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"unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_exponential.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_exponential.html", "unified_diff": "@@ -37,15 +37,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_gamma.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_gamma.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_normal.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_normal.html", "unified_diff": "@@ -37,15 +37,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_t.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.standard_t.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.triangular.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.triangular.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.uniform.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.uniform.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.vonmises.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.vonmises.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/generator.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/generator.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/index.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/index.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/legacy.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/legacy.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n"}, {"source1": "./usr/share/doc/python-numpy/html/reference/random/multithreading.html", "source2": "./usr/share/doc/python-numpy/html/reference/random/multithreading.html", "unified_diff": "@@ -37,15 +37,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \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": "@@ -37,15 +37,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n \n \n \n \n@@ -521,30 +521,30 @@\nIn [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-7.25 ms +- 74.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-14.7 ms +- 17.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.44 ms +- 38.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+7.52 ms +- 18.9 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-6.09 ms +- 42.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-17.3 ms +- 64.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.15 ms +- 20 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+7.51 ms +- 16.8 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-29.6 ms +- 3.38 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-34 ms +- 1.45 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+12.7 ms +- 63.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+18.4 ms +- 29.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n
integers
is now the canonical way to generate integer\n random numbers from a discrete uniform distribution. This replaces both\n randint
and the deprecated random_integers
.
The rand
and randn
methods are only available through the legacy\n@@ -571,21 +571,21 @@\n
Standard Exponentials (standard_exponential
)
In [6]: rng = np.random.default_rng()\n \n In [7]: rng.random(3, dtype=np.float64)\n-Out[7]: array([0.19452855, 0.8623312 , 0.87792506])\n+Out[7]: array([0.96395281, 0.04954194, 0.25917649])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.06192333, 0.66640437, 0.17871135], dtype=float32)\n+Out[8]: array([0.7857391 , 0.9779958 , 0.98701364], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([156, 162, 124], dtype=uint8)\n+Out[9]: array([62, 82, 77], dtype=uint8)\n
Optional out
argument that allows existing arrays to be filled for\n select distributions
Uniforms (random
)
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([0.01987656, 0.69498653])\n+Out[12]: array([0.86816272, 0.21975435])\n \n In [13]: print(existing)\n-[0.01987656 0.69498653 0. 0. ]\n+[0.86816272 0.21975435 0. 0. ]\n
Optional axis
argument for methods like choice
,\n permutation
and shuffle
that controls which\n axis an operation is performed over for multi-dimensional arrays.
Added a method to sample from the complex normal distribution\n (complex_normal)
With the legacy polynomial module, a linear fit (i.e. polynomial of degree 1)\n could be applied to these data with polyfit
:
In [4]: np.polyfit(x, y, deg=1)\n-Out[4]: array([1.04481468, 0.25605254])\n+Out[4]: array([0.84792215, 0.07212898])\n
