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">array([0.22978782, 0.08194945, 0.27917787])</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([0.1394285 , 0.46245968, 0.24632704], dtype=float32)</span>\n+<span class=\"gh\">Out[8]: </span><span class=\"go\">array([0.0332737 , 0.40015244, 0.07163864], 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([140, 155, 8], dtype=uint8)</span>\n+<span class=\"gh\">Out[9]: </span><span class=\"go\">array([ 42, 218, 66], 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([0.12454461, 0.18662093])</span>\n+<span class=\"gh\">Out[12]: </span><span class=\"go\">array([0.71719118, 0.0370398 ])</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\">[0.12454461 0.18662093 0. 0. ]</span>\n+<span class=\"go\">[0.71719118 0.0370398 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([[ 2, 2, 1, 1, 1],</span>\n-<span class=\"go\"> [ 6, 6, 5, 5, 5],</span>\n-<span class=\"go\"> [10, 10, 9, 9, 9]])</span>\n+<span class=\"go\">array([[ 0, 3, 1, 1, 0],</span>\n+<span class=\"go\"> [ 4, 7, 5, 5, 4],</span>\n+<span class=\"go\"> [ 8, 11, 9, 9, 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([[ 3, 0, 1, 2],</span>\n-<span class=\"go\"> [ 7, 4, 5, 6],</span>\n-<span class=\"go\"> [11, 8, 9, 10]])</span>\n+<span class=\"go\">array([[ 3, 2, 1, 0],</span>\n+<span class=\"go\"> [ 7, 6, 5, 4],</span>\n+<span class=\"go\"> [11, 10, 9, 8]])</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-1.02 ms +- 53.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.57 ms +- 68.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+935 us +- 46.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.54 ms +- 43.2 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-1 ms +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.88 ms +- 18.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+799 us +- 18.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.88 ms +- 14.3 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-2.47 ms +- 22.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-7.95 ms +- 3.34 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.34 ms +- 29.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.47 ms +- 19.3 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([0.26314191, 0.23305205, 0.21018643])\n+Out[7]: array([0.22978782, 0.08194945, 0.27917787])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.1394285 , 0.46245968, 0.24632704], dtype=float32)\n+Out[8]: array([0.0332737 , 0.40015244, 0.07163864], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([140, 155, 8], dtype=uint8)\n+Out[9]: array([ 42, 218, 66], 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([0.12454461, 0.18662093])\n+Out[12]: array([0.71719118, 0.0370398 ])\n \n In [13]: print(existing)\n-[0.12454461 0.18662093 0. 