{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.3NC9y4Zn/b1/g2o_0~20230806-4.1_amd64.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.3NC9y4Zn/b2/g2o_0~20230806-4.1_amd64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,5 +1,5 @@\n \n 7a70a93557ad59803549023510875e0b 124044 libdevel optional libg2o-dev_0~20230806-4.1_amd64.deb\n- 30e3c1536afea58b59bf8024993ea27a 208954148 doc optional libg2o-doc_0~20230806-4.1_all.deb\n+ 31a972c765b72e0b686440140c338def 208954200 doc optional libg2o-doc_0~20230806-4.1_all.deb\n d4242aa7ff5fb47d13fa60f4ef9c72a6 145195648 debug optional libg2o0t64-dbgsym_0~20230806-4.1_amd64.deb\n 31bab9ec6b05f5832876625acd245c08 826636 libs optional libg2o0t64_0~20230806-4.1_amd64.deb\n"}, {"source1": "libg2o-doc_0~20230806-4.1_all.deb", "source2": "libg2o-doc_0~20230806-4.1_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 2024-02-28 09:35:01.000000 debian-binary\n--rw-r--r-- 0 0 0 162180 2024-02-28 09:35:01.000000 control.tar.xz\n--rw-r--r-- 0 0 0 208791776 2024-02-28 09:35:01.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 162244 2024-02-28 09:35:01.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 208791764 2024-02-28 09:35:01.000000 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": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: libg2o-doc\n Source: g2o\n Version: 0~20230806-4.1\n Architecture: all\n Maintainer: Debian Science Maintainers \n-Installed-Size: 313544\n+Installed-Size: 313543\n Section: doc\n Priority: optional\n Homepage: http://www.g2o.org\n Description: C++ framework for optimizing graph-based nonlinear error functions\n A wide range of problems in robotics as well as in computer-vision involve the\n minimization of a non-linear error function that can be represented as a graph.\n Typical instances are simultaneous localization and mapping (SLAM) or bundle\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}, {"source1": "xz --list", "source2": "xz --list", "unified_diff": "@@ -1,13 +1,13 @@\n Streams: 1\n Blocks: 1\n- Compressed size: 158.4 KiB (162180 B)\n+ Compressed size: 158.4 KiB (162244 B)\n Uncompressed size: 770.0 KiB (788480 B)\n Ratio: 0.206\n Check: CRC64\n Stream Padding: 0 B\n Streams:\n Stream Blocks CompOffset UncompOffset CompSize UncompSize Ratio Check Padding\n- 1 1 0 0 162180 788480 0.206 CRC64 0\n+ 1 1 0 0 162244 788480 0.206 CRC64 0\n Blocks:\n Stream Block CompOffset UncompOffset TotalSize UncompSize Ratio Check\n- 1 1 12 0 162144 788480 0.206 CRC64\n+ 1 1 12 0 162208 788480 0.206 CRC64\n"}]}, {"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": "@@ -180,15 +180,15 @@\n -rw-r--r-- 0 root (0) root (0) 2075 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/tutorial_slam2d.cpp.gz\n -rw-r--r-- 0 root (0) root (0) 2021 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/types_tutorial_slam2d.cpp\n -rw-r--r-- 0 root (0) root (0) 1613 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/types_tutorial_slam2d.h\n -rw-r--r-- 0 root (0) root (0) 1871 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_point_xy.cpp\n -rw-r--r-- 0 root (0) root (0) 2148 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_point_xy.h\n -rw-r--r-- 0 root (0) root (0) 1879 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_se2.cpp\n -rw-r--r-- 0 root (0) root (0) 2156 2023-08-06 13:01:18.000000 ./usr/share/doc/libg2o-dev/examples/tutorial_slam2d/vertex_se2.h\n--rw-r--r-- 0 root (0) root (0) 605290 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/g2o.pdf.gz\n+-rw-r--r-- 0 root (0) root (0) 605018 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/g2o.pdf.gz\n drwxr-xr-x 0 root (0) root (0) 0 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/\n -rw-r--r-- 0 root (0) root (0) 9737 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h.html\n -rw-r--r-- 