With the new polynomial API, the fit
\n class method is preferred:
In [5]: p_fitted = np.polynomial.Polynomial.fit(x, y, deg=1)\n \n In [6]: p_fitted\n-Out[6]: Polynomial([4.95771861, 4.70166607], domain=[0., 9.], window=[-1., 1.], symbol='x')\n+Out[6]: Polynomial([3.88777865, 3.81564967], domain=[0., 9.], window=[-1., 1.], symbol='x')\n
Note that the coefficients are given in the scaled domain defined by the\n linear mapping between the window
and domain
.\n convert
can be used to get the\n coefficients in the unscaled data domain.
In [7]: p_fitted.convert()\n-Out[7]: Polynomial([0.25605254, 1.04481468], domain=[-1., 1.], window=[-1., 1.], symbol='x')\n+Out[7]: Polynomial([0.07212898, 0.84792215], domain=[-1., 1.], window=[-1., 1.], symbol='x')\n
polynomial
package#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([1.04481468, 0.25605254])\n+Out[4]: array([0.84792215, 0.07212898])\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([4.95771861, 4.70166607], domain=[0., 9.], window=[-1.,\n+Out[6]: Polynomial([3.88777865, 3.81564967], 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([0.25605254, 1.04481468], domain=[-1., 1.], window=[-1.,\n+Out[7]: Polynomial([0.07212898, 0.84792215], 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/reference/routines.polynomials.laguerre.html", "source2": "./usr/share/doc/python-numpy/html/reference/routines.polynomials.laguerre.html", "unified_diff": "@@ -38,15 +38,15 @@\n \n \n \n \n \n \n \n- \n+ \n \n \n \n
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2392, 2401, 2404, 2427, 2429, 2441, 2451, 2454, 2641, 2644, 2662],\n \"2000\": [514, 526, 2525, 2613],\n \"20000\": [2316, 2359, 2361, 2406, 2408, 2456, 2583],\n \"2000000\": 2631,\n \"20000000e\": 897,\n \"2001\": [55, 2322, 2326, 2329, 2330, 2331, 2335, 2339, 2340, 2366, 2371, 2373, 2374, 2375, 2378, 2382, 2383, 2413, 2418, 2420, 2421, 2422, 2425, 2429, 2430],\n \"2002\": [55, 360, 2316, 2323, 2353, 2361, 2367, 2400, 2408, 2414, 2450],\n@@ -33731,14 +33727,15 @@\n \"21869\": 2589,\n \"21870\": 2589,\n \"219\": 2463,\n \"21925\": 2594,\n \"21949\": 2589,\n \"21951\": 2589,\n \"21952\": 2589,\n+ \"21975435\": 2461,\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, 2513, 2517, 2519, 2534, 2588, 2631, 2633, 2637, 2653, 2662],\n \"220\": [2238, 2576],\n \"22004\": 2594,\n \"22014\": 2594,\n \"22030\": 2590,\n@@ -34158,15 +34155,15 @@\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, 528, 544, 624, 645, 660, 667, 669, 680, 870, 881, 893, 917, 1056, 1070, 1083, 1135, 1142, 1143, 1240, 1349, 1526, 1527, 1542, 1581, 1606, 1651, 1694, 1702, 1834, 1907, 2089, 2090, 2091, 2204, 2239, 2240, 2251, 2265, 2266, 2323, 2325, 2337, 2348, 2349, 2367, 2380, 2390, 2396, 2414, 2427, 2438, 2446, 2461, 2463, 2473, 2476, 2491, 2513, 2515, 2519, 2567, 2602, 2604, 2622, 2623, 2631, 2637, 2641, 2650, 2653, 2655, 2662],\n+ \"25\": [10, 50, 54, 55, 56, 58, 69, 140, 361, 377, 409, 418, 440, 481, 489, 520, 526, 528, 544, 624, 645, 660, 667, 669, 680, 870, 881, 893, 917, 1056, 1070, 1083, 1135, 1142, 1143, 1240, 1349, 1526, 1527, 1542, 1581, 1606, 1651, 1694, 1702, 1834, 1907, 2089, 2090, 2091, 2204, 2239, 2240, 2251, 2265, 2266, 2323, 2325, 2337, 2348, 2349, 2367, 2380, 2390, 2396, 2414, 2427, 2438, 2446, 2463, 2473, 2476, 2491, 2513, 2515, 2519, 2567, 2602, 2604, 2622, 2623, 2631, 2637, 2641, 2650, 2653, 2655, 2662],\n \"250\": [361, 2463],\n \"2500\": 652,\n \"25000\": 652,\n \"25003\": 2604,\n \"25043\": 2604,\n \"25049\": 2604,\n \"25054\": 2622,\n@@ -34273,15 +34270,14 @@\n \"25553\": 2622,\n \"25557246e\": 438,\n \"25570\": 2622,\n \"25584\": 2606,\n \"25585\": 2606,\n \"25599\": 2606,\n \"256\": [69, 186, 287, 333, 418, 438, 514, 624, 827, 999, 1172, 1241, 1259, 1324, 1414, 1478, 1933, 1998, 2158, 2290, 2303, 2304, 2461, 2462, 2613, 2633, 2646],\n- \"25605254\": 2488,\n \"2561\": 2615,\n \"25618\": 2606,\n \"25619\": 2606,\n \"25620\": 2606,\n \"25630\": 2606,\n \"25643\": 2606,\n \"25645\": 2606,\n@@ -34309,14 +34305,15 @@\n \"25802\": 2622,\n \"25812\": 2622,\n \"25816\": 2622,\n \"2584\": 28,\n \"25866\": 2622,\n \"25911\": 2622,\n \"25914\": 2622,\n+ \"25917649\": 2461,\n \"25943\": 2622,\n \"25954\": 2622,\n \"25d0\": 32,\n \"25j\": 2655,\n \"25t03\": 55,\n \"25x\": 138,\n \"26\": [29, 30, 40, 50, 54, 55, 58, 63, 79, 146, 944, 2204, 2208, 2310, 2333, 