0. ]\n+[0.71719118 0.0370398 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([[ 2, 2, 1, 1, 1],\n- [ 6, 6, 5, 5, 5],\n- [10, 10, 9, 9, 9]])\n+array([[ 0, 3, 1, 1, 0],\n+ [ 4, 7, 5, 5, 4],\n+ [ 8, 11, 9, 9, 8]])\n \n In [18]: rng.shuffle(a, axis=1) # Shuffle in-place\n \n In [19]: a\n Out[19]:\n-array([[ 3, 0, 1, 2],\n- [ 7, 4, 5, 6],\n- [11, 8, 9, 10]])\n+array([[ 3, 2, 1, 0],\n+ [ 7, 6, 5, 4],\n+ [11, 10, 9, 8]])\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([ 1.14016016, -1.11946821])</span>\n+<span class=\"gh\">Out[4]: </span><span class=\"go\">array([ 1.06730508, -0.0899841 ])</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([4.01125252, 5.13072073], domain=[0., 9.], window=[-1., 1.], symbol='x')</span>\n+<span class=\"gh\">Out[6]: </span><span class=\"go\">Polynomial([4.71288877, 4.80287287], 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([-1.11946821, 1.14016016], domain=[-1., 1.], window=[-1., 1.], symbol='x')</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">Polynomial([-0.0899841 , 1.06730508], 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([ 1.14016016, -1.11946821])\n+Out[4]: array([ 1.06730508, -0.0899841 ])\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([4.01125252, 5.13072073], domain=[0., 9.], window=[-1.,\n+Out[6]: Polynomial([4.71288877, 4.80287287], 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([-1.11946821, 1.14016016], domain=[-1., 1.], window=[-1.,\n+Out[7]: Polynomial([-0.0899841 , 1.06730508], 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": "@@ -32337,29 +32337,28 @@\n \"009a87ff44bf\": 42,\n \"00j\": [514, 642, 2167],\n \"01\": [41, 42, 45, 54, 55, 62, 147, 162, 163, 164, 335, 361, 410, 514, 547, 557, 566, 669, 897, 1088, 1094, 1586, 1594, 1601, 1658, 1715, 1772, 1829, 1885, 2104, 2160, 2323, 2329, 2367, 2373, 2414, 2420, 2525, 2657],\n \"010\": [653, 1602, 1659, 1716, 1773, 1830, 1886],\n \"0100\": 1519,\n \"01041667\": 1585,\n \"011\": [1602, 1659, 1716, 1773, 1830, 1886],\n- \"01125252\": 2488,\n \"012\": [1602, 1659, 1716, 1773, 1830, 1886],\n \"0123456789\": [303, 2132],\n \"01280782\": [2335, 2378, 2425],\n \"016\": [648, 653],\n \"01652764\": 2634,\n \"01666667\": 1544,\n \"016j\": 2105,\n \"01831564\": 1660,\n \"018318\": [2353, 2400, 2450],\n \"01j\": 514,\n \"01t00\": [55, 62, 361, 2525],\n \"01t08\": 2525,\n \"01t12\": 55,\n- \"02\": [54, 55, 147, 162, 163, 410, 547, 566, 669, 1335, 1586, 1601, 1658, 1715, 1772, 1816, 1829, 1885, 2104, 2461],\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\": 2643,\n \"02332573e\": 54,\n \"0253\": 2651,\n \"02654825\": 1891,\n@@ -32367,16 +32366,18 @@\n \"02755911\": 2665,\n \"027559113243068367\": 2665,\n \"02785049\": 1867,\n \"02i\": [513, 2643],\n \"03\": [55, 67, 163, 566, 669, 1335, 1586, 1816, 2657],\n \"03125\": [1585, 2491],\n \"0326911\": [2335, 2378, 