0 root (0) root (0) 1537 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__dep__incl.map\n -rw-r--r-- 0 root (0) root (0) 32 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__dep__incl.md5\n -rw-r--r-- 0 root (0) root (0) 39947 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__dep__incl.png\n -rw-r--r-- 0 root (0) root (0) 503 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__incl.map\n -rw-r--r-- 0 root (0) root (0) 32 2024-02-28 09:35:01.000000 ./usr/share/doc/libg2o-dev/html/FlexLexer_8h__incl.md5\n"}, {"source1": "./usr/share/doc/libg2o-dev/g2o.pdf.gz", "source2": "./usr/share/doc/libg2o-dev/g2o.pdf.gz", "unified_diff": null, "details": [{"source1": "g2o.pdf", "source2": "g2o.pdf", "unified_diff": null, "details": [{"source1": "pdftotext {} -", "source2": "pdftotext {} -", "unified_diff": "@@ -1,12 +1,12 @@\n g2o: A general Framework for (Hyper) Graph Optimization\n Giorgio Grisetti, Rainer Ku\u0308mmerle, Hauke Strasdat, Kurt Konolige\n email: {grisetti,kuemmerl}@informatik.uni-freiburg.de\n strasdat@gmail.com konolige@willowgarage.com\n-May 12, 2024\n+June 15, 2025\n In this document we describe a C++ framework for performing the optimization of nonlinear least\n squares problems that can be embedded as a graph or in a hyper-graph. A hyper-graph is an extension\n of a graph where an edge can connect multiple nodes and not only two. Several problems in robotics and\n in computer vision require to find the optimum of an error function with respect of a set of parameters.\n Examples include, popular applications like SLAM and Bundle adjustment.\n In the literature, many approaches have been proposed to address this class of problems. The naive\n implementation of standard methods, like Levenberg-Marquardt or Gauss-Newton can lead to acceptable\n"}]}]}]}, {"source1": "xz --list", "source2": "xz --list", "unified_diff": "@@ -1,25 +1,25 @@\n Streams: 1\n Blocks: 13\n- Compressed size: 199.1 MiB (208791776 B)\n+ Compressed size: 199.1 MiB (208791764 B)\n Uncompressed size: 307.7 MiB (322652160 B)\n Ratio: 0.647\n Check: CRC64\n Stream Padding: 0 B\n Streams:\n Stream Blocks CompOffset UncompOffset CompSize UncompSize Ratio Check Padding\n- 1 13 0 0 208791776 322652160 0.647 CRC64 0\n+ 1 13 0 0 208791764 322652160 0.647 CRC64 0\n Blocks:\n Stream Block CompOffset UncompOffset TotalSize UncompSize Ratio Check\n- 1 1 12 0 15757564 25165824 0.626 CRC64\n- 1 2 15757576 25165824 7828092 25165824 0.311 CRC64\n- 1 3 23585668 50331648 8131408 25165824 0.323 CRC64\n- 1 4 31717076 75497472 17411756 25165824 0.692 CRC64\n- 1 5 49128832 100663296 19987876 25165824 0.794 CRC64\n- 1 6 69116708 125829120 18910724 25165824 0.751 CRC64\n- 1 7 88027432 150994944 15805272 25165824 0.628 CRC64\n- 1 8 103832704 176160768 14873500 25165824 0.591 CRC64\n- 1 9 118706204 201326592 18244168 25165824 0.725 CRC64\n- 1 10 136950372 226492416 19024532 25165824 0.756 CRC64\n- 1 11 155974904 251658240 19785264 25165824 0.786 CRC64\n- 1 12 175760168 276824064 16661544 25165824 0.662 CRC64\n- 1 13 192421712 301989888 16369940 20662272 0.792 CRC64\n+ 1 1 12 0 15757480 25165824 0.626 CRC64\n+ 1 2 15757492 25165824 7827744 25165824 0.311 CRC64\n+ 1 3 23585236 50331648 8131540 25165824 0.323 CRC64\n+ 1 4 31716776 75497472 17412628 25165824 0.692 CRC64\n+ 1 5 49129404 100663296 19987840 25165824 0.794 CRC64\n+ 1 6 69117244 125829120 18910932 25165824 0.751 CRC64\n+ 1 7 88028176 150994944 15805372 25165824 0.628 CRC64\n+ 1 8 103833548 176160768 14873100 25165824 0.591 CRC64\n+ 1 9 118706648 201326592 18244516 25165824 0.725 CRC64\n+ 1 10 136951164 226492416 19024536 25165824 0.756 CRC64\n+ 1 11 155975700 251658240 19785352 25165824 0.786 CRC64\n+ 1 12 175761052 276824064 16661704 25165824 0.662 CRC64\n+ 1 13 192422756 301989888 16368884 20662272 0.792 CRC64\n"}]}]}]}