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-1.36 ms +- 20.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.62 ms +- 49.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.27 ms +- 70.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.42 ms +- 36.3 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-732 us +- 10.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.92 ms +- 8.05 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.34 ms +- 31.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+3.57 ms +- 32.6 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-2.68 ms +- 22.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-5.06 ms +- 11.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.46 ms +- 43.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+9.8 ms +- 44.7 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([0.91438936, 0.36837562, 0.67942914])\n+Out[7]: array([0.51511368, 0.90237344, 0.35387021])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.38148934, 0.36816353, 0.74277556], dtype=float32)\n+Out[8]: array([0.5956226 , 0.48274744, 0.5823649 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 84, 151, 84], dtype=uint8)\n+Out[9]: array([198, 64, 189], 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([0.49547114, 0.12719573])\n+Out[12]: array([0.71740836, 0.37834741])\n \n In [13]: print(existing)\n-[0.49547114 0.12719573 0. 0. ]\n+[0.71740836 0.37834741 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([ 1.13325949, -1.09876797])\n+Out[4]: array([ 1.14318524, -0.6599364 ])\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([4.00089972, 5.09966769], domain=[0., 9.], window=[-1., 1.], symbol='x')\n+Out[6]: Polynomial([4.48439716, 5.14433357], 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([-1.09876797, 1.13325949], domain=[-1., 1.], window=[-1., 1.], symbol='x')\n+Out[7]: Polynomial([-0.6599364 , 1.14318524], 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([ 1.13325949, -1.09876797])\n+Out[4]: array([ 1.14318524, -0.6599364 ])\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([4.00089972, 5.09966769], domain=[0., 9.], window=[-1.,\n+Out[6]: Polynomial([4.48439716, 5.14433357], 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([-1.09876797, 1.13325949], domain=[-1., 1.], window=[-1.,\n+Out[7]: Polynomial([-0.6599364 , 1.14318524], 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": "@@ -32307,15 +32307,14 @@\n \"00012\": 137,\n \"00016403\": 1640,\n \"0001e10\": [91, 548],\n \"0002\": 2607,\n \"0003\": [1596, 1653, 1710, 1767, 1824, 1879, 1893, 2607],\n \"0003163943736596009\": 2315,\n \"00032805\": 1697,\n- \"00089972\": 2488,\n \"000e\": [410, 2656],\n \"000j\": 1335,\n \"001\": [335, 420, 947, 1541, 1596, 1602, 1653, 1659, 1710, 1716, 1767, 1773, 1824, 1830, 1879, 1886, 1893, 2160],\n \"0010\": 1519,\n \"0011\": 138,\n \"001637457145670096\": 2315,\n \"001j\": [1596, 1653, 1710, 1767, 1824, 1879, 1893],\n@@ -32375,23 +32374,23 @@\n \"04\": [54, 55, 164, 410, 547, 1586, 2463, 2594, 2657],\n \"0400\": 360,\n \"04097352\": 2633,\n \"04166667\": [1544, 1585],\n \"04211c6\": 2521,\n \"04551152e\": 2104,\n \"04719755\": 1911,\n- \"05\": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2461, 2646],\n+ \"05\": [55, 91, 163, 410, 548, 669, 