2425],\n+ \"0332737\": 2461,\n \"0361\": 2607,\n \"03703704\": 1809,\n+ \"0370398\": 2461,\n \"03943254e\": 2104,\n \"03968254\": [1113, 1543],\n \"0396842\": 680,\n \"03t13\": 55,\n \"04\": [54, 55, 164, 410, 547, 1586, 2463, 2594, 2658],\n \"0400\": 360,\n \"04097352\": 2634,\n@@ -32394,33 +32395,37 @@\n \"0596779\": 1153,\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+ \"06730508\": 2488,\n \"06959433e\": [420, 947],\n \"07\": [55, 164, 547, 896, 897, 1335, 2170, 2508],\n \"07106781e\": 514,\n+ \"07163864\": 2461,\n \"07407407\": 1809,\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+ \"08194945\": 2461,\n \"08333333\": [1645, 1871],\n \"08405657\": 1867,\n \"0855\": 2641,\n \"08553692\": 38,\n \"085537\": 2641,\n \"0856306\": 1154,\n \"08618131\": 1526,\n \"08703704\": [1113, 1543],\n \"087300000000000003\": [2346, 2392, 2441],\n+ \"0899841\": 2488,\n \"09\": [55, 2171, 2252, 2323, 2367, 2414],\n \"090097550553843\": 2641,\n \"09417735\": [349, 2457, 2638],\n \"0943951\": 1911,\n \"09640474436813\": 675,\n \"09861229\": [657, 2654],\n \"0999755859375\": 62,\n@@ -32655,15 +32660,14 @@\n \"1179\": 2463,\n \"118\": 2463,\n \"1180339887498949\": 2115,\n \"11803399\": 1228,\n \"11885628817151628\": 2458,\n \"119\": 2616,\n \"11902\": 2544,\n- \"11946821\": 2488,\n \"11981\": 2544,\n \"11982\": 2544,\n \"11985\": 2541,\n \"11986\": 2541,\n \"11987\": 2541,\n \"11992\": 2544,\n \"11995\": 2541,\n@@ -32714,15 +32718,14 @@\n \"123456789a12\": 25,\n \"123456789a123456789b\": 25,\n \"1234567e\": 2172,\n \"12346\": 2462,\n \"1235\": 2091,\n \"123abc\": [302, 305, 2131, 2134],\n \"124\": [98, 905],\n- \"12454461\": 2461,\n \"125\": [470, 660, 1114, 1142, 1645, 1651, 1899, 1900, 2239, 2339, 2382, 2429, 2460, 2491, 2658, 2665],\n \"12589991e\": 645,\n \"126\": [863, 1048, 1116, 1904],\n \"1261\": 2612,\n \"12658\": 2560,\n \"12697628\": 2634,\n \"127\": [62, 66, 514, 863, 1048, 1102, 1116, 1904, 2301, 2302, 2462, 2463, 2464, 2583, 2638],\n@@ -32745,15 +32748,14 @@\n \"130\": 409,\n \"1300000\": 2634,\n \"13020\": 2549,\n \"13026\": 2549,\n \"13028\": 2549,\n \"13038\": 2549,\n \"13041\": 2549,\n- \"13072073\": 2488,\n \"13090\": [186, 827, 999, 1172, 1259, 1414, 1933],\n \"131\": [527, 2463],\n \"1312\": 485,\n \"13182611e\": [420, 947],\n \"132\": [527, 2330, 2374, 2421, 2463, 2636],\n \"1324274851176597e\": 1867,\n \"133\": [527, 2330, 2374, 2421],\n@@ -32799,22 +32801,20 @@\n \"13845\": 2552,\n \"13867\": 2552,\n \"1387\": 2612,\n \"13899\": 2560,\n \"139\": 655,\n \"13905\": 2552,\n \"13933\": 2552,\n- \"1394285\": 2461,\n \"13984\": 2552,\n \"13994\": 2552,\n \"13d7934\": 13,\n \"13j\": 1915,\n \"14\": [9, 47, 54, 55, 56, 57, 58, 59, 98, 107, 108, 114, 140, 141, 142, 270, 366, 367, 375, 460, 476, 486, 530, 542, 544, 546, 566, 645, 661, 880, 893, 905, 963, 968, 969, 1069, 