{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/tmp.Vcup8s9YLr/b1/scipy_1.6.0-2_arm64.changes", "source2": "/srv/reproducible-results/rbuild-debian/tmp.Vcup8s9YLr/b2/scipy_1.6.0-2_arm64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,4 +1,4 @@\n \n- c4c50f254a2aa5fe161bb4c2a3eddfaf 24073120 doc optional python-scipy-doc_1.6.0-2_all.deb\n+ b216382097cff2733119cdf70707a486 24073076 doc optional python-scipy-doc_1.6.0-2_all.deb\n 1cbbf41414f224b5fff80ac8bb245d74 78571952 debug optional python3-scipy-dbg_1.6.0-2_arm64.deb\n a667eb1c216973c2e7315995347667e1 11597588 python optional python3-scipy_1.6.0-2_arm64.deb\n"}, {"source1": "python-scipy-doc_1.6.0-2_all.deb", "source2": "python-scipy-doc_1.6.0-2_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 2021-01-16 12:26:56.000000 debian-binary\n--rw-r--r-- 0 0 0 113068 2021-01-16 12:26:56.000000 control.tar.xz\n--rw-r--r-- 0 0 0 23959860 2021-01-16 12:26:56.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 113064 2021-01-16 12:26:56.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 23959820 2021-01-16 12:26:56.000000 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": "./usr/share/doc/python-scipy-doc/html/generated/scipy.cluster.hierarchy.ClusterNode.pre_order.html", "source2": "./usr/share/doc/python-scipy-doc/html/generated/scipy.cluster.hierarchy.ClusterNode.pre_order.html", "unified_diff": "@@ -104,15 +104,15 @@\n
\n
\n \n
\n

scipy.cluster.hierarchy.ClusterNode.pre_order\u00b6

\n
\n
\n-ClusterNode.pre_order(self, func=<function ClusterNode.<lambda> at 0xffff95d43ee0>)[source]\u00b6
\n+ClusterNode.pre_order(self, func=<function ClusterNode.<lambda> at 0xffffa05b6ee0>)[source]\u00b6\n

Perform pre-order traversal without recursive function calls.

\n

When a leaf node is first encountered, func is called with\n the leaf node as its argument, and its result is appended to\n the list.

\n

For example, the statement:

\n 
ids = root.pre_order(lambda x: x.id)\n
\n"}, {"source1": "./usr/share/doc/python-scipy-doc/html/generated/scipy.optimize.brute.html", "source2": "./usr/share/doc/python-scipy-doc/html/generated/scipy.optimize.brute.html", "unified_diff": "@@ -102,15 +102,15 @@\n
\n
\n \n
\n

scipy.optimize.brute\u00b6

\n
\n
\n-scipy.optimize.brute(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0xffff97013820>, disp=False, workers=1)[source]\u00b6
\n+scipy.optimize.brute(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0xffffa18a5820>, disp=False, workers=1)[source]\u00b6\n

Minimize a function over a given range by brute force.

\n

Uses the \u201cbrute force\u201d method, i.e., computes the function\u2019s value\n at each point of a multidimensional grid of points, to find the global\n minimum of the function.

\n

The function is evaluated everywhere in the range with the datatype of the\n first call to the function, as enforced by the vectorize NumPy\n function. The value and type of the function evaluation returned when\n"}, {"source1": "./usr/share/doc/python-scipy-doc/html/generated/scipy.stats.median_abs_deviation.html", "source2": "./usr/share/doc/python-scipy-doc/html/generated/scipy.stats.median_abs_deviation.html", "unified_diff": "@@ -102,15 +102,15 @@\n

\n
\n \n
\n

scipy.stats.median_abs_deviation\u00b6

\n
\n
\n-scipy.stats.median_abs_deviation(x, axis=0, center=<function median at 0xffff9e8a6e50>, scale=1.0, nan_policy='propagate')[source]\u00b6
\n+scipy.stats.median_abs_deviation(x, axis=0, center=<function median at 0xffffa510be50>, scale=1.0, nan_policy='propagate')[source]\u00b6\n

Compute the median absolute deviation of the data along the given axis.

\n

The median absolute deviation (MAD, [1]) computes the median over the\n absolute deviations from the median. It is a measure of dispersion\n similar to the standard deviation but more robust to outliers [2].

\n

The MAD of an empty array is np.nan.

\n
\n

New in version 1.5.0.

\n"}, {"source1": "./usr/share/doc/python-scipy-doc/html/generated/scipy.stats.multiscale_graphcorr.html", "source2": "./usr/share/doc/python-scipy-doc/html/generated/scipy.stats.multiscale_graphcorr.html", "unified_diff": "@@ -102,15 +102,15 @@\n
\n
\n \n
\n

scipy.stats.multiscale_graphcorr\u00b6

\n
\n
\n-scipy.stats.multiscale_graphcorr(x, y, compute_distance=<function _euclidean_dist at 0xffff9675b310>, reps=1000, workers=1, is_twosamp=False, random_state=None)[source]\u00b6
\n+scipy.stats.multiscale_graphcorr(x, y, compute_distance=<function _euclidean_dist at 0xffffa0fcd310>, reps=1000, workers=1, is_twosamp=False, random_state=None)[source]\u00b6\n

Computes the Multiscale Graph Correlation (MGC) test statistic.

