{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/tmp.nLMBYAupZM/b1/scipy_1.6.0-2_armhf.changes", "source2": "/srv/reproducible-results/rbuild-debian/tmp.nLMBYAupZM/b2/scipy_1.6.0-2_armhf.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,4 +1,4 @@\n \n- eafdd458ada4e1ba7b3b75e4a2434284 24079956 doc optional python-scipy-doc_1.6.0-2_all.deb\n+ 1cf67fcad8c590331d976182be4b1790 24080296 doc optional python-scipy-doc_1.6.0-2_all.deb\n cd0d3cc67e9aeabb40de129e45d2859b 73534364 debug optional python3-scipy-dbg_1.6.0-2_armhf.deb\n f367e4916728e6a7ae168c57d7ca64ba 11272072 python optional python3-scipy_1.6.0-2_armhf.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 113028 2021-01-16 12:26:56.000000 control.tar.xz\n--rw-r--r-- 0 0 0 23966736 2021-01-16 12:26:56.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 113036 2021-01-16 12:26:56.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 23967068 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 0xf2b7fc40>)[source]\u00b6
\n+ClusterNode.pre_order(self, func=<function ClusterNode.<lambda> at 0xb17f0c40>)[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 0xf369a028>, disp=False, workers=1)[source]\u00b6
\n+scipy.optimize.brute(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0xb2ba7028>, 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 0xf62d32b0>, scale=1.0, nan_policy='propagate')[source]\u00b6
\n+scipy.stats.median_abs_deviation(x, axis=0, center=<function median at 0xb574e2b0>, 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 0xf23dc388>, 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 0xb206d970>, 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- \"0xf23dc388\": 3127,\n- \"0xf2b7fc40\": 61,\n- \"0xf369a028\": 1545,\n- \"0xf62d32b0\": 3045,\n+ \"0xb17f0c40\": 61,\n+ \"0xb206d970\": 3127,\n+ \"0xb2ba7028\": 1545,\n+ \"0xb574e2b0\": 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"}]}]}]}]}]}