1083, 1143, 1229, 1247, 1312, 1466, 1577, 1695, 1704, 1711, 1758, 1764, 1867, 1873, 1986, 2086, 2089, 2115, 2168, 2205, 2206, 2208, 2209, 2210, 2222, 2224, 2225, 2237, 2240, 2246, 2333, 2342, 2377, 2385, 2424, 2432, 2457, 2461, 2463, 2490, 2513, 2519, 2531, 2542, 2543, 2544, 2545, 2546, 2547, 2553, 2554, 2559, 2560, 2561, 2566, 2572, 2576, 2583, 2584, 2585, 2586, 2588, 2591, 2596, 2599, 2600, 2622, 2627, 2634, 2637, 2640, 2644, 2652, 2656, 2658, 2665],\n- \"140\": [2238, 2461, 2576],\n- \"14016016\": 2488,\n+ \"140\": [2238, 2576],\n \"14022471\": [2387, 2434],\n \"14036\": 2560,\n \"14039\": 2560,\n \"14042\": 2552,\n \"14043\": 2552,\n \"14044\": 2552,\n \"14045\": 2552,\n@@ -33023,15 +33023,15 @@\n \"15302337\": 523,\n \"1534\": 485,\n \"15355\": 2566,\n \"15385\": 2566,\n \"154\": [2463, 2665],\n \"15427\": 2566,\n \"15463\": 2566,\n- \"155\": [2461, 2463, 2636],\n+ \"155\": [2463, 2636],\n \"15534\": 2566,\n \"15648\": 2566,\n \"15666\": 2572,\n \"15675\": 2562,\n \"15676\": 2562,\n \"15677\": 2562,\n \"15679\": 2562,\n@@ -33317,15 +33317,14 @@\n \"18629\": 2576,\n \"1863\": 2611,\n \"18636\": 2574,\n \"18638\": 2574,\n \"18657\": 2576,\n \"18658\": 2576,\n \"18661\": 2574,\n- \"18662093\": 2461,\n \"18666\": 2576,\n \"1867\": 1643,\n \"18671\": 2574,\n \"18695\": 2576,\n \"18697\": 2576,\n \"187\": [2332, 2376, 2423],\n \"1870\": 2611,\n@@ -33669,15 +33668,14 @@\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 \"210\": 363,\n \"21001\": 2588,\n- \"21018643\": 2461,\n \"21029\": 2588,\n \"210306068529402873165736369884012333109\": [2275, 2280],\n \"21048\": 2586,\n \"211\": 2491,\n \"21106\": 2586,\n \"21120\": 2599,\n \"21130\": 2588,\n@@ -33735,30 +33733,30 @@\n \"21595\": 2594,\n \"216\": 2665,\n \"21623\": [2588, 2594],\n \"21627\": 2594,\n \"21645\": 2594,\n \"21663\": 2588,\n \"21760\": 2622,\n- \"218\": [566, 2463],\n+ \"218\": [566, 2461, 2463],\n \"21807\": 2594,\n \"2184\": 2225,\n \"21866\": 2589,\n \"21867\": 2589,\n \"21868\": 2589,\n \"21869\": 2589,\n \"21870\": 2589,\n \"219\": 2463,\n \"21925\": 2594,\n \"21949\": 2589,\n \"21951\": 2589,\n \"21952\": 2589,\n \"21976\": 2594,\n \"21995\": 2594,\n- \"22\": [21, 22, 29, 30, 40, 47, 52, 54, 55, 58, 59, 98, 107, 108, 109, 163, 270, 336, 378, 425, 485, 628, 658, 669, 880, 905, 1045, 1069, 1113, 1211, 1229, 1294, 1312, 1336, 1337, 1340, 1341, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1358, 1448, 1466, 1514, 1521, 1522, 1543, 1591, 1905, 1908, 1913, 1968, 1986, 2091, 2208, 2236, 2237, 2342, 2461, 2513, 2517, 2519, 2534, 2588, 2634, 2636, 2640, 2656, 2665],\n+ \"22\": [21, 22, 29, 30, 40, 47, 52, 54, 55, 58, 59, 98, 107, 108, 109, 163, 270, 336, 378, 425, 485, 628, 658, 669, 880, 905, 1045, 1069, 1113, 1211, 1229, 1294, 1312, 1336, 1337, 1340, 1341, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1358, 