896, 1095, 1871, 2083, 2173, 2353, 2400, 2450, 2646],\n \"0500\": 360,\n \"05093587\": 2633,\n \"05208333\": 1585,\n \"05263157894736836\": 2257,\n \"0549999999999997\": 2083,\n \"055\": 2083,\n \"0596779\": 1153,\n- \"06\": [55, 566, 2083, 2461, 2508],\n+ \"06\": [55, 566, 2083, 2508],\n \"0614962j\": [439, 453],\n \"0625\": [418, 624, 1645],\n \"06369197489564249\": 2458,\n \"06381726\": 349,\n \"0660\": [302, 2131],\n \"06959433e\": [420, 947],\n \"07\": [55, 164, 547, 896, 897, 1335, 2170, 2508],\n@@ -32414,16 +32413,14 @@\n \"087300000000000003\": [2346, 2392, 2441],\n \"09\": [55, 2171, 2323, 2367, 2414],\n \"090097550553843\": 2640,\n \"09417735\": [349, 2457, 2637],\n \"0943951\": 1911,\n \"09640474436813\": 675,\n \"09861229\": [657, 2653],\n- \"09876797\": 2488,\n- \"09966769\": 2488,\n \"0999755859375\": 62,\n \"099975586\": 62,\n \"0a1\": 577,\n \"0a2\": 577,\n \"0b00000011\": [1519, 2235],\n \"0b1\": [34, 50, 577, 2602],\n \"0b2\": 577,\n@@ -32717,15 +32714,14 @@\n \"125\": [470, 660, 1114, 1142, 1645, 1651, 1899, 1900, 2239, 2339, 2382, 2429, 2460, 2491, 2657, 2664],\n \"12589991e\": 645,\n \"126\": [863, 1048, 1116, 1904],\n \"1261\": 2612,\n \"12658\": 2560,\n \"12697628\": 2633,\n \"127\": [62, 66, 514, 863, 1048, 1102, 1116, 1904, 2301, 2302, 2462, 2463, 2464, 2583, 2637],\n- \"12719573\": 2461,\n \"12736\": [2548, 2549],\n \"12767\": 2548,\n \"12768\": 2548,\n \"12769\": 2548,\n \"12773\": 2548,\n \"128\": [56, 62, 69, 114, 514, 863, 1048, 1098, 1116, 1335, 1904, 2091, 2162, 2270, 2275, 2280, 2285, 2290, 2299, 2300, 2301, 2302, 2303, 2348, 2458, 2462, 2463, 2464, 2525, 2535, 2566, 2583, 2622, 2637, 2646],\n \"12811363\": [349, 2457, 2637],\n@@ -32748,15 +32744,14 @@\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, 2635],\n \"1324274851176597e\": 1867,\n \"133\": [527, 2330, 2374, 2421],\n- \"13325949\": 2488,\n \"13392\": 2551,\n \"13394\": 2551,\n \"13396\": 2551,\n \"134\": [542, 968, 969],\n \"13406828\": 2458,\n \"13421\": 2566,\n \"1344\": [270, 880, 1069, 1229, 1312, 1466, 1986],\n@@ -32863,14 +32858,15 @@\n \"14297\": 2552,\n \"143\": 2463,\n \"14301\": 2555,\n \"14302\": 2555,\n \"14307\": 2555,\n \"14308\": 2555,\n \"14309\": 2555,\n+ \"14318524\": 2488,\n \"14322\": 2552,\n \"14325\": 2560,\n \"14330\": 2555,\n \"14335\": 2560,\n \"14338\": 2555,\n \"14339\": 2555,\n \"14340\": 2553,\n@@ -32888,14 +32884,15 @@\n \"14420\": 2556,\n \"14421\": 2556,\n \"14423\": 2553,\n \"14423775e\": 438,\n \"14430\": 2556,\n \"14432\": 2556,\n \"14433\": 2556,\n+ \"14433357\": 2488,\n \"14434\": 2553,\n \"14436\": 2556,\n \"14446\": 2556,\n \"14456\": 2557,\n \"14458\": 2557,\n \"1446\": 2612,\n \"14463\": 2557,\n@@ -32976,15 +32973,15 @@\n \"1500000\": 2633,\n \"15006\": 2588,\n \"15012\": 2560,\n \"15028c96\": 2666,\n \"15038\": 2559,\n \"15067\": 2559,\n \"15068\": 2560,\n- \"151\": [2358, 2405, 2455, 2461, 2463],\n+ \"151\": [2358, 2405, 2455, 2463],\n \"15113\": 2560,\n \"15118\": 2566,\n \"15119\": 2566,\n \"15121\": 2572,\n \"15158\": 2561,\n \"15159\": 2561,\n \"15161\": 2561,\n@@ -33334,15 +33331,15 @@\n \"188\": [2463, 2635],\n \"1885\": 2611,\n \"18874\": 2576,\n \"1887902\": 1911,\n \"18880\": 2576,\n \"18884\": 2583,\n \"18887\": 2575,\n- \"189\": 2327,\n+ \"189\": [2327, 2461],\n \"18906\": 