\n

Specifically, for each point, MGC finds the \\(k\\)-nearest neighbors for\n one property (e.g. cloud density), and the \\(l\\)-nearest neighbors for\n the other property (e.g. grass wetness) [1]. This pair \\((k, l)\\) is\n called the \u201cscale\u201d. A priori, however, it is not know which scales will be\n most informative. So, MGC computes all distance pairs, and then efficiently\n computes the distance correlations for all scales. The local correlations\n"}, {"source1": "./usr/share/doc/python-scipy-doc/html/searchindex.js", "source2": "./usr/share/doc/python-scipy-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -4343,18 +4343,18 @@\n \"0rc1\": [32, 3376, 3383, 3386],\n \"0rc2\": [3372, 3376],\n \"0th\": [385, 386, 510, 515, 1785, 1786],\n \"0x00\": 562,\n \"0x7fe753763180\": 545,\n \"0x_3\": 3427,\n \"0x_4\": 3427,\n- \"0xffff95d43ee0\": 61,\n- \"0xffff9675b310\": 3127,\n- \"0xffff97013820\": 1545,\n- \"0xffff9e8a6e50\": 3045,\n+ \"0xffffa05b6ee0\": 61,\n+ \"0xffffa0fcd310\": 3127,\n+ \"0xffffa18a5820\": 1545,\n+ \"0xffffa510be50\": 3045,\n \"100\": [10, 16, 41, 101, 143, 280, 287, 288, 356, 464, 497, 498, 510, 512, 513, 515, 563, 716, 1405, 1446, 1537, 1538, 1541, 1542, 1557, 1560, 1561, 1563, 1565, 1566, 1572, 1575, 1578, 1579, 1582, 1586, 1588, 1624, 1626, 1627, 1628, 1631, 1632, 1638, 1639, 1640, 1641, 1647, 1651, 1653, 1654, 1659, 1667, 1676, 1691, 1694, 1697, 1699, 1701, 1702, 1705, 1727, 1739, 1741, 1743, 1748, 1750, 1751, 1754, 1755, 1756, 1759, 1767, 1785, 1796, 1812, 2366, 2439, 2446, 2475, 2508, 2533, 2561, 2658, 2673, 2678, 2679, 2701, 2704, 2749, 2754, 2815, 2834, 2862, 2900, 2903, 2905, 2906, 2908, 2910, 2912, 2919, 2923, 2925, 2926, 2927, 2928, 2929, 2938, 2940, 2943, 2946, 2951, 2952, 2953, 2954, 2955, 2957, 2960, 2962, 2963, 2965, 2966, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2992, 2994, 2996, 2997, 2998, 2999, 3000, 3001, 3004, 3005, 3006, 3007, 3008, 3009, 3012, 3013, 3014, 3015, 3018, 3020, 3023, 3024, 3025, 3026, 3028, 3029, 3031, 3032, 3033, 3035, 3036, 3037, 3038, 3040, 3041, 3044, 3045, 3046, 3048, 3052, 3127, 3132, 3134, 3135, 3136, 3138, 3140, 3143, 3144, 3146, 3151, 3152, 3153, 3156, 3161, 3162, 3163, 3165, 3174, 3192, 3198, 3215, 3223, 3242, 3244, 3245, 3247, 3250, 3251, 3252, 3254, 3260, 3261, 3265, 3266, 3270, 3273, 3275, 3278, 3279, 3280, 3282, 3283, 3286, 3287, 3316, 3323, 3327, 3328, 3345, 3348, 3383, 3385, 3390, 3394, 3396, 3406, 3417, 3419, 3422, 3423, 3427, 3428, 3429, 3430, 3431, 3480, 3516],\n \"1000\": [100, 287, 371, 401, 485, 501, 742, 1049, 1537, 1539, 1549, 1550, 1555, 1556, 1631, 1638, 1639, 1651, 1662, 1676, 1679, 1688, 1689, 1691, 1692, 1694, 1699, 1701, 1703, 1711, 1715, 1745, 1746, 1777, 1802, 1815, 2362, 2365, 2621, 2658, 2659, 2679, 2701, 2747, 2749, 2750, 2759, 2900, 2903, 2905, 2906, 2909, 2910, 2911, 2912, 2916, 2918, 2920, 2923, 2925, 2926, 2927, 2928, 2929, 2938, 2940, 2941, 2943, 2945, 2946, 2951, 2952, 2953, 2954, 2955, 2957, 2960, 2962, 2963, 2965, 2966, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3004, 3005, 3006, 3007, 3012, 3013, 3014, 3015, 3020, 3024, 3025, 3026, 3027, 3028, 3029, 3031, 3032, 3033, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3044, 3048, 3052, 3127, 3132, 3133, 3134, 3135, 3136, 3138, 3139, 3140, 3143, 3144, 3147, 3149, 3151, 3152, 3153, 3157, 3161, 3162, 3163, 3164, 3165, 3171, 3196, 3220, 3241, 3244, 3248, 3250, 3254, 3260, 3261, 3265, 3266, 3273, 3275, 3278, 3279, 3280, 3282, 3283, 3287, 3289, 3292, 3293, 3306, 3316, 3321, 3326, 3329, 3342, 3361, 3372, 3396, 3400, 3406, 3417, 3423, 3427, 3430, 3431],\n \"10000\": [280, 282, 1572, 1663, 1669, 1687, 1729, 1802, 2645, 2913, 3019, 3154, 3155, 3431],\n \"100000\": [10, 1565, 1715, 1743, 2908, 3011, 3215],\n \"1000000\": [1343, 2532, 3045, 3129],\n \"10000000\": [20, 1550, 1557],\n \"100000000\": [282, 2367, 2368],\n"}]}]}]}]}]}