1448, 1466, 1514, 1521, 1522, 1543, 1591, 1905, 1908, 1913, 1968, 1986, 2091, 2208, 2236, 2237, 2342, 2513, 2517, 2519, 2534, 2588, 2634, 2636, 2640, 2656, 2665],\n \"220\": [2238, 2576],\n \"22004\": 2594,\n \"22014\": 2594,\n \"22030\": 2590,\n \"22031\": 2590,\n \"22032\": 2590,\n \"22033\": 2590,\n@@ -33893,14 +33891,15 @@\n \"22967\": 2596,\n \"22968\": 2596,\n \"22969\": 2596,\n \"22970\": 2596,\n \"22971\": 2596,\n \"22972\": 2596,\n \"22976\": 2596,\n+ \"22978782\": 2461,\n \"22982\": 2599,\n \"22989\": 2596,\n \"22997\": 2599,\n \"22998\": 2599,\n \"22e\": 457,\n \"23\": [1, 8, 10, 18, 32, 43, 47, 54, 55, 58, 62, 63, 75, 79, 98, 135, 139, 163, 270, 287, 329, 336, 483, 513, 628, 669, 880, 905, 917, 1029, 1069, 1104, 1229, 1241, 1312, 1324, 1466, 1478, 1545, 1707, 1748, 1777, 1903, 1913, 1986, 1998, 2154, 2208, 2235, 2237, 2256, 2321, 2340, 2342, 2365, 2383, 2412, 2430, 2513, 2519, 2542, 2552, 2595, 2622, 2627, 2634, 2636, 2640, 2644, 2647, 2656, 2665],\n \"230\": 409,\n@@ -33935,15 +33934,14 @@\n \"23226\": 2597,\n \"23229\": 2599,\n \"23240\": 2599,\n \"23275\": 2599,\n \"23297648\": 349,\n \"233\": [28, 2463],\n \"23302\": 2599,\n- \"23305205\": 2461,\n \"23314\": 2599,\n \"23322\": 2599,\n \"23341\": 2597,\n \"23342\": 2597,\n \"23343\": 2597,\n \"23344\": 2597,\n \"23345\": 2597,\n@@ -34109,15 +34107,14 @@\n \"24614\": 2602,\n \"24615\": 2602,\n \"24616\": 2602,\n \"24617\": 2602,\n \"24622\": 2602,\n \"24629\": 2602,\n \"24630\": 2602,\n- \"24632704\": 2461,\n \"24634\": 2622,\n \"24637\": 2602,\n \"24638\": 2602,\n \"24647\": 2602,\n \"24648\": 2602,\n \"24653\": 2602,\n \"24659\": 2602,\n@@ -34345,15 +34342,14 @@\n \"26137788e\": 2104,\n \"26157\": 2625,\n \"262\": 2665,\n \"26268\": 2625,\n \"26285\": 2625,\n \"26292\": 2625,\n \"26313\": 2625,\n- \"26314191\": 2461,\n \"26388\": 2625,\n \"26393\": 2622,\n \"26452\": 2625,\n \"26501\": 2625,\n \"26579\": 2625,\n \"26580\": 2625,\n \"26590556\": 2634,\n@@ -34493,14 +34489,15 @@\n \"27735\": 2629,\n \"27736\": 2629,\n \"27807\": 2629,\n \"27808\": 2629,\n \"27896\": 2629,\n \"278dd2a\": 13,\n \"2791\": 2615,\n+ \"27917787\": 2461,\n \"27935\": 2630,\n \"2794155\": 2665,\n \"27950\": 2630,\n \"27955\": 2630,\n \"27958\": 2630,\n \"2795853\": 533,\n \"27959\": 2630,\n@@ -34577,15 +34574,15 @@\n \"28657\": 28,\n \"28790332\": 2665,\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, 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, 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 \"290\": 2300,\n \"29001ed\": 13,\n \"290301\": 55,\n \"292\": 55,\n \"29229249\": 524,\n \"29239766\": 54,\n \"2925464970228\": 657,\n@@ -34818,14 +34815,15 @@\n \"3x4x5\": 2664,\n \"3x8000\": [2350, 2397, 2447],\n \"4\": [4, 5, 9, 12, 14, 25, 29, 30, 32, 37, 38, 39, 40, 47, 50, 54, 55, 56, 57, 58, 59, 61, 63, 65, 66, 67, 