2576,\n \"18915\": 2575,\n \"18921\": 2575,\n \"18922\": 2575,\n \"18923\": 2575,\n \"18924\": 2575,\n \"18925\": 2575,\n@@ -33458,15 +33455,15 @@\n \"1971\": 55,\n \"1972\": [351, 2105, 2166, 2167, 2316, 2327, 2329, 2356, 2361, 2372, 2373, 2403, 2408, 2419, 2420, 2453],\n \"19745\": 2579,\n \"1975\": [136, 523, 524, 566],\n \"19754\": 2583,\n \"19775622\": 2640,\n \"1977562209304\": 2640,\n- \"198\": [36, 2664],\n+ \"198\": [36, 2461, 2664],\n \"1980\": [62, 637, 641, 642, 655, 658, 1526],\n \"19803\": 2583,\n \"19805\": 2583,\n \"1985\": [653, 1897],\n \"19857\": 2583,\n \"1986\": [136, 523, 524],\n \"19879\": 2583,\n@@ -33508,15 +33505,15 @@\n \"1l_0\": 1822,\n \"1rc1\": 577,\n \"1st\": [36, 74, 642, 2616, 2635, 2639],\n \"1th\": [661, 2168],\n \"1type\": 2554,\n \"1xn\": 2663,\n \"2\": [0, 1, 2, 4, 5, 9, 12, 13, 14, 19, 20, 24, 25, 26, 29, 30, 31, 32, 34, 36, 37, 38, 43, 47, 48, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62, 65, 66, 68, 69, 70, 72, 73, 74, 75, 76, 79, 86, 88, 89, 90, 95, 96, 97, 98, 99, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133, 134, 135, 136, 137, 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[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@@ -33742,15 +33739,15 @@\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, 2633, 2635, 2639, 2655, 2664],\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, 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\"27021\": 2624,\n@@ -34617,15 +34614,15 @@\n \"3057\": 2615,\n \"307\": [1149, 2664],\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, 2635, 2639, 2655, 2664],\n+ \"31\": [54, 55, 146, 336, 514, 669, 1088, 1094, 1638, 1711, 1867, 1913, 2204, 2236, 2327, 2331, 2375, 2422, 2461, 2513, 2520, 2635, 2639, 2655, 2664],\n \"31018314\": 2458,\n \"3105\": 2614,\n \"3108\": 2614,\n \"3117\": 2614,\n \"3118\": [57, 2547],\n \"31183145\": 2664,\n \"312088\": 1867,\n@@ -34647,15 +34644,15 @@\n \"3173\": 2617,\n \"3175\": 2617,\n \"317811\": 28,\n \"317j\": [411, 617],\n \"318\": 1526,\n \"3192\": 2615,\n \"31962608\": [196, 836, 1008, 1179, 1266, 1421, 1940],\n- \"32\": [1, 13, 21, 50, 54, 55, 56, 59, 61, 62, 63, 65, 69, 74, 137, 144, 215, 270, 336, 390, 434, 514, 584, 661, 880, 893, 1027, 1069, 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2461, 2462, 2508, 2513, 2520, 2521, 2522, 2535, 2543, 2544, 2545, 2546, 2547, 2557, 2562, 2564, 2572, 2574, 2579, 2582, 2587, 2588, 2599, 2602, 2606, 2607, 2617, 2622, 2635, 2637, 2639, 2643, 2644, 2646, 2647, 2650, 2655, 2656, 2657, 2664],\n \"320\": 1149,\n \"32000\": 2094,\n \"32119158\": 1867,\n \"323\": [260, 421, 948, 1059, 1222, 1305, 1350, 1459, 1579, 1590, 1604, 1605, 1639, 1649, 1661, 1662, 1696, 1706, 1718, 1719, 1753, 1763, 1775, 1776, 1810, 1820, 1832, 1833, 1866, 1875, 1887, 1888, 1979, 2312],\n \"3263\": 2617,\n \"32767\": 535,\n \"32768\": 535,\n@@ -34681,15 +34678,15 @@\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, 2463, 2491, 2566, 2583, 2639, 2655, 2664],\n+ \"34\": [12, 28, 144, 441, 1880, 1919, 2208, 2461, 2463, 2491, 2566, 2583, 2639, 2655, 2664],\n \"340\": [2238, 2576],\n \"34132519\": [680, 2657],\n \"3421\": 2615,\n \"343\": 2664,\n \"34317802\": 1153,\n \"34376245\": 2633,\n \"3456\": 13,\n@@ -34706,14 +34703,15 @@\n \"34960421\": 680,\n \"3497\": 2615,\n \"35\": [409, 489, 669, 870, 1056, 2204, 2325, 2369, 2416, 2572, 2633, 2639, 2655, 2664],\n \"350\": [544, 635],\n \"3504\": 2617,\n \"3534857623790153\": 666,\n \"35355339\": 1636,\n+ \"35387021\": 2461,\n \"3541\": 2615,\n \"35489284e\": 2104,\n \"36\": [58, 137, 355, 1752, 1761, 2204, 2225, 2323, 2367, 2414, 2461, 2463, 2491, 2536, 2647, 2655, 2657, 2664],\n \"360\": [544, 2103, 2238, 2576],\n \"36045180e\": 147,\n \"3608\": 2615,\n \"361\": [1344, 1346, 1522, 1908],\n@@ -34721,33 +34719,31 @@\n \"3628523\": 2458,\n \"36363636\": 136,\n \"3644j\": [415, 621],\n \"365\": [544, 1344, 1346, 1522, 1908],\n \"366\": 55,\n \"36674\": 2098,\n \"36787944\": [1660, 1774],\n- \"36816353\": 2461,\n- \"36837562\": 2461,\n \"37\": [59, 60, 136, 1913, 2083, 2204, 2236, 2463, 2639, 2655, 2664],\n \"370\": 652,\n \"3701\": 2615,\n \"37079802\": [349, 2637],\n \"3712\": 2615,\n \"3728\": 2615,\n \"3728973198\": 2594,\n \"3743\": 2615,\n \"375\": [1634, 1663],\n \"37601032\": [2387, 2434],\n \"377\": 28,\n \"378\": 652,\n+ \"37834741\": 2461,\n \"379\": 533,\n \"3793\": 2615,\n \"38\": [59, 542, 968, 969, 1545, 1752, 1761, 2107, 2204, 2208, 2313, 2333, 2361, 2377, 2408, 2424, 2510, 2572, 2655, 2664],\n \"380\": [2238, 2576],\n- \"38148934\": 2461,\n \"38268343\": 642,\n \"38268343j\": 642,\n \"3832\": 2615,\n \"38434191e\": 660,\n \"38446749\": 1867,\n \"385\": [2270, 2300],\n \"3854\": [287, 1241, 1324, 1478, 1998],\n@@ -34826,15 +34822,15 @@\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, 2566, 2605, 2622, 2633, 2637, 2655, 2662, 2663, 2664],\n+ \"42\": [31, 58, 63, 147, 349, 669, 896, 897, 974, 1029, 2090, 2208, 2256, 2332, 2376, 2423, 2457, 2461, 2566, 2605, 2622, 2633, 2637, 2655, 2662, 2663, 2664],\n \"420\": [2238, 2576],\n \"42016704\": 2664,\n \"4206\": 2617,\n \"4220\": 2617,\n \"4223\": 2617,\n \"4225\": 2617,\n \"423\": 55,\n@@ -34850,26 +34846,26 @@\n \"42667924\": 1154,\n \"4267\": 2617,\n \"4270\": 2617,\n \"4276\": 2617,\n \"429\": 136,\n \"4294967293\": 2637,\n \"4294967296\": [196, 836, 1008, 1179, 1266, 1421, 1940],\n- \"43\": [2208, 2583, 2633, 2639, 2655, 2664],\n+ \"43\": [2208, 2461, 2583, 2633, 2639, 2655, 2664],\n \"430148\": 2633,\n \"43014843\": 2633,\n \"43181166\": 2458,\n \"4354\": 2617,\n \"4359\": 2617,\n \"4368\": [287, 1241, 1324, 1478, 1998],\n \"4375\": 2491,\n \"43857224\": 1153,\n \"43887844\": [349, 2457, 2637],\n \"43999999999998\": 1114,\n- \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2463, 2624, 2655, 2656, 2657, 2664],\n+ \"44\": [16, 1525, 1758, 1905, 1907, 1922, 2208, 2461, 2463, 2624, 2655, 2656, 2657, 2664],\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@@ -34899,15 +34895,15 @@\n \"4532\": [409, 661, 2168],\n \"4545724517479104\": 2460,\n \"45560727e\": 54,\n \"456\": 1921,\n \"4567\": 2642,\n \"45674898e\": 566,\n \"45a3d84\": 2521,\n- \"46\": [409, 523, 905, 1707, 2204, 2208, 2639, 2655],\n+ \"46\": [409, 523, 905, 1707, 2204, 2208, 2461, 2639, 2655],\n \"460\": [2238, 2576],\n \"46009194e\": 566,\n \"4602\": 2618,\n \"4610935\": 457,\n \"4613\": 2618,\n \"4628\": 2618,\n \"46351241j\": 2081,\n@@ -34943,27 +34939,28 @@\n \"48\": [58, 270, 355, 880, 1069, 1142, 1229, 1312, 1466, 1867, 1986, 2208, 2239, 2323, 2367, 2414, 2655, 2657],\n \"480\": [238, 