2377, 2424, 2513, 2519, 2534, 2536, 2537, 2577, 2599, 2601, 2622, 2625, 2629, 2637, 2653, 2654, 2662],\n@@ -34643,15 +34640,15 @@\n \"3340\": [287, 1241, 1324, 1478, 1998],\n \"3348\": 2617,\n \"33486982e\": 438,\n \"3361\": [99, 906],\n \"3364\": 2615,\n \"3373\": 2615,\n \"33872321e\": 2104,\n- \"34\": [12, 28, 144, 441, 1880, 1919, 2208, 2461, 2463, 2491, 2566, 2583, 2637, 2653, 2662],\n+ \"34\": [12, 28, 144, 441, 1880, 1919, 2208, 2463, 2491, 2566, 2583, 2637, 2653, 2662],\n \"340\": [2238, 2576],\n \"34132519\": [680, 2655],\n \"3421\": 2615,\n \"343\": 2662,\n \"34317802\": 1153,\n \"34376245\": 2631,\n \"3456\": 13,\n@@ -34785,15 +34782,15 @@\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, 2631, 2635, 2653, 2660, 2661, 2662],\n+ \"42\": [31, 58, 63, 147, 349, 669, 896, 897, 974, 1029, 2090, 2208, 2256, 2332, 2376, 2423, 2457, 2566, 2605, 2622, 2631, 2635, 2653, 2660, 2661, 2662],\n \"420\": [2238, 2576],\n \"42016704\": 2662,\n \"4206\": 2617,\n \"4220\": 2617,\n \"4223\": 2617,\n \"4225\": 2617,\n \"423\": 55,\n@@ -34820,15 +34817,15 @@\n \"4354\": 2617,\n \"4359\": 2617,\n \"4368\": [287, 1241, 1324, 1478, 1998],\n \"4375\": 2491,\n \"43857224\": 1153,\n \"43887844\": [349, 2457, 2635],\n \"43999999999998\": 1114,\n- \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2463, 2624, 2653, 2654, 2655, 2662],\n+ \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2461, 2463, 2624, 2653, 2654, 2655, 2662],\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@@ -34844,15 +34841,15 @@\n \"4476\": 2621,\n \"4483\": 2617,\n \"4485\": 2617,\n \"4486\": 2617,\n \"449294e\": 2170,\n \"44948974\": 2655,\n \"44999999925494177\": 2115,\n- \"45\": [12, 18, 38, 58, 94, 105, 130, 339, 666, 851, 866, 944, 1025, 1026, 1031, 1051, 1212, 1882, 1886, 2103, 2204, 2461, 2633, 2640, 2653, 2662],\n+ \"45\": [12, 18, 38, 58, 94, 105, 130, 339, 666, 851, 866, 944, 1025, 1026, 1031, 1051, 1212, 1882, 1886, 2103, 2204, 2633, 2640, 2653, 2662],\n \"450\": 55,\n \"45000005\": 2115,\n \"45038594\": [349, 2635],\n \"45053314\": 2631,\n \"4511316\": 2662,\n \"4520525295346629\": 1228,\n \"4532\": [409, 661, 2168],\n@@ -34956,15 +34953,15 @@\n \"50491123200\": 55,\n \"50491123200000000\": 55,\n \"50629471\": 1154,\n \"5063\": 2620,\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, 2653, 2662],\n+ \"51\": [58, 520, 1654, 1752, 1761, 1867, 1870, 2083, 2204, 2331, 2339, 2356, 2375, 2382, 2403, 2422, 2429, 2453, 2461, 2622, 2653, 2662],\n \"510\": [143, 570],\n \"5100\": 2620,\n \"5104\": 2620,\n \"51182162\": 2662,\n \"512\": [69, 893, 1083, 1143, 1247, 2115, 2240, 2542, 2547, 2662],\n \"51327412\": 1891,\n \"5136\": 2620,\n@@ -34980,15 +34977,15 @@\n \"51658839e\": 1586,\n \"517\": 10,\n \"5170\": 2620,\n \"5184\": 2620,\n \"51851852\": 1809,\n \"519928\": 2631,\n \"51992837\": 2631,\n- \"52\": [10, 50, 62, 457, 1638, 1647, 1650, 2204, 2208, 2554, 2631, 2637, 2644, 2653, 2655, 2656, 2662],\n+ \"52\": [10, 50, 62, 457, 1638, 1647, 1650, 2204, 2208, 2461, 2554, 2631, 2637, 2644, 2653, 2655, 2656, 2662],\n \"5203\": 2620,\n \"5225\": 2620,\n \"5231\": 2620,\n \"52338984\": [2345, 2391, 2439],\n \"52359878\": 1911,\n \"52380952e\": 1816,\n \"5240\": 2620,\n@@ -35113,35 +35110,35 @@\n \"6143849206349179\": [1113, 1543],\n \"6176\": [99, 906],\n \"61799388\": 1911,\n \"618\": 669,\n \"6180\": [2353, 2400, 2450],\n \"61988120985\": [2323, 2367, 2414],\n \"61c54bbd\": 42,\n- \"62\": [542, 968, 969, 2323, 2333, 2367, 2377, 2414, 2424, 2653],\n+ \"62\": [542, 968, 969, 2323, 2333, 2367, 2377, 2414, 2424, 2461, 2653],\n \"6208\": 2522,\n \"6227766\": 514,\n \"623\": [2394, 2444],\n \"62318272\": [2319, 2363, 2410],\n \"62341325\": 514,\n \"62374854\": 2662,\n \"624\": [2300, 2370, 2394, 2417, 2444, 2462],\n \"625\": [99, 478, 645, 906, 2390, 2438],\n \"6273591314603949\": [2335, 2378, 2425],\n \"62949953e\": 645,\n- \"63\": [58, 514, 669, 1748, 1777, 2336, 2463, 2520, 2566, 2594, 2638, 2653],\n+ \"63\": [58, 514, 669, 1748, 1777, 2336, 2461, 2463, 2520, 2566, 2594, 2638, 2653],\n \"631198583\": 55,\n \"631198588\": 55,\n \"63317787e\": [2166, 2167],\n \"63526532\": 2458,\n \"636363636364\": [2353, 2400, 2450],\n \"63696169\": 2631,\n \"6376\": 2522,\n \"6390\": [2353, 2400, 2450],\n- \"64\": [1, 5, 13, 21, 30, 50, 55, 56, 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