68, 69, 76, 78, 89, 90, 95, 96, 97, 98, 99, 104, 105, 107, 108, 109, 110, 111, 113, 115, 118, 120, 121, 127, 129, 130, 132, 134, 135, 136, 138, 140, 141, 144, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 167, 171, 176, 177, 185, 194, 213, 214, 215, 234, 239, 262, 264, 270, 280, 284, 287, 290, 295, 302, 305, 336, 337, 338, 339, 340, 341, 342, 345, 348, 352, 353, 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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, 2461, 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@@ -34881,15 +34879,15 @@\n \"42667924\": 1154,\n \"4267\": 2617,\n \"4270\": 2617,\n \"4276\": 2617,\n \"429\": 136,\n \"4294967293\": 2638,\n \"4294967296\": [196, 836, 1008, 1179, 1266, 1421, 1940],\n- \"43\": [2208, 2583, 2634, 2640, 2656, 2665],\n+ \"43\": [2208, 2461, 2583, 2634, 2640, 2656, 2665],\n \"430148\": 2634,\n \"43014843\": 2634,\n \"43181166\": 2458,\n \"4354\": 2617,\n \"4359\": 2617,\n \"4368\": [287, 1241, 1324, 1478, 1998],\n \"4375\": 2491,\n@@ -34930,21 +34928,20 @@\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- \"46245968\": 2461,\n \"4628\": 2618,\n \"46351241j\": 2081,\n \"46368\": 28,\n \"464\": 680,\n \"4642\": 2618,\n \"465\": [58, 1157],\n \"4656\": 2618,\n@@ -35065,15 +35062,15 @@\n \"52359878\": 1911,\n \"52380952e\": 1816,\n \"5240\": 2620,\n \"5251\": 2620,\n \"526\": 2463,\n \"5260\": [2353, 2400, 2450],\n \"5271\": 42,\n- \"53\": [162, 457, 1638, 2208, 2461, 2551, 2554, 2656, 2665],\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 \"5363922081269535\": 2458,\n@@ -35081,15 +35078,15 @@\n \"536870910\": 1902,\n \"5374\": 2621,\n \"53814331\": 2665,\n \"5388\": 2621,\n \"5390\": 2621,\n \"5393\": 2621,\n \"5396\": [287, 1241, 1324, 1478, 1998],\n- \"54\": [38, 59, 523, 1638, 1711, 1764, 2208, 2641, 2656],\n+ \"54\": [38, 59, 523, 1638, 1711, 1764, 2208, 2461, 2641, 2656],\n \"540\": [2238, 2576],\n \"54030231\": 2665,\n \"54030231j\": 2665,\n \"541\": 2463,\n \"54117\": 1644,\n \"5424\": 2621,\n \"54323428\": [2345, 2391, 2439],\n@@ -35124,15 +35121,15 @@\n \"5620499351813308\": 86,\n \"5625\": 2491,\n \"56294995342131\": 2083,\n \"5640\": [2353, 2400, 2450],\n \"567\": 2643,\n \"56826729e\": 2104,\n \"56917101\": 2634,\n- \"57\": [58, 669, 2204, 2322, 2366, 2413, 2461, 2463, 2491, 2636, 2656],\n+ \"57\": [58, 669, 2204, 2322, 2366, 2413, 2463, 2491, 2636, 2656],\n \"5707963267948966\": [102, 125],\n \"57079633\": [94, 105, 130, 1911, 2238, 2665],\n \"5708\": [412, 618],\n \"57115742\": 524,\n \"57136612e\": 2173,\n \"57510612\": 660,\n \"576\": 2615,\n@@ -35275,15 +35272,15 @@\n \"6586976\": 1149,\n \"6590\": 2522,\n \"6596\": 2522,\n \"6596288841243357\": 2458,\n \"659885634118668e\": 645,\n \"65_535\": 62,\n \"65x\": 2594,\n- \"66\": [13, 58, 1922, 2547, 2656, 2665],\n+ \"66\": [13, 58, 1922, 2461, 