858, 1041, 1207, 1290, 1444, 1964],\n \"4800\": 57,\n \"48000000\": 2633,\n \"4809319\": 1153,\n \"4816\": [99, 906],\n \"48219694e\": [421, 948],\n+ \"48274744\": 2461,\n \"4836\": 2618,\n \"484\": [2517, 2583, 2625],\n+ \"48439716\": 2488,\n \"4853\": 2618,\n 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2630, 2633, 2636, 2637, 2639, 2642, 2643, 2645, 2646, 2647, 2655, 2656, 2663, 2664, 2666],\n \"90\": [25, 29, 33, 37, 39, 55, 68, 78, 363, 1910, 2082, 2103, 2248, 2463, 2610, 2652, 2655],\n+ \"90237344\": 2461,\n \"90384387e\": 2104,\n \"90476190e\": 1816,\n \"90909091\": 136,\n \"909297\": 2640,\n \"90929743\": 2664,\n \"91\": 2655,\n \"91275558\": 2633,\n- \"91438936\": 2461,\n \"916666666666666\": 1240,\n- \"92\": [98, 2461, 2655, 2657],\n+ \"92\": [98, 2655, 2657],\n \"921fb54442d18p\": 2520,\n \"9223372036854775807\": 2646,\n \"9223372036854775808\": 2646,\n \"92346708\": 349,\n \"92362781e\": 2104,\n \"92387953\": 642,\n \"92387953j\": 642,\n"}]}]}]}]}, {"source1": "python3-numpy_2.2.2+ds-1_amd64.deb", "source2": "python3-numpy_2.2.2+ds-1_amd64.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 2025-01-20 17:07:13.000000 debian-binary\n--rw-r--r-- 0 0 0 20784 2025-01-20 17:07:13.000000 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--wide --decompress --hex-dump=.gnu_debuglink {}", "source2": "readelf --wide --decompress --hex-dump=.gnu_debuglink {}", "comments": ["error from `readelf --wide --decompress --hex-dump=.gnu_debuglink {}`:", "readelf: Error: no .dynamic section in the dynamic segment"], "unified_diff": "@@ -1,7 +1,7 @@\n \n Hex dump of section '.gnu_debuglink':\n 0x00000000 30356437 36393164 37326435 64383766 05d7691d72d5d87f\n 0x00000010 32353734 33633336 35346133 38313866 25743c3654a3818f\n 0x00000020 30336566 63652e64 65627567 00000000 03efce.debug....\n- 0x00000030 dfcaad74 ...t\n+ 0x00000030 3d43b2c2 =C..\n \n"}]}]}]}]}, {"source1": "python3-numpy-dbgsym_2.2.2+ds-1_amd64.deb", "source2": "python3-numpy-dbgsym_2.2.2+ds-1_amd64.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 2025-01-20 17:07:13.000000 debian-binary\n -rw-r--r-- 0 0 0 2312 2025-01-20 17:07:13.000000 control.tar.xz\n--rw-r--r-- 0 0 0 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int_default_descriptor\n float_default_descriptor\n"}, {"source1": "readelf --wide --decompress --hex-dump=.strtab {}", "source2": "readelf --wide --decompress --hex-dump=.strtab {}", "unified_diff": "@@ -3397,364 +3397,364 @@\n 0x0000d420 0075696e 745f6173 5f6e756d 62657200 .uint_as_number.\n 0x0000d430 756c6f6e 675f6173 5f6e756d 62657200 ulong_as_number.\n 0x0000d440 756c6f6e 676c6f6e 675f6173 5f6e756d ulonglong_as_num\n 0x0000d450 62657200 68616c66 5f61735f 6e756d62 ber.half_as_numb\n 0x0000d460 65720063 666c6f61 745f6173 5f6e756d er.cfloat_as_num\n 0x0000d470 62657200 63646f75 626c655f 61735f6e ber.cdouble_as_n\n 0x0000d480 756d6265 7200636c 6f6e6764 6f75626c umber.clongdoubl\n- 0x0000d490 655f6173 5f6e756d 62657200 63634c46 e_as_number.ccLF\n- 0x0000d4a0 3966625a 2e6f005f 5f73766d 6c5f6461 9fbZ.o.__svml_da\n+ 0x0000d490 655f6173 5f6e756d 62657200 63634662 e_as_number.ccFb\n+ 0x0000d4a0 6d417257 2e6f005f 5f73766d 6c5f6461 mArW.o.__svml_da\n 0x0000d4b0 636f735f 64617461 5f696e74 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