2547, 2656, 2665],\n \"6600475\": 1149,\n \"6602\": 2522,\n \"6606\": 2522,\n \"6611\": 2522,\n \"6614\": 2522,\n \"6618\": 2522,\n \"6621\": 2522,\n@@ -35324,15 +35321,15 @@\n \"6765\": 28,\n \"6771\": 2522,\n \"6775\": 2522,\n \"6780\": 2522,\n \"6781\": 2522,\n \"6783\": 2522,\n \"6785\": 2522,\n- \"68\": [2461, 2656, 2658, 2665],\n+ \"68\": [2656, 2658, 2665],\n \"6805\": [2353, 2400, 2450],\n \"6807\": 2522,\n \"68080986\": 349,\n \"6813\": 2522,\n \"6817\": 2522,\n \"6819\": 2522,\n \"68206631e\": 2104,\n@@ -35378,15 +35375,17 @@\n \"70710678\": [216, 247, 641, 1199, 1216, 1282, 1299, 1436, 1453, 1636, 1642, 1956, 1973, 2103],\n \"70710678118654746\": 637,\n \"70710678j\": 641,\n \"7083\": 2529,\n \"71\": [354, 650, 2460, 2656],\n \"71080601\": 2658,\n \"71238898\": 1911,\n+ \"71288877\": 2488,\n \"71387850e\": 1586,\n+ \"71719118\": 2461,\n \"718281\": 431,\n \"71828182845904523536028747135266249775724709369995\": 76,\n \"71828183\": [38, 2665],\n \"718282\": 2641,\n \"7183\": 2641,\n \"7185\": [99, 906],\n \"71946897\": 1153,\n@@ -35511,14 +35510,15 @@\n \"7954\": 2527,\n \"7955\": 2527,\n \"79579319e\": 1867,\n \"79694221e\": 1586,\n \"7972\": 2527,\n \"7976931348623157e\": 1335,\n \"79769313e\": 1335,\n+ \"799\": 2461,\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, 529, 530, 533, 538, 541, 543, 544, 546, 548, 554, 565, 566, 577, 608, 622, 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2656, 2657, 2658, 2659, 2664, 2665],\n \"80\": [69, 396, 409, 446, 977, 2248, 2346, 2357, 2383, 2392, 2404, 2430, 2441, 2454, 2588, 2622, 2647, 2648, 2656],\n@@ -35526,14 +35526,15 @@\n \"8000\": [2350, 2352, 2397, 2399, 2447, 2449],\n \"80000000000000204\": [1113, 1543],\n \"8005\": 2527,\n \"801\": [941, 1114, 1123, 1124, 1131],\n \"8010\": 2527,\n \"8020\": 2527,\n \"8024\": 2527,\n+ \"80287287\": 2488,\n \"8031\": 2527,\n \"804\": 2508,\n \"8044\": 2527,\n \"8058837395885292\": 666,\n \"80b3a34\": 2614,\n \"81\": [1650, 1884, 2634, 2640, 2644, 2656, 2665],\n \"81299683\": 2634,\n@@ -35664,14 +35665,15 @@\n \"9304e\": 2091,\n \"931\": 2587,\n \"9317\": 2532,\n \"9319\": 2532,\n \"9339\": 2532,\n \"9340\": 2532,\n \"934284\": 349,\n+ \"935\": 2461,\n \"93657855\": 349,\n \"9371\": 2532,\n \"9372\": 2532,\n \"9373\": 2532,\n \"9374\": 2532,\n \"9376\": 2532,\n \"9377\": 2532,\n@@ -35683,15 +35685,15 @@\n \"940\": 2587,\n \"941257\": 2634,\n \"94125714\": 2634,\n \"94708397920832\": 2641,\n \"9475673279178444\": 2348,\n \"94864945\": 2665,\n \"94909878\": [2387, 2434],\n- \"95\": [37, 39, 646, 2353, 2400, 2450, 2461, 2634, 2653, 2656],\n+ \"95\": [37, 39, 646, 2353, 2400, 2450, 2634, 2653, 2656],\n \"9504637\": 2665,\n \"950684\": 2634,\n \"95068423\": 2634,\n \"9555\": [2533, 2534],\n \"9556\": [2533, 2534],\n \"9557\": [2533, 2534],\n \"9558\": [2533, 2534],\